Reviewerly/exHarmony huggingface dataset:
Penerapan metode k-means clustering data COVID-19 di Provinsi Jakarta

The disease epidemic that attacked the respiratory area and was detected in Indonesia starting in early 2020 is the Corona Virus (COVID-19). This virus's spread is relatively easy, namely through droplets from infected patients, so that the spread is very rapid. This research was conducted to cluster the data on Covid-19 cases in Jakarta Province considering that Jakarta is the starting point for the first case of Corona in Indonesia and until now has become one of the most significant contributors to COVID-19 issues in Indonesia, namely as of December 2020 positive cases of Covid-19 reached 154,000. Souls with the healing of 139.0000 souls. The grouping was carried out based on positive and dead patients from each urban village in Jakarta Province. This study uses the k-means Method to cluster in the handling of COVID-19 cases with 2 clusters. Data distribution in cluster 1 consists of 173 data and 18 data in cluster 2. The use of k-means in this study provides information on areas with the highest and lowest number of positive cases and the highest and lowest cure rates that can be used as an evaluation in handling the Covid-virus 19.
Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction

A new technique for progressive visual secret sharing (PVSS) with adaptive priority weight is proposed in this paper. This approach employs the bitwise and eXclusive-OR (XOR) based approaches for generating a set of shared images from a single secret image. It effectively overcomes the former scheme limitation on dealing with an odd number of stacked or collected shared images in the recovery process. The presented technique works well when the number of stacked shared images is odd or even. As documented in experimental results, the proposed method offers good results over binary, grayscale, and color images with a perfectly reconstructed secret image. In addition, the performance of the proposed method is also supported with theoretical analysis showing its lossless ability to recover the secret image. However, it can be considered as a strong substitutive candidate for implementing a PVSS system.
Application of Recommendation Systems Based on Deep Learning

With the rapid development of mobile Internet and self-media, it is becoming more and more convenient for people to obtain information, and the problem of information overload has increasingly affected people’s sense of use. The emergence of recommendation systems can help solve the problem of information overload, but in the big data environment, the traditional recommendation system technology can no longer meet the needs of recommending more personalized, more real-time, and more accurate information to users. In recent years, deep learning has made breakthroughs in the fields of natural language understanding, speech recognition, and image processing. At the same time, deep learning has been integrated into the recommendation system, and satisfactory results have also been achieved. However, how to integrate massive multi-source heterogeneous data and build more accurate user and item models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences between recommendation systems based on deep learning and traditional recommendation systems.
Controllable Emphatic Speech Synthesis based on Forward Attention for Expressive Speech Synthesis

In speech interaction scenarios, speech emphasis is essential for expressing the underlying intention and attitude. Recently, end-to-end emphatic speech synthesis greatly improves the naturalness of synthetic speech, but also brings new problems: 1) lack of interpretability for how emphatic codes affect the model; 2) no separate control of emphasis on duration and on intonation and energy. We propose a novel way to build an interpretable and controllable emphatic speech synthesis framework based on forward attention. Firstly, we explicitly model the local variation of speaking rate for emphasized words and neutral words with modified forward attention to manifest emphasized words in terms of duration. The 2-layers LSTM in decoder is further divided into attention-RNN and decoder-RNN to disentangle the influence of emphasis on duration and on intonation and energy. The emphasis information is injected into decoder-RNN for highlighting emphasized words in the aspects of intonation and energy. Experimental results have shown that our model can not only provide separate control of emphasis on duration and on intonation and energy, but also generate more robust and prominent emphatic speech with high quality and naturalness.
Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network

Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.
Multi-Objective Big Data View Materialization Using NSGA-II

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.
Algebraic Collision Attacks on Keccak

In this paper, we analyze the collision resistance of the two smallest versions of Keccak which have a width of 200 and 400 bits respectively. We show that algebraic and linearization techniques can serve collision cryptanalysis by using some interesting properties of the linear part of the round function of Keccak. We present an attack on the Keccak versions that could be used in lightweight cryptography reduced to two rounds. For Keccak[40, 160] (resp. Keccak[72, 128] and Keccak[144, 256]) our attack has a computational complexity of 273 (resp. 252.5 and 2101.5) Keccak calls.
Psychosocial Approach to Cyber Threat Intelligence

Cyber attackers continuously show new levels of intention by performing more sophisticated attacks on networks and important infrastructures (e.g., hospitals).This is an urgent situation calling for a swift improvement for cyber defenders.Hence, a paradigm shift is necessary to ameliorate the effectiveness of current practices.Behavioural, social and psychological related information about the attackers is considered in this paper, important elements of the Cyber Threat Intelligence (CTI) that improve cyber defense practices.The aims of this paper are to firstly provide a review of relevant behavioural and social theories and models that can be used for better capturing the attackers' characteristics and then to utilize them by giving insights on more realistic security measurements. Behaviour and Social TheoriesResearch efforts [8] are being conducted, in combating cybercrime study technical and human factors, to determine cybercriminal behaviours.This is achieved by using multidisciplinary approaches from various scientific domains (e.g., social sciences, criminology, anthropology, cyberpsychology).Behavioural scientists claim that there is not adequate knowledge about the behaviour of the user (whether legitimate or illegal) in relation to Information Technologies (IT) in general, and
Quantum Computation in a Human Environment

In the present article, we discuss quantum computation by incorporating the quantum mechanical phenomena to our everyday world. While traditional computers are the consequence of the mechanical synthesis of human body and mind acts in an embodied reality, quantum computation is the consequence of translating a subatomic scaled reality into human acts. Quantum computers are based on quantum mechanics that encompasses properties such as superposition, entanglement and reversibility of time. These properties enable computer information processing and memory to be transformed into a quantum superposition of qubit states, that opens up for numerous potential future applications. As with every new technology, the development of quantum computers is embedded in expectations, a phenomenon that historically has been shown to be problematic. These expectations generate the idea of the supremacy of the technology which hides the importance of the human environment in which the new technology will be applied. This article aims to place these expectations in a human context to show that in fact for every case, it is more a matter of human supremacy than supremacy of the technology being developed.
Discriminant Mutual Information for Text Feature Selection

In text classification tasks, the high dimensionality of data would result in a high computational complexity and decrease the classification accuracy because of high correlation between features; so, it is necessary to execute feature selection. In this paper, we propose a Discriminant Mutual Information (DMI) criterion to select features for text classification tasks. DMI measures the discriminant ability of features from two aspects. One is the mutual information between features and the label information. The other is the discriminant correlation degree between a feature and a target feature subset based on the label information, which could be used for judging whether a feature is redundant in the target feature subset. Thus, DMI is a de-redundancy text feature selection method considering discriminant information. In order to prove the superiority of DMI, we compare it with the state-of-the-art filter methods for text feature selection and conduct experiments on two datasets: Reuters-21578 and WebKB. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are taken as the subsequent classifiers. Experimental results shows that the proposed DMI has significantly improved the classification accuracy and F1-score of both Reuters-21578 and WebKB.
How to encourage lecturer performance in research through servant leadership, organizational commitment, and tacit knowledge sharing

The purpose of the present study was to analyze the role of servant leadership, organizational commitment, and tacit knowledge sharing in enhancing research performance. To this end, a survey of 400 randomly recruiting private university lecturers was conducted. The data were analyzed by using SEM covariant. The analysis result showed that leadership that tends to exhibit service and sacrifice towards its subordinate improved organizational commitment, particularly in the continuance and normative dimensions. Tacit knowledge sharing was found to promote the affective dimension of lecturers' corporate responsibility to research performance. Tacit knowledge sharing plays a mediating role between servant leadership and commitment organization and research performance. It was found to determine the research performance. The present study calls for knowledge management for tacit knowledge sharing to improve performance from a theoretical perspective. This can be done by focusing on tacit knowledge sharing about research, especially concerning research methodology and practical method development. From a practical standpoint, the study calls for extending and developing a structured tacit knowledge sharing supported by sharing culture among the lecturers.
Enterprise Performance Optimization Management Decision-Making and Coordination Mechanism Based on Multiobjective Optimization

Today’s society is a society of the knowledge economy, and the competition of enterprises is the competition of talents. The rapid development of science and technology and the fierce development of market competition have made the importance of performance management increasingly prominent in corporate management. The purpose of performance management is to explore and deal with some of the effects of various factors on employee performance and to tap the potential of employees, improve employee performance, and also bring a qualitative leap to the performance of the organization. The improvement of the employee performance management level has laid a solid foundation for the improvement of the organizational performance management level. However, there are still some difficulties in the implementation of performance management in my country at this stage, and the management effect is not obvious. Therefore, building a scientific, reasonable, and complete multiobjective optimization-based corporate performance optimization management decision-making and coordination mechanism is the primary task of today’s enterprises. This article will give a brief theoretical overview of the combination of system management theory and behavior management theory, MBO target management, and KPI indicators, build a multiobjective optimization model on an effective theoretical basis, and use genetic algorithms to obtain a weak Pareto effective solution that can optimize the enterprise with consideration of performance appraisal indicators. It also builds an agency model and an analysis of employee incentive plans, which clearly shows the relationship between the company and the management and employees, conducts a cross analysis of the needs of the company’s management and employees, and puts forward the best corporate performance considering the needs of employees; among them, the multiobjective optimization of corporate performance increased by 14% under the optimal management decision.
Motivational Embodied Conversational Agent for Brain Injury Rehabilitation

The design and development of a motivational embodied conversational agent for brain injury rehabilitation is discussed. Results for initial prototype design and implementation, and alpha and beta testing phases are presented. Key aspects identified during development included supporting user engagement via personalization and choice-making; integrating behaviour change principles into dialogues; addressing clinical needs of cognitive fatigue and memory loss within conversation structure; and optimizing feasibility of use in a real-life clinical setting.
A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.
A New Hybrid Technique for Face Identification Based on Facial Parts Moments Descriptors

Robust facial feature extraction is an effective and important process for face recognition and identification system. The facial features should be invariant to scaling, translation, illumination and rotation, several feature extraction techniques may be used to increase the recognition accuracy. This paper inspects three-moment invariants techniques and then determines how is influenced by the variation which may happen to the various shapes of the face (globally and locally) Globally means the whole face shapes and locally means face part's shape (right eye, left eye, mouth, and nose). The proposed technique is tested using CARL database images. The proposal method of the new method that collects the robust features of each method is trained by a feed-forward neural network. The result has been improved and achieved an accuracy of 99.29%.
Text Categorization Techniques: A Survey

The amount of data being generated during recent times has been exponentially huge. The data mainly comprises of unstructured data in the form of textual information like emails, tweets, articles etc. To gain information from these textual data, traditional way of analyzing cannot be used. There is a need for efficient techniques for analyzing these data. Text mining is defined as the process of transforming this unstructured data into understandable and meaningful information. Text Mining is a subfield of Artificial Intelligence which aims to automatically process the data and gain insights from the huge voluminous data. In this paper, several techniques used for classifying the data have been discussed. An overview about the dimensionality reduction methodology and how it can enhance the categorization process has been highlighted. It also aims with a future research scope in extending this categorization process along with dimensionality reduction procedures.
Smart Door for Handicapped People via Face Recognition and Voice Command Technique

Smart home, Face recognition, face detection, handicapped people, Raspberry Pi
Required Moment Sets: Enhanced Controllability Analysis for Nonlinear Aircraft Models

Attainable moment sets (AMS) are a powerful method to assess aircraft controllability. However, as attainable moment sets only take into account the achievable moment set of the control effectors, they do not assess the required moment set to fulfill the aircraft mission requirements. This paper proposes to calculate a corresponding required moment set (RMS) which defines a set of moments sufficient for fulfilling aircraft controllability requirements in the mission flight envelope. The paper applies the required moment set approach to a nonlinear simulation model of an electric vertical take off vehicle (eVTOL) transition drone in hover. By comparing the required moment set to the AMS of the aircraft model, moment set margins are derived and used to assess the controllability of the considered aircraft. The results indicate that the combined evaluation directly identifies critical moment channels and margins, which is advantageous when compared to a pure AMS-based evaluation. The proposed approach enables the execution of simulation-based assessments in aircraft design and flight control development. In the early stages of aircraft design, required moment sets can support sizing, positioning and tilting of control effectors (e.g., propulsive elements) to fit the AMS to the actual required force and moment set for the specific system.
Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages

With the advent of social media, we have seen a proliferation of data and public discourse. Unfortunately, this includes offensive content as well. The problem is exacerbated due to the sheer number of languages spoken on these platforms and the multiple other modalities used for sharing offensive content (images, gifs, videos and more). In this paper, we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance, Tamil and Latin). Our solution ranked number one for the Malayalam dataset and ranked 4th and 5th for Tamil and Kannada, respectively.
The Source-Target Domain Mismatch Problem in Machine Translation

While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in.As a result, people often talk about different things in different parts of the world.In this work we study the effect of local context in machine translation and postulate that this causes the domains of the source and target language to greatly mismatch.We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence for its existence.We conclude with an empirical study of how sourcetarget domain mismatch affects training of machine translation systems on low resource languages.While this may severely affect back-translation, the degradation can be alleviated by combining back-translation with selftraining and by increasing the amount of target side monolingual data.
Distillation of weighted automata from recurrent neural networks using a spectral approach

This paper is an attempt to bridge the gap between deep learning and grammatical inference. Indeed, it provides an algorithm to extract a (stochastic) formal language from any recurrent neural network trained for language modelling. In detail, the algorithm uses the already trained network as an oracle—and thus does not require the access to the inner representation of the black-box—and applies a spectral approach to infer a weighted automaton. As weighted automata compute linear functions, they are computationally more efficient than neural networks and thus the nature of the approach is the one of knowledge distillation. We detail experiments on 62 data sets (both synthetic and from real-world applications) that allow an in-depth study of the abilities of the proposed algorithm. The results show the WA we extract are good approximations of the RNN, validating the approach. Moreover, we show how the process provides interesting insights toward the behavior of RNN learned on data, enlarging the scope of this work to the one of explainability of deep learning models.