Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment. In this work, we propose the Embodied Scene Description, which exploits the embodiment ability of the agent to find an optimal viewpoint in its environment for scene description tasks. A learning framework with the paradigms of imitation learning and reinforcement learning is established to teach the intelligent agent to generate corresponding sensorimotor activities. The proposed framework is tested on both the AI2Thor dataset and a real world robotic platform demonstrating the effectiveness and extendability of the developed method.

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1 imes$ and $2 imes$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.

The total influence of a function is a central notion in analysis of Boolean functions, and characterizing functions that have small total influence is one of the most fundamental questions associated with it. The KKL theorem and the Friedgut junta theorem give a strong characterization of such functions whenever the bound on the total influence is $o(log n)$. However, both results become useless when the total influence of the function is $omega(log n)$. The only case in which this logarithmic barrier has been broken for an interesting class of functions was proved by Bourgain and Kalai, who focused on functions that are symmetric under large enough subgroups of $S_n$.

In this paper, we build and improve on the techniques of the Bourgain-Kalai paper and establish new concentration results on the Fourier spectrum of Boolean functions with small total influence. Our results include:

1. A quantitative improvement of the Bourgain--Kalai result regarding the total influence of functions that are transitively symmetric.

2. A slightly weaker version of the Fourier--Entropy Conjecture of Friedgut and Kalai. This weaker version implies in particular that the Fourier spectrum of a constant variance, Boolean function $f$ is concentrated on $2^{O(I[f]log I[f])}$ characters, improving an earlier result of Friedgut. Removing the $log I[f]$ factor would essentially resolve the Fourier--Entropy Conjecture, as well as settle a conjecture of Mansour regarding the Fourier spectrum of polynomial size DNF formulas.

Our concentration result has new implications in learning theory: it implies that the class of functions whose total influence is at most $K$ is agnostically learnable in time $2^{O(Klog K)}$, using membership queries.

In the era of Internet of Things (IoT), Malware has been proliferating exponentially over the past decade. Traditional anti-virus software are ineffective against modern complex Malware. In order to address this challenge, researchers have proposed Hardware-assisted Malware Detection (HMD) using Hardware Performance Counters (HPCs). The HPCs are used to train a set of Machine learning (ML) classifiers, which in turn, are used to distinguish benign programs from Malware. Recently, adversarial attacks have been designed by introducing perturbations in the HPC traces using an adversarial sample predictor to misclassify a program for specific HPCs. These attacks are designed with the basic assumption that the attacker is aware of the HPCs being used to detect Malware. Since modern processors consist of hundreds of HPCs, restricting to only a few of them for Malware detection aids the attacker. In this paper, we propose a Moving target defense (MTD) for this adversarial attack by designing multiple ML classifiers trained on different sets of HPCs. The MTD randomly selects a classifier; thus, confusing the attacker about the HPCs or the number of classifiers applied. We have developed an analytical model which proves that the probability of an attacker to guess the perfect HPC-classifier combination for MTD is extremely low (in the range of $10^{-1864}$ for a system with 20 HPCs). Our experimental results prove that the proposed defense is able to improve the classification accuracy of HPC traces that have been modified through an adversarial sample generator by up to 31.5%, for a near perfect (99.4%) restoration of the original accuracy.

Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.

Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable.

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.

Bitvector filtering is an important query processing technique that can significantly reduce the cost of execution, especially for complex decision support queries with multiple joins. Despite its wide application, however, its implication to query optimization is not well understood.

In this work, we study how bitvector filters impact query optimization. We show that incorporating bitvector filters into query optimization straightforwardly can increase the plan space complexity by an exponential factor in the number of relations in the query. We analyze the plans with bitvector filters for star and snowflake queries in the plan space of right deep trees without cross products. Surprisingly, with some simplifying assumptions, we prove that, the plan of the minimal cost with bitvector filters can be found from a linear number of plans in the number of relations in the query. This greatly reduces the plan space complexity for such queries from exponential to linear.

Motivated by our analysis, we propose an algorithm that accounts for the impact of bitvector filters in query optimization. Our algorithm optimizes the join order for an arbitrary decision support query by choosing from a linear number of candidate plans in the number of relations in the query. We implement our algorithm in Microsoft SQL Server as a transformation rule. Our evaluation on both industry standard benchmarks and customer workload shows that, compared with the original Microsoft SQL Server, our technique reduces the total CPU execution time by 22%-64% for the workloads, with up to two orders of magnitude reduction in CPU execution time for individual queries.

Software development has been changing rapidly. This development process can be influenced through changing developer friendly approaches. We can save time consumption and accelerate the development process if we can automatically guide programmer during software development. There are some approaches that recommended relevant code snippets and APIitems to the developer. Some approaches apply general code, searching techniques and some approaches use an online based repository mining strategies. But it gets quite difficult to help programmers when they need particular type conversion problems. More specifically when they want to adapt existing interfaces according to their expectation. One of the familiar triumph to guide developers in such situation is adapting collections and arrays through automated adaptation of object ensembles. But how does it help to a novice developer in real time software development that is not explicitly specified? In this paper, we have developed a system that works as a plugin-tool integrated with a particular Data Mining Integrated environment (DMIE) to recommend relevant interface while they seek for a type conversion situation. We have a mined repository of respective adapter classes and related APIs from where developer, search their query and get their result using the relevant transformer classes. The system that recommends developers titled automated objective ensembles (AOE plugin).From the investigation as we have ever made, we can see that our approach much better than some of the existing approaches.

Coordinating the operation of combined heat and power plants (CHPs) and heat pumps (HPs) at the interface between heat and power systems is essential to achieve a cost-effective and efficient operation of the overall energy system. Indeed, in the current sequential market practice, the heat market has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an electricity-aware heat unit commitment model. Coordination is achieved through bid validity constraints, which embed the techno-economic linkage between heat and electricity outputs and costs of CHPs and HPs. This approach constitutes a novel market mechanism for the coordination of heat and power systems, defining heat bids conditionally on electricity market prices. The resulting model is a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish power and heat system, and show that the proposed model yields a 4.5% reduction in total operating cost of heat and power systems compared to a traditional decoupled unit commitment model, while reducing the financial losses of each CHP and HP due to invalid bids by up-to 20.3 million euros.

Behavioral research can provide important insights for SE practices. But in performing it, many studies of SE are committing a normative fallacy - they misappropriate normative and prescriptive theories for descriptive purposes. The evidence from reviews of empirical studies of decision making in SE suggests that the normative fallacy may is common. This article draws on cognitive psychology and behavioral economics to explains this fallacy. Because data collection is framed by narrow and empirically invalid theories, flawed assumptions baked into those theories lead to misleading interpretations of observed behaviors and ultimately, to invalid conclusions and flawed recommendations. Researchers should be careful not to rely solely on engineering methods to explain what people do when they do engineering. Instead, insist that descriptive research be based on validated descriptive theories, listen carefully to skilled practitioners, and only rely on validated findings to prescribe what they should do.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

Are you looking for a content management system (CMS) that will help you create the digital content you need? With so many options on the market, it’s challenging to know which one is the best CMS software for your business. On this page, we’ll take a look at the six best CMS’s for website development […]

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By Jeff Mulhollem Penn State News The first harvest of 34 acres of fast-growing shrub willow from a Penn State demonstration field this winter is a milestone in developing a sustainable biomass supply for renewable energy and bio-based economic development, … Continue reading →

With all due respect to Greta Thunberg and Billie Eilish, nobody had a better 2019 than Baby Yoda. The real star of the Disney+ flagship Star Wars series The Mandalorian, the little green puppeteering/CGI marvel (aka "the Child") might be the most adorable creature ever created.…

What am I supposed to say here?…

When it comes to the music scene in the midst of the coronavirus pandemic, the math is pretty simple: No shows equals no revenue.…

A data warehouse is constructed using the relational mapping of a transactional database without reconstructing the data relationships of the transactional database. First, an application programmer analyzes an object model in order to describe facts and dimensions using the objects, attributes, and paths of the object model. Each of the dimensions has an identifier that correlates an item in the transactional database to a dimension record in the data warehouse. The fact and dimension descriptions are saved to a description file. Second, a Data Warehouse Engine (DWE) then access the description file and uses the object model, fact and dimension descriptions, and object-relational mapping to map transactional data to the data warehouse.

A method, system and computer program product for intelligently responding to hardware failures so as to optimize system performance. An administrative server monitors the utilization of the hardware as well as the software components running on the hardware to assess a context of the software components running on the hardware. Upon detecting a hardware failure, the administrative server analyzes the hardware failure to determine the type of hardware failure and analyzes the properties of the workload running on the failed hardware. The administrative server then responds to the detected hardware failure based on various factors, including the type of the hardware failure, the properties of the workload running on the failed hardware and the context of the software running on the failed hardware. In this manner, by taking into consideration such factors in responding to the detected hardware failure, a more intelligent response is provided that optimizes system performance.

An approach for introspection of a software component and generation of a conditional memory dump, a computing device executing an introspection program with respect to the software component is provided. An introspection system comprises one or more conditions for generating the conditional memory dump based on operations of the software component. In one aspect, a computing device detects, through an introspection program, whether the one or more conditions are satisfied by the software component based on information in an introspection analyzer of the introspection program. In addition, the computing device indicates, through the introspection program, if the one or more conditions are satisfied by the software component. In another aspect, responsive to the indication, the computing device generates the conditional memory dump through the introspection program.

A system, method and computer program product for avoiding a processing flaw in a computer processor triggered by a predetermined sequence of hardware events. The system may include a detecting unit and a power-on reset unit. The detecting unit detects that the predetermined sequence of hardware events is going to occur at the computer processor. The power-on reset unit initializes the computer processor to a state stored in computer memory in response to detecting the sequence of hardware events.

A packet transmission/reception apparatus and method is provided. The packet transmission method of the present invention includes acquiring a source payload including partial source symbols from a source block, generating a source packet including the source payload and an identifier (ID) of the source payload, generating a repair packet including a repair payload corresponding to the source payload and an ID of the repair payload, generating a Forward Error Correction (FEC) packet block including the source and repair packets, and transmitting the FEC packet block. The source payload ID includes a source payload sequence number incrementing by 1 per source packet. The packet transmission/reception method of the present invention is advantageous in improving error correction capability and network resource utilization efficiency.

A virtual machine (VM) migration from a source virtual machine monitor (VMM) to a destination VMM on a computer system. Each of the VMMs includes virtualization software, and one or more VMs are executed in each of the VMMs. The virtualization software allocates hardware resources in a form of virtual resources for the concurrent execution of one or more VMs and the virtualization software. A portion of a memory of the hardware resources includes hardware memory segments. A first portion of the memory segments is assigned to a source logical partition and a second portion is assigned to a destination logical partition. The source VMM operates in the source logical partition and the destination VMM operates in the destination logical partition. The first portion of the memory segments is mapped into a source VMM memory, and the second portion of the memory segments is mapped into a destination VMM memory.

A processor having a streaming unit is disclosed. In one embodiment, a processor includes one or more execution units configured to execute instructions of a processor instruction set. The processor further includes a streaming unit configured to execute a first instruction of the processor instruction set, wherein executing the first instruction comprises the streaming unit loading a first data stream from a memory of a computer system responsive to execution of a first instruction. The first data stream comprises a plurality of data elements. The first instruction includes a first argument indicating a starting address of the first stream, a second argument indicating a stride between the data elements, and a third argument indicative of an ending address of the stream. The streaming unit is configured to output a second data stream corresponding to the first data stream.

A system, method and computer program product for allocating shared resources. Upon receiving requests for resources, the cost of bundling software in a virtual machine (VM) image is automatically generated. Software is selected by the cost for each bundle according to the time required to install it where required, offset by the time to uninstall it where not required. A number of VM images having the highest software bundle value (i.e., highest cost bundled) is selected and stored, e.g., in a machine image store. With subsequent requests for resources, VMs may be instantiated from one or more stored VM images and, further, stored images may be updated selectively updated with new images.

The systems and methods described herein may be used to implement a shared dynamic-sized data structure using hardware transactional memory to simplify and/or improve memory management of the data structure. An application (or thread thereof) may indicate (or register) the intended use of an element of the data structure and may initialize the value of the data structure element. Thereafter, another thread or application may use hardware transactions to access the data structure element while confirming that the data structure element is still part of the dynamic data structure and/or that memory allocated to the data structure element has not been freed. Various indicators may be used determine whether memory allocated to the element can be freed.

A system and method for reordering storage levels in a virtualized environment includes identifying a virtual machine (VM) to be transitioned and determining a new storage level order for the VM. The new storage level order reduces a VM live state during a transition, and accounts for hierarchical shared storage memory and criteria imposed by an application to reduce recovery operations after dynamic resource allocation actions. The new storage level order recommendation is propagated to VMs. The new storage level order applied in the VMs. A different storage-level order is recommended after the transition.

A usage allocation of a hardware resource to each of a number of workloads over time is determined using a demand model. The usage allocation of the resource includes a current and past actual usage allocation of the resource, a future projected usage allocation of the resource, and current and past actual usage of the resource. A capacity allocation of the resource is determined using a capacity model. The capacity allocation of the resource includes a current and past capacity and a future projected capacity of the resource. Whether a gap exists between the usage allocation and the capacity allocation is determined using a mapping model. Where the gap exists between the usage allocation of the resource and the capacity allocation of the resource, a user is presented with options determined using the mapping model and selectable by the user to implement a remediation strategy to close the gap.

A method of reducing the downward flow of air currents on the leeward side of an emissions emitting structure including the step of using a system that includes components chosen from the group consisting of one or more mechanical air moving devices; physical structures; and combinations thereof to create an increase in the air pressure within a volume of air on the leeward side of an emissions emitting structure having emissions that become airborne. The increased air pressure prevents or lessens downward flow of emissions that would occur without the use of the system and increases the safety by which one can travel a road or other transportation route that might otherwise be visually obscured by the emissions and the safety of the property and those within the area where emissions occur.

The disclosure relates to the implementation of multi-tasking on a digital signal processor. Blocking functions are arranged such that they do not make use of a processor's hardware stack. Respective function calls are replaced with a piece of inline assembly code, which instead performs a branch to the correct routine for carrying out said function. If a blocking condition of the blocking function is encountered, a task switch can be done to resume another task. Whilst the hardware stack is not used when a task switch might have to occur, mixed-up contents of the hardware stack among function calls performed by different tasks are avoided.

An adaptable integrated circuit is disclosed having a plurality of heterogeneous computational elements coupled to an interconnection network. The interconnection network changes interconnections between the plurality of heterogeneous computational elements in response to configuration information. A first group of computational elements is allocated to form a first version of a functional unit to perform a first function by changing interconnections in the interconnection network between the first group of heterogeneous computational elements. A second group of computational elements is allocated to form a second version of a functional unit to perform the first function by changing interconnections in the interconnection network between the second group of heterogeneous computational elements. One or more of the first or second group of heterogeneous computational elements are reallocated to perform a second function by changing the interconnections between the one or more of the first or second group of heterogeneous computational elements.

Mechanisms are provided for offloading a workload from a main thread to an assist thread. The mechanisms receive, in a fetch unit of a processor of the data processing system, a branch-to-assist-thread instruction of a main thread. The branch-to-assist-thread instruction informs hardware of the processor to look for an already spawned idle thread to be used as an assist thread. Hardware implemented pervasive thread control logic determines if one or more already spawned idle threads are available for use as an assist thread. The hardware implemented pervasive thread control logic selects an idle thread from the one or more already spawned idle threads if it is determined that one or more already spawned idle threads are available for use as an assist thread, to thereby provide the assist thread. In addition, the hardware implemented pervasive thread control logic offloads a portion of a workload of the main thread to the assist thread.

Efficient data processing apparatus and methods include hardware components which are pre-programmed by software. Each hardware component triggers the other to complete its tasks. After the final pre-programmed hardware task is complete, the hardware component issues a software interrupt.

A processor receives an instruction operation (OP) code from a verification system. The instruction OP code includes instruction bits and forced event bits. The processor identifies a forced event based upon the forced event bits, which is unrelated to an instruction that corresponds to the instruction bits. In turn, the processor executes the forced event.

Disclosed is a novel system and method to select software components. A set of available software components are accessed. Next, one or more dimensions are defined. Each dimension is an attribute to the set of available software components. A set of coherence distances between each pair of the available software components in the set of available software components is calculated for each of the dimensions that have been defined. Each of the coherence distances are combined between each pair of the available software components that has been calculated in the set of the coherence distances into an overall coherence degree for each of the available software components. Using the overall coherence degree, one or more software components are selected to be included in a software bundle.

A method for recommending at least one artifact to an artifact user is described. The method includes obtaining user characteristic information reflecting preferences, particular to the artifact user, as to a desired artifact. The method also includes obtaining first metadata about each of one or more candidate artifacts, and scoring, as one or more scored artifacts, each of the one or more candidate artifacts by evaluating one or more criteria, not particular to the artifact user, applied to the first metadata. The method further includes scaling, as one or more scaled artifacts, a score of each of the one or more scored artifacts, by evaluating the suitability of each of the one or more scored artifacts in view of the user characteristic information. The method lastly includes recommending to the artifact user at least one artifact from among the one or more scaled artifacts based on its scaled score.

An aspect of the present invention identifies differences between source codes (e.g. of different versions of a software), when each source code is organized using incorporated files. In one embodiment, in response to receiving identifiers of a first and second source codes (each source code being organized as a corresponding set of code files), listings of the instructions in the first and second source codes are constructed. Each listing is constructed, for example, by replacing each incorporate statement in the source code with instructions stored in a corresponding one of code files. The differences between the first and second source codes are then found by comparing the constructed listings of instructions.

A system and method may generate unit tests for source code concurrently with API documentation. The system may receive a source code file including several comments sections. Each comments section may include a description of a source code unit such as a class, method, member variable, etc. The description may also correspond to input and output parameters the source code unit. The system and method may parsing the source code file to determine a source code function type corresponding to the unit description and copy the unit description to a unit test stub corresponding to the function type. A developer or another module may then complete the unit test stub to transform each stub into a complete unit test corresponding to the source code unit. Additionally, the system and method may execute the unit test and generate a test result indication for each unit test.

In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors The system includes a plurality of processors of two or more different processor types. According to the recited method, machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). A program module applicability analyzer (PMAA) determines whether a first processor of a first processor type is capable of running a first program module without compiling the first program module. Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution.

In one embodiment of the invention, a method is disclosed for modifying a pre-existing application program for multi-processing and/or distributed parallel processing. The method includes searching an application program for a computational loop; analyzing the computational loop to determine independence of the computational transactions of the computational loop; and replacing the computational loop with master code and slave code to provide master-slave execution of the computational loop in response to analyzing the computational loop to determine independence of the computational transactions of the computational loop. Multiple instances of the modified application program are executed to provide multi-processing and/or distributed parallel processing.

Embodiments of the present disclosure provide a method and a system for upgrading software. The method includes: a client reports a software upgrade request to a server, wherein the upgrade request carries file information of the local software to be upgraded; the server determines the difference with the latest version software according to the file information of the software to be upgraded in the upgrade request, and generates upgrade instruction information according to the difference and sends it to the client; the client downloads and updates the relevant files and performs the relevant local upgrade operations according to the instructions in received upgrade instruction information. Technical solutions of the present disclosure can save bandwidth resources and reduce the workload for upgrading software.

A software streaming platform may be implemented that predictively chooses units of a program to download based on the value of downloading the unit. In one example, a program is divided into blocks. The sequence in which blocks of the program historically have been requested is analyzed in order to determine, for a given history, what block is the next most likely to be requested. Blocks then may be combined into chunks, where each chunk represents a chain of blocks that have a high likelihood of occurring in a sequence. A table is then constructed indicating, for a given chunk, the chunks that are most likely to follow the given chunk. Based on the likelihood table and various other considerations, the value of downloading particular chunks is determined, and the chunk with the highest expected value is downloaded.

A firmware update method and apparatus of a set-top box for a digital broadcast system is provided. A firmware update method of a set-top box for a digital broadcast system includes determining whether a newly received Code Version Table (CVT) following a public CVT which has been previously received and stored is the public CVT or a filtering CVT; and updating, when the newly received CVG is the filtering CVT, the firmware of the set-top box with a filtering firmware indicated by the filtering CVT.

A plurality of Field Programmable Gate Arrays (FPGA), high performance transceivers, and memory devices provide a verification module for timing and state debugging of electronic circuit designs. Signal value compression circuits and gigabit transceivers embedded in each FPGA increase the fanout of each FPGA. Ethernet communication ports enable remote software debugging of processor instructions.

Methods and apparatus to generate and use content-aware watermarks are disclosed herein. In a disclosed example method, media composition data is received and at least one word present in an audio track of the media composition data is selected. The word is then located in a watermark.

An audio signal decoder has a time warp contour calculator, a time warp contour data rescaler and a warp decoder. The time warp contour calculator is configured to generate time warp contour data repeatedly restarting from a predetermined time warp contour start value, based on time warp contour evolution information describing a temporal evolution of the time warp contour. The time warp contour data rescaler is configured to rescale at least a portion of the time warp contour data such that a discontinuity at a restart is avoided, reduced or eliminated in a rescaled version of the time warp contour. The warp decoder is configured to provide the decoded audio signal representation, based on an encoded audio signal representation and using the rescaled version of the time warp contour.

A time warp contour calculator for use in an audio signal decoder receives an encoded warp ratio information, derives a sequence of warp ratio values from the encoded warp ratio information, and obtains warp contour node values starting from a time warp contour start value. Ratios between the time warp contour node values and the time warp contour starting value are determined by the warp ratio values. The time warp contour calculator computes a time warp contour node value of a given time warp contour node, on the basis of a product-formation having a ratio between the time warp contour node values of the intermediate time warp contour node and the time warp contour starting value and a ratio between the time warp contour node values of the given time warp contour node and of the intermediate time warp contour node as factors.

A method includes displaying a first application view. A first input is detected, and an application view selection mode is entered for selecting one of concurrently open applications for display in a corresponding application view. An initial group of open application icons in a first predefined area and at least a portion of the first application view adjacent to the first predefined area are concurrently displayed. The initial group of open application icons corresponds to at least some of the concurrently open applications. A gesture is detected on a respective open application icon in the first predefined area, and a respective application view for a corresponding application is displayed without concurrently displaying an application view for any other application in the concurrently open applications. The open application icons in the first predefined area cease to be displayed, and the application view selection mode is exited.