Reviewerly/exHarmony huggingface dataset:
SiamGauss: Siamese region proposal network with Gaussian head for visual object tracking

We propose SiamGauss, a Siamese region proposal network with a Gaussian head for single-target visual object tracking for aerial benchmarks. Visual tracking in aerial videos faces unique challenges due to the large field of view resulting in small size objects, similar looking objects (confusers) in close proximity, occlusions, and fast motion due to simultaneous object and camera motion. In Siamese tracking, a cross-correlation ration is performed in the embedding space to obtain a similarity map of the target within a search frame, which is then used to localize the target. The proposed Gaussian head helps suppress the activation produced in the similarity map on confusers present in the search frame during training while boosting the confidence on the target. This activation suppression improves the confuser awareness of our tracker. In addition, improving the activation on the target helps maintain tracking consistency in fast motion. Our proposed Gaussian head is only applied during training and introduces no additional computational overhead during inference while tracking. Thus, SiamGauss achieves fast runtime performance. We evaluate our method on multiple aerial benchmarks showing that SiamGauss performs favorably with state-of-the-art trackers while rating at a frame rate of 96 frames per second.
Virtual talk assistance for Dumb and Deaf

For the Deaf and Dumb community, the use of Information and Communication Technology has increased the ease of life for them. Deaf and dumb people communicate with help of sign language to pass their messages to each other. In this way, they can’t express their ideas the exact way, they want. The implementation of Speech To Text and Text To Speech technique for deaf and dumb people to make them communicate better. Here the compared elaborated research work done in the same field which may be helpful in identifying the drawbacks as well as methods to improvise the present technology. It presents a detailed methodology used in numerous research work and advancements for the deaf and dumb community.
Intelligent Solutions for Manipulating Purchasing Decisions of Customers Using Internet of Things during Covid-19 Pandemic

It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks.
IR Evaluation and Learning in the Presence of Forbidden Documents

Many IR collections contain forbidden documents (F-docs), i.e. documents that should not be retrieved to the searcher. In an ideal scenario F-docs are clearly flagged, hence the ranker can filter them out, guaranteeing that no F-doc will be exposed. However, in real-world scenarios, filtering algorithms are prone to errors. Therefore, an IR evaluation system should also measure filtering quality in addition to ranking quality. Typically, filtering is considered as a classification task and is evaluated independently of the ranking quality. However, due to the mutual affinity between the two, it is desirable to evaluate ranking quality while filtering decisions are being made. In this work we propose nDCGf, a novel extension of the nDCGmin metric[14], which measures both ranking and filtering quality of the search results. We show both theoretically and empirically that while nDCGmin is not suitable for the simultaneous ranking and filtering task, nDCGf is a reliable metric in this case.
Design of a Public Cultural Service System for the Elderly Based on Intelligent Computing

Public cultural services for the elderly are part of the government-led public services for society in order to meet the needs of people’s spiritual and cultural life. As the global problem of ageing becomes increasingly serious, the problem of balancing the supply and demand of public cultural services for the elderly has become increasingly serious. How to realize a public cultural service system for the elderly with wide coverage and effective integration of resources has become a pressing problem to be solved. In order to solve the above problems, this article designs a public cultural service system for the elderly based on intelligent computing. Firstly, the problems that lead to the inefficient use of public cultural resources for the elderly are analyzed. Secondly, it attempts to introduce an advanced intelligent computing technology (Cuckoo Search) to build a three-dimensional public cultural resource sharing system for the elderly. Then, the standard Cuckoo algorithm is improved by using chaotic mapping and dynamic step size for the characteristics of public cultural services in order to improve the convergence speed and the accuracy of the search. The experimental results show that the designed system can effectively integrate the grassroots resources of cultural institutions such as libraries, cultural centres, and cultural service centres, verifying its effectiveness and feasibility.
A Three-Stage Anomaly Detection Framework for Traffic Videos

As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research.
Personalizing the Sequencing of Learning Activities by Using the Q-Learning and the Bayesian Knowledge Tracing

In this paper, we present an approach for personalizing the sequencing of learning activities that relies on the Q-learning. The Q-learning learns a sequencing policy to select learning activities that aims to maximize the learning gain of students. On the one hand, the core of this approach is the use of the Bayesian knowledge tracing (BKT) to model the student knowledge state and to define the Q-Learning reward function. On the other hand, we defined with experts rules to generate simulated students. These simulated data were used to initialize the Q-table of the Q-Learning and answer its“cold start” problem. We present empirical results showing that the sequencing policy learned from the expert-based initialization of the Q-table provides the system with an efficient strategy to improve the students’ knowledge states in comparaison with the Q-table randomly initialized. We further show that Q-Learning approach based on the knowledge states of the students inferred by the BKT are promising way for adaptive instruction in intelligent tutoring systems.
Lexical Resource Creation and Evaluation: Sentiment Analysis in Marathi

In India, the raise of regional language contents over social media, websites, blogs and news article are exponentially increasing because of ease of use of technology, and people are expressing their thoughts, opinion more conveniently and powerfully over Internet. In this paper, we evaluate the challenges of sentiment analysis in Marathi by setting up a baseline, where we produced an annotated dataset, however, initially, we created an annotated dataset consisting of Marathi news scraped from various newspaper/channel websites. Furthermore, domain experts annotated Marathi news with positive, negative and neutral polarity. And we used machine learning models such as logistic regression, Stochastic Gradient Decent (SGD), support vector machine (SVM), nearest neighbour, neural network, decision tree (DT), Naïve Bayes (NB) and proposed ensemble-based model for sentiment analysis to demonstrate effectiveness. In experimentation, the proposed ensemble classifier outperforms other classifiers with an accuracy of 94.16% and an F-score of 97.02% for fivefold validation. Also, for tenfold validation, the accuracy is 95.07%, and the F-score is 96.93%.
Single User Group Recommendations

Going to restaurants is also a social activity; people often go to restaurants with family, friends, or colleagues. However, most restaurant finder systems, such as TripAdvisor, allow users to search for restaurants matching only one user’s preferences. We present here a system GUI aiming at extending such systems to support the organizer of an event in finding a proper restaurant for her group. The organizer is responsible for expressing the group members’ preferences, analyzing the recommendations, and finally selecting a restaurant. We have identified three recommendation techniques (popularity-based, relevance-based, and critiquing-based) to support such a task. These techniques make different assumptions on the amount of information about the group members’ preferences available to the organizer, and they support alternative choice patterns. Moreover, the proposed system supports in the final decision-making stage by: a) indicating the extent to which a recommended restaurant is attractive to each group member, w.r.t. the entered preferences, b) suggesting a good choice for the group, and c) illustrating the similarity of other groups which have previously bookmarked one of the recommended restaurants.
Thermographic image super-resolution based on neural networks

The continuous development of thermographic technology has led to the overall improvement of instruments used in medicine, surveillance, or military systems. However, thermographic imaging cameras still have a high cost compared to other alternatives on the market, such as visible light cameras and a much lower spatial resolution. Super-resolution is a technique that improves the visual quality of an image through software processing. This work studies three neural networks architectures based on deep learning capable of performing super-resolution of RGB images at x2 and x4 scales: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN), Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR), and Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR). These architectures, in this work, have been trained as a super-resolution system using thermographic images as input data. The evaluation was carried out using thermographic images from different thermographic cameras. The performance assessment was carried out using the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM). In addition, low-resolution images from a low-cost thermographic camera were used as input to the neural networks to study the feasibility of this method.
Stepping Into the Next Decade of Ubiquitous and Pervasive Computing: UbiComp and ISWC 2021

The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) took place from September 21–26 virtually, around the world; co-located with the International Symposium on Wearable Computers (ISWC). This article discusses some of the interesting research published in the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, as well as those accepted at ISWC this year—presented virtually with Live Q&A over Whova, Zoom, and Gather.Town. Furthermore, it covers some of the interesting plenary events, such as the Keynotes by Cynthia Breazeal (MIT Media Labs) and John LePore (Perception), as well as the panel discussion on the 30th anniversary of Mark Weiser’s paper on the Vision of a Computer for the 21st Century. Finally, some of the interesting highlights from the ISWC gadget-show and design-exhibition, alongside multiple virtual workshops as part of the adjunct program have been presented.
ADPL

Traditional dialogue summarization models rely on a large-scale manually-labeled corpus, lacking generalization ability to new domains, and domain adaptation from a labeled source domain to an unlabeled target domain is important in practical summarization scenarios. However, existing domain adaptation works in dialogue summarization generally require large-scale pre-training using extensive external data. To explore the lightweight fine-tuning methods, in this paper, we propose an efficient Adversarial Disentangled Prompt Learning (ADPL) model for domain adaptation in dialogue summarization. We introduce three kinds of prompts including domain-invariant prompt (DIP), domain-specific prompt (DSP), and task-oriented prompt (TOP). DIP aims to disentangle and transfer the shared knowledge from the source domain and target domain in an adversarial way, which improves the accuracy of prediction about domain-invariant information and enhances the ability for generalization to new domains. DSP is designed to guide our model to focus on domain-specific knowledge using domain-related features. TOP is to capture task-oriented knowledge to generate high-quality summaries. Instead of fine-tuning the whole pre-trained language model (PLM), we only update the prompt networks but keep PLM fixed. Experimental results on the zero-shot setting show that the novel design of prompts can yield more coherent, faithful, and relevant summaries than baselines using the prefix-tuning, and perform at par with fine-tuning while being more efficient. Overall, our work introduces a prompt-based perspective to the zero-shot learning for dialogue summarization task and provides valuable findings and insights for future research.
Unsupervised Product Offering Title Quality Scores

The title of a product offering is the consolidation of a product's characteristics in textual format for user consumption. The low quality of the textual content of a product's title can negatively influence the entire shopping experience. The negative experience can start with the impossibility of discovering a desired product, going from problems in identifying a product and its characteristics up to the purchase of an unwanted item. A solution to this problem is to establish an indicator that automatically describes the quality of the product title. With this assessment, it is possible to notify sellers who have registered products with poor quality titles and encourage revisions or suggest improvements. The focus of this work is to show how it is possible to assign a score that indicates the descriptive quality of product offers in an e-commerce marketplace environment using unsupervised methods.
Fast and effective pedestrian detection based on low-level visual features combination

Automatic pedestrian detection is a crucial task for intelligent vehicles as it helps avoid car-to-pedestrian accidents. Recently, several approaches have been proposed to improve detection accuracy, such as the channel-based family, which is based on the aggregation of channel features (e.g., HOG + LUV) to represent the shape of pedestrians. Previous handcrafted-based detectors significantly improved the detection performance by applying different filters to channel features. However, when there are similar-looking background objects, their detection performance suffers due to the lack of texture features. Another drawback of these approaches lies in their high computational cost, limiting their use for real-world applications, especially for resource constrained systems. To mitigate these drawbacks while maintaining a distinguishable detection accuracy, we propose a method that combines a simple but effective texture descriptor based on local binary patterns with HOG + LUV. The concatenated features are used to train a boosted decision trees classifier. To evaluate our approach, we carried out extensive experiments on four well-known datasets, including INRIA, ETH, KITTI, and Caltech. Experimental results show that the proposed method achieves a competitive performance to the channel-based detectors with significantly lower computational cost; it runs at 91.64 and 6.08 times faster than the closest competitors detectors on the INRIA and Caltech datasets, respectively. Furthermore, it outperforms these methods when evaluating under the corrected annotations of the Caltech dataset.
A Content Recommendation Policy for Gaining Subscribers

How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.
Auto-eD: A visual learning tool for automatic differentiation

Most fields of scientific inquiry require the evaluation of derivatives to calculate and optimize quantities of interest.Automatic differentiation is a set of techniques that allow the differentiation of computer programs to machine precision without requiring full symbolic derivatives (Baydin et al., 2017;Griewank, 1989).The great success of machine learning algorithms, and neural networks in particular, was partly enabled by the celebrated backpropagation algorithm (Werbos, 1990), which is a special case of automatic differentiation.Given the rapidly increasing interest in algorithms that rely on automatic differentiation, and the evolution towards differential programming paradigms (Innes et al., 2019), it is important that students be taught the basics of this key family of algorithms.
A Blockchain-Based Cross-Domain Data Sharing Scheme for VANETs

Recently, the rapid development of 5G communication technology enables the interaction and data sharing in the vehicular ad-hoc networks, which further improves the vehicular safety and travel efficiency. However, the current researches of vehicular data sharing and management only focus on a single domain, while fail to adapt to the multi-domain scenarios. Moreover, the issues of privacy preserving and resistance of attacks are also not considered. To this end, we propose a hierarchical data sharing and management system for vehicular ad-hoc networks by combining blockchain and trusted execution environments to achieve transparent data transmission and privacy preserving, wherein the consortium blockchain is regarded as the underlying platform, and the trusted execution environments using Intel SGX is leveraged to guarantee the secure execution of smart contract. Finally, the security analysis and performance evaluation are provided to show that our system can perform well in terms of privacy preserving and data sharing efficiency.
SEGBFT: A Scalable Consensus Protocol for Consortium Blockchain

Blockchain technology is moving towards the 3.0 era, and the consortium blockchain's commercial application value has become the main concern and research hotspot. Consensus protocol is used to solve the consistency problem of blockchain system, and it directly determines the performance, fault tolerance and correctness of blockchain, PBFT algorithm is the most used consensus algorithm in consortium blockchain, but it has the problems of poor scalability and does not support nodes to dynamically join or exit the system. In this paper, we propose the SEGBFT algorithm, which can reduce the communication complexity from to . In the simulation test, its throughput is close to twice that of PBFT algorithm, and the average transaction delay is less than half of that of PBFT algorithm. Moreover, the Byzantine Grouping Algorithm and reorganization mechanism proposed by SEGBFT realize the dynamic adjustment of the number of nodes in the blockchain network.
Blockchain Technology Research Hotspots from the Perspective of Smart City

It is very important to understand the current situation of blockchain technology on a global scale. The large-scale application of blockchain technology is an inevitable trend. This article revolves around published papers related to blockchain technology, using keywords and abstract content to search for Chinese papers and English papers. Relevance analysis and sorting through the retrieved documents with six core layers of blockchain respectively: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, the study found that China's research is more preference and application of landing and industry, while international research is more preferred to the research of the underlying technology of finance and blockchain. This article illustrates the current status of blockchain technology and applications from the smart city, and contribute to scholars and technicians who are interested in or research blockchain.