Reviewerly/exHarmony huggingface dataset:
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues.

Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A key stumbling block in making progress towards this goal is a lack of appropriate training data, stemming from the high complexity of sign annotation and a limited supply of qualified annotators. In this work, we introduce a new scalable approach to data collection for sign recognition in continuous videos. We make use of weakly-aligned subtitles for broadcast footage together with a keyword spotting method to automatically localise sign-instances for a vocabulary of 1,000 signs in 1,000 hours of video. We make the following contributions: (1) We show how to use mouthing cues from signers to obtain high-quality annotations from video data - the result is the BSL-1K dataset, a collection of British Sign Language (BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train strong sign recognition models for co-articulated signs in BSL and that these models additionally form excellent pretraining for other sign languages and benchmarks - we exceed the state of the art on both the MSASL and WLASL benchmarks. Finally, (3) we propose new large-scale evaluation sets for the tasks of sign recognition and sign spotting and provide baselines which we hope will serve to stimulate research in this area.
Face Detection Based on Image Stitching for Class Attendance Checking

Traditional attendance checking relies on teachers to name students in the class, which has many shortcomings, including time wastage and fake sign-ins. We use an online system to allow the students to sign in and take advantage of the face detection technology to help teachers check attendance in their classes. Because large classrooms require teachers to take multiple photos to include all students, picture stitching is required before face detection can be performed. We need to solve the problems of image registration, image fusion, adaptation of shooting angles, the proportion of overlapping parts of images, and the problem that YOLOv3 cannot detect small faces. Our research focuses on achieving a good result in stitching and improving the accuracy of face detection. The proposed system has tried to give teachers some good advice on photo shooting angles and overlapping proportions of multiple photos. Also, we make some improvements in feature point extraction and small faces detection algorithm etc. The experimental results show that the system can identify nearly 100% faces in the photos.
“Bibliosphere” Journal over Fifteen-Year Period: Analytical Review of Methodological and General Theoretical Articles

In 2005, the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences established the “Bibliosphere” Journal, well known in our sector multidisciplinary periodical on library science, bibliography, computer science, book studies and related cultural issues. It formed the synthetic heading covering issues common to the entire bibliosphere, and the word “Bibliosphere” has become a professional term with its own definition. The Journal constantly maintains the columns on library science, bibliology, bibliography science and others. The article presents the review of the publications of synthetic nature that have methodological and general theoretical significance for the complex of the related disciplines that form bibliosphere: library science, bibliography science, bibliology, book culture and computer science. Maintaining the column “Methodology of Scientific Research Works” is specific particular to “Bibliosphere” Journal and refers to its undoubted success. From the very first issues, the Journal crossed the planned regional scale and declared itself as a serious periodical of theoretical and methodological level.
Exploring the Performance Characteristics of the Naïve Bayes Classifier in the Sentiment Analysis of an Airline’s Social Media Data

Airline operators get much feedback from their customers which are vital for both operational and strategic planning.Social media has become one of the most popular platforms for obtaining such feedback.However, to analyze, categorize, and generate useful insight from the huge quantity of data on social media is not a trivial task.This study investigates the capability of the Naïve Bayes classifier for analyzing sentiments of airline image branding.It further examines the impact of data size on the accuracy of the classifier.We collected data about some online conversations relating to an incident where an airline's security operatives roughly handled a passenger as a case study.It was reported that the incident resulted in a loss of about $1 billion of the company's corporate value.Data were extracted from twitter, preprocessed and analyzed using the Naïve Bayes Classifier.The findings showed a 62.53% negative and 37.47% positive sentiments about the incident with a classification accuracy of over 0.97.To assess the impact of training size on the accuracy of the classifier, the training sets were varied into different sizes.A direct linear relationship between the training size and the classifier's accuracy was observed.This implies that large training data sets have the potentials for increasing the classification accuracy of the classifier.However, it was also observed that a continuous increase in the classification size could lead to overfitting.Hence there is a need to develop mechanisms for determining optimum training size for finest accuracy of the classifier.The negative perceptions of customers could have a damaging effect on a brand and ultimately lead to a catastrophic loss in the organization.
Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

With the development of motorization, road traffic crashes have become the leading cause of death in many countries.Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions.To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation.Aggressive driving behaviors create many kinds of roadway traffic safety hazards.Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety.This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status.Histogram of gradients (HOG) was applied to extract facial features.Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion.Extracted features with its probability were sent to Bayesian Gaussian process classier as input.Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features.An overall accuracy rate of 86.2% was achieved in this study.Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.
On the potential for detecting scientific issues and controversies on Twitter : A method for investigation conversations mentioning research

In this study, we demonstrate how to collect Twitter conversations emanating from or referring to scientific papers. We propose segmenting the conversational threads into smaller segments and then ...
Black-box Adversarial Sample Generation Based on Differential Evolution

Deep Neural Networks (DNNs) are being used in various daily tasks such as object detection, speech processing, and machine translation. However, it is known that DNNs suffer from robustness problems -- perturbed inputs called adversarial samples leading to misbehaviors of DNNs. In this paper, we propose a black-box technique called Black-box Momentum Iterative Fast Gradient Sign Method (BMI-FGSM) to test the robustness of DNN models. The technique does not require any knowledge of the structure or weights of the target DNN. Compared to existing white-box testing techniques that require accessing model internal information such as gradients, our technique approximates gradients through Differential Evolution and uses approximated gradients to construct adversarial samples. Experimental results show that our technique can achieve 100% success in generating adversarial samples to trigger misclassification, and over 95% success in generating samples to trigger misclassification to a specific target output label. It also demonstrates better perturbation distance and better transferability. Compared to the state-of-the-art black-box technique, our technique is more efficient. Furthermore, we conduct testing on the commercial Aliyun API and successfully trigger its misbehavior within a limited number of queries, demonstrating the feasibility of real-world black-box attack.
Data Prediction Model Using Combination of Clustering and Fuzzy Technique

The analysis of environmental daily evaporation plays a vital role in the field of agriculture.It is very essential to know the daily evaporation rate of a particular area for proper cultivation.So, we need a standard prediction model which can predict the daily evaporation.In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area.The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output.The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value.The finding of the paper is applicable in environmental science, geological science and agriculture.
Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews

Product reviews are a huge source of natural language data in e-commerce applications.Several millions of customers write reviews regarding a variety of topics.We categorize these topics into two groups as either "category-specific" topics or as "generic" topics that span multiple product categories.While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category.In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach.We show that our proposed method can work at scale on real product reviews at Tokopedia 1 , a major e-commerce platform in Indonesia.Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.
New Hybrid Proposed Solution for Video Steganography Based on Clustering Algorithm

Through the advent of digital information, information security has taken on a vital role. Use of the internet freely for contact has increased the attacks on users. Data protection is the present problem relating to privacy and security during storage and communication. This paper deals with a multilayered protected channel's proposal to transfer confidential data/video through a channel. The hidden video is initially encrypted using the algorithm NOLSB. The created cipher video is hidden in a larger sized video file. In addition, this video file is encrypted using the (m, k) firm technique to optimize resource efficiency and bandwidth optimization. Video shares are then sent through all the network's channels to ensure security. This approach ensures that video files could be retrieved at the end receiver even though any shares were lost over the network, without the sender needing to resend the video file.
<i>Cyberinsights</i> : It's Complicated

[ ]North Korea's hacking teams are engaging in cyber-criminal activities, including some large breaches of financial institutions around the globe, to help prop up the nation's economy 1 What Makes Them Tick Diving a little deeper on the respective group's motivation, five key objectives can be recognized: [ ]we are observing an increase in the theft of healthcare intellectual property, because it is seen as a means for advancing these countries' health systems [ ]most recently, some APT groups also have added air-gap capabilities to their toolbelt 9 Various malware strains with this type of capability have been detected recently, and in principle, they all take a similar approach: penetrate the connected part of the network, infect USB drives that are used within the environment, eventually land on an airgapped system, perform desired action (e g , data collection), and wait for another USB connection to transfer data back into the connected environment 10 This approach by cyber adversaries requires patience but is likely to succeed eventually According to a recent study, 45% of the analyzed breaches could be attributed to the healthcare sector, with related costs of $17 billion during 2019—amounting to an average impact of $429 per breached record 15 According to data from the Department of Health & Human Services' “Wall of Shame” for the period of 2010–18, not only has the number of breaches (500 records/event) risen steadily at about 7% a year, it increased by 36% from 2018 to 2019—the highest single-year increase so far
The unified approach to a definition of effective conductivity index of percolating cluster in macroscopically disordered environments

Abstract The paper proposes the unified approach to a definition of effective conductivity index of percolating cluster’s t fractal, quasi-fractal and n homogeneous parts. The approach is based on probabilistic-statistical nature of percolation cluster’s formation. Two functions t (ϑ) and n (ϑ) are presented. By the use of simple exponentiations they allow describing the behavior of maximum of critical macroscopically disordered environments’ effective conductivity in case of the given values of concentration ϑ elements of conductive component from percolation threshold ϑ = ϑ c to their entire consolidation ϑ = 1.
On the constructions of $n$-cycle permutations

Any permutation polynomial is an $ n $-cycle permutation. When $n$ is a specific small positive integer, one can obtain efficient permutations, such as involutions, triple-cycle permutations and quadruple-cycle permutations. These permutations have important applications in cryptography and coding theory. Inspired by the AGW Criterion, we propose criteria for $ n $-cycle permutations, which mainly are of the form $ x^rh(x^s) $. We then propose unified constructing methods including recursive ways and a cyclotomic way for $ n $-cycle permutations of such form. We demonstrate our approaches by constructing three classes of explicit triple-cycle permutations with high index and two classes of $ n $-cycle permutations with low index.
Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) provide on-demand service at a price with a small delay. We propose a QoS-differentiated model where multiple SLAs deliver both on-demand service for latency-critical users and delayed services for delay-tolerant users at lower prices. Two architectures are considered to fulfill SLAs. The first is based on priority queues. The second simply separates servers into multiple modules, each for one SLA. As an ecosystem, we show that the proposed framework is dominant-strategy incentive compatible. Although the first architecture appears more prevalent in the literature, we prove the superiority of the second architecture, under which we further leverage queueing theory to determine the optimal SLA delays and prices. Finally, the viability of the proposed framework is validated through numerical comparison with the on-demand service and it exhibits a revenue improvement in excess of 200%. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.
Trends in the Development of Forensic Face Recognition

The mobility of digital technologies results in the increased number of conducted forensic facial recognitions on video materials obtained during investigations of criminal incidents. Unfortunately, a significant amount of research ends with the conclusion that it is impossible to identify the person in question, because there is not a sufficient number of anatomical features displayed on the video. The author has tried to analyze and reflect on the existing problems, to offer their solutions. To overcome the negative trends in forensic facial recognition a complex of measures is suggested: 1) to improve the quality of methodological support of expert research; 2) to use the entire spectrum of human appearance characteristics (including complex and dynamic) for solving the identifications tasks; 3) to modernize comparison methods through introduction of information technologies; 4) to expand the scope of legal regulation of forensic expertise. In the context of the author’s comprehensive cross-sectoral approach to the procedure of appointment and conduct of identificational facial recognition, its effectiveness will depend on the quality of enhancement of the methodological, legal and technical support for expert research of the entire volume of visual information about a person captured on a video recording.
Fast, Structured Clinical Documentation via Contextual Autocomplete

We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical concepts as a doctor drafts a note by leveraging features from both unstructured and structured medical data. By constraining our architecture to shallow neural networks, we are able to make these suggestions in real time. Furthermore, as our algorithm is used to write a note, we can automatically annotate the documentation with clean labels of clinical concepts drawn from medical vocabularies, making notes more structured and readable for physicians, patients, and future algorithms. To our knowledge, this system is the only machine learning-based documentation utility for clinical notes deployed in a live hospital setting, and it reduces keystroke burden of clinical concepts by 67% in real environments.
Challenges of Implementing Knowledge Management Practices in Nepalese Financial Institutions

Knowledge management (KM) is the research field that still faces some lack of cumulative theoretical development and empirical studies. Despite several opportunities, one faces many challenges to explore these concepts and their implementation at organizational level. The main objective of this study was to explore the challenges of implementing Knowledge Management practice in Nepalese financial institutions. A convergent-parallel mixed method research was designed to explore the prevailing challenges of KM implementation in banking and financial sector in Nepal. In this study, a sample of 385 respondents was selected randomly from four different types of financial institutions of Kathmandu, Nepal. Quantitative as well as Qualitative analysis was carried out to draw the conclusion. This study identified major challenges faced by Nepalese financial institutions and the ways to resolve them and concluded that knowledge management as a practice and behavior could be the most influential strategy in banking and financial sector if opportunities were exploited eradicating the challenges properly.
MessyTable: Instance Association in Multiple Camera Views

We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. Project page: $\href{https://caizhongang.github.io/projects/MessyTable/}{\text{MessyTable}}$
Normal-bundle Bootstrap

Probabilistic models of data sets often exhibit salient geometric structure. Such a phenomenon is summed up in the manifold distribution hypothesis, and can be exploited in probabilistic learning. Here we present normal-bundle bootstrap (NBB), a method that generates new data which preserve the geometric structure of a given data set. Inspired by algorithms for manifold learning and concepts in differential geometry, our method decomposes the underlying probability measure into a marginalized measure on a learned data manifold and conditional measures on the normal spaces. The algorithm estimates the data manifold as a density ridge, and constructs new data by bootstrapping projection vectors and adding them to the ridge. We apply our method to the inference of density ridge and related statistics, and data augmentation to reduce overfitting.
An Explicit Semantics for Event-B Refinements

We present a semi-shallow embedding in Coq of Event-B’s notions of abstract machine and refinement. The abstract structure of Event-B developments, including machines and refinement annotations, can be represented within Coq’s type system, using inductive and dependent types. This formalization allows us to reason at the meta-level on machines and their behaviors, considered as first-class citizens. We show how this formalization of Event-B structures into Coq allows us to model Rodin’s proof obligations, and prove how these obligations entail correctness properties of a given Event-B project. Moreover, the correctness of a given refinement is now an explicit theorem, instead of being an implicit consequence of a set of proof obligations.
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data

With a proliferation of generic domain-adaptation approaches, we report a simple yet effective technique for learning difficult per-pixel 2.5D and 3D regression representations of articulated people. We obtained strong sim-to-real domain generalization for the 2.5D DensePose estimation task and the 3D human surface normal estimation task. On the multi-person DensePose MSCOCO benchmark, our approach outperforms the state-of-the-art methods which are trained on real images that are densely labelled. This is an important result since obtaining human manifold's intrinsic uv coordinates on real images is time consuming and prone to labeling noise. Additionally, we present our model's 3D surface normal predictions on the MSCOCO dataset that lacks any real 3D surface normal labels. The key to our approach is to mitigate the "Inter-domain Covariate Shift" with a carefully selected training batch from a mixture of domain samples, a deep batch-normalized residual network, and a modified multi-task learning objective. Our approach is complementary to existing domain-adaptation techniques and can be applied to other dense per-pixel pose estimation problems.