Reviewerly/exHarmony huggingface dataset:
The 3D simulation of Lorentz Force based on augmented reality technology

Abstract The Lorentz force concept is classified as an abstract to be understood. The direction of the current, the direction of the magnetic field and the direction of the force generated are difficult to understand without real media. This article will describe the Lorentz Force style simulation by utilizing augmented reality technology. The development of Lorentz force media uses 4D stages (define, design, develop, and disseminate). Interactive media based on augmented reality Lorentz force can display the Lorentz style concept by making changes to magnetic field variables and electric currents. The process of developing this media has been guided by pedagogical content knowledge (PCK). Our simulation successfully displays the relationship between the magnetic field and electric current which is directly proportional to the Lorentz force. AR-based Lorentz force simulation has fulfilled the principle of technological pedagogical content knowledge (TPACK).
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV With Thrust Vectoring Rotors

Abstract In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robot is learned using the policy transfer from the learned controller of the quadcopter (comparatively simple UAV design without thrust vectoring). This approach allows learning a control policy for systems with multiple inputs and multiple outputs. The performance of the learned policy is evaluated by physics-based simulations for the tasks of hovering and way-point navigation. The flight simulations utilize a flight controller based on reinforcement learning without any additional PID components. The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter). We demonstrate the robustness of our learned policy by showing the recovery of the tilt-rotor platform in the simulation from various non-static initial conditions in order to reach a desired state. The developmental policy for the tilt-rotor UAV also showed superior fault tolerance when compared with the policy learned from the scratch. The results show the ability of the presented approach to bootstrap the learned behavior from a simpler system (lower-dimensional action-space) to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster.
Conversational Agents to Promote Children’s Verbal Communication Skills

The fundamentals of verbal communication skills are developed during childhood, and existing studies pinpoint the benefits of stimulating language and expression skills from an early age. Our research is a preliminary evaluation of conversational technology to support this process. In this paper, we describe the design process of a speech-based conversational agent for children, which involved a Wizard-of-Oz empirical study with 20 primary school children aged 9–10 y.o. in order to identify the design guidelines for the automated version of the system. Our agent is called ISI, is integrated into a web application and exploits oral and visual interaction modes. ISI enables children to practice verbal skills related to the description of a person's physical characteristics. It provides opportunities for them to learn and use words and linguistic constructs. Also, ISI permits to develop their body awareness and self-expression (when describing their self) or the attention to "the other" (when describing someone else). ISI engages users in a speech-based conversational flow composed of two main repeated steps. It talks to the children and stimulates them with questions about a specific part of their body (e.g., "What color is your hair?"). When the users describe the required feature adequately, ISI provides a cheerful real-time visual representation of the answer; otherwise, it provides hints.
A Survey on Blockchain for Healthcare Informatics and Applications

Blockchain, cryptographically linked blocks of data, is the key technology behind the infamous cryptocurrency Bitcoin, however, blockchain can serve more use cases than just cryptocurrency. The technology has use cases in any industry that generates and transfers data. In a use case such as cryptocurrency the blockchain is used to manage transactions from peer-to-peer in a way that does not allow for that transaction's data to be manipulated by one of the involved parties or a third party. This is a valuable process that other industries could leverage. One such industry that could benefit from applying blockchain technology for a multitude of cases is the healthcare industry. Primary care doctors could be connected to a blockchain system that allowed them to access patient data and securely monitor the respective patient, pharmaceutical companies could increase their products validity in the marketplace with individual product tracking, and the insurance industry could trust all the information about a client. All of these are possible use cases for blockchain in the healthcare industry. Currently, the healthcare industry is not leveraging the blockchain at scale. Within this paper we are going to explore the possibilities for blockchain technology and the use cases in the healthcare industry specifically, and how the different industries within the healthcare industry can implement a blockchain system.
A Shoulder-Surfing Resistant Scheme Embedded in Traditional Passwords

Typing passwords is vulnerable to shoulder-surfing attacks.We proposed a shoulder-surfing resistant scheme embedded in traditional textual passwords in this study.With the proposed scheme, when the password field is on focus, a pattern appears in it as a hint to tell the user how to enter a password.Following the hint, the user needs to skip some characters while typing the password.The characters to be skipped are randomly selected so that an observer will not be able to see the whole password even if the authentication procedure was recorded.We evaluated the proposed scheme in a usability study.Compared to traditional passwords, our scheme achieved a similar level of accuracy while only required marginal additional time to authenticate users.Participants also expressed significantly higher acceptance of the new technique for security-sensitive applications and gave it significantly higher ratings in perceived security, shoulders-surfing resistance, camera-recording resistance, and guessattack resistance.
On using Theorem Proving for Cognitive Agent-oriented Programming

© 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reservedDemonstrating reliability of cognitive multi-agent systems is of key importance. There has been an extensive amount of work on logics for verifying cognitive agents but it has remained mostly theoretical. Cognitive agent-oriented programming languages provide the tools for compact representation of complex decision making mechanisms, which offers an opportunity for applying a theorem proving approach. We base our work on the belief that theorem proving can add to the currently available approaches for providing assurance for cognitive multi-agent systems. However, a practical approach using theorem proving is missing. We explore the use of proof assistants to make verifying cognitive multi-agent systems more practical.
Network in Sequential Form: Combine Tree Structure Components into Recurrent Neural Network

Abstract Smaller units nestled in larger units-a natural language that is hierarchically ordered. Smaller sections are replaced because of the completion of the bigger constituencies. The basic LSTM architecture does not have a clear choice for modeling the hierarchy of components as separate neurons do not require knowledge to be monitored on various time scales. The idea in this paper is to introduce inductive bias by grouping the neurons. The modifications we create to the master input vector are changed in all neurons following the order of the specified neuron and forget windows. Four activities on which we can achieve strong care unattended sorting, logical inference, and language modeling and guided syntax evaluation.
Turkish Speech Recognition Techniques and Applications of Recurrent Units (LSTM and GRU)

A typical solution of Automatic Speech Recognition (ASR) problems is realized by feature extraction, feature classification, acoustic modeling and language modeling steps. In classification and modeling steps, Deep Learning Methods have become popular and give more successful recognition results than conventional methods. In this study, an application for solving ASR problem in Turkish Language has been developed. The data sets and studies related to Turkish Language ASR problem are examined. Language models in the ASR problems of agglutative language groups such as Turkish, Finnish and Hungarian are examined. Subword based model is chosen in order not to decrease recognition performance and prevent large vocabulary. The recogniton performance is increased by Deep Learning Methods called Long-Short Term Memory (LSTM) Neural Networks and Gated Recurrent Unit (GRU) in the classification and acoustic modeling steps. The recognition performances of systems including LSTM and GRU are compared with the the previous studies using traditional methods and Deep Neural Networks. When the results were evaluated, it is seen that LSTM and GRU based Speech Recognizers performs better than the recognizers with previous methods. Final Word Error Rate (WER) values were obtained for LSTM and GRU as 10,65% and 11,25%, respectively. GRU based systems have similar performance when compared to LSTM based systems. However, it has been observed that the training periods are short. Computation times are 73.518 and 61.020 seconds respectively. The study gave detailed information about the applicability of the latest methods to Turkish ASR research and applications.
End2End Acoustic to Semantic Transduction

In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism. It reliably selects contextual acoustic features in order to hypothesize semantic contents. An initial architecture capable of extracting all pronounced words and concepts from acoustic spans is designed and tested. With a shallow fusion language model, this system reaches a 13.6 concept error rate (CER) and an 18.5 concept value error rate (CVER) on the French MEDIA corpus, achieving an absolute 2.8 points reduction compared to the state-of-the-art. Then, an original model is proposed for hypothesizing concepts and their values. This transduction reaches a 15.4 CER and a 21.6 CVER without any new type of context.
New First-Order Secure AES Performance Records

Being based on a sound theoretical basis, masking schemes are commonly applied to protect cryptographic implementations against Side-Channel Analysis (SCA) attacks. Constructing SCA-protected AES, as the most widely deployed block cipher, has been naturally the focus of several research projects, with a direct application in industry. The majority of SCA-secure AES implementations introduced to the community opted for low area and latency overheads considering Application-Specific Integrated Circuit (ASIC) platforms. Albeit a few, those which particularly targeted Field Programmable Gate Arrays (FPGAs) as the implementation platform yield either a low throughput or a not-highly secure design.In this work, we fill this gap by introducing first-order glitch-extended probing secure masked AES implementations highly optimized for FPGAs, which support both encryption and decryption. Compared to the state of the art, our designs efficiently map the critical non-linear parts of the masked S-box into the built-in Block RAMs (BRAMs).The most performant variant of our constructions accomplishes five first-order secure AES encryptions/decryptions simultaneously in 50 clock cycles. Compared to the equivalent state-of-the-art designs, this leads to at least 70% reduction in utilization of FPGA resources (slices) at the cost of occupying BRAMs. Last but not least, we provide a wide range of such secure and efficient implementations supporting a large set of applications, ranging from low-area to high-throughput.
DATA: Digital Archiving and Transformed Analytics

As cloud service becomes more and more capable, available and powerful, wiseCIO has emerged from an innovative roadmap toward archival Content Management Service (aCMS) and massive Content Delivery Service (mCDS) in support of Anything-as-a-Service (XaaS) via Digital Archiving and Transformed Analytics (DATA); DATA aims to automate UBC with FAST solutions throughout a feasible, analytical, scalable and testable approach.This paper, based on the novel wiseCIO (web-based intelligent service engaging Cloud Intelligence Outlet), presents digital archiving and transformed analytics via machine learning automata for intelligent UBC processes to liaise with Universal interface for human-computer interaction, enable Brewing aggregation (differing from traditional web browsing), and engage Centered user experience.As one of the most practical aspects of artificial intelligence, machine learning is applied to analytical model building and massive and/or multidimensional Online Analytical Processing (mOLAP) for more intelligent cloud service with little explicit coding required.DATA is central to useful information via archival transformation and analytics, and utilizable intelligence for Business, Education and Entertainment (iBEE) in support of decision-making.As a result, DATA orchestrates wiseCIO to promote AC-TiVE XaaS that enables accessibility, contextuality and traceability of information for vast engagement with various cloud services, such as aCMS (archival Content Management Service), COSA (Context-Oriented Screening Aggregation), DASH (Deliveries Assembled for fast Search and Hits), OLAS (Online Learning via Analytical Synthesis), REAP (Rapid Extension and Active Presentation), and SPOT (Special Points On Top) with great ease.
An External Knowledge Enhanced Graph-based Neural Network for Sentence Ordering

As an important text coherence modeling task, sentence ordering aims to coherently organize a given set of unordered sentences. To achieve this goal, the most important step is to effectively capture and exploit global dependencies among these sentences. In this paper, we propose a novel and flexible external knowledge enhanced graph-based neural network for sentence ordering. Specifically, we first represent the input sentences as a graph, where various kinds of relations (i.e., entity-entity, sentence-sentence and entity-sentence) are exploited to make the graph representation more expressive and less noisy. Then, we introduce graph recurrent network to learn semantic representations of the sentences. To demonstrate the effectiveness of our model, we conduct experiments on several benchmark datasets. The experimental results and in-depth analysis show our model significantly outperforms the existing state-of-the-art models.
Soft Skills and ICT Tools for Final Qualification Assessment in Universities of Ukraine and India in COVID-19 Framework

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of university summative assessment process. Qualification assessment for Foreign Languages major programs is a strict regimen that involves different stages (oral and written exams, final project viva, internal and external review). This study presents a comprehensive analysis of the practices of Borys Grinchenko Kyiv University (Kyiv, Ukraine) and International Business School (Hyderabad, India) digital qualification assessment for students of European (French, Italian, Spanish, English, German) and Asian (Mandarin, Japanese) Languages major programs, employed in the year 2020 due to quarantine measures. The survey and analysis of different ICT tools is used to translate real life qualification assessment practices into online blended format. The investigation identifies various groups of applied digital skills and soft skills, utilized through qualification assessment process by primary subjects (students). The choice of the considered cases of university Final Qualification Assessment for Foreign Languages major programs is informed by the need to estimate similar and divergent socio-cultural parameters of digital literacy development and application in multi-cultural context. Comparison is provided for integral and differential parameters of Final Qualification Assessment in the universities of Ukraine and India under survey.
Deep Learning based Ensemble Approach to Predict Student Academic Performance: Case Study

Traditional practices of education, medicine, and economics often use collective expert methods to find acceptable solutions to complex issues. The computer model of such methods is an ensemble classification which combines prediction of a number base classifier trained by various machine learning techniques. The predictive sample in the educational process is always not balanced - it leads to a shift in estimates. The basic idea of this work is that the binary forecasting on unbalanced datasets is carried out by a Meta-classifier - an ensemble of different classification algorithm (Decision tree J48, Naive base NB, Multilayer perception MLP, k- Neighbour Nearest k-NN and Support Vector Machine SVM) which trained by stacking, bagging and boosting procedures on SMOTE, RUS, ROS balanced samples. The experiments conducted on moderately unbalanced dataset of 350 tuples of student data with using simple voting Meta-ensemble models, show that the best overall accuracy A and F1 measure of minority class were achieved by the Meta-ensembles containing 5-15 members classifiers trained on samples prepared by SMOTE. It is also proposed that these of Meta-ensembles can be used to resolve quantification learning problem - novel task to predict collective performance. As in our recent works the deterministic outputs of Meta-ensemble members can be transformed to probabilistic outputs and then to prediction of class frequency. After analyzing the results of quantification, it becomes obvious that the most optimal design of the meta-ensembles is to collect pre-boosted classifiers trained on the SMOTE sample.
εpsolute: Efficiently Querying Databases While Providing Differential Privacy

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure data privacy, while allowing efficient querying. A rich collection of attacks on such systems has emerged. Even with strong cryptography, just communication volume or access pattern is enough for an adversary to succeed.
SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts

Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing research over many different domains, but it is not widely discussed in the legal domain. A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA. To the best of our knowledge, there is no publicly available dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion texts. Therefore, creating a publicly available dataset for the research of ABSA for the legal domain can be considered as a task with significant importance. In this study, we introduce a manually annotated legal opinion text dataset (SigmaLaw-ABSA) intended towards facilitating researchers for ABSA tasks in the legal domain. SigmaLaw-ABSA consists of legal opinion texts in the English language which have been annotated by human judges. This study discusses the sub-tasks of ABSA relevant to the legal domain and how to use the dataset to perform them. This paper also describes the statistics of the dataset and as a baseline, we present some results on the performance of some existing deep learning based systems on the SigmaLaw-ABSA dataset.
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current offline RL benchmarks commonly have a large reality gap, because they involve large datasets collected by highly exploratory policies, and the trained policy is directly evaluated in the environment. In real-world situations, running a highly exploratory policy is prohibited to ensure system safety, the data is commonly very limited, and a trained policy should be well validated before deployment. In this paper, we present a near real-world offline RL benchmark, named NeoRL, which contains datasets from various domains with controlled sizes, and extra test datasets for policy validation. We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward. The empirical results demonstrate that the tested offline RL algorithms become less competitive to the deterministic policy on many datasets, and the offline policy evaluation hardly helps. The NeoRL suit can be found at http://polixir.ai/research/neorl. We hope this work will shed some light on future research and draw more attention when deploying RL in real-world systems.
An Intent-based System Configuration Design for IT/NW Services with Functional and Quantitative Constraints

Network service providers need to appropriately design systems and carefully configuring the settings and parameters to ensure that the systems keep running consistently and deliver the desired services. This can be a heavy and error-prone task. Intent-based system design methods have been developed to help with such tasks. These methods receive service-level requirements and generate service configurations to fulfill the given requirements. One such method is search-based system design, which can flexibly generate systems of various architectures. However, it has difficulty dealing with constraints on the quantitative parameters of systems, e.g., disk volume, RAM size, and QoS. To deal with practical cases, intent-based system design engines need to be able to handle quantitative parameters and constraints. In this work, we propose a new intent-based system design method based on search-based design that augments search states with quantitative constraints. Our method can generate a system that meets both functional and quantitative service requirements by combining a search-based design method with constraint checking. Experimental results show that our method can automatically generate a system that fulfills all given requirements within a reasonable computation time.