Reviewerly/exHarmony huggingface dataset:
Learning Efficient Representations for Keyword Spotting with Triplet Loss

In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most notably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also improve the current best published SOTA for Google Speech Commands dataset V1 10 + 2 -class classification by about 34%, achieving 98.55% accuracy, V2 10 + 2-class classification by about 20%, achieving 98.37% accuracy, and V2 35-class classification by over 50%, achieving 97.0% accuracy. (Code is available at https://github.com/roman-vygon/triplet_loss_kws).
Assessing the learning and transfer of gaze behaviours in immersive virtual reality

Abstract Virtual reality (VR) has clear potential for improving simulation training in many industries. Yet, methods for testing the fidelity, validity and training efficacy of VR environments are, in general, lagging behind their adoption. There is limited understanding of how readily skills learned in VR will transfer, and what features of training design will facilitate effective transfer. Two potentially important elements are the psychological fidelity of the environment, and the stimulus correspondence with the transfer context. In this study, we examined the effectiveness of VR for training police room searching procedures, and assessed the corresponding development of perceptual-cognitive skill through eye-tracking indices of search efficiency. Participants ( n = 54) were assigned to a VR rule-learning and search training task (FTG), a search only training task (SG) or a no-practice control group (CG). Both FTG and SG developed more efficient search behaviours during the training task, as indexed by increases in saccade size and reductions in search rate. The FTG performed marginally better than the CG on a novel VR transfer test, but no better than the SG. More efficient gaze behaviours learned during training were not, however, evident during the transfer test. These findings demonstrate how VR can be used to develop perceptual-cognitive skills, but also highlight the challenges of achieving transfer of training.
Chatbot Development as a Digital Learning Tool to Increase Students’ Research Knowledge

The research aimed to: 1) develop the chatbot; 2) evaluate its effectiveness; and 3) investigate its effects on students&amp;rsquo; research knowledge. The sample consisted of 36 Thai university students. The research instruments consisted of: 1) the chatbot; 2) an evaluation form; 3) an effectiveness questionnaire; and 4) research tests. Data analysis used was mean, standard deviation, content analysis and a t-test. The findings indicated that: 1) the chatbot was evaluated by experts with the applicability at a very high level ( = 4.67, S.D. = 0.08) with recommendation to add more research content and interactive learning. The pilot test was done with 14 non-target group of students. Students perceived the chatbot&amp;rsquo;s effectiveness at a high level ( = 4.43, S.D. = 0.35) with comments to add more examples and graphics to make the chatbot more interesting; 2) the 36 target group of Thai university students perceived the chatbot as an effective technology to use as a digital learning tool at a high level ( = 4.37, S.D. = 0.48). They thought that chatbot technology was easy to use, easy to understand, innovative and fun for learning. They could get answers instantly and be able to seek specific information without waiting for responses. However, in response to questions not matched keywords specified, further details of finding proper answers such as links should be provided instead of leaving those questions unanswered. Also, the chatbot only provided responses when typing correctly so there should be an option to choose from a list of questions or keywords; and 3) the post-test scores were significantly higher than the pre-test scores at the 0.05 level of significance. In conclusion, using chatbot technology in education settings to increase students&amp;rsquo; research knowledge gave positive results as it led to positive learning outcomes and helped provide better personalized learning experience for students.
Training Using a Commercial Immersive Virtual Reality System on Hand–Eye Coordination and Reaction Time in Young Musicians: A Pilot Study

The implementation of virtual reality (VR) opens up a wide range of possibilities for the development of dexterity, speed and precision of movements. The aim of this study was to investigate whether immersive VR training affected the hand–eye coordination and reaction time in students of the state music school. This study implemented a single-group pre-post study design. This study enrolled 14 individuals, submitted to a 15 min training session of the immersive music game “Beat Saber”, once a day for 5 consecutive days. The plate-tapping test (PTT) and the ruler-drop test (Ditrich’s test) were used to assess the reaction time. Trial-making test (TMT) A and TMT B were used to assess coordination and visual attention. Analysis of the results showed a statistically significant improvement in hand–eye coordination and reaction time of music school students using the TMT-A (p &lt; 0.002), TMT-B (p &lt; 0.001), Ditrich’s test for the non-dominant hand (0.025) and PTT (0.0001) after applying a week-long training period in immersive VR. The results obtained in the present study show that the VR system, along with the immersive music game, has the potential to improve hand–eye coordination and reaction time in young musicians, which may lead to the faster mastering of a musical instrument.
Arbitrary controlled-phase gate on fluxonium qubits using differential ac Stark shifts

Large scale quantum computing motivates the invention of two-qubit gate schemes that not only maximize the gate fidelity but also draw minimal resources. In the case of superconducting qubits, the weak anharmonicity of transmons imposes profound constraints on the gate design, leading to increased complexity of devices and control protocols. Here we demonstrate a resource-efficient control over the interaction of strongly-anharmonic fluxonium qubits. Namely, applying an off-resonant drive to noncomputational transitions in a pair of capacitively-coupled fluxoniums induces a $\text{ZZ}$ interaction due to unequal ac Stark shifts of the computational levels. With a continuous choice of frequency and amplitude, the drive can either cancel the static $\text{ZZ}$ term or increase it by an order of magnitude to enable a controlled-phase (CP) gate with an arbitrary programmed phase shift. The cross-entropy benchmarking of these non-Clifford operations yields a sub $1%$ error, limited solely by incoherent processes. Our result demonstrates the advantages of strongly-anharmonic circuits over transmons in designing the next generation of quantum processors.
Axion-Like Particles, Two-Higgs-Doublet Models, Leptoquarks, and the Electron and Muon g − 2

Data from the Muon g-2 experiment and measurements of the fine structure constant suggest that the anomalous magnetic moments of the muon and electron are at odds with standard model expectations. We survey the ability of axion-like-particles, two-Higgs-doublet models and leptoquarks to explain the discrepancies. We find that accounting for other constraints, all scenarios except the Type-I, Type-II and Type-Y two-Higgs-doublet models fit the data well.
Orbital transformations to reduce the 1-norm of the electronic structure Hamiltonian for quantum computing applications

Reducing the complexity of quantum algorithms to treat quantum chemistry problems is essential to demonstrate an eventual quantum advantage of noisy-intermediate scale quantum devices over their classical counterpart. Significant improvements have been made recently to simulate the time-evolution operator $U(t)={e}^{i\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\mathcal{H}}t}$, where $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\mathcal{H}}$ is the electronic structure Hamiltonian, or to simulate $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\mathcal{H}}$ directly (when written as a linear combination of unitaries) by using block encoding or qubitization techniques. A fundamental measure quantifying the practical implementation complexity of these quantum algorithms is the so-called 1-norm of the qubit representation of the Hamiltonian, which can be reduced by writing the Hamiltonian in factorized or tensor-hypercontracted forms, for instance. In this paper, we investigate the effect of classical preoptimization of the electronic structure Hamiltonian representation, via single-particle basis transformation, on the 1-norm. Specifically, we employ several localization schemes and benchmark the 1-norm of several systems of different sizes (number of atoms and active space sizes). We also derive a formula for the 1-norm as a function of the electronic integrals and use this quantity as a cost function for an orbital-optimization scheme that improves over localization schemes. This paper gives more insights about the importance of the 1-norm in quantum computing for quantum chemistry and provides simple ways of decreasing its value to reduce the complexity of quantum algorithms.
SummScreen: A Dataset for Abstractive Screenplay Summarization

We introduce SUMMSCREEN, a summarization dataset comprised of pairs of TV series transcripts and human written recaps.The dataset provides a challenging testbed for abstractive summarization for several reasons.Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript.These details must be found and integrated to form the succinct plot descriptions in the recaps.Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief.This information is rarely contained in recaps.Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics.Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors.An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts.Human evaluation and qualitative analysis reveal that our nonoracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors.Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions.
NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly.In this paper, we investigate TSC in news articles, a much less researched TSC domain despite the importance of news as an essential information source in individual and societal decision making.We introduce NewsMTSC, a high-quality dataset for TSC on news articles with key differences compared to established TSC datasets, including, for example, different means to express sentiment, longer texts, and a second test-set to measure the influence of multi-target sentences.We also propose a model that uses a BiGRU to interact with multiple embeddings, e.g., from a language model and external knowledge sources.The proposed model improves the performance of the prior state-of-the-art from F 1 m = 81.7 to 83.1 (real-world sentiment distribution) and from F 1 m = 81.2 to 82.5 (multi-target sentences).
Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning

This research was conducted to apply the KNN (K-Nearest Neighbor) algorithm in conducting sentiment analysis of Twitter users on issues related to government policies regarding Online Learning. Research using Tweet data as much as 1825 Indonesian tweet data data were collected from February 1, 2020 to September 30, 2020. Using the python library, Tweepy. word weighting using TF-IDF, will be classified into two classes of sentiment values, positive and negative. After testing with K of 20, the highest accuracy results were obtained when K = 10 with an accuracy value of 84.65% with a precision of 87%, a recall of 86% f measure 87% and an error rate of 0.12% and a tendency was also obtained. public opinion on online learning tends to be positive.
Non Verbal Communication in Software Engineering – An Empirical Study

Communication among humans consists of both verbal and non verbal components. The latter may sometimes express concepts or ideas not conveyable by the former. This is also true in Software Engineering. This paper first analyses theoretically the role of non verbal communication in software development teams, using the framework provided by distributed cognition as a conceptual palette and as a point of reference. Then, it presents an empirical investigation involving 38 IT professionals from Russia, sharing their experiences in communicating and interacting when developing software artifacts. The results of this empirical investigation are consistent with many of the ideas underlying a distributed approach to cognition. In addition, our findings provide valuable insights to make communication more effective in software development teams, while defining a new framework for follow-up studies.
WOVe: Incorporating Word Order in GloVe Word Embeddings

Word vector representations open up new opportunities to extract useful information from unstructured text. Defining a word as a vector made it easy for the machine learning algorithms to understand a text and extract information from. Word vector representations have been used in many applications such word synonyms, word analogy, syntactic parsing, and many others. GloVe, based on word contexts and matrix vectorization, is an effective vector-learning algorithm. It improves on previous vector-learning algorithms. However, the GloVe model fails to explicitly consider the order in which words appear within their contexts. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. Experimental results show that our Word Order Vector (WOVe) word embeddings approach outperforms unmodified GloVe on the natural language tasks of analogy completion and word similarity. WOVe with direct concatenation slightly outperformed GloVe on the word similarity task, increasing average rank by 2%. However, it greatly improved on the GloVe baseline on a word analogy task, achieving an average 36.34% improvement in accuracy.
An Energy-Efficient Authentication Scheme Based on Chebyshev Chaotic Map for Smart Grid Environments

Electric vehicle charging is becoming more commonplace, but a number of challenges remain. For example, the wireless communications between vehicle users and aggregators can be subject to exploitation and hence, several authentication schemes have been designed to support varying levels of privacy protection. However, there are a number of limitations observed in existing authentication schemes, and examples include lack of anonymity and not considering charging peak in their design (and consequently, not meeting low energy consumption requirement in smart grid environments). More recently, there have been attempts to utilize Chevyshev chaotic map in the design of authentication mechanism, with the aims of reducing computational costs yet achieving high security. However, the security requirements of Chebyshev polynomials pose new challenges to the construction of Chebyshev chaotic maps-based authentication schemes. To solve these limitations, we propose an efficient Chebyshev polynomials algorithm by adopting a square matrix-based binary exponentiation algorithm to provide secure and efficient Chebyshev polynomial computation. We further construct an energy-efficient authentication and key negotiation scheme for the smart grid environments based on the proposed algorithm. Compared with five other competing schemes, our proposed authentication scheme achieves reduced computational and communication costs. In addition, the ProVerif tool is used to analyze the security of our proposed authentication scheme. The results show that the proposed scheme outperforms these five other schemes in terms of computation and communication overheads while achieving privacy preserving.
Assessment of Online Environment and Digital Footprint Functions in Higher Education Analytics

The article is devoted to learning analytics problems associated with the digital culture development in the university educational space and with the student activity control in the vocational training process. The empirical basis of the study was a series of surveys conducted by the Center for Sociological Research of the Peter the Great Polytechnic University in 2018–2020. To systematize the information on the traces of students’ activity in the digital space, the method of constructing a personal mental map, reflecting the cognitive characteristics of the student’s interactive actions in the network, was used. Because of the analysis of the mental maps, the general structure of the personal digital footprint was identified, which is significant for analytics of the student’s academic history and self-assessment of his activities in professional development. In conclusion, the constructive role of digital technology in assessing and modeling the educational process is emphasized. The study of students’ digital footprints on the university platforms, supplemented by the study of their activity in social networks, allows the development of educational modeling aimed at creating a more adequate set of competencies and soft skills of the graduate.
Security issues and defensive approaches in deep learning frameworks

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications. However, the security issues of deep learning frameworks are among the main risks preventing the wide application of it. Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life. We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them. We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive approaches. Moreover, we analyze a case of the physical-world use of deep learning security issues. In addition, we discuss future directions and open issues in deep learning frameworks. We hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.
Retweet communities reveal the main sources of hate speech

We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018-2020.
Rethinking Perturbations in Encoder-Decoders for Fast Training

We often use perturbations to regularize neural models.For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time.Thus, this study addresses the question of whether these approaches are efficient enough for training time.We compare several perturbations in sequence-to-sequence problems with respect to computational time.Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.Our code is publicly available at https://github.com/takase/rethink_perturbations.
Single-Shot Error Correction of Three-Dimensional Homological Product Codes

The conditions for the robustness of quantum codes against unreliable measurements are explored: A strict criterion is provided, as well as benchmarks and conceptual tools needed for building practical devices.
RefSum: Refactoring Neural Summarization

Although some recent works show potential complementarity among different state-of-theart systems, few works try to investigate this problem in text summarization.Researchers in other areas commonly refer to the techniques of reranking or stacking to approach this problem.In this work, we highlight several limitations of previous methods, which motivates us to present a new framework Refactor that provides a unified view of text summarization and summaries combination.Experimentally, we perform a comprehensive evaluation that involves twenty-two base systems, four datasets, and three different application scenarios.Besides new state-of-the-art results on CNN/DailyMail dataset (46.18 ROUGE-1), we also elaborate on how our proposed method addresses the limitations of the traditional methods and the effectiveness of the Refactor model sheds light on insight for performance improvement.Our system can be directly used by other researchers as an offthe-shelf tool to achieve further performance improvements.We open-source all the code and provide a convenient interface to use it:https://github.com/yixinL7/Refactoring-Summarization.