Reviewerly/exHarmony huggingface dataset:
A CNN Based Speech Recognition Approach for Voice Controlled Elevator

Disabled people face many challenges in day-to-day life. One of the challenges is to use a lift in large buildings. Being unable to use a lift, taking stairs has increased the number of accidents and injuries. To avoid such scenarios, the proposed system acts as an intermediate between humans and machines. Convolutional Neural Network (CNN) based speech recognition method used in the proposed system recognizes the voice command given by the person. The Mel Frequency Cepstral Coefficient (MFCC) features are used. Passengers can give voice commands in Marathi regional language and English language. The system will be useful to communicate with ease and also contactless. Performance of proposed algorithm verified on Marathi, English and both voice commands. This experimentation gave an accuracy of 95%, 85%, and 75% on Marathi, Englishand both language voice commands respectively. The lift is fully automated and there is no need for human intervention which provides all the disabled people with ease of life.
Action anticipation using latent goal learning

To get something done, humans perform a sequence of actions dictated by a goal. So, predicting the next action in the sequence becomes easier once we know the goal that guides the entire activity. We present an action anticipation model that uses goal information in an effective manner. Specifically, we use a latent goal representation as a proxy for the "real goal" of the sequence and use this goal information when predicting the next action. We design a model to compute the latent goal representation from the observed video and use it to predict the next action. We also exploit two properties of goals to propose new losses for training the model. First, the effect of the next action should be closer to the latent goal than the observed action, termed as "goal closeness". Second, the latent goal should remain consistent before and after the execution of the next action which we coined as "goal consistency". Using this technique, we obtain state-of-the-art action anticipation performance on scripted datasets 50Salads and Breakfast that have predefined goals in all their videos. We also evaluate the latent goal-based model on EPIC-KITCHENS55 which is an unscripted dataset with multiple goals being pursued simultaneously. Even though this is not an ideal setup for using latent goals, our model is able to predict the next noun better than existing approaches on both seen and unseen kitchens in the test set. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Tutorial on Stance Detection

Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.
Real-time Vision-based Bangla Sign Language Detection using Convolutional Neural Network

Sign Language is an essential means of communication for people with speech and hearing impairment. In spite of this, there are no effective tools to assist the social interaction between Bangla sign language speakers and non-sign language speakers. Our main objective is to implement an automated translation system that is capable of translating Bangla sign language to Bangla text using common computing environments such as a computer and a generic webcam. The dataset has been created for this project with 1500 images for 10 signs. A seven-layered custom sequential CNN model has been trained and validated with the processed dataset. For real-time detection, we have extracted the region of interest and then detected the specified sign. The system runs in real-time and can provide output from a video feed with a time delay of 120.6 ms. Our system has been tested for an accuracy of 97.0%.
An enhancement method of waveform simulation in dual-color laser field imaging based on Internet of Things

Abstract In view of the fact that the existing methods are easy to be affected by nonlinear factors in the incident process of the light source, resulting in the received shock wave inconsistent with the reality and large calculation error, a two-color laser field imaging waveform simulation and enhancement method based on the Internet of things is proposed. According to the automatic transformation characteristics of augmented reality technology, the waveform simulation control process of two-color laser field imaging is constructed. The Gaussian distribution characteristics are used to analyze the target, the optical grid and double oscillation device are used to control the results, the spectrum Fourier transform is used to adjust the phase of the waveform, and finally the simulated modulated laser waveform is output. Through the comparison of laser waveform spectrum and peak value, the results show that this method is closest to the actual laser waveform, saves calculation time, and has good practical application effect.
SNIP-FSL: Finding task-specific lottery jackpots for few-shot learning

Foresight pruning is an effective approach for deriving a compact sub-network in resource-constrained scenarios. However, most existing methods neglect the fact that scarce resources usually imply insufficient trainable data, thereby considerably affecting the pruning performance. In this paper, we propose a novel single-shot pruning method, named SNIP-FSL, for these few-shot tasks to achieve a trade-off between resources and data scale. Considering the pretrained weights as a special initialization state, we argue that there are task-sensitive high-performance sparse subnetworks in the few-shot learning process, termed “task-specific lottery jackpots.” By designing an effective parameter significance criterion, we obtained these jackpots without additional time-consuming searches and iterations. Furthermore, SNIP-FSL is designed to distinguish task-specific lottery jackpots based on historical experience; thus, it can be easily integrated into most transfer- and meta-based methods. For example, we obtain a winning submodel that has only 30% parameters without compromising accuracy in vanilla ProtoNet. Extensive experimental results reveal that SNIP-FSL attains excellent performance compared with several state-of-the-art foresight pruning methods under both transfer- and meta-paradigms.
An Analysis of User Profiles from Covid-19 Questions in Stack Overflow

The COVID-19 pandemic brought many changes in society, with one of the most important being an explosion of software development concerning technological solutions for combatting its crippling effects. In this global crisis, many software enthusiasts, combined with seasoned developers and specialists turned their attention to Questions and Answers platforms such as Stack Overflow to expand their knowledge and ask questions regarding their COVID-19 related solutions. This paper examines the different characteristics of these users, dividing them into Newcomers and Oldcomers and pinpoints popularity differences, scientific and technological backgrounds by analyzing key technologies, as well as the role of gender in their participation.
SecWalk: Protecting Page Table Walks Against Fault Attacks

The correct execution of a memory load and store is essential for the flawless execution of a program. However, as soon as devices are deployed in hostile environments, fault attacks can manipulate memory operations and subsequently alter the execution of a program. While memory accesses for simple processors with direct memory access can efficiently be protected against fault attacks, larger processors with virtual addressing lack this protection. However, the number of systems with larger application-class processors is growing, leaving many applications unprotected. It requires new countermeasures to efficiently protect memory accesses of processors with virtual memory against fault attacks. In this work, we present SecWalk, a design to efficiently protect all memory accesses of a program in the virtual and physical memory domain against fault attacks. We enhance residual-based pointer protection with a hardware-based secure page table walk inside the memory management unit. The page table walk securely translates a protected virtual address to a protected physical address by exploiting the redundancy properties of encoded addresses and a linking mechanism in the memory management unit. Furthermore, we extend the protection domain for virtual addresses to the TLB to also protect fast translations. To evaluate the overhead, we integrate SecWalk to an FPGA-based open-source RISC-V core, where SecWalk extends the area of the design by 10 %. The software evaluation on a set of microbenchmarks shows an average code overhad of 11.05 % and runtime overhead of 7.17 %. To show the applicability on real-life applications, we port the microkernel seL4 to SecWalk, which yields a code overhead of 13.1 % and a runtime overhead of 11.6 %. The evaluation shows the overhead is small considering that SecWalk automatically protects all memory accesses of arbitrary applications against faults.
A New Cascade-Hybrid Recommender System Approach for the Retail Market

By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also formalized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender’s performance.
Analysis of Sentiments in Movie Reviews using Supervised Machine Learning Technique

Nearly all high-flying branches of machine learning technique is sentiment analysis. A mechanism text summarization process is used determine to decide that document&#x0027;s author&#x0027;s intent. This article proposes to implement and test a system for the automated sentiment analysis of film reviews. In comparison to other studies that foe us solely on sentimental orientation (Positive versus negative), the proposed approach conducts fine-grained research to evaluate both the viewer&#x0027;s sentimental orientation and sentimental intensity against different facets of the film. This article presents an evaluation of the outcome achieved by applying Variational Forest, XGBoost, Linear Regression, Naive Bayes (NB), Maximum Entropy, and Vector Support Machine (SVM). The experimentation shows that by providing lofty dynamic factors to the film, acting, and storyline facets of a film, we achieved the maximum accurateness in the study of the film reviews.
An Instrumented Apartment to Monitor Human Behavior: A Pilot Case Study in the NeuroTec Loft

For patients suffering from neurodegenerative disorders, the behavior and activities of daily living are an indicator of a change in health status, and home-monitoring over a prolonged period of time by unobtrusive sensors is a promising technology to foster independent living and maintain quality of life. The aim of this pilot case study was the development of a multi-sensor system in an apartment to unobtrusively monitor patients at home during the day and night. The developed system is based on unobtrusive sensors using basic technologies and gold-standard medical devices measuring physiological (e.g., mobile electrocardiogram), movement (e.g., motion tracking system), and environmental parameters (e.g., temperature). The system was evaluated during one session by a healthy 32-year-old male, and results showed that the sensor system measured accurately during the participant's stay. Furthermore, the participant did not report any negative experiences. Overall, the multi-sensor system has great potential to bridge the gap between laboratories and older adults' homes and thus for a deep and novel understanding of human behavioral and neurological disorders. Finally, this new understanding could be utilized to develop new algorithms and sensor systems to address problems and increase the quality of life of our aging society and patients with neurological disorders.
Platform Multiverse: Discontent and Disconnection among Alt-Rights

Background: This article explores the platform multiverse of alt-rights, whereby groups and individuals, ideas, and discourses move across and between social platforms to avoid detection, suspension, and moderation. Analysis: This study examines disconnection practice among alt-rights as part of an affiliative assemblage that extends across multiple platforms and works to orient the disconnected toward reconnection. Conclusion and implications: Tracing this alt-right multiverse and the spatial and temporal intensities of disconnection practice exposes the discourses, habits, and interactions of alt-rights across new social platforms.
A Novel Fault-Tolerant Approach to Web Service Composition upon the Edge Computing Environment

In the edge environment, combining existing simple services to build value-added services that to meet users’ needs has become a research hotspot of great practical value. With the increasing popularity of the edge computing paradigm, a large number of web services with similar functions have been created and deployed. Aiming at efficient and trustworthy composition of edge services, we proposed a novel fault-tolerant approach (FTSC) for edge service composition. Employs Primary-Backup (PB) fault-tolerant model to ensure edge service execution under the fault background, and leverages Deep-Q-learning-Network (DQN)-based algorithm for identifying the optimal service composition. For the validation purpose, we conducted extensive simulations based on the real dataset, which showed the proposed method clearly outperforms the traditional ones in terms of edge service completion rate, service active time and resource utilization.
Content-Based Image Retrieval System Using Fuzzy Colour and Local Binary Pattern with Apache Lucene

The explosive increase and ubiquitous accessibility of visual data on the computer, web, and even smartphones have led to the prosperity of research on image retrieval system. With the ignorance of the visual information and the content of the image in the retrieval process, methods such as text-based image retrieval can lead to inconsistency and inaccuracy between the text search and the image result. A more precise yet powerful technique known as content-based image retrieval (CBIR) can analyse and store the visual information of the image in feature vector representation. However, the big challenge is the semantic gap and intention gap to retrieve relevant images. Numerous CBIR methods have been developed by researchers to identify the best approach. This paper proposed a technique using a combination of fuzzy colour and local binary patterns (LBPs), where the ten bins and 24 bins output from the fuzzy colour system are mapped into LBP histogram. The indexing and searching process utilized Apache Lucene, where the inverted index data structure is applied to boost up the retrieval speed. The proposed method is compared and benchmarked with other techniques such as region-based HSV colour histogram, IOSB SIFT, and traditional fuzzy colour and texture histogram (FCTH). The evaluation is based on the indexing time, searching time, rotation, and scaling invariant, as well as the ability to retrieve mostly similar images. The proposed fuzzy colour and local binary pattern (FCLBP) method passed all the criteria with better accuracy as well as short indexing and searching time.
What do cross-country Bitcoin holdings tell us? Monetary and institutional discontent vs financial development

Cryptocurrencies show tremendous growth by market capitalization, however Bitcoin cross-country holdings are still in question. The purpose of the paper is to show that inflation discontent with the rule of law failures can explain why residents of different countries are prone to cryptocurrency holdings. The level of financial development is also considered. A hypothesis is proposed for more complex and segmented motives of Bitcoin holdings, tested by the OLS method. Single- and multi-factor regressions with independent variables are used, which can validate cross-country Bitcoin holdings in terms of inflation discontent, quality of institutions and financial development. Regression results confirm the idea of more segmented motives to hold Bitcoins. First, the hedge against inflation motive is rooted in the institutional weakness of central banks, and the regression results show that inflation variables are the most significant. Second, the hedge against institutional risks of asset ownership motive, based on the lack of rule of law and the relevant variable, is best performing among other institutional variables. Third, it is wrong to neglect financial development. However, it only plays a role in interaction with better innovation performance, meaning that crypto investors try not only to diversify their portfolios, but also to profit from involving in a sector with promising technological perspectives. The main takeaway is that institutional factors help explain why people in countries with worsened inflation and institutional performance tend to hold a large fraction of Bitcoins in assets. Obviously, monetary and institutional fragility is underestimated in the general discussion about the nature of digital money.
Using an Evaluation Grid to Holistically Assess Library Databases

When evaluating subscription-based databases, academic librarians primarily rely on cost-per-use statistics to guide their decision-making. Although this metric is useful, it does not provide a complete picture of a resource’s value to the institution. To adopt a more holistic approach, the University of Mary Washington implemented an evaluation grid, which was used to assess 61 products the library subscribed to during the 2019–2020 academic year. In addition to product cancelations, results were used to identify areas for improvement. Using an evaluation grid enabled UMW Libraries to make high-quality renewal and cancelation decisions in an objective and transparent manner and effectively communicate these decisions to various stakeholders. Libraries wishing to holistically assess their databases can adapt the evaluation grid to meet their local needs.
Fundamental Approach to University Education and Training of IT Professionals

The paper is devoted to education and training of professionals in the IT field in classical universities in the context of the implementation of the Federal Project “Human Resources for the Digital Economy” of the National Program “Digital Economy of the Russian Federation”. The general approach, goals and content of fundamental education and specialized training of Bachelors, Masters and PhD-holders to complex, innovative and research activities have been determined. The structure of Bachelor, Master and PhD programs of the Institute of Applied Mathematics and Computer Science of Tomsk State University modernized on the basis of the above approach, is presented. The paper pays special attention to the training of T-Shaped specialists in IT in classical universities, dwells on the fundamental education of IT specialists. The results of the coordination of programs with employers and students are given.
XCoref: Cross-document Coreference Resolution in the Wild

Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may expose news readers to bias by word choice and labeling. For example, coreferential mentions of “direct talks between U.S. President Donald Trump and Kim” such as “an extraordinary meeting following months of heated rhetoric” or “great chance to solve a world problem” form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., “Donald Trump,” but also abstractly defined concepts, such as groups of persons, “caravan of immigrants,” events and actions, e.g., “marching to the U.S. border.” In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the “wild” of political news articles.
GAN-Based Inter-Class Sample Generation for Contrastive Learning of Vein Image Representations

Biometric vein recognition using deep learning has shown promising performance recently. However, due to the lack of vein image training data, the performance is greatly limited. Although the use of traditional data augmentation can alleviate this problem to some extent, it can only expand the intra-class samples and is fundamentally limited to the intra-class space, resulting in limited performance improvement. In this paper, we tackle this deep learning data shortage problem by proposing a GAN-based framework that can generate arbitrary-pattern vein images, which can augment the training data with new vein classes. The proposed generation framework consists of three progressive synthetic steps, namely, generation of random vein patterns in the binary space, refinement of the binary vein patterns, and rendering them into grayscale vein images. Moreover, we use the synthetic dataset to pretrain the feature embedding network via unsupervised contrastive learning, which allows learning data augmentation-invariant and instance-separating representations. After that, we further fine-tune the embedding network on the real training data in a supervised manner. Our results show that high-fidelity and diverse vein image samples can be generated to alleviate the problem of data shortage and improve the learning of feature representations for biometric vein verification.