Reviewerly/exHarmony huggingface dataset:
The Application of the Simulation Method in the in Foreign Language Teaching in Higher Education Institutions, the Cognitive Linguistic Approach

This article presents the results of the research aimed at proving the efficiency of using the simulation method in combination with the cognitive linguistic approach in teaching students a foreign language. The experimental group students studied the foreign (English) language course during the semester of the 2019-2020 academic year using simulations. The methodology consisted of the performance of roles by the students who actively interacted for the implementation of assigned tasks. Kravchenko's cognitive linguistic algorithm was used to develop the skills of English grammar. This allowed us to establish the similarities between the temporal forms of English and Ukrainian words in discourse. The efficiency was evaluated through the test, developed by the author based on the exercises from the book by Holitsynskyi. Diagnostics of the students' level of motivation to learn a foreign language using simulations and cognitive linguistic approach was performed with the help of the Self-Assessment Form Student test, described in the EU manual. The statistical significance of the study results was established using Pearson's χ2 test. The correlation between the factor and resultant characteristics was established using Tschuprow's contingency coefficient. This was revealed through the methodology of applying the cognitive linguistic approach regarding the training of the skills in choosing the tense forms of a verb in English and the method of simulations in the development of language competencies of the business foreign language. It was found that the proportion of experimental group students with a high and sufficient level of success in learning a foreign language doubled, exceeding the same indicator in the control group. The total number of students with very high, high and average levels of development of skills in choosing the tense forms of the verb in English increased by 36%. The influence of the skills of choosing the tense forms of the verb on the development of the skills of using grammatical structures found in the process of the simulation was recorded. The results of the study can be applied in teaching students a foreign language in the institutions of higher and secondary education, as well as in the system of post-graduate education. The problems of individual students' passivity regarding their participation in simulations, failure to use a foreign language in group work require further research. Received: 19 March 2021 / Accepted: 20 May 2021 / Published: 8 July 2021
HLIPSCS: A Rapid and Efficient Algorithm for Image Contrast Enhancement

Contrast is an important feature in an image because the low-contrast effect causes poor details visibility, and the high-contrast effect leads to better-perceived details.The low-contrast effect happens due to many unavoidable reasons, and the demand for high-quality images entails the production of improved quality images.This obligates processing the low-contrast effect properly with an algorithm that works rapidly and preserves the essential image information.Many of the existing algorithms do not provide desirable results as they may amplify the brightness, deliver deficient colors, introduce insufficient contrast, or generate processing artifacts.Hence, a new HLIPSCS algorithm is introduced to avoid the aforesaid drawbacks and involves many processing concepts related to hyperbolic, logarithmic, and statistical approaches.It starts by applying two different hyperbolic methods, then their results are merged via an altered logarithmic approach.Next, statistical and contrast stretching approaches are applied to provide the anticipated outcome.The HLIPSCS algorithm is applied on different real contrast-degraded grayscale and color images, compared with eight different algorithms and the comparison outputs are evaluated using three different image evaluation methods.For the made comparisons and experiments, the HLIPSCS showed processing superiority as it delivered top performances in respect of image evaluation scores.Finally, the results of HLIPSCS have a way better appearance in the matters of brightness, contrast, and color representations.
Multi‐party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross

Abstract Dark pools are financial trading venues where orders are entered and matched in secret so that no order information is leaked. By preventing information leakage, dark pools offer the opportunity for large volume block traders to avoid the costly effects of market impact. However, dark pool operators have been known to abuse their privileged access to order information. To address this issue, we introduce a provably secure multi‐party computation mechanism that prevents an operator from accessing and misusing order information. Specifically, we implement a secure emulation of Turquoise Plato Uncross, Europe's largest dark pool trading mechanism, and demonstrate that it can handle real world trading throughput, with guaranteed information integrity.
Modelling schemes for multi-party blockchain-based systems to support integrity analysis

Multi-party systems are important for business processes but can be complex. Blockchain facilitates trust in multi-party systems by providing transparency, decentralised control, and immutable transaction history, to improve security and accountability between parties. The use of cryptographic hashes and the continual validation of the shared ledger in a blockchain system provides parties with data integrity for historical transactions and process integrity for smart contract execution. However, in the design of a broader system combining a blockchain with off-chain components, it is not always clear how system-level integrity is supported. This paper proposes two modelling schemes to better understand blockchain's support for integrity in multi-party blockchain-based systems. The schemes model interactions between components in an architecture as security protocols, for analysis by standard techniques and tools. We first illustrate how blockchain-based systems can be abstractly modelled directly as security protocols. Then we show how blockchain-specific issues such as consensus-based ‘forking’ (also known as ‘orphan blocks’ or ‘uncle blocks’) can be encoded. This allows transaction reordering behaviour to manifest in the model, and allows design mitigation for that problem to be checked. We illustrate our approach with analyses of three design alternatives for possible enhancements to a multi-party system for sharing trade certificates.
Gillian, Part II: Real-World Verification for JavaScript and C

Abstract We introduce verification based on separation logic to Gillian, a multi-language platform for the development of symbolic analysis tools which is parametric on the memory model of the target language. Our work develops a methodology for constructing compositional memory models for Gillian, leading to a unified presentation of the JavaScript and C memory models. We verify the JavaScript and C implementations of the AWS Encryption SDK message header deserialisation module, specifically designing common abstractions used for both verification tasks, and find two bugs in the JavaScript and three bugs in the C implementation.
Impact of the industrial revolution 4.0 on higher education in Vietnam: challenges and opportunities

With a lofty goal and mission, it is the mission of cultivating people. Higher education has an important role to play in the development of society. Education not only trains people with good knowledge and expertise, but also trains people with good moral and physical qualities, effectively serving the socio-economic development associations of each country, each ethnic group. The industrial revolution 4.0 with its rapid development speed and profound impacts on all areas of the social life of each country, if left behind of this revolution, the backward development is also inevitable. On the contrary, if you make good use of the advantages of this revolution, the opportunities are huge. In the digital era, higher education will change profoundly from the educational environment, the role of teachers and learners to teaching methods. Currently, not only Vietnam but also many countries around the world are facing the big challenge of a shortage of highly qualified, specialized and skilled workers. Therefore, in order to renovate higher education to meet the requirements of the labor market, it is necessary to raise awareness and renew thinking on higher education development; renovating training programs and methods.
Contract-Theory-Based Secure Spectrum Sharing Framework in Internet of Vehicles

Internet of vehicles (IoV) enables a large number of applications for consumers by integrating massive electronics, which aggravates the spectrum scarcity and data security, and degrades the quality of services (QoS) and experiences of consumers obviously. Focusing on enhancing the security and improving spectrum utilization, this article proposes a contract-theory-based secure spectrum sharing framework, which adopts the consortium blockchain to guarantee security of spectrum sharing and utilizes the practical Byzantine fault tolerant consensus algorithm to achieve consensus in IoV. Smart contracts are then formed based on insurance contracts to promote primary users to share their idle spectrum resources. A preference-based spectrum allocation algorithm is designed to maximize the total utility of secondary users (SUs). Simulation results show that, compared with the existing schemes, the proposed scheme achieves better performance in terms of the utility of SUs and the spectrum utilization greatly, which provides more access opportunities to consumers in IoV.
High-Precision Binary Object Detector Based on a BSF-XNOR Convolutional Layer

Recently, building an efficient and robust model for object detection has attracted the attention of the vision community. Although binary networks have a fast inference speed, they cannot be used directly on mobile devices such as unmanned aerial vehicles (UAVs) because of their low detection accuracy. Different from improving the detection accuracy of a binary network by adjusting the network structure or adjusting the update gradient, we propose an improved binary neural network based on the block scaling factor XNOR (BSF-XNOR) convolutional layer. In addition, we propose a two-level densely connected network structure, which further enhances the network layer's feature representation capabilities. Experiments using the TensorFlow framework prove the effectiveness of our algorithm in improving accuracy. Compared with the original standard XNOR network, the mean average precision (mAP) detected by our algorithm on the PASCAL VOC dataset was improved. The experimental results on the VisDrone2019 UAV dataset confirm that our method achieves a better balance between speed and accuracy than previous methods. Our algorithm aims to guide and deploy high-precision binary networks on the embedded device and solves the problem of low-precision binary networks.
Single-Port Homodyne Detection in a Squeezed-State Interferometry with Optimal Data Processing

Performing homodyne detection at a single output port of a squeezed-state light interferometer and then separating the measurement quadrature into several bins can realize superresolving and supersensitive phase measurements. However, the phase resolution and the achievable phase sensitivity depend on the bin size that is adopted in the data processing. By maximizing classical Fisher information, we analytically derive an optimal value of the bin size and the associated best sensitivity for the case of three bins, which can be regarded as a three-outcome measurement. Our results indicate that both the resolution and the achievable sensitivity are better than that of the previous binary–outcome case. Finally, we present an approximate maximum Likelihood estimator to asymptotically saturate the ultimate lower bound of the phase sensitivity.
Entangled sideband control scheme via frequency-comb-type seed beam

We report a control scheme of entangled sideband modes without coherent amplitude by employing a frequency-comb-type seed beam. In this scheme, each tooth of the frequency comb serves as a control field for the corresponding downconversion mode. Consequently, all the degrees of freedom can be actively controlled, and the entanglement degrees are higher than 6.7 dB for two pairs of sidebands. We believe that this scheme provides a simple solution for the control of sideband modes, which could be further applied to achieve compact channel multiplexing quantum communications.
Analysis of formal characteristics of text in the CPACT Research: Enhancing the LIWC linguistic processing for the Czech language

Aim: This paper describes how psycholinguistic and psychodiagnostic fields have adopted quantitative text analysis to process spoken Czech. This method employs computer-assisted linguistic procedures to categorize and quantify formal characteristics (such as morphology, semantics, etc.) of recorded texts.Method: The study’s sample size is 200 people who were selected using age, gender, and level of education to reflect the same proportion of representation of the target groups as is found in the total Czech population. The processes of lemmatization (the identification of a lexical unit as a dictionary entry) and unambiguity (the removal of ambiguity in interpreting a particular word or homonymy) are used in formal text analysis.Findings: In total, CPACT studies use 212 linguistic variables, which is a substantial number. So the output is much larger than the Linguistic Processes module in the LIWC 2015 program, which processes 29 grammatical/summary variables. The linguistic variables processed by LIWC are limited, but the grammatical categories and subcategories used in the CPACT study allow for a much more in-depth exploratory study.Implications/Novel Contribution: The results of this study provide new information on the experimental application of quantitative psycholinguistic analysis to formal parameters. It’s a fascinating strategy, and it yields many interesting hypotheses and study directions. Research into this area, whether by linguists or psychologists, has the potential to reveal surprising new insights into the makeup and dynamics of human communication.
An Approach of Binary Neural Network Energy-Efficient Implementation

Binarized neural networks (BNNs), which have 1-bit weights and activations, are well suited for FPGA accelerators as their dominant computations are bitwise arithmetic, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. However, the energy efficiency of these accelerators is still restricted by the abundant redundancies in BNNs. This hinders their deployment for applications in smart sensors and tiny devices because these scenarios have tight constraints with respect to energy consumption. To overcome this problem, we propose an approach to implement BNN inference while offering excellent energy efficiency for the accelerators by means of pruning the massive redundant operations while maintaining the original accuracy of the networks. Firstly, inspired by the observation that the convolution processes of two related kernels contain many repeated computations, we first build one formula to clarify the reusing relationships between their convolutional outputs and remove the unnecessary operations. Furthermore, by generalizing this reusing relationship to one tile of kernels in one neuron, we adopt an inclusion pruning strategy to further skip the superfluous evaluations of the neurons whose real output values can be determined early. Finally, we evaluate our system on the Zynq 7000 XC7Z100 FPGA platform. Our design can prune 51 percent of the operations without any accuracy loss. Meanwhile, the energy efficiency of our system is as high as 6.55 × 105 Img/kJ, which is 118× better than the best accelerator based on an NVDIA Tesla-V100 GPU and 3.6× higher than the state-of-the-art FPGA implementations for BNNs.
Probabilistic Sufficient Explanations

Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed. In this paper, we introduce probabilistic sufficient explanations, which formulate explaining an instance of classification as choosing the "simplest" subset of features such that only observing those features is "sufficient" to explain the classification. That is, sufficient to give us strong probabilistic guarantees that the model will behave similarly when all features are observed under the data distribution. In addition, we leverage tractable probabilistic reasoning tools such as probabilistic circuits and expected predictions to design a scalable algorithm for finding the desired explanations while keeping the guarantees intact. Our experiments demonstrate the effectiveness of our algorithm in finding sufficient explanations, and showcase its advantages compared to Anchors and logical explanations.
Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

Soft spelling mistakes are a class of mistakes that is widespread among native Arabic speakers and foreign learners alike.Some of these mistakes are typographical in nature.They occur due to orthographic variations of some Arabic letters and the complex rules that dictate their correct usage.Many people forgo these rules, and given the identical phonetic sounds, they often confuse such letters.In this paper, we investigate how to use machine learning to correct such mistakes given that there are no sufficient datasets to train the correction models.Soft errors detection and correction is an active field in Arabic natural language processing.We generate training datasets using proposed transformed input approach and stochastic error injection approach.These approaches are applied to two acclaimed datasets that represent Classical Arabic and Modern Standard Arabic.We treat the problem as character-level, one-to-one sequence transcription problem.This one-to-one transcription of mistakes that include omissions and deletions is possible with adopted simple transformations.This approach permits using bidirectional long short-term memory (BiLSTM) models that are more effective to train compared to other alternatives such as encoder-decoder models.Based on investigating multiple alternatives, we recommend a configuration that has two BiLSTM layers, and is trained using the stochastic error injection approach with error injection rate of 40%.The best model corrects 96.4% of the injected errors and achieves a low character error rate of 1.28% on a real test set of soft spelling mistakes
Training machine learning models faster with Dask

Machine learning (ML) relies on stochastic algorithms, all of which rely on gradient approximations with "batch size" examples.Growing the batch size as the optimization proceeds is a simple and usable method to reduce the training time, provided that the number of workers grows with the batch size.In this work, we provide a package that trains PyTorch models on Dask clusters, and can grow the batch size if desired.Our simulations indicate that for a particular model that uses GPUs for a popular image classification task, the training time can be reduced from about 120 minutes with standard SGD to 45 minutes with a variable batch size method.
τ-Access policy: Attribute-based encryption scheme for social network based data trading

The rapid development of social technology has replaced physical interaction in the trading market. The implication of this technology is to provide access to the right information at the right time. The drawback of these technologies is that the eaves dropper can remove the user from the network and can create proxy participants. In this paper, we discuss how a social network overcome and prevent these data trading issues. To maintain the security of data trading, we applied ABE technique based on DBDH to secure data trading network. Our proposed τ-access policy scheme provides the best solution for the betterment of data trading network in terms of security. In τ-access policy scheme, the users can encrypt and decrypt Private Transactions Information (PTI) using our proposed τ-access policy. The security propertiesof τ-access policy are data confidentiality, data integrity, authenticity, non-repudiation, and unforgeability. The efficiency of our scheme is 77.73%, which is more suitable for data trading markets and trading strategies.
AI patents and the self-assembling machine

Legal scholarship has begun to consider the implications of algorithmic pattern recognition systems, colloquially dubbed “artificial intelligence” or “AI,” for intellectual property law. This emerging literature includes several analyses that breathlessly proclaim the imminent overthrow of intellectual property systems as we now know them. Indeed, some commentators have prophesied the demise of patentable innovation under the influence of AI research and development tools. Although AI systems pose fundamental challenges to the many areas of law and legal institutions, careful consideration suggests that intellectual property generally, and the patent system particularly, encompasses sufficient flexibilities to address AI innovation. In many cases, previous accommodation of biotechnology within the patent system points the way similar accommodation of AI tools. However, the incorporation of AI innovation into patents reveals a significant gap in patent doctrine regarding causation, which deserves resolution quite apart from the furore over the intersection of AI and patents.
Enhancing audio quality for expressive Neural Text-to-Speech

Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings. However, not all speaking styles are easy to model: highly expressive voices are still challenging even to recent TTS architectures since there seems to be a trade-off between expressiveness in a generated audio and its signal quality. In this paper, we present a set of techniques that can be leveraged to enhance the signal quality of a highly-expressive voice without the use of additional data. The proposed techniques include: tuning the autoregressive loop's granularity during training; using Generative Adversarial Networks in acoustic modelling; and the use of Variational Auto-Encoders in both the acoustic model and the neural vocoder. We show that, when combined, these techniques greatly closed the gap in perceived naturalness between the baseline system and recordings by 39% in terms of MUSHRA scores for an expressive celebrity voice.
A Real-Time Automated System for Dual-Aperture Common-Path Interferometer Phase-Shifting

We report a novel fully real-time automatized optomechatronic dual-aperture common-path interferometer system for obtaining the phase difference between two interferograms by using the technique of phase-shifting interferometry. A motorized system is used to shift an additional phase transversally to the optical axis by ruling translation. For each high-resolution ruling displacement step of 0.793 μm, an interferogram is recorded by a CCD camera. The phase difference between the two successive recorded interferograms is then automatically calculated by computational self-calibrated algorithms. The proposed device provides more accurate measuring than typically used manual processes. Real-time phase differences are obtained from a robust low-cost optomechatronic system. Analytical calculation of the phase is performed automatically without the requirement of additional or external tools and processes, reducing the significant rework delay. A set of 47 interferograms were captured in real time then recorded and analyzed, obtaining an average phase shifting of 2.483 rad. Analytic explanation and experimental results are presented.