Reviewerly/exHarmony huggingface dataset:
Time-Adaptive Statistical Test for Random Number Generators

The problem of constructing effective statistical tests for random number generators (RNG) is considered. Currently, there are hundreds of RNG statistical tests that are often combined into so-called batteries, each containing from a dozen to more than one hundred tests. When a battery test is used, it is applied to a sequence generated by the RNG, and the calculation time is determined by the length of the sequence and the number of tests. Generally speaking, the longer is the sequence, the smaller are the deviations from randomness that can be found by a specific test. Thus, when a battery is applied, on the one hand, the “better” are the tests in the battery, the more chances there are to reject a “bad” RNG. On the other hand, the larger is the battery, the less time it can spend on each test and, therefore, the shorter is the test sequence. In turn, this reduces the ability to find small deviations from randomness. To reduce this trade-off, we propose an adaptive way to use batteries (and other sets) of tests, which requires less time but, in a certain sense, preserves the power of the original battery. We call this method time-adaptive battery of tests. The suggested method is based on the theorem which describes asymptotic properties of the so-called p-values of tests. Namely, the theorem claims that, if the RNG can be modeled by a stationary ergodic source, the value − l o g π ( x 1 x 2 … x n ) / n goes to 1 − h when n grows, where x 1 x 2 … is the sequence, π ( ) is the p-value of the most powerful test, and h is the limit Shannon entropy of the source.
Generating Fact Checking Summaries for Web Claims

We present SUMO, a neural attention-based approach that learns to establish the correctness of textual claims based on evidence in the form of text documents (e.g., news articles or Web documents).SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim.Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation.This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim.However, this design of claimdriven attention does not capture the contextual information in documents properly.We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues.We show the efficacy of our approach on datasets concerning political, healthcare, and environmental issues.
Support vector machine based feature extraction for gender recognition from objects using lasso classifier

Abstract Object detection and gender recognition were two different categories to be classified in a single section is a complicated task and this approach helps in supporting the blind people for an artificial vision. In this paper, our method to the betters vision sensation of blind persons by conversion of visualized data to audio data. Therefore this artificial intelligence model helps in detecting the objects as well as human face recognition with gender classification based on face recognition approach. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform (MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression (LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR-89.6%, EN-93.5%, LR-93.2% and proposed approach (LRGS) with 98.4% accurate detection rate with prediction name of classes.
Improvised Approach for Real Time Patient Health Monitoring System Using IoT

This paper presents the design details of its implementation of an IoT-based Real Time Health Monitoring System for the monitoring of a patient and provides. The proposed Design permits the monitoring of health related dangers and deals with economy in health care expenditure through effective interpretation and distribution of patient data. This paper works on the concept of reducing human presence in monitoring a patient’s problems and sending vital details to the Doctors and Caretakers on a real time basis. This proposal helps saving in and alerts the Doctors/Caretakers in an emergency situation to ensure critical moments are not wasted. The outcome of the proposed system is to provide exact collected medical services to patients through records and information through sensors which monitor the patient’s details relating to Pulse, temperature, Body Tilt Angle and Glucose Level. This information is sent to the patient’s Doctor/Caretaker using a cloud based App on a real time basis.
Dynamical Triangulation Induced by Quantum Walk

We present the single-particle sector of a quantum cellular automaton, namely a quantum walk, on a simple dynamical triangulated $2-$manifold. The triangulation is changed through Pachner moves, induced by the walker density itself, allowing the surface to transform into any topologically equivalent one. This model extends the quantum walk over triangular grid, introduced in a previous work, by one of the authors, whose space-time limit recovers the Dirac equation in (2+1)-dimensions. Numerical simulations show that the number of triangles and the local curvature grow as $t^\alpha e^{-\beta t^2}$, where $\alpha$ and $\beta$ parametrize the way geometry changes upon the local density of the walker, and that, in the long run, flatness emerges. Finally, we also prove that the global behavior of the walker, remains the same under spacetime random fluctuations.
Mobile Assisted Language Learning

One of the requirements to get competitive human resources internationally is mastery technology in learning.On the other hand, means that human has the ability to use information and communication technology to communicate with cognitive and technical skills.In line with the its definition, MALL is the massive solution.Mobile assisted language learning or language learning utilization based mobile phones.The merits of this study be worthy of helping learners easier understand while language learning process is occurring either guided face to face in the classroom or self-learning out of classroom.Study used classroom action research in gaining data.Seeing initial students' ability, this study designed into three cycles in gathered data.However, 30 students at forth semester of English Department were participants of this study.Study noted the using MALL potential to ameliorate students' ability in the end of cycle.In the final analysis results declares using MALL is pretty effective to encourage student's motivation, built-up selflearning language, aids to enrich vocabulary, promote speaking, grammar and listening skill were greater.Implicitly, MALL has merit as a reference for design massive curriculum and materials development.
Open Problems in a Logic of Gossips

Gossip protocols are programs used in a setting in which each agent holds a secret and the aim is to reach a situation in which all agents know all secrets. Such protocols rely on a point-to-point or group communication. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. The advantage of the use of epistemic logic is that the resulting protocols are very concise and amenable for a simple verification. Recently, we introduced a natural modal logic that allows one to express distributed epistemic gossip protocols and to reason about their correctness. We proved that the resulting protocols are implementable and that all aspects of their correctness, including termination, are decidable. To establish these results we showed that both the definition of semantics and of truth of the underlying logic are decidable. We also showed that the analogous results hold for an extension of this logic with the 'common knowledge' operator. However, several, often deceptively simple, questions about this logic and the corresponding gossip protocols remain open. The purpose of this paper is to list and elucidate these questions and provide for them an appropriate background information in the form of partial of related results.
Comparison of data recovery techniques on master file table between Aho-Corasick and logical data recovery based on efficiency

Data recovery is one of the tools used to obtain digital forensics from various storage media that rely entirely on the file system used to organize files in these media.In this paper, two of the latest techniques of file recovery from file system (new technology file system (NTFS)) logical data recovery, Aho-Corasick data recovery were studied, examined and a practical comparison was made between these two techniques according to the speed and accuracy factors using three global datasets.It was noted that all previous studies in this field completely ignored the time criterion despite the importance of this standard.On the other hand, algorithms developed with other algorithms were not compared.The proposed comparison of this paper aims to detect the weaknesses and strength of both algorithms to develop a new algorithm that is more accurate and faster than both algorithms.The paper concluded that the logical algorithm was superior to the Aho-Corasick algorithm according to the speed criterion, whereas the algorithms gave the same results according to the accuracy criterion.The paper leads to a set of suggestions for future research aimed at achieving a highly efficient and high-speed data recovery algorithm such as the file-carving algorithm.
A factorisation algorithm in Adiabatic Quantum Computation

The problem of factorising positive integer N into two integer factors x and y is first reformulated as an optimisation problem over the positive integer domain of either of the Diophantine polynomials or , of each of which the optimal solution is unique with , and x = 1 if and only if N is prime. An algorithm in the context of Adiabatic Quantum Computation is then proposed for the general factorisation problem.
Tweeki: Linking Named Entities on Twitter to a Knowledge Graph

To identify what entities are being talked about in tweets, we need to automatically link named entities that appear in tweets to structured KBs like WikiData.Existing approaches often struggle with such short, noisy texts, or their complex design and reliance on supervision make them brittle, difficult to use and maintain, and lose significance over time.Further, there is a lack of a large, linked corpus of tweets to aid researchers, along with lack of gold dataset to evaluate the accuracy of entity linking.In this paper, we introduce (1) Tweeki, an unsupervised, modular entity linking system for Twitter, (2) Tweeki-Data, a large, automatically-annotated corpus of Tweets linked to entities in WikiData, and (3) TweekiGold, a gold dataset for entity linking evaluation.Through comprehensive analysis, we show that Tweeki is comparable to the performance of recent state-of-the-art entity linkers models, the dataset is of high quality, and a use case of how the dataset can be used to improve downstream tasks in social media analysis (geolocation prediction).
Development of Game Modification Using Blended Learning in Physical Education, Sports, and Health For Senior High School Students

The results of the research were: (1) attractiveness criteria (90.06%), (2) ease criteria (91.35%), (3) clarity criteria (92.58%), (4) effectiveness criteria (90, 41%).The average (91.18%)category is very valid.Based on a large group trial (91.18%) and a small group trial (90.97), this indicates an increase in the yield of 0.21%.Effectiveness test results at each meeting, namely: (1) between the first meeting and the second meeting the average increase of 0.38 with a percentage of 0.48%, (2) between the second and third meetings the average increase of 1.25 with a percentage 1.56%.Overall it was obtained 90.92% with a very complete category.Efficiency test obtained time efficiency, namely: (1) between the first and second meetings obtained 00:02:28, (2) between the second and third meetings obtained 00:01:08.The total time of the three meetings is 266: 02: 11 and the average obtained is 03:19:32.It can be concluded that time efficiency occurs in studying the material and working on the questions in each meeting.The results of the attraction test obtained results from questionnaires in 3 meetings to test the effectiveness of the number of items as many as: (1) 5 items for meeting I (face to face), (2) 7 items for meeting II (offline), (3) 5 items for meeting III (online) items and from these results show the percentage of attractiveness of game modification products in physical education, sports, and health learning (PJOK) using blended learning for: (1) 92.88% meeting I (face to face), (2) 93.48% of meeting II (offline), (3) 93.69% of meeting III (online) with very valid criteria.
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue

As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information.In this study, we propose a novel and extensible dialogue generation method by leveraging sellers' historical dialogue information, which can be both accessible and informative.By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers' historical dialogues, which can further enhance the current dialogue generation quality.Unlike prior dialogue generation efforts, we treat each seller's historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs.Extensive experimental results show that the proposed approach can generate highquality responses that cater to specific sellers' characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.
Fuzzy-Logic-Based Integration of Web Contextual Linguistic Structures for Enriching Conceptual Visual Representations

Due to the difficulty of automatically mapping visual features with semantic descriptors, state-of-the-art frameworks have exhibited poor performance in terms of coverage and effectiveness for indexing the visual content. This prompted us to investigate the use of both the web as a large information source from where to extract relevant contextual linguistic information and bimodal visual-textual indexing as a technique to enrich the vocabulary of index concepts. Our proposal is based on the signal/semantic approach for multimedia indexing which generates multifacetted conceptual representations of the visual content. We propose to enrich these image representations with concepts automatically extracted from the visual contextual information. We specifically target the integration of semantic concepts, which are more specific than the initial index concepts since they represent the visual content with greater accuracy and precision. Also, we aim to correct the faulty indexes resulting from the automatic semantic tagging. Experimentally, the details of the prototyping are given and the presented technique is tested in a web-scale evaluation on 30 queries representing elaborate image scenes.
Unsupervised Evaluation for Question Answering with Transformers

It is challenging to automatically evaluate the answer of a QA model at inference time.Although many models provide confidence scores, and simple heuristics can go a long way towards indicating answer correctness, such measures are heavily dataset-dependent and are unlikely to generalise.In this work, we begin by investigating the hidden representations of questions, answers, and contexts in transformer-based QA architectures.We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct.Our method does not require any labelled data and outperforms strong heuristic baselines, across 2 datasets and 7 domains.We are able to predict whether or not a model's answer is correct with 91.37% accuracy on SQuAD, and 80.7% accuracy on SubjQA.We expect that this method will have broad applications, e.g., in semi-automatic development of QA datasets.
Organization of Education Using Modern Distance Learning Technologies in the Context of the COVID-19 Pandemic (on the example of Russian law schools)

The purpose of the article was to find the key problems of transferring the educational process into a distance form, and options for their solution. The changes that have taken place in the world over the past year have posed new challenges for society to which it should have been able to react. The sphere of education found itself in a rather difficult situation. It had to be reorganized in a short time and began to function in a remote communication mode. The author's methodology was based on an empirical study (a survey of teachers and students) to determine the degree of their readiness for a global transition to distance learning. Moreover, sociological, systemic analysis and synthesis scientific methods were used. During the study, the authors have concluded that neither teachers nor trainees were ready for the transformation that took place in the field of education. The problems faced by the participants in the educational process are both technical and psychological. In the technical sphere, there was an acute shortage of equipment used by trainees in the educational process. In addition, the lack of Internet traffic provided made it difficult to exchange information between participants in the educational process.In the psychological sphere, the difficulties have boiled down to the fact that in the new environment the role and degree of responsibility of trainees have increased significantly, which was highly negatively assessed by them. Teachers of the older age group have turned out to be practically unprepared for mastering new methods using technical teaching means. The learning process was mainly reduced to self-preparation of students, during which they were offered to read lecture materials, self-test, independent problem solving and other tasks. At the same time, the interaction between a teacher and trainees was minimized. Some options for the implementation of techniques were proposed in the article. They will help in the future to grade a number of problems, increase the degree of interaction between the teacher and students, making remote learning more convenient and comfortable for many.
Transferring Tree Ensembles to Neural Networks

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn. Many implementations can only produce trees in an offline manner and in a greedy manner. We explore ways to convert existing GBDT implementations to known neural network architectures with minimal performance loss in order to allow decision splits to be updated in an online manner and provide extensions to allow splits points to be altered as a neural architecture search problem. We provide learning bounds for our neural network and demonstrate that our non-greedy approach has comparable performance to state-of-the-art offline, greedy tree boosting models.
Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering

Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering a group of data points lying in a union of low-dimensional subspaces. However, SSC may suffer from heavy computational burden. Applying Orthogonal Matching Pursuit (OMP) on SSC may accelerate the computation but the trade-off is the loss of clustering accuracy. In this letter, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and remain computationally efficient by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SSC. The proposed control matrix can be applied to other selection strategies for data point. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) have demonstrated that our algorithm outperforms other methods in clustering accuracy.
Querying Across Genres for Medical Claims in News

We present a query-based biomedical information retrieval task across two vastly different genres -newswire and research literaturewhere the goal is to find the research publication that supports the primary claim made in a health-related news article.For this task, we present a new dataset of 5,034 claims from news paired with research abstracts.Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list.We compare the classical IR approach using BM25 with more recent transformerbased models.Our results show that crossgenre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.
DeepVS: an efficient and generic approach for source code modelling usage

The source code suggestions provided by current integrated development environment (IDEs) are mostly dependent on static type learning. These suggestions often end up proposing irrelevant suggestions for a peculiar context. Recently, deep learning-based approaches have shown great potential in the modelling of source code for various software engineering tasks. However, these techniques lack adequate generalisation and resistance to acclimate the use of such models in a real-world software development environment. This Letter presents DeepVS, an end-to-end deep neural code completion tool that learns from existing codebases by exploiting the bidirectional gated recurrent unit (BiGRU) neural net. The proposed tool is capable of providing source code suggestions instantly in an IDE by using pre-trained BiGRU neural net. The evaluation of this work is two-fold, quantitative and qualitative. Through extensive evaluation on ten real-world open-source software systems, the proposed method shows significant performance enhancement and its practicality. Moreover, the results also suggest that DeepVS tool is capable of suggesting zero-day (unseen) code tokens by learning coding patterns from real-world software systems.