Sergey Brin is telling employees to stop making old products and start improving new ones. "For example, said Chief Executive Eric Schmidt, Google plans to combine its spreadsheet, calendar and word-processing programs into one suite of Web-based applications."

Roblox is introducing new safety features for children under the age of 13, following criticism of how it protects younger users. The free online gaming platform, which has around 70 million […]

The post Roblox introduces new safety features, Australia bans Under 16s from social media appeared first on Tech Digest.

If you are home alone tonight on Halloween and fancy something spooky and electronic to listen to, please allow me to direct you to the annual Homebrew Electronica horrorthon! Promising “spooky bangers, creepy electronica and twisted soundscapes for Halloween night”,...

This paper describes a new approach to document classification based on visual features alone. Text-based retrieval systems perform poorly on noisy text. We have conducted series of experiments using cosine distance as our similarity measure, selecting varying numbers local interest points per page, and varying numbers of nearest neighbour points in the similarity calculations. We have found that a distance-based measure of similarity outperforms a rank-based measure except when there are few interest points. We show that using visual features substantially outperforms textbased approaches for noisy text, giving average precision in the range 0.4-0.43 in several experiments retrieving scientific papers.

An OCR free word spotting method is developed and evaluated under a strong experimental protocol. Different feature sets are evaluated under the same experimental conditions. In addition, a tuning process in the document segmentation step is proposed which provides a significant reduction in terms of processing time. For this purpose, a complete OCR-free method for word spotting in printed documents was implemented, and a document database containing document images and their corresponding ground truth text files was created. A strong experimental protocol based on 800 document images allows us to compare the results of the three feature sets used to represent the word image.

One of the challenges in Dynamic Software Product Line (DSPL) is how to support the coordination of multiple instances of a service feature. In particular, there is a need for a decentralized decision-making capability that will be able to seamlessly integrate new instances of a service feature without an omniscient central controller. Because of the need for decentralization, we are investigating principles from self-organization in biological organisms. As an initial proof of concept, we have applied three bio-inspired techniques to a simple smart home scenario: quorum sensing based service activation, a firefly algorithm for synchronization, and a gossiping (epidemic) protocol for information dissemination. In this paper, we first explain why we selected those techniques using a set of motivating scenarios of a smart home and then describe our experiences in adopting them.

A feature model (FM) defines the valid combinations of features, whose combinations correspond to a program in a Software Product Line (SPL). FMs may evolve, for instance, during refactoring activities. Developers may use a catalog of refactorings as support. However, the catalog is incomplete in principle. Additionally, it is non-trivial to propose correct refactorings. To our knowledge, no previous analysis technique for FMs is used for checking properties of general FM refactorings (a transformation that can be applied to a number of FMs) containing a representative number of features. We propose an efficient encoding of FMs in the Alloy formal specification language. Based on this encoding, we show how the Alloy Analyzer tool, which performs analysis on Alloy models, can be used to automatically check whether encoded general and specific FM refactorings are correct. Our approach can analyze general transformations automatically to a significant scale in a few seconds. In order to evaluate the analysis performance of our encoding, we evaluated in automatically generated FMs ranging from 500 to 2,000 features. Furthermore, we analyze the soundness of general transformations.

Feature modeling is an approach to represent commonalities and variabilities among products of a product line. Context-aware applications use context information to provide relevant services and information for their users. One of the challenges to build a context-aware product line is to develop mechanisms to incorporate context information and adaptation knowledge in a feature model. This paper presents UbiFEX, an approach to support feature analysis for context-aware software product lines, which incorporates a modeling notation and a mechanism to verify the consistency of product configuration regarding context variations. Moreover, an experimental study was performed as a preliminary evaluation, and a prototype was developed to enable the application of the proposed approach.

E-mail spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients' system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning techniques have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should counteract with it. To cope with this, parameters optimization and feature selection have been used to reduce processing overheads while guaranteeing high detection rates. However, previous approaches have not taken into account feature variable importance and optimal number of features. Moreover, to the best of our knowledge, there is no approach which uses both parameters optimization and feature selection together for spam detection. In this paper, we propose a spam detection model enabling both parameters optimization and optimal feature selection; we optimize two parameters of detection models using Random Forests (RF) so as to maximize the detection rates. We provide the variable importance of each feature so that it is easy to eliminate the irrelevant features. Furthermore, we decide an optimal number of selected features using two methods; (i) only one parameters optimization during overall feature selection and (ii) parameters optimization in every feature elimination phase. Finally, we evaluate our spam detection model with cost-sensitive measures to avoid misclassification of legitimate messages, since the cost of classifying a legitimate message as a spam far outweighs the cost of classifying a spam as a legitimate message. We perform experiments on Spambase dataset and show the feasibility of our approaches.

The increased volume of accessible internet data creates an opportunity for researchers and practitioners to improve time series forecasting for many indicators. In our study, we assess the value of web traffic data in forecasting the number of short-term visitors travelling to Australia. We propose a feature-based model selection framework which combines random forest with feature ranking process to select the best performing model using limited and informative number of features extracted from web traffic data. The data was obtained for several tourist attraction and tourism information websites that could be visited by potential tourists to find out more about their destinations. The results of random forest models were evaluated over 3- and 12-month forecasting horizon. Features from web traffic data appears in the final model for short term forecasting. Further, the model with additional data performs better on unseen data post the COVID19 pandemic. Our study shows that web traffic data adds value to tourism forecasting and can assist tourist destination site managers and decision makers in forming timely decisions to prepare for changes in tourism demand.

With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms.

Screen-shooting will lead to information leakage. Anti-screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti-screen-shooting.

In today's world, malware has become a part and threat to our computer systems. All electronic devices are very susceptible/vulnerable to various threats like different types of malware. There is one subset of malware called ransomware, which is majorly used to have large financial gains. The attacker asks for a ransom amount to regain access to the system/data. When dynamic technique using machine learning is used, it is very important to select the correct set of features for the detection of a ransomware attack. In this paper, we present two novel algorithms for the detection of ransomware attacks. The first algorithm is used to assign the time stamp to the features (API calls) for the ordering and second is used for the ordering and ranking of the features for the early detection of a ransomware attack.

A well-documented architecture can greatly improve comprehension and maintainability. However, shorter release cycles and quick delivery patterns results in negligence of architecture. In such situations, the architecture can be recovered from its current implementation based on considering dependency relations. In literature, structural and semantic dependencies are commonly used software features, and directory information along with co-change/change history information are among rarely utilised software features. But, they are found to help improve architecture recovery. Therefore, we consider investigating various features that may further improve the accuracy of existing architecture recovery techniques and evaluate their feasibility by considering them in different pairs. We compared five state-of-the-art methods under different feature subsets. We identified that two of them commonly outperform others but surprisingly with low accuracy in some evaluations. Further, we propose a new subset of features that reflects more accurate user perceptions and hence, results in improving the accuracy of architecture recovery techniques.

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

Aim/Purpose: Based on the stimulus-organism-response (SOR) model, the current study investigated social commerce functions as an innovative retailing technological support by selecting the three most appropriate features for the Chinese online shopping environment with respective value co-creation intentions. Background: Social commerce is the customers’ online shopping touchpoint in the latest retail era, which serves as a corporate technological tool to extend specific customer services. Although social commerce is a relatively novel platform, limited theoretical attention was provided to determine retailers’ approaches in employing relevant functions to improve consumer experience and value co-creation. Methodology: A questionnaire was distributed to Chinese customers, with 408 valid questionnaires being returned and analyzed through Structural Equation Modeling (SEM). Contribution: The current study investigated the new retail concept and value co-creation from the consumer’s perspective by developing a theoretical model encompassing new retail traits and consumer value, which contributed to an alternative theoretical understanding of value creation, marketing, and consumer behaviour in the new retail business model. Findings: The results demonstrated that value co-creation intention was determined by customer experience, hedonic experience, and trust. Simultaneously, the three factors were significantly influenced by interactivity, personalisation, and sociability features. Specifically, customers’ perceptions of the new retail idea and the consumer co-creation value were examined. Resultantly, this study constructed a model bridging new retail characteristics with consumer value. Recommendations for Practitioners: Nonetheless, past new retail management practice studies mainly focused on superficial happiness in the process of human-computer interaction, which engendered a computer system design solely satisfying consumers’ sensory stimulation and experience while neglecting consumers’ hidden value demands. As such, a shift from the subjective perspective to the realisation perspective is required to express and further understand the actual meaning and depth of consumer happiness. Recommendation for Researchers: New retailers could incorporate social characteristics on social commerce platforms to improve the effectiveness of marketing strategies while increasing user trust to generate higher profitability. Impact on Society: The new retail enterprises should prioritise consumers’ acquisition of happiness meaning and deep experience through self-realisation, cognitive improvement, identity identification, and other aspects of consumer experiences and purchase processes. By accurately revealing and matching consumers’ fundamental perspectives, new retailers could continuously satisfy consumer requirements in optimally obtaining happiness. Future Research: Future comparative studies could be conducted on diverse companies within the same industry for comprehensive findings. Moreover, other underlying factors with significant influences, such as social convenience, group cognitive ability, individual family environment, and other external stimuli were not included in the present study examinations.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

Zhigang WU,Yaohui ZHU, Vol.E107-D, No.11, pp.1385-1395
This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.
Publication Date: 2024/11/01

...

Google Flights has released a new feature that allows travelers worldwide to find the best possible airfare deal with one click. The feature is currently being rolled out and should be available globally over the next two weeks. Google Flights Introduces “Cheapest” Tab Look for the new “Cheapest” tab (next to the “Best” tab) to […]

The post Google Flights Just Announced a New Feature To Find Cheap Flights appeared first on Clark Howard.

Apple’s shares fell 1.7 per cent on Tuesday, a day after the US tech giant unveiled its new iPhones with AI

This tutorial will show you how to manage featured and special products in Shopify templates.

The post Shopify. How to manage featured and special products appeared first on Template Monster Help.

Facebook, TikTok, Twitter, YouTube and Instagram apps are seen on a smartphone in this illustration taken, July 13, 2021.— Reuters

Meta-owned Instagram might be working to add a new AI-backed feature that will allow users to create profile pictures using Meta’s artificial...

A representational image shows an illustration of the WhatsApp logo. — Unsplash

WhatsApp is set to revamp its feature for muting notifications from group chats in an upcoming update, making it simpler for users to better understand how this feature works.

A eclectic group of stars - including reality TV's Ariana Madix, Broadway belter Idina Menzel, hip-hop's T-Pain, members of the WNBA champions New York Liberty and country duo Dan + Shay - will feature in this year's Macy's Thanksgiving Day Parade.

The new Big Ten truly comes to full bore for Maryland this weekend with its first-ever West Coast conference trip to No. 1 Oregon, bringing with it a host of logistical tweaks and challenges.

The news about the International Symposium "Nature and Governance – Biodiversity Data, Science, and the Policy Interface" and the official EU BON Kickoff Meeting has been reflected in the January newsletter of the The Global Biodiversity Information Facility (GBIF).

GBIF only features high end biodiversity news and major projects. EU BON project summary is placed in the collaborations section of the newsletter and the two events are included in the Upcoming Events, pointed out to the readers' attention.

To view the GBIF newsletter for January, please go to: http://www.gbif.org/communications/resources/newsletters/, or see the newsletter PDF attached below.

We are happy to announce that earlier this summer EU BON has been selected to be featured as a successful EU-funded project. The DG Research & Innovation communication team has interviewed our project co-ordinator Christoph Häuser and the resulting article - Combining citizen and satellite biodiversity data - is now a fact!

The news item focuses on EU BON's efforts to bring together biodiversity and Earth observation data, that are accumulated from data sources ranging from the individual citizen scientist, researchers to the most technologically advanced satellites in one EU-wide initiative. 

"Information on life on Earth is crucial to addressing global and local challenges, from environmental pressures and societal needs, to ecology and biodiversity research questions," commented Christoph Häuser in his interview.

View the full story on the Horizon 2020 site.

A recent story, featured on CORDIS News, focuses on EU BON and its achievements. Based on a recent project's publication in ‘Nature – Scientific Reports’ and information from the project, the new item discusses important project outcomes, including the European Biodiversity Portal, and their contribution to drawing a more accurate picture of current biodiversity to aid efforts for sustainable governance of natural resources.

Here is a snippet from the news feature on CORDIS: 

A recent article published in the journal ‘Scientific Reports’ states, ‘Monitoring schemes provide an important source of information on biodiversity change, guiding further research, conservation assessment and planning.’ The article cites The European Biodiversity Portal, designed and implemented by the EU-funded EU BON project that offers researchers, policy-makers, and others interested in biodiversity, easy access to insights on trends and modelling techniques.

The project worked on the establishment and adoption of new data standards, the development of tools to enable collaborative research and the encouragement of citizen-scientists. As a result, EU BON, by building the European Biodiversity Observation Network, has created advanced techniques for data analysis along with new approaches for modelling and strategies for future biodiversity monitoring.

Read the full publication here.

Town Clerk Ben Kaufman said the new design featuring Brookline's bird "celebrity" is to encourage turnout.

The post New Brookline ‘I voted’ stickers will feature a familiar face: a turkey appeared first on Boston.com.

By popular demand, there is now a new feature in the sitewide settings that allows hiding series in certain languages. Once you've selected the languages to ignore, shows in these language will be hidden from: the homepage (if "Include shows not in my watchlist when in their 1st season" is enabled) and the homepage discovery widget, recent, calendar, browse, the TV Schedule and the discovery charts - hot | upcoming | trending | top from my creators | recommended. Basically, ignoring a language hides all shows in it everywhere possible. Let me know how that works for you!

To help you navigate the messy world of streaming services availability, Next Episode has a few new features: All lists (search, browse, top/discovery charts, watchlist, track episodes) now also show streaming availability You can now filter by streaming services availability In addition, you can add the streaming services you use to My Services and filter through that as wellThis is not just for US - your country should've been automatically detected, but if not - you can easily switch it here. Basically, it should now be easier than ever to find out where your stuff is streaming and find new great shows on the streaming services you use. Our movies section will get the same treatment soon. Let me know your thoughts and as always - if you appreciate my efforts here - consider getting Premium. Thank you!

On Sept. 17, JLG introduced new features for its ES electric scissor lifts, E18 vertical lifts and E18 stock picker, including standard AC drive motors, lithium-ion battery options and a range-extending genset charging option.

HOK designed the Institute for Specialized Medicine & Intervention’s new clinic in Quebec as a spa-like experience for underserved patients. Supporting this modern, comforting, healing environment, HOK specified Rockfon Medical Plus acoustic stone wool ceiling panels for all of ISMI’s treatment rooms.

Mecanoo and Beyer Blinder Belle designed The New York Public Library’s completely renovated Stavros Niarchos Foundation Library. The design team selected Rockfon Alaska acoustic stone wool ceiling panels to create a cohesive, timeless appearance while providing best-level sound absorption and sustainability benefits.

Longtime Rockfon partner Sesco Group designed and renovated its boardroom to feature Rockfon’s Mono Acoustic ceiling system – the first installation of the product in Ontario, Canada.

Bon’s line of contractor-grade hand tools and materials for the building trades has been expanded to nearly 6,000 products and includes more than 200 new items in the company’s 2013 Master Catalog.

Washington – A recently released OSHA fact sheet on preventing amputations from food slicers and meat grinders incorporates findings from the agency’s new injury reporting requirements.

For Thomas Marino, president of Advanced Technologies, Baton Rouge, La., “green” projects were nothing new. He had done projects that involved low-power, solar or other green elements. But when he ran into local attorney Joe Simmons at a trade show, the extremely environmentally conscious lawyer asked him to do the security and home automation for the custom home he was building. The home was new construction in a community of older homes and would feature as many green elements as possible, from the material in the countertops to the security and audio elements.

ASSA ABLOY Global Solutions — Critical Infrastructure (Booth #8101) is showcasing its robust ALWIN security management software as part of a total access management solution for critical infrastructure customers in the U.S. at ISC West.

Washington – OSHA has updated an online tool aimed at teaching small-business owners and workers how to locate workplace hazards.

The “Editor’s Choice” and more new products and solutions as described by manufacturers.

The System Clean Up Utility feature effectively enables a swift and thorough cleanup of invalid or outdated access control data, resulting in enhanced system management and security.

This enables the ability to integrate Google Nest products with ClareOne empowers its partners to deliver comprehensive home automation and security through an intuitive unified touchscreen with the ability to add automations.