Reviewerly/exHarmony huggingface dataset:
Student Study Timeline Prediction Model Using Naïve Bayes Based Forward Selection Feature

The student study period is one of the factors that show a student's academic performance. Universities are required to be able to keep students able to complete their studies on time so that there is no buildup of the number of students who have not graduated. Therefore, from the academic data students conducted data mining classification using naïve Bayes algorithm. But because of the many attributes, to speed up this naïve Bayes modeler, it is supported by the selection of the forward selection feature. In the Selection process, the feature generates 5 selected attributes that affect the dataset. While from this classification process obtained the accuracy value of the prediction model naïve Bayes increased from 90.00% to 92.94% after adding a forward selection feature. With this high accuracy score, prediction models can be applied in policymaking to prevent students from graduating on time.
Text Similarity Measurement Method Based on BiLSTM-SECapsNet Model

Text similarity measurement is a basic task in natural language processing and widely used in information retrieval, automatic question answering, machine translation, etc. Because most traditional statistical-based methods for text similarity measurement cannot efficiently extract the semantic information of the text, we propose a BiLSTM-SECapsNet hybrid model based on BiLSTM, CapsNet, and SENet for text similarity measurement. We employed the siamese BiLSTM network as sequential inference models to extract the global features. The coattention mechanism is employed to generate attention weight between the text features, which will collect local inference over sequences. The CapsNet network is also introduced to catch the local features of the text. And then the SENet network is used to automatically calibrate the importance of each local feature to obtain the local feature matrix. After that, the feature matrix is fused again and the BiLSTM network is used to extract the context information to obtain the similarity matrix of the two texts. At last, the semantic similarity of the text is measured through the fusion, pooling, and fully connected layer. The experimental results based on the Quora Questions Pairs data set show that the accuracy of the method is 87.31, and the F-measure is 87.35. Compared with other networks, the effectiveness of the method has been improved.
Automatic Table Detection, Structure Recognition and Data Extraction from Document Images

In the recent advancement, the extensive usage of electronic devices to photograph and upload documents, the requirement for extracting the information present in the unstructured document images is becoming progressively intense. The major obstacle to the objective is, these images often contain information in tabular form and extracting the data from table images presents a series of challenges due to the various layouts and encodings of the tables. It includes the accurate detection of the table present in an image and eventually recognizing the internal structure of the table and extracting the information from it. Although some progress has been made in table detection, obtaining the table contents is still a challenge since this involves more fine-grained table structure (rows and columns) recognition. The digitization of critical information has to be carried out automatically since there are millions of documents. Based on the motivation that AI-based solutions are automating many processors, this work comprises three different stages: First, the table detection using Faster R-CNN algorithm. Second, table internal structure recognition process using morphology operation and refine operation and last the table data extraction using contours algorithm. The dataset used in this work was taken from the UNLV dataset.
Tunable Activation Functions for Deep Neural Networks

The performance of artificial neural networks significantly depends on the choice of the nonlinear activation function of the neuron. Usually this choice comes down to an empirical one from a list of universal functions that have shown satisfactory results on most tasks. However this approach does not lead to optimal training in terms of model convergence over a certain number of epochs. We proposed tunable polynomial activation function for artificial neuron. Parameters of this function can be adjusted during learning procedure along with synaptic weights. The proposed function can take the form of universal ones due to its polynomial properties. Adjustable form tunable polynomial function leads to the fastest convergence of the model and more accurate training due to the possibility of using a smaller training step that has been shown experimentally. Improved convergence allows to apply tunable activation function to various deep learning problems.
Effects of Normalization on Rotation Based Perturbation Techniques for Privacy Preservation

Data is increasing rapidly with the vast development of technologies. Mining is mandatory to retrieve knowledge from these data. But, the data may contain sensitive information of the individuals such as medical diagnostic reports which they don’t want to reveal. Privacy Preserving Data Mining (PPDM) helps in this regard to keep sensitive data private as well as preserving the data utility. Besides other PPDM techniques, rotation based perturbation contributes to satisfying both aspects of PPDM i.e., individuals’ privacy and data utility. Also, normalization techniques have a significant impact in the field of PPDM. In this work, the effects of three well-known normalization approaches (i.e., z-score, min-max and decimal scaling) on rotation based perturbation techniques called 2-Dimensional Rotation Transformation (2DRT) and 3-Dimensional Rotation Transformation (3DRT), are analyzed. The effects of normalization over rotation based perturbation techniques are investigated using three benchmark classifiers with five UCI data set. The empirical analysis elucidates that 3DRT provides significant privacy preservation and data utility maintenance than 2DRT for z-score normalization.
Minimum-time optimal control for a quadcopter trajectory through gates with arbitrary pose

Drone racing has become popular in recent years, both in the manned and unmanned versions. However, currently manned races show more level of acrobatic behaviors through more challenging scenarios. Autonomous quadcopters are still limited to technological proprioceptive and exteroceptive challenges, as well as algorithmic limitations. This paper proposes an approach for unmanned quadcopters to maneuver through gates with arbitrary poses solving a minimum-time optimal control problem. The results show, in a dynamic simulator, that the quad copter is able to successfully circumvent the nuances of the environment passing through the gates in minimum time satisfying the motion constraints and planning the motion ahead.
WITHDRAWN: Distance learning and its impact on student’s creativity material: A practical experience for students of the qweismeh brigade

This article has been retracted at the request of the Publisher and Editor. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal .
The Sound Monad: A Philosophical Perspective on Sound Design

Abstract This article aims at sketching a philosophical theory of sound based on the perspective of sound designers: unique agents blurring the boundaries between engineering, music, acoustics and sound-based art. After having introduced the general framing in Section 1, focusing on a short history of the theory and practice of sound design, in Section 2 we propose a reading of sound as monad. We derive such intuition from the technology of digital sampling of audio signals, based on the decomposition of complex sound waves in a number of elementary sinusoidal waves. Thus, in Section 3, we attempt at grounding the resulting “sound-atom” on Leibniz’s notion of monad, intended both as a “simple substance without parts” and as a “nucleus of forces in statu possibilitatis .” The insight is resumed and further discussed in Section 4, where we draw our conclusions by demonstrating the fitness of such framing with regards to the standpoint of sound design, while accounting for the work of sound artists Carsten Nicolai and Ryoji Ikeda.
Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders

The process of creating a meaningful and perceptually pleasing color palette is an incredibly difficult task for the inexperienced practitioner. In this paper we show that the Variational Auto Encoder can be a powerful creative tool for the generation of novel color palettes as well as their extraction from visual mediums. Our proposed model is capable of extracting meaningful color palettes from images, and simultaneously learns an internal representation which allows for the sampling of novel color palettes without any additional input.
Trusted Computing Architecture for Secure Network Functions

Currently, Network Function Virtualization (NFV) has become a basic technology to support the development of the 5G communication network. Meanwhile, the security protection technology of network function software has also become an important part of solving the 5G security problems. Therefore, we established a simulation network environment for network slicing by using open source software. Then, we designed a trusted computing architecture for security protection technology of network function in this simulation environment. Then, through experiments, we tested the static integrity security protection of network functions and proved that this architecture can resist the tampering attack. Finally, the performance test shows the usability of our trusted computing architecture.
Improved Learning Outcomes to Understand The Contents And Mandate of Poetry Using The Survey-Question-Read-Reflect-Recited-Review (SQ4R) Model for Grade IV Elementary School Students

Abstract:  This study aims to increase the learning outcomes of understanding the contents and mandate of poetry using the SQ4R model in class IV SDN 02 Pojok Blitar District. This study uses a qualitative approach with a type of classroom action research (CAR). Data from this research are data derived from students' knowledge, skills, and attitudes. then the data is processed in the form of teacher activities and student activities. The results of the study showed that the application of the SQ4R model could improve learning outcomes understanding the content and mandate of poetry covering an average percentage of knowledge values reaching 86% with KBK 90%, skill scores reaching 83%, and all attitude criteria were seen in class IV SDN Pojok 02 Blitar district Keywords : learning outcomes, poetry, SQ4R model
Image Compression Based on DCT and Adaptive Grid Scanning

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.
Recognizing Skeleton-Based Hand Gestures by a Spatio-Temporal Network

A key challenge in skeleton-based hand gesture recognition is the fact that a gesture can often be performed in several different ways, with each consisting of its own configuration of poses and their spatio-temporal dependencies. This leads us to define a spatio-temporal network model that explicitly characterizes these internal configurations of poses and their local spatio-temporal dependencies. The model introduces a latent vector variable from the coordinates embedding to characterize these unique fine-grained configurations among joints of a particular hand gesture. Furthermore, an attention scorer is devised to exchange joint-pose information in the encoder structure, and as a result, all local spatio-temporal dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.
Time-Aware Representation Learning of Knowledge Graphs

Representation learning is a fundamental task in knowledge graph-related research and applications. Most existing approaches learn representations for entities and relations only based on static facts, where temporal information has been ignored completely. This paper aims to learn time-aware representations for entities and relations in knowledge graphs. Based on how temporal information affects the learned embeddings, we propose three assumptions and build three different models, BTS, ETS, and RTS, respectively. In these models, we build two separate embedding spaces for entities and relations, the standard translation condition is checked after projecting embedding vectors between these spaces by model-specific transformations. As to the performance, the proposed RTS model achieves state-of-the-art results in three experiments conducted on two datasets: YAGO11k and Wikidata12k, which validates the effectiveness of our model. Comparing the results of all three models, we find that relation embeddings are time-sensitive and form natural ordering, while the effects of time on entity embeddings can be safely ignored for translation-based methods. Experiments also show that our findings can be used to simplify other existing models like HyTE.
A Secure and Scalable IoT Consensus Protocol

Several consensus algorithms have been proposed as a way of resolving the Byzantine General problem with respect to blockchain consensus process. However, when these consensus algorithms are applied to a distributed, asynchronous network, some suffer with security and/or scalability issues, while others suffer with liveness and/or safety issues. This is because the majority of research have not considered the importance of liveness and safety, with respect to the integrity of the consensus decision. In this paper, a novel solution to this challenge is presented, a solution that protects blockchain transactions from fraudulent or erroneous mis-spends. This consensus protocol uses a combination of probabilistic randomness, an isomorphic balance authentication, error detection and synchronised time restrictions, when assessing the authenticity and validity of IoT request. Designed to operate in a distributed asynchronous network, this approach increases scalability while maintaining a high transactional throughput, even when faced with Byzantine failure.
Can the Blockchain Facilitate the Development of an Interport Community?

Seaports are part of a complex and information-intensive maritime supply chain that includes a set of organizations and operators who need to exchange a large amount of information and data with each other. As supply chains become increasingly integrated and connected, the connectivity of stakeholders must be ensured not only within individual ports but also between ports. This paper explores the application prospects and practical implications of the application of Blockchain technology for the establishment of an interport community within which different ports organized as a network can exchange information and data in a secure and effective way. With the support of SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, this paper addresses several research questions concerning the practical impacts, benefits, pros and cons, economic and technical barriers related to the implementation of Blockchain technology to support the creation of an interport community.
Extended First-Order Step Theories

Active Logic is a conceptual system, the principles of which are satisfied with formalisms of reasoning, which allow correlating the results of reasoning with moments in time and ensuring tolerance to contradictions. At present, tolerance to contradictions (paraconsistency) as applied to Active Logic systems has received theoretical justification in the works of the authors of this report and refers to the so-called formalisms of step theories, combining the principles of Active Logic and logical programming. In particular, it was proved that the argumentative semantics of the formalism of extended step theories, which have two types of negation and have more developed expressive capabilities, are paranormal. The formalism introduced in this paper has even greater expressive capabilities. Keeping all the controls for the process of reasoning in time, this formalism allows one to more adequately represent facts about the subject area by using the language of first-order logic instead of the language of propositional logic. For the formalism of extended step theories of the first order, a declarative semantics is proposed, its paraneosistance is shown, and the expressive possibilities of this formalism in relation to the tasks of action planning are demonstrated using the example of the monkey and the banana.
Using Cognitive Interest Graph and Knowledge-activated Attention for Learning Resource Recommendation

Recently, a number of deep-learning based approaches have proved effective for personalized learning resource recommendation (PLR), but they ignore the impact of complex transitions in a learner’s interests and their cognitive ability. This undermines the accomplishment of personalized learning. This paper proposes a new method for learning session-based recommendation which uses a graph neural network (GNNs) and a cognitive interest graph that, for brevity, is referred to as CIGNN. It models the click sequence and exercise sequence in a learning session separately as a session graph and a cognitive graph, then creates a composite cognitive interest graph. CIGNN also uses a novel knowledge-activated attention mechanism that makes uses of the knowledge mastery of learners and their past behavior to actively adapt to their learning interests, so that representations of their preferences change as they acquire knowledge. A further novel feature of CIGNN is its use of an interest-aware semantic graph attention network to extract semantic information regarding different types of learning interests by drawing on meta-paths of different importance. Extensive experiments were conducted using real-world data and the results show that CIGNN can outperform existing baseline approaches for PLR-related tasks. We also provide some case studies that illustrate how CIGNN can help learners to master knowledge.
Towards Finding Best Linear Codes for Side-Channel Protections

Side-channel attacks aim at extracting secret keys from cryptographic devices. Ran- domly masking the implementation is a provable way to protect the secrets against this threat. Recently, various masking schemes have converged to the “code-based masking” philosophy. In code-based masking, different codes allow for different levels of side-channel security. In practice, for a given leakage function, it is important to select the code which enables the best resistance, i.e., which forces the attacker to capture and analyze the largest number of side-channel traces. This paper is a first attempt to address the constructive selection of the optimal codes in the context of side-channel countermeasures, in particular for code-based masking when the device leaks information in the Hamming weight leakage model. We show that the problem is related to the weight enumeration of the extended dual of the masking code. We first present mathematical tools to study those weight enumeration polynomials, and then provide an efficient method to search for good codes, based on a lexicographic sorting of the weight enumeration polynomial from lowest to highest degrees.
Design and implementation of earthquake disaster assessment system under different intensity based on residential area

Most of the existing earthquake disaster assessment systems take districts, counties or townships as evaluation units. Due to the lack of data accuracy, there is a big gap between the evaluation results and the actual losses. With the development of technology, the acquisition and update of residential data is more convenient and rapid. Compared with the evaluation system based on districts, counties or townships, the results of the evaluation system based on residential areas are more detailed and accurate. In this paper, the existing calculation model as the skeleton, ArcEngine as the GIS development platform, using C / S structure to design the seismic disaster assessment system under different intensity based on residential area. The system takes the residential area as the evaluation unit. According to the selected assessment area, the system evaluates the damage of buildings and casualties of earthquake disasters according to the five grades of VI, VII, VIII, IX and X. The results can provide technical support for governments at all levels and relevant departments to carry out regional earthquake risk assessment and earthquake disaster pre assessment.
Computer "MIR" is a Prototype of Modern Personal Computer

Present article considers the history of Soviet computer technology in Ukraine in 1950-1970. The implementation of the ideas of V.M. Glushkov in the construction of new computers, especially the MIR series, which gradually approached the modern personal computer, and are its forerunners. The decision of the country's leadership in 1969 to copy Western computer technology instead of developing original designs interrupted development. Particular attention is paid to the impact of the "MIR" series on the economy and communication of cybernetics with the public.