The internet is still convinced a Nintendo Switch 2 announcement is going to happen this month, as a part of a major third-party open world game is also rumoured. It’ll […]

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We're excited to announce that Search Central Live is coming to Buenos Aires on March 5, 2024. Following successful events throughout the world last year, we're continuing our mission to help website owners to enhance their site's performance in Google Search.

This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

Interactive video is increasingly becoming a more and more dominant feature of our media platforms. Especially due to the popular YouTube annotations framework, integrating graphical annotations in a video has become very fashionable these days. However, the current options are limited to a few graphical shapes for which the user can define as good as no dynamic behaviour. Despite the enormous demand for easy-creatable, interactive video there are no such advanced tools available.

In this article we describe an innovative approach, to realize dynamics and interactivity of video annotations. First we explain basic concepts of video-markup like the generic element model and visual descriptors. After that we introduce the event-tree model, which can be used to define event-handling in an interactive video formally as well as visually. By combining these basic concepts, we can give an effective tool to the video community for realizing interactive and dynamic video in a simple, intuitive and focused way.

Malgré un télescopage plus que dommageable avec l’affaire Ferrand – il aurait déjà dû démissionner – François Bayrou a annoncé un train de mesures visant à moraliser visant à encadrer les élus. Et bien celles-ci vont dans le bon sens. Bien sûr, on pourra...

No Comment : commémoration du 106e anniversaire de l'Armistice 

No Comment : Tyrannosaure Rex, un mannequin pas comme les autres

Une gigantesque panne d'électricité a affecté lundi une large partie du Pakistan, dont ses plus grandes villes Lahore et Karachi, avant que les autorités n'annoncent en soirée le retour progressif du courant.

Les coupures d'électricité sont fréquentes au Pakistan, englué depuis des années dans de graves problèmes budgétaires et énergétiques, et dont le système de distribution est complexe et vétuste.

La panne a été causée par un dysfonctionnement survenu à 07H30 locales (02H30 GMT) à la suite de mesures d'économies prises sur le réseau.

"Nous espérons que le courant sera rétabli partout dans le pays d'ici ce (lundi) soir", a déclaré dans un communiqué vidéo le ministre de l'Energie, Khurram Dastagir Khan.

La nuit avançant, il a assuré sur Twitter que l'électricité commençait à être rétablie progressivement.

Le ministère a tweeté que le courant était revenu dans le centre d'Islamabad et à Gujranwala, à quelque 200 km au sud-est de la capitale.

M. Khan a également retweeté le message d'un usager qui se félicitait qu'il y ait à nouveau "de la lumière à Lahore", la deuxième ville la plus peuplée du pays, avec plus de 10 millions d'habitants.

La panne a été causée par une variation de la fréquence électrique sur le réseau national, au redémarrage des unités de production électrique lundi matin.

Ces unités sont temporairement éteintes la nuit en hiver pour économiser du carburant, a expliqué M. Khan à la presse.

La plupart des hôpitaux, industries et institutions gouvernementales sont équipées de générateurs. Mais les ménages et petits commerces n'ont souvent pas les moyens de s'offrir un tel équipement.

Dans le nord du Pakistan, les températures devaient tomber lundi soir en dessous de 0°. Le chauffage au gaz est le plus répandu, mais n'est pas non plus toujours très fiable, les délestages étant fréquents en raison d'une pénurie de gaz.

Les réseaux de téléphonie mobile ont été également perturbés par la panne, selon un tweet du régulateur pakistanais des télécoms.

L'économie pakistanaise est déjà chancelante avec une inflation galopante, une devise nationale - la roupie - en chute libre et des réserves de change au plus bas. Une telle coupure d'électricité ne fait qu'accroître la pression sur les petits commerces.

A Rawalpindi, ville voisine de la capitale, Muhammad Iftikhar Sheikh, 71 ans, un vendeur d'électroménager, a déploré ne pas pouvoir tester ses appareils devant les clients.

"Les clients n'achètent jamais sans essayer d'abord", pestait-il. Du coup, "on est tous assis là, à ne rien faire".

Dans les écoles, les cours ont souvent eu lieu dans la pénombre, pour celles ne disposant pas d'éclairage sur batterie.

A Karachi (sud), où les températures étaient plus élevées, un commerçant a indiqué à l'AFP craindre que son stock entier de produits laitiers ne soit perdu, sans réfrigération.

Et Khurrum Khan, un imprimeur de 39 ans, voyait les commandes s'empiler, sans pouvoir y répondre. Les problèmes d'électricité sont "une malédiction permanente dont nos gouvernements n'ont pas réussi à se débarrasser", regrettait-il.

Une panne similaire en janvier 2021 avait plongé l'essentiel de ce pays de 220 millions d'habitants dans le noir pendant plusieurs heures, après qu'un dysfonctionnement technique dans le sud eut déclenché une réaction en chaîne dans plusieurs centrales électriques.

Les coupures d'électricité sont un problème récurrent au Pakistan. Mais la situation s'est encore détériorée ces derniers mois, la situation financière dramatique du pays aggravant un peu plus ses difficultés d'approvisionnement énergétique.

Les ménages ont été touchés, mais l'industrie également, notamment le textile, qui représente environ 60% des exportations pakistanaises.

We introduce a unique inductive bias to improve the reconstruction quality of Neural Radiance Fields (NeRF), NeRF employs the Fourier transform to map 3D coordinates to a high-dimensional space, enhancing the representation of high-frequency information in scenes. However, this transformation often introduces significant noise, affecting NeRF's robustness. Our approach allocates attention effectively by segregating channels within NeRF using attention-based gating units. We conducted experiments on an open-source data set to demonstrate the effectiveness of our method, which leads to significant improvements in the quality of synthesised new-view images compared to state-of-the-art methods. Notably, we achieve an average PSNR increase of 0.17 compared to the original NeRF. Furthermore, our method is implemented through a carefully designed special Multi-Layer Perceptron (MLP) architecture, ensuring compatibility with most existing NeRF-based methods.

This article studies the application of artificial intelligence technology in natural resource planning and management. This article first introduces the background of NR and AI intelligent technology, then conducts academic research and summary on NR planning management and AI intelligent technology. Then, an algorithm model based on multi-objective intelligent planning algorithm is established. Finally, simulation experiments are conducted, and experiments summary and discussion are provided. The experimental results show that the average efficiency value of the four stages of NR planning and management before use is 5.25, and the average efficiency value of the four stages of NR planning and management after use is 7. The difference in the average efficiency value before and after use is 1.75. It can be seen that the use of AI intelligent technology can effectively improve the efficiency of natural resource planning and management.

This study is unique in how it looks at how crowdfunding on the recurring pledge platform Patreon is associated with the frequency of video creation. It analyses factors that make video creators on YouTube more likely to crowdfund on Patreon. It finds channels that upload more frequently, younger channels, channels with more subscribers and views per video, and channels that shared their Facebook pages were more likely to crowdfund on that platform.

Aim/Purpose: This study empirically analyzed and examined the effectiveness of the online advocacy banners on customers’ reactions to make replacements with the similar products in their shopping carts. Background: When a product in a shopping cart is removed, it might be put back into the cart again during the same purchase or it may be bought in the future. Otherwise, it might be abandoned and replaced with a similar item based on the customer’s enquiry list or on the recommendation of banners. There is a lack of understanding of this phenomenon in the existing literature, pointing to the need for this study. Methodology: With a database from a Taiwanese e-retailer, data were the tracks of empirical webpage clickstreams. The used data for analyses were particularly that the products were purchased again or replaced with the similar ones upon the advocacy banners being shown when they were removed from customers’ shopping carts. Few pre-defined Apriori rules as well as similarity algorithm, Jaccard index, were applied to derive the effectiveness. Contribution: This study addressed a measurement challenge by leveraging the information from clickstream data – particularly clickstream data behavior. These data are most useful to observe the real-time behavior of consumers on websites and also are applied to studying click-through behavior, but not click-through rates, for web banners. The study develops a new methodology to aid advertisers in evaluating the effectiveness of their banner campaign. Findings: The recommending/advocating titles of “you probably are interested” and “the most viewed” are not significantly effective on saving back customers’ removed products or repurchasing similar items. For the banners entitled “most buy”, “the most viewed” might only show popularity of the items, but is not enough to convince them to buy. At the current stage on the host website, customers may either not trust in the host e-retailer or in such mechanism. Additionally, the advocating/recommending banners only are effective on the same customer visits and their effects fade over time. As time passes, customers’ impressions of these banners may become vague. Recommendations for Practitioners: One managerial implication is more effective adoption of advocacy/recommendation banners on e-retailing websites. Another managerial implication is the evaluation of the advocacy/recommendation banners. By using a data mining technique to find the association between removed products and restored ones in e-shoppers’ shopping carts, the approach and findings of this study, which are important for e-retailing marketers, reflect the connection between the usage of banners and the personalized purchase changes in an individual customer’s shopping cart. Recommendation for Researchers: This study addressed a new measurement which challenges to leverage the information from clickstream data instead of click-through rates – particularly retailing webpages browsing behavior. These data are most useful to observe the real-time behavior of consumers on websites and also are applied to studying click-through behavior. Impact on Society: Personalization has become an important technique that allows businesses to improve both sales and service relationships with their online customers. This personalization gives e-marketers the ability to deliver real effectiveness in the use of banners. Future Research: The effectiveness is time- and case-sensible. Business practitioners and academic researchers are encouraged to apply the mining methodology to longevity studies, specific marketing campaigns of advertising and personal recommendations, and any further recommendation algorithms.

Aim/Purpose: This study investigated factors influencing the intention to engage in green innovation among small and medium enterprises (SMEs) in the tourism sector, using an integrated approach from the pro-environmental planned behavior (PEPB) and technology organization environment (TOE) models. Background: Green innovation is a long-term strategy aimed at addressing environmental challenges in the Indonesian tourism sector, especially those related to SMEs in culinary, accommodation, transportation, and creative industries. While prior research primarily focused on innovation characteristics and various behavioral intentions towards new technologies, this study pioneered an approach to understanding green innovation practices among SMEs by examining behavioral intention and the influence of internal organizational and external environmental factors. This was achieved through the PEPB model, which extends the theory of planned behavior (TPB) by incorporating perceived authority support and perceived environmental concern and integrating it with the TOE model. This comprehensive approach was crucial for understanding SME motivations, needs, and challenges in adopting green innovation, thereby supporting environmental sustainability. Methodology: Data were collected through offline and online questionnaires and interviews with 405 SMEs that had implemented green innovation as respondents. The theoretical model was tested using partial least squares structural equation modeling (PLS-SEM) with top-level constructs. Contribution: This research contributed to the development and validation of an integrated model for green innovation in SMEs, offering insights and recommendations for all stakeholders in the tourism sector to formulate effective green innovation strategies. Findings: This research revealed that the integrated model of pro-environmental planned behavior and technology organization environment successfully explained 71% of the factors influencing the intention to engage in green innovation for SMEs in the tourism sector. Perceived authority support emerged as the strongest factor, while perceived behavioral control was identified as a weaker factor. Recommendations for Practitioners: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Recommendation for Researchers: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Impact on Society: Examining the factors influencing the intention to engage in green innovation among SMEs in the tourism sector carried significant social implications. The findings contributed to recommending strategies for businesses and stakeholders such as the government, investors, and tourists to collectively strive to minimize environmental damage in tourist areas through the implementation of green innovation. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope to include diverse regions and industries and using additional approaches, such as leadership theory and management commitment theories, can increase the R-squared value. Additionally, broadening the profile of interviewees to obtain a more comprehensive understanding of the intention to engage in green innovation should be considered.

DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

Unmanned stores are one of the latest conceptual developments in retail and have received much attention, especially in the context of COVID-19-related social restrictions and the associated changes in consumer behaviour. The concept considers the latest technological developments and promises to offer various benefits to consumers and retailers based on artificial intelligence and automation. Using a German sample, this paper aims to evaluate consumers' acceptance of and intention to use the most prominent innovative solutions in unmanned stores. A modified technology acceptance model (TAM) as a theoretical framework was applied to the study. The results of the structural equation modelling make two contributions to the existing literature: First, the acceptance criteria for unmanned stores have not been analysed previously. Second, the modified TAM could be confirmed in this study. We provide empirical evidence suggesting that significant numbers of consumers accept unmanned stores, especially if the stores are strategically located and when individuals have a high innovation affinity.

Aim/Purpose: This study seeks to uncover the perceived quality factors of computer-mediated communication in business planning in which communication among teammates is crucial for collaboration. Background: Computer-mediated communication has made communicating with teammates easier and more affordable than ever. What motivates people to use a particular CMC technology during business planning is a major concern in this research. Methodology: This study seeks to address the issues by applying the concept of Information Product Quality (IPQ). Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), an experimental study was conducted to identify the factors that are perceived as most relevant. Contribution: The findings in this study will help developers find a more customer-oriented approach to developing CMC technology design, specifically useful in collaborative work, such as business planning. Findings: This study extracted the three specific quality factors to use CMC technology in business planning: informational, physical, and service. Future Research: Future research will shed more light on the generality of these findings. Future studies should be extended to other population and contextual situations in the use of CMC.

An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.

COVID-19 created a surge in e-commerce usage, leading to fierce channel competition between the manufacturer's online sales and the offline retailer. Hence, the imperative need for effective and innovative optimisation strategies to mitigate channel competition. Manufacturer-coupons are widely practiced in market, yet research on the importance they play in coordinating channel competition to achieve optimisation in channel distributions is scarce. This research addresses this gap by examining the effectiveness of manufacturer-coupons on the coordination of the manufacturer's online sales and offline retailer's sales. The findings indicate that issuing a manufacturer-coupon to the customers who buy from the offline retailer reduces the competition in the different channel distributions, but cost sharing of the retailer coupon is a better strategy. We thus examine if profit sharing is an effective strategy to facilitate the use of manufacturer-coupon in the market. After comparing different scenarios, we conclude that advanced profit-sharing can be effective in making manufacturer-coupon prevalent in the market and thus alleviate channel competition effectively.

This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members' risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer's value-added services in their direct channel will improve the direct price while decreasing the retail price. Consumers' channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increases while the retail price decreases in the manufacturer's value-added services. The retailer's risk aversion has a greater influence on price decisions than that of the manufacturer.

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a […]

The post Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach appeared first on UMBC ebiquity.

Results using the reinforcement learning technique on two SAT benchmarks using a D-Wave 2000Q quantum processor showed significantly better solutions with fewer samples compared to the best-known quantum annealing techniques.

The post Paper: Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata appeared first on UMBC ebiquity.

Prior research on mergers and acquisitions (M&As) has substantially advanced our understanding of how isolated acquirer- and deal-specific factors affect abnormal returns. However, investors are likely to perceive and evaluate M&As holistically—that is, as complex configurations (i.e., Gestalts) of characteristics, rather than as a list of independent factors. Yet, extant M&A literature has not addressed why and how configurations of factors elicit positive or negative reactions. In other words, overlooking the interdependent nature of factors known to influence acquisition success has limited our understanding of both M&As and investor judgment. Taking an inductive approach to addressing this important issue, this study relies on fuzzy set methodology. Our results provide compelling evidence that investor perceptions of M&A announcements are not only configurational in nature but also characterized by equifinality - or the presence of multiple paths to success - and asymmetric causality - that is, configurations that represent bad deals are not simply a mirror image of good deals, but differ fundamentally. By constructing a typology of "good" and "bad" deals as perceived by market participants, we develop a mid-range theory of M&A stock market performance. As such, this study offers novel theoretical and empirical insights to scholars, and implications for practitioners.

Studies suggest that category-spanning organizations receive lower evaluation and perform worse than organizations focused on a single category. We propose that (1) these effects are contingent on clients' theory of value and that as clients expect more sophisticated services, they tend to value category spanners more positively and (2) the evaluation of producers mediates the relationship between category spanning and performance. We test our hypotheses using original data on corporate legal services in three markets (London, New York City, and Paris) over the decade 2000-2010. We find that (1) category spanners receive a better evaluation, and more so when their categorical combination is more inclusive and (2) evaluation mediates significantly the relationship between category spanning and performance. This study enriches our understanding of how audiences apprehend a whole market category system and why organizations span categories.

Hollie Peabody returns to work in the prisons service after getting to the semi-finals of The Voice.

Fresh on the heels of a TiKtok ban, the US Government has banned Kapsery Software in the US due to its ties to the Russian Government. Ban Implementation Details On June 20, 2024, the https://www.reuters.com/technology/biden-ban-us-sales-kaspersky-software-over-ties-russia-source-says-2024-06-20/US government announced new restrictions on inbound sales of Kaspersky software in the US will come into effect on September 29, 2024, giving businesses 100 days to transition to alternative cybersecurity solutions. The ban will also apply to software up...

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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.

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