Reviewerly/exHarmony huggingface dataset:
Genomic Sequence Data Compression using Lempel Ziv Welch Algorithm with Indexed Multiple Dictionary

With the advancement in technology and development of High Throughput System (HTS), the amount of genomic data generated per day per laboratory across the globe is surpassing the Moore’s law. The huge amount of data generated is of concern to the biologists with respect to their storage as well as transmission across different locations for further analysis. Compression of the genomic data is the wise option to overcome the problems arising from the data deluge. This paper discusses various algorithms that exists for compression of genomic data as well as a few general purpose algorithms and proposes a LZW-based compression algorithm that uses indexed multiple dictionaries for compression. The proposed method exhibits an average compression ratio of 0.41 bits per base and an average compression time of 6.45 secs for a DNA sequence of an average size 105.9 KB.
Facial Modelling and Animation: An Overview of The State-of-The Art

Animating human face presents interesting challenges because of its familiarity as the face is the part utilized to recognize individuals. This paper reviewed the approaches used in facial modeling and animation and described their strengths and weaknesses. Realistic face animation of computer graphic models of human faces can be hard to achieve as a result of the many details that should be approximated in producing realistic facial expressions. Many methods have been researched to create more and more accurate animations that can efficiently represent human faces. We described the techniques that have been utilized to produce realistic facial animation. In this survey, we roughly categorized the facial modeling and animation approach into the following classes: blendshape or shape interpolation, parameterizations, facial action coding system-based approaches, moving pictures experts group-4 facial animation, physics-based muscle modeling, performance driven facial animation, visual speech animation.
Temporal Consistency-Based Loss Function for Both Deep Q-Networks and Deep Deterministic Policy Gradients for Continuous Actions

Artificial intelligence (AI) techniques in power grid control and energy management in building automation require both deep Q-networks (DQNs) and deep deterministic policy gradients (DDPGs) in deep reinforcement learning (DRL) as off-policy algorithms. Most studies on improving the stability of DRL have addressed these with replay buffers and a target network using a delayed temporal difference (TD) backup, which is known for minimizing a loss function at every iteration. The loss functions were developed for DQN and DDPG, and it is well-known that there have been few studies on improving the techniques of the loss functions used in both DQN and DDPG. Therefore, we modified the loss function based on a temporal consistency (TC) loss and adapted the proposed TC loss function for the target network update in both DQN and DDPG. The proposed TC loss function showed effective results, particularly in a critic network in DDPG. In this work, we demonstrate that, in OpenAI Gym, both “cart-pole” and “pendulum”, the proposed TC loss function shows enormously improved convergence speed and performance, particularly in the critic network in DDPG.
Evaluation of User Experience Information Systems Using Heuristic Evaluation (Case Study of STMIK Amik Riau Student Portal)

The academic information system is an important system to supports lecture activities. Is used by almost all elements in the university, both students, lecturers, staff and leaders. This research uses Heuristic Evaluation as an inference method to assess the components of learnability, efficiency, memorability, errors, and satisfaction. for various cases, such as designing academic and corporate websites with reference to these problems, it is necessary to evaluate the usability of STMIK Amik Riau information system. STMIK Amik Riau implements an integrated information system to support fast and real-time information management processes of STMIK Amik Riau information system includes various services such as E-KRS, E-KTM, E-EDOM, and other information. The aim is to identify problems related to the usability of the website. The data collection method in this research was carried out using questionnaire, containing a list of questions distributed via google form to respondents, its about 100 students of STMIK Amik Riau. Based on the analysis conducted using the Heuristic Evaluation method, the evaluation results of STMIK Amik Riau web portal have met the usability criteria with an average of 78.71915% where P &gt; 60% and provides user satisfaction in accessing STMIK Amik Riau web portal. STMIK Amik Riau's web interface design is quite good. These results are based on the results of Likert scale score which states that the respondents agree.
News Video Classification Model Based on ResNet-2 and Transfer Learning

A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.
Distribution level electric current consumption and meteorological data set of the east region of Paraguay

This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area.
Attribute Set-Based Boolean Keyword Search over Encrypted Personal Health Records

A personal health record (PHR) is an electronic application which enables patients to collect and share their health information. With the development of cloud computing, many PHR services have been outsourced to cloud servers. Cloud computing makes it easier for patients to manage their personal health records and makes it easier for doctors and researchers to share and access this information. However, due to the high sensitivity of PHR, a series of security protections are needed to protect them, such as encryption and access control. In this article, we propose an attribute set-based Boolean keyword search scheme, which can realize fine-grained access control and Boolean keyword search over encrypted PHR. Compared with the existing attribute-based searchable encryption, our solution can not only improve the flexibility in specifying access policies but also perform Boolean keyword search, which can meet the needs of large-scale PHR users. Furthermore, we simulate our scheme, and the experimental results show that our scheme is practical for PHR systems in cloud computing.
Horizontal Network Organization Form Of Undergraduates’ Research Activities Based On Interdisciplinary Interaction

The article is devoted to the problem of organizing network interdisciplinary interaction in the process of research work of undergraduates.Aiming to improve the organizational and methodological tools for the development of research competencies of undergraduates in philology, the authors propose their own concept and specific ways to solve the problem: to form new skills of research activities based on the integration of professional and interdisciplinary knowledge using a horizontal network form of research work.The organization of a "virtual" research space within the framework of network interaction of program participants is carried out through the creation of web discussion platforms, web seminars, where managers pose professional problems to undergraduates in the humanities, and undergraduates of other professional fields provide technical or mathematical assistance in solving them with further critical discussion of problems and their assessment.The article illustrates interdisciplinary research tasks for philology students based on textual analysis and computer modeling of I. A. Bunin's journalistic texts.A distinctive feature of the conducted research is the implementation of a network format of dialogical interaction of participants through critical discussion and practical solutions from various points of view of interdisciplinary professional problems.The uniqueness of the presented approach lies in the fact that for the first time in the practice of teaching undergraduates of opposing training professional fields (mathematical and humanitarian), actual ways to overcome the isolation of scientific cultures have been found.
GENERAL CLASSIFICATION OF THE DOCUMENT: CHANGES AND ADDITIONS

Object, theme, purpose of work. The article is devoted to one of major problems of typology of document: general classification of document. A research purpose is to optimize the chart of general classification of document, specify some terms and offer material for a further discussion for development of typology of document. Method or methodology of realization of work. The research methodology is based on the information and communication approach, which allows to determine the features of the classification of the document, which ensure the fixation of information in the document and its transmission in society. The study is aimed at developing a general theory of the document, so take into account primarily the features inherent in all documents, regardless of their purpose. A comparative analysis of various concepts of the general classification of the document and the proposed terms and concepts is carred out. Presenting main material. The classification of the document is considered on the following blocks of facets: Types of the document on features of the information carrier; Types of the document on features of fixing of the information (creation of the document); Types of the document on sign means of fixing and transfer of the information (on a signaling component); Types of document on the possibilities of perception of information (perceptual component); Types of the document according to its information component; Types of the document according to the circumstances of the document’s existence in the external environment. The composition of the blocks has been revised; some facets have been added, others have been removed from the general document classification table. The names and definitions of certain types of documents have been clarified. Industry of application of results. Research results will be drawn in educational and practical activity in science and industries, related to information and documentation. Conclusions. As a result of research the signs on which it is necessary to carry out the general classification of the document, names and sequence of blocks from which the general multifaceted classification consists, the characteristic of separate types of the document are specified. A promising direction for the further development of the general classification of the document should be both the solution of problems inherent in this classification, and the wider popularization of already achieved results, their implementation in various fields of document science.
CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion

In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by using rectangular bounding boxes, the previous methods may be insufficient for working with more challenging instances of text. To solve this problem, this paper proposes a scene text detection network based on cross-scale feature fusion (CSFF-Net). The framework is based on the lightweight backbone network Resnet, and the feature learning is enhanced by embedding the depth weighted convolution module (DWCM) while retaining the original feature information extracted by CNN. At the same time, the 3D-Attention module is also introduced to merge the context information of adjacent areas, so as to refine the features in each spatial size. In addition, because the Feature Pyramid Network (FPN) cannot completely solve the interdependence problem by simple element-wise addition to process cross-layer information flow, this paper introduces a Cross-Level Feature Fusion Module (CLFFM) based on FPN, which is called Cross-Level Feature Pyramid Network (Cross-Level FPN). The proposed CLFFM can better handle cross-layer information flow and output detailed feature information, thus improving the accuracy of text region detection. Compared to the original network framework, the framework provides a more advanced performance in detecting text images of complex scenes, and extensive experiments on three challenging datasets validate the realizability of our approach.
The Impact of Supplemental Word Recognition Strategies on Students with Reading Difficulties

The purpose of this study was to examine the impact of supplemental word recognition strategies on students receiving intensive (Tier III) remedial reading instruction.Two-hundred and thirteen middle school students with reading difficulties received an intensive reading intervention (i.e., Corrective Reading Decoding strand) delivered by trained school staff (n = 21) at three middle schools.The 213 fifth through eighth grade students (121 males, 92 females) enrolled in three northwestern school districts (two rural and one urban).Of the 213 students, one-hundred and seven also received supplemental word recognition instruction using Reading Excellence: Word Attack and Rate Development Strategies (REWARDS).Using a pre-post quasi-experimental design, the effects of the Corrective Reading Decoding (CRD) program was compared to CRD combined with the REWARDS program.The REWARDS program produced statistically and educationally significant changes in the basic reading skills of students over and above Corrective Reading Decoding alone.Students in the REWARDS condition demonstrated statistically significant mean changes on the WJ-III Basic Reading Skills cluster and associated subtests and the DIBELS ORF probe compared to those in the comparison condition.Results, implications, and limitations are discussed.
SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems

Blockchain technology provides a “tamper-proof distributed ledger” for its users. Typically, to ensure the integrity and immutability of the transaction data, each node in a blockchain network retains a full copy of the ledger; however, this characteristic imposes an increasing storage burden upon each node with the accumulation of data. In this paper, an off-chain solution is introduced to relieve the storage burden of blockchain nodes while ensuring the integrity of the off-chain data. In our solution, an off-chain remote DB server stores the fully replicated data while the nodes only store the commitments of the data to verify whether the off-chain data are tampered with. To minimize the influence on performance, the nodes will store data locally at first and transfer it to the remote DB server when otherwise idle. Our solution also supports accessing all historical data for newly joined nodes through a snapshot mechanism. The solution is implemented based on the Hyperledger Fabric (HLF). Experiments show that our solution reduces the block data for blockchain nodes by 93.3% compared to the original HLF and that our advanced solution enhances the TPS by 9.6% compared to our primary solution.
A Collaborative Robot in the Classroom: Designing 21st Century Engineering Education Together

New and interactive robot arms have found their way to the factory floor: the collaborative robots (cobots). Cobots execute simple and routine tasks with the accuracy and precision of tradional industrial robotics. Although cobots often operate highly autonomous, they require human interferance to function. Both production workers and engineers play an essential role in the creation and maintenance of these 'human￾cobot collaborations'. The rising number of cobots in industry increasingly calls for current and future generations of production workers and engineers who are capable of fulfilling their role in human-cobot collaboration. From a development point-of-view, engineering education is a powerful vehicle to prepare production workers and engineers for human-cobot collaboration. However, it is unclear what knowledge, skills, abilities, and other requirements (KSAOs) production workers and engineers need to create and maintain human-cobot collaboration and what engineering education allows the development of these KSAOs. Therefore, our goal was to investigate how engineering education could prepare future production workers and engineers for human-cobot collaboration. We used the O*NET Content Model to deductively analyse 60 interviews about human-cobot collaboration in Dutch industry. Results illustrate how 31 KSAOs were found relevant for the design, programming, operation, and repair of human-cobot collaboration and how these KSAOs were distributed amongst production workers and engineers. Together with a community of practice, we used these insights to design a 240-hour vocational education course on human-cobot collaboration. Key decisions, course content, learning dimensions, and examination compentents are elaborated upon.
A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks. Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colony-based Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.
Airborne Computer System Based Collision-Free Flight Path Finding Strategy Design for Drone Model

An airborne computer system is avionics-based model that consists of an electronic system and a specifically designed equipment.In this paper, we propose a collision-free path finding flight strategy that enhances the output performance of a drone system for free-navigation between buildings.This strategy is based on a reactive method that requires metaheuristic algorithms to process environmental information during drone flying.In addition, a robust controller is designed to achieve drone flight and reach the target point.The goal of this paper is to discuss the determination of the shortest flight path with the minimum cost function evaluation and this flight path should avoid the collision by using four heuristic algorithms including: Chaotic Particle Swarm Optimization (CPSO) algorithm, Fire-Fly (FF) algorithm, Bees (B) algorithm and a hybrid optimization algorithm that combines the CPSO, FF and B algorithms.To fly the drone and to follow the generated flightpath, six adaptive PID controllers are designed in order to control the highly nonlinear model and under-actuated drone system using an on-line CPSO algorithm to learn and tune the eighteen control gain parameters.The purpose of this control design is to precisely and quickly obtain robust thrust forces control actions to control the attitude and altitude of the drone model.The numerical results of the proposed flightpath-finding algorithms and robust control strategy confirm that the hybrid (CPSOFFB) flightpath algorithm has the minimum number of iterations and evaluation functions as well as free navigation and the shortest flight path length generated.Moreover, the proposed six adaptive PID controller results show that the four thrust forces control actions are smooth and accurately generated making the drone take off and follow the desired flight path quickly with the minimum number of cost function and around ±10 cm minimum error tracking translation location.The maximum overshoot of the altitude did not exceed 10 cm in the transient state and the orientation error of the drone is approximately zero in the steady-state.To demonstrate the effectiveness of the proposed flight path finding and control strategy, a comparison is made with the results other types of algorithms.
Classification Algorithms for Software Defect Prediction: A Systematic Literature Review

Within Software Engineering, it is essential to build quality software. An obstacle to the after mentioned are the defects that can be found in any phase of software development. That is why the area of software defect prediction emerged, in which different algorithms have already been evaluated, studied, and proposed. The objective of this paper is to carry out a Systematic Literature Review, to know which are the classification algorithms that help to predict software defects. In the same way, it is intended to know the features, metrics and what has been the precision of the classification algorithms for software defect prediction. This paper shows that the most widely used classifiers to predict defects are Naive Bayes and Random Forest, while those that show the best results are Naive Bayes and Boosting. Finally, it is highlighted that Precision and Recall are the most used metrics for model validation.