Reviewerly/exHarmony huggingface dataset:
Enhancing Multi-factor User Authentication for Electronic Payments

Security is beginning to be more and more important for electronic transaction nowadays, and the need for security is becoming more important than ever before. A variety of authentication techniques have been established to ensure the security of electronic transactions. The usage of electronic payment systems has grown significantly in recent years. To secure confidential user details from attacks, the finance sector has begun to implement multi-factor authentication. Multi-factor authentication is a device access management strategy that enables an individual to move through various authentication phases. For each of the previous tasks, attempts have been created to secure the electric payment process by using various authentication methods, and despite the advantages of theirs, each had a downside. Authentication based on password is one of the most common ways for users to authenticate in numerous online transaction applications. Inhere, electronic payments authentication mechanism which mainly relies on the traditional password only authentication cannot efficiently resist to the latest password wondering and password cracking strikes. In order to handle the problem, this paper proposes an authentication algorithm for electric payments by adding a multi-factor mechanism on the existing user authentication mechanism. The enhancement concentrates on enhancing the user authentication elements of multiple factors using the biometric technique. The software is developed using an android simulator, and a platform that helps developers to evaluate an application without the requirement for the device to be built on a real device. The proposed system has two phases, namely: registration stage and an authentication stage. The suggested authentication protocol gives users safe access to the authorization through multi-factor authentication using their password and fingerprint. In order to ensure unauthorized users cannot easily break into the mobile application, the proposed enhancement would provide better security.
Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings

Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yorùbá which uses bare nouns but marks these features contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yorùbá into English. We investigate how the systems what extent they identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperforms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT outperforms the NMT models for 1 category.
Human-Robot Collaboration in the Society of the Future: A Survey on the Challenges and the Barriers

The robotization of industries and daily life activities had long been transformed from innovation and curiosity to reality. The use of collaborative robots (cobots) has now become widespread, capable of safely working together with a human in a single working space and increasing the efficiency of work in professions that are cannot be automated easily so far. The researchers and economists are making optimistic projections of increasing the rate of adoption of robots and cobots both in industries and in everyday human activities. However, there are difficulties and problems on the way to mass robotization, and construction of the socio-cyber-physical environment, including fear and distrust of robots, differences in the attitude towards robots in different countries and social groups, incomprehension of the relevance of robotization, etc. In addition, there are well-known problems, that are still under discussion and have no ready solutions. In this article we consider the current state of affairs in the sphere of deployment of cobots in the world and Russia, consider optimistic forecasts of the process of robotization, but also form a list of known and potential problems and negative factors, that could potentially hamper the implementation of plans for mass robotization and cobotization of industries and society, and provide our common vision of the solution of these problems.
Rancang Bangun Sistem Informasi Penerimaan Peserta Didik Baru Berbasis Website Dengan Metode Spiral

Penerimaan Peserta Didik Baru (PPDB) is an annual agenda for every institution or educational institution in order to accept new students in its environment. Often these agencies or institutions often experience problems in managing the administration of new students, one of which is Raudhatul Athfal Sirojul Falah. The problems that occur in administrative matters include the frequent problems of inputting new student data and it is not organized so that the data becomes difficult to find. So that in this study a PPDB information system will be created to solve this problem. The information system that will be created is in the form of a UML (Unified Modeling Language) design, ERD (Entity Relationship Diagram), and LRS (Logical Record Structure) as well as a website with the PHP programming language, CodeIgniter framework, and bootstrap library. The result of this research is the construction of a PPDB information system at RA Sirojul Falah in the form of a website using the spiral method with the hope of helping the PPDB RA Sirojul Falah committee in managing new student data effectively and efficiently.
The effect of the extended bridge on the Timbre of the Sarasvati Veena: a numerical and experimental study

An attempt is made to establish the dependence of the timbre of the Sarasvati veena, a South Indian string instrument, on its extended bridge. The equation of motion for a plucked string vibrating against the extended boundary at the bridge is solved numerically. The solution is analyzed for timbre characteristics, which show energy redistribution among the higher harmonics, as well as revival of higher harmonics with time. The variation of timbre with bridge slope and curvature are analyzed. These results are validated by comparison with corresponding analysis of acoustic data gathered from experiments performed on a sonometer, whose knife edge is replaced by the bridge of the veena.
Automated, Explainable Rule Extraction from MAP-Elites Archives

Quality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user. They are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000 s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.
Designing the Profile of a Student of a General Education School in the Context of the System-Activity and Outcome-Based Approach

At present goals and objectives of the education development are associated with ensuring the global competitiveness of the Russian education in general and guaranteeing the entry of the Russian Federation into the top 10 countries in the world in terms of the quality of general education, in particular.&#x0D; To achieve these national goals and objectives aimed at the development of education, the purposeful activity of general education institutions is required. They should focus on the design of modern school models to transform them into a new type of schools that will be able to improve the quality of education and ensure the school graduates’ training and preparation for their future adult life in the context of sustainable technological changes in the production, economic and social spheres.&#x0D; The article presents the approaches used by the staff of the Educational Complex "Nasha Shkola" in Novosibirsk to the construction of the Student’s Profile as a target reference point for the educational content standard of the System "Nasha Shkola" in the context of the system-activity and outcome-based approaches.&#x0D; The paper also considers the list of universal competencies and literacy groups that are formed by the educational and technological environment of the school and allow creating conditions for the personal and professional success of each student.
Keyword-Aware Encoder for Abstractive Text Summarization

Text summarization aims to produce a brief statement covering main points. Human beings would intentionally look for key entities and key concepts when summarizing a text. Fewer efforts are needed to write a high-quality summary if keywords in the original text are provided. Inspired by this observation, we propose a keyword-aware encoder (KAE) for abstractive text summarization, which extracts and exploits keywords explicitly. It enriches word representations by incorporating keyword information and thus leverages keywords to distill salient information. We construct an attention-based neural summarizer equipped with KAE and evaluate our model extensively on benchmark datasets of various languages and text lengths. Experiment results show that our model generates competitive results comparing to state-of-the-art methods.
Sentiment Analysis in Agriculture

Sentiment analysis is currently the most actively researched topic in the field of natural language processing, however, despite it being such a powerful tool, it is not very widely used in the agrarian sector. This research focuses on the discovery and analysis of scientific literature related to Sentiment analysis in agriculture, to provide an overview of how and where Sentiment analysis is used in the agrarian sector and which methods are most commonly used. This article also discusses which applications of Sentiment analysis yield the most benefits and suggests a direction for future research.
Gaussian Metric Learning for Few-Shot Uncertain Knowledge Graph Completion

Recent advances in relational information extraction have allowed to automatically construct large-scale knowledge graphs (KGs). Nevertheless, an automatic process entails that a significant amount of uncertain facts are introduced into KGs. Uncertain knowledge graphs (UKGs) such as NELL and Probase model this kind of uncertainty as confidence scores associated to facts for providing more precise knowledge descriptions. Existing UKG completion methods require sufficient training examples for each relation. However, most relations only have few facts in real-world UKGs. To solve the above problem, in this paper, we propose a novel method to complete few-shot UKGs based on Gaussian metric learning (GMUC) which could complete missing facts and confidence scores with few examples available. By employing a Gaussian-based encoder and metric function, GMUC could effectively capture uncertain semantic information. Extensive experiments conducted over various datasets with different uncertainty levels demonstrate that our method consistently outperforms baselines.
Quantum Bose–Einstein Statistics for Indistinguishable Concepts in Human Language

We investigate the hypothesis that within a combination of a ‘number concept’ plus a ‘substantive concept’, such as ‘eleven animals’, the identity and indistinguishability present on the level of the concepts, i.e., all eleven animals are identical and indistinguishable, gives rise to a statistical structure of the Bose–Einstein type similar to how Bose–Einstein statistics is present for identical and indistinguishable quantum particles. We proceed by identifying evidence for this hypothesis by extracting the statistical data from the World-Wide-Web utilizing the Google Search tool. By using the Kullback–Leibler divergence method, we then compare the obtained distribution with the Maxwell–Boltzmann as well as with the Bose–Einstein distributions and show that the Bose–Einstein’s provides a better fit as compared to the Maxwell–Boltzmann’s.
Vulnerability Analysis of Internet Devices from Indonesia Based on Exposure Data in Shodan

Abstract The growth of internet-enabled devices has increased interest in cybersecurity. In 2014, Project SHINE (SHodan INtelligence Extraction) published a report of large-scale security assessments for devices connected to the Internet. However, the number of IP addresses harvested from Indonesia in 2014 is very small. There were 7.182 IP address from Indonesia. It was about 0,0032% from the total 2.186.971 IP addresses. In this paper, we propose an initiative to gather all information for all Autonomous System Number (AS Number) from Indonesia in Shodan. We have gathered a dataset about all information of AS Numbers in Indonesia such as 12.787 unique ports, 79 unique operating systems, 409 unique products, 3.634 unique domains, 145.543 unique IP addresses, and 790 unique organizations. We use the K-Means algorithm to cluster all AS Numbers into several classes according to the exposure level in shodan. Based on the result, we have 4 classes of AS Numbers. There are 1.075 AS Numbers in class:0 (no information in Shodan yet), 614 AS Numbers in class:1 (exposure level = low), 9 AS Numbers in class:2 (exposure level = medium), and 1 AS Number in class:3 (exposure level = high). This information can be used to warn the organizations that manage AS Numbers in Indonesia to be aware of the security and the threats to their systems.
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification

Video-based person re-identification (Re-ID) aims to retrieve video sequences of the same person under non-overlapping cameras. Previous methods usually focus on limited views, such as spatial, temporal or spatial-temporal view, which lack of the observations in different feature domains. To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID. More specifically, we design a trigeminal feature extractor to jointly transform raw video data into spatial, temporal and spatial-temporal domain. Besides, inspired by the great success of vision transformer, we introduce the transformer structure for video-based person Re-ID. In our work, three self-view transformers are proposed to exploit the relationships between local features for information enhancement in spatial, temporal and spatial-temporal domains. Moreover, a cross-view transformer is proposed to aggregate the multi-view features for comprehensive video representations. The experimental results indicate that our approach can achieve better performance than other state-of-the-art approaches on public Re-ID benchmarks. We will release the code for model reproduction.
Fast and Accuracy Camera Calibration Based on Tsai Two-Step Method

High precision camera calibration is the premise of establishing 3D surface measurement system with excellent performance. Therefore, it is necessary to conduct systematic research on camera calibration method to improve measurement efficiency and accuracy. The original Tsai Two-Step method had two main disadvantages: firstly, different points on different planes should be adopted for camera calibration; Secondly, only radial distortion of the lens is considered. Many scholars optimized the method, but most of them optimized one of its shortcomings. In this paper, comprehensive optimization was considered to improve the two shortcomings together. At the same time, Taylor series expansion method is used to process the data and improve the calibration speed and accuracy. The improved method is proved to be feasible by comparing experiments with the Zhang's calibration method.
A Novel Web Service Discovery Method Combining Semantic Interface Similarity and Context Similarity

Abstract Web service discovery is to find the most relevant services to satisfy the requester queries by means of similarity matching between Web services and a service requirement. We propose a novel Web service discovery method based on semantic similarity measure combing I/O similarity and context similarity. A new similarity measurement between terms is put forward as the basis of Web service similarity. Similarity between terms is represented by the similarity of two concepts containing them, and is calculated by means of the length of the shortest path between two concepts. Three kinds of linked relations are considered, i.e. ISA, HASA and antonymy relations. The experiments demonstrate that the proposed approach can improve the accuracy of concept similarity measure. Service discovery based on the similarity measure has better query performance than previous discovery methods in the precision, recall and F-measure.
Quantum random number generator with discarding-boundary-bin measurement and multi-interval sampling

A quantum random number generator (QRNG) provides a reliable means for the generation of true random numbers. The inherent randomness of the vacuum fluctuations makes the quantum vacuum state a superior source of entropy. However, in practice, the raw sequences of QRNG are inevitably contaminated by classical technical noise, which compromises the security of the QRNG. Min-entropy conditioned on the classical noise is a useful method that can quantify the side-information independent randomness. To improve the extractable randomness from the raw sequences arising from the quantum vacuum-based QRNG, we propose and experimentally demonstrate two approaches, discarding-boundary-bin measurement and multi-interval sampling. The first one increases the conditional min-entropy at a low quantum-to-classical-noise ratio. The latter exploits parallel sampling using multiple analog-to-digital converters (ADCs) and effectively overcomes the finite resolution limit and uniform sampling of a single ADC. The maximum average conditional min-entropy can reach 9.2 per sample when combining these two approaches together in contrast to 6.93 with a single 8-bit ADC.
Federated Learning for Advanced Manufacturing Based on Industrial IoT Data Analytics

The recent surge in the volume of real-time condition-monitoring data generated from connected devices such as from Industrial Internet of Things (IIoT), has enabled wide spread adoption of machine learning techniques to improve the efficiency, effectiveness and intelligence of the industrial process. However, a key obstacle to this whole adoption is that the data is distributed across multiple industrial assets and are usually not shared across the industry partners as they are constrained by complicated administrative process, industry competition concerns and the need to comply to privacy laws. Federated Learning, a privacy preserving machine learning technique coined by Google, is a likely and efficient technique to overcome this obstacle. Federated Learning enables multiple industries to train the condition-monitoring data without revealing their corresponding data and assets. In this chapter, we present our proposed decentralized federated learning based solution and its integration with an IIoT smart manufacturing platform. We demonstrate the effectiveness of our platform with real world datasets and different machine learning algorithms. We observe that the proposed federated learning approach can achieved an equivalent performance compared to the traditional centralized-based learning approach in terms of accuracy and efficiency.
Lessons on Parameter Sharing across Layers in Transformers

We propose a parameter sharing method for Transformers (Vaswani et al., 2017). The proposed approach relaxes a widely used technique, which shares parameters for one layer with all layers such as Universal Transformers (Dehghani et al., 2019), to increase the efficiency in the computational time. We propose three strategies: Sequence, Cycle, and Cycle (rev) to assign parameters to each layer. Experimental results show that the proposed strategies are efficient in the parameter size and computational time. Moreover, we indicate that the proposed strategies are also effective in the configuration where we use many training data such as the recent WMT competition.
Developing Value Networks for Game 2048 with Reinforcement Learning

The game 2048 is a stochastic single-player game and several computer players have been developed in not only research work but also student projects. Among them, the most successful approach is based on N-tuple networks trained by reinforcement learning methods. Though there have been several works on computer players with deep neural networks, their performance were not as good in most cases. In our previous work, we designed policy networks and applied supervised learning, which resulted in an average score of 215,802. In this study, we tackle the problem with value networks and reinforcement learning methods, since value networks are important to combine with game-tree search methods. We investigate the training methods in several aspects, including batches of training, use of symmetry, network structures, and use of game-specific tricks. We then conduct a training for 240 hours with the best configuration. With the best value network obtained, we achieved an average score of 228,100 with the greedy (1-ply search) play, and furthermore an average score of 406,927 by combining it with the 3-ply expectimax search.
Euclidean Distance Based Similarity Measurement and Ensuing Ranking Scheme for Document Search from Outsourced Cloud Data

In this paper, we propose the Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme to aid effective document search from outsourced cloud data. It is another attempt to find an alternative to binary based approaches. In this approach, the User or the Data owner needs to filter out the suitable keywords for the document and then the index is prepared. To provide security and privacy, both the data and the index are encrypted and moved to the cloud space. The application of Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme for document searching takes place after the authorized user requests for the documents through query terms. Initially the authorized user sends a query to Cloud Service Provider to retrieve all the documents which are mapped with the keywords provided by him. The proposed algorithm calculates the distance between the query terms and the index terms. The minimum the distance, the more it is closer towards each other and vice-versa. Our Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme greatly enhances the system functionality by sending the most relevant documents instead of transmitting all documents back. The experimental validations are performed on RFC and FIRE dataset. Through experimental analysis, we prove that our proposed approach is secure and efficient as well as exhibits better recall and precision rate in the IR system to deal with the document-retrieval process.