Reviewerly/exHarmony huggingface dataset:
DEVELOPING ENGLISH LEARNING MATERIALS FOR FRONT OFFICE STUDENTS IN TOURISM FACULTY OF TRIATMA MULYA UNIVERSITY

Teaching English especially in front office department was less textbook and dominated by memorization of vocabularies. This research aims to develop English learning materials in the form of textbook for front office students in Tourism Faculty, Triatma Mulya University. The method of the current study is Design and Development. In developing the learning material, this research uses the DnD model by Richey and Klein. Some processes were carried out, namely identifying learning materials needed, developing learning materials in the form of books and evaluating the results of material development. The results of the study found that there are six types of material that need by front office students namely (1) front office department coordination to other departments, (2) telephone operator, (3) reservations, (4) reception, (5) porter/uniformed service, and (6) procedure handling complaint. The results of expert tests showed the score of the learning material developed belongs to good category.
Robot's velocity optimization for planned path by hybrid adaptive dimensionality with GSO algorithm

The robot velocity is one of the important factors to identify a mobile robot efficiency. This paper will present simple but efficient approach for controlling the robot's velocity in order to stabilize its movement when cornering and/or avoiding obstacles crush with suitable fast and safe navigation speed. In our previous work, we introduce a hybrid adaptive dimensionality with the Glowworm Swarm Optimization (GSO) algorithm approach to find an optimal path from initial point to the goal point. This will be extended in the current paper by using the path that was obtained by this approach to decide if the robot should change its velocity to avoid collision. Robot velocity controlling is an important in the following states. (1) When the robot navigates in the tight free area. (2) When the robot cornering around the angles of its path heading to its goal. This paper proposes an effective approach to robot velocity control that the robot selects the possible fastest velocity. To adopt this approach, two important factors must be taken into account to control the speed of robot in the above two cases, angle's value at each path's node as well as the number of obstacles that are surrounding this node which are located in the nearest two levels of this node toward the robot motion. The proposed approach successfully made the robot move around in its environments with adaptively controlling the velocity.
Pedestrian and Objects Detection by Using Learning Complexity-Aware Cascades

Due to the considerable technological development in all joints of life, the trend has become significant towards automating various processes in daily life, such as smart cities, the Internet of things, and cloud services. One of the most crucial applications is self-driving cars, which will be a quantum leap in this field. The main problem with these vehicles will be how to provide the necessary accuracy to deal with various situations, such as sudden stops and pedestrian crossing. In this paper, we propose an effective method for automating autonomous vehicles by improving their ability to make appropriate decisions at the right time. For this, we rely on sequential training that is aware of the complexity. The system is trained and provided to the vehicles, where the presence of pedestrians is detected using machine learning algorithms, such as a deep convolutional neural network (CNN). The findings obtained in this research suggest a clear improvement in the vehicle's ability to make decisions and a great speed in responding to the event and parking the vehicle when passing by.
Arabic light-based stemming: a comparative study among ligh10 stemmer, P-stemmer, and Conditional light stemmer

Arabic stemming is a key stage in natural language processing's preprocessing (NLP). It takes affixes out of words. It improves text classification (TC) as well as information retrieval (IR). Light-based stemming and root-based stemming are the two types of stem. When compared to root-based stemming, light-based stemming consumes more energy. Only suffixes and prefixes are removed from the words. The light10 stemmer, the p-stemmer, and conditional light stemming (CondLight) are three well-known methods of light stemming. Prefixes and suffixes are removed by Light10 stemmers under a few conditions. Only prefixes are removed by the P-stemmer, while the CondLight stemmer is the same as the Light10 stemmer but with eight conditions. We measured the extent of improvement in Arabic TC by evaluating the stemmers. Three classifiers employ the Support Vector Machine (SVM), the k-nearest neighbor algorithm (KNN), Nave Bays (NB), and statistical similarity measurement. With stemming, the outcome indicates a small improvement (about 2 percent improvement).
Enhancement and Lightweight Fusion U-Net for Segmentation of Cardiac Structures in MRI

Magnetic resonance imaging technology and the segmentation process are essential parts in obtaining accurate diagnosis of cardiac diseases. Recently, deep learning networks have shown progress in achieving good accuracy. However, the 2D network may not produce the required accuracy, and the use of 3D networks is restricted because of the computation and storage complexities. In this paper, a lightweight fusion U-Net (LWFU-Net) is proposed to achieve the trade-off between segmentation accuracy and complexities. LWFU-Net is a new variant of U-Net and LVRV-Net. It enhances 2D information by using a propagation flow approach with multipath and multilevel fusion. LWFU-Net operates via three stages. First, the adjacent slices of target slice are fused by using traditional average method, generating context information. Subsequently, the target slice is segmented similar to U-Net network, where the context information is fused with the target slice and the decoding results via concatenation operator and then processes information by using convolutional layers. Finally, the deep supervision generates the final outcome. The proposed networks are evaluated on the public Automated Cardiac Diagnosis Challenge dataset by using fivefold cross validation. LWFU-Net outperforms single-path LWU-Net and agrees with the LVRV-Net. The best performance of LWFU-Net exhibits mean dice similarity scores of 0.939 left ventricular cavity, 0.861 right ventricular cavity, and 0.885 left ventricular myocardium over the subtest of 10% of training set. Results demonstrate that LWFU-Net has an acceptable usage of 51 MB (weight memory), 13,274,252 (trainable parameters), and 20 G flop. It achieves a speedup of 6 for the training and approximately 2 for the testing.
A Topic Modeling for Clustering Arabic Documents

Topic modeling is a type of statistical data mining technique for discovering the abstract “topics” that occur in a collection of articles or documents and the most widely used topic modeling technique is LDA. In our paper, we tested the effectiveness of a recently developed topic modeling approach (LDA2Vec) that was introduced by Chris Moody with our Arabic dataset. LDA2Vec is a hybrid approach of LDA and a highly popular word-embedding model (Word2Vec). Our goal is to find a method for automatically clustering Arabic documents by topic and categorizing them for use in a recommendation system and searching. The performance of the model was evaluated using a corpus of Arabic documents divided into nine categories. Despite the grammatical variations between Arabic and English, the model worked well with the Arabic language when it was implemented, as we observed in our study. As a conclusion of our findings, LDA2Vec gave (82.40%) accuracy over topics for test documents, which is greater than LDA accuracy (67.96%), which was evaluated with the same dataset.
Role of A.M. Bochever in the development of specialized bibliography and the Central Scientific Agricultural Library

The article presents the main stages of the A.M. Bochever’s biography, a known specialist in special bibliography and library science, who worked in the Central Scientific Agricultural Library (CSAL) for 27 years and 19 years of that period he held the position of the director. A.M. Bochever acted as an author and editor of different bibliographic textbooks. He published above 100 works; in particular he was a co-author of a textbook on agricultural bibliography “Literature on Agriculture”. He made a&#x0D; substantial contribution to the theory of library science and&#x0D; bibliography, as well as the establishment and development of&#x0D; the CSAL. The A.M. Bochever’s views are presented with&#x0D; respect to bibliography and its place in the society, the&#x0D; problems of a library, the separation of functions of a library&#x0D; and scientific and technical information bodies, the role of&#x0D; coordination and cooperation in the activity of agricultural&#x0D; libraries. He regarded bibliography as a basis for all types of&#x0D; scientific and technical information, emphasized its social and&#x0D; cultural role. He proposed to consider the bibliography of&#x0D; agricultural literature as an independent branch of general&#x0D; bibliography. He promoted creating in the CSAL of a&#x0D; bibliographical information system comprising alert (an&#x0D; initiator of creation), recommendatory, retrospective and&#x0D; current bibliography. In the 1960s he organized in the CSAL&#x0D; the Centralized Bibliographical Information (CBI): the&#x0D; distribution of a ready catalog card which was both the&#x0D; centralized catalogization of publications on agriculture, as&#x0D; well as the current bibliographical information. He advocated&#x0D; for coordination of the work of libraries and their cooperation&#x0D; in library service. The A.M. Bochever’s ideas were reflected in&#x0D; series of industry-wide methodical documents, including&#x0D; “Regulation on the Unified System of Scientific Technical&#x0D; Information of the USSR State Agriculture Committee”.
Cloud Forensics: Challenges and Solutions (Blockchain Based Solutions)

Cloud computing is an on-demand service provided for computer resources, data storage and enhancing computing power. Digital forensics is used to help forensic investigators extract evidence against cloud/ cybercriminals and maintain the integrity and security of data stored in a cloud environment. Based on the prior research in this area concerning existing challenges and solutions, this survey focuses on exploring the problems and their proposed solutions on the basis of a detailed literature survey. It critically explores and reviews the prevailing challenges and solutions through an in-depth review of the cloud forensic area. The paper highlights all the current problems in cloud forensics and their solutions previously identified by the researchers to help investigators probe any criminal incident. The three categorization model explores the challenges and solutions of the existing methods and offers directions for future research in this area. Finally, this survey paper can be considered an initiative to carry out research and develop cloud forensic-able services for the cloud environment.&#x0D; INDEX TERMS: blockchain based solution, cloud forensics, cloud forensics challenges, cloud forensics methodologies, cloud forensics solutions
Generating Face Image Dataset using a 3D Head Model

Many classification algorithms depend on a training dataset to be able to find appropriate parameters. The training dataset can significantly affect the resulting quality of the classifier, hence it is necessary to use as good dataset for a given task as possible. Either there already is an available ready-to-use dataset or a new one must be created. It is a rather tedious and time consuming process to create a new dataset from scratch because many algorithms usually require a large amount of tagged data. For many specific tasks, the only way is to create a new dataset simply because there are no sets available. For the purposes of training and testing face detection and recognition algorithms, we created a generator producing a large number of images containing a human head in various poses and positions placed in a predefined scene. The synthetically generated dataset may complement the dataset generated by real data or may be used separately.
A Real-Time Blinking Reminder Supportive System for Eye Care of Computer Gamers

The most common human trouble in today's world is the ocular disease. About 75% of human beings use spectacles due to eye problems. Using electronic devices is the root cause of this issue. While using these devices, people don't blink their eyes occasionally. They continuously watch the screen of the devices while playing games or doing any official work. The proposed system may create a solution to this problem. Especially during playing computer games blink rate of gamers decrease significantly. A survey is done in this work to justify the number of blinking of a computer gamer. To recognize human eyes and eye blinking Haar feature-based classifier is used. It can detect a human face as well as eye position. The main motive of this system is to alert the user to blink their eyes several times. Generally, a person needs to blink their eyes approximately twelve times in a minute to keep their eyes healthy. The proposed system generates an audio alert to its user if a gamer doesn't blink a minimum number of times in one minute and acts as a supportive system for eye care of gamers.
Common-mode Current Reduction by Applying Mode-conversion Suppression Technique to Power Delivery Network as Side-channel Attack Countermeasure

The common-mode current flowing through a power cable contains the secret information of a cryptographic module that allows an attacker to eavesdrop from a remote location. Mode conversion conveys secret information as side-channel information from the normal-mode noise to the common-mode current at the connector section where the imbalance factor between the power cable and the trace on a power delivery network (PDN) is discontinuous. This common-mode current is generated due to mode conversion and flows through a power cable as side-channel information. We apply the mode-conversion suppression technique at the discontinuity point on a PDN to reduce the common-mode current as a side-channel attack (SCA) countermeasure. We place a capacitor at the discontinuity point to suppress mode conversion by reducing normal-mode voltage. Therefore, the common-mode current in a power cable should enhance the SCA resistance of the cryptographic module. We experimentally confirmed that installing a capacitor at the discontinuity point of the imbalance factor on a PDN efficiently suppresses mode conversion and reduces the common-mode current to counter SCAs from outside the cryptographic module.
Word Reading, Reading Comprehension, and English Performance among Elementary Pupils: Basis for Designing Intervention Program

The issues and concerns regarding the reading competency of the majority of elementary school students have long been a source of contention in public schools. In order to meet the Department of Education's reading competency target for all primary grades, teachers have faced significant obstacles in implementing the innumerable strategies that have been developed. As evidenced by the large number of studies that demonstrate a link between reading competency and other findings such as academic achievement, literacy, and other factors, developing reading skills is perilous for learners' cognitive education. It is the teacher's instinctive strength in dealing with these problems in the broad sense of learners' reading competency and its relationship with their academic performance that is taken into consideration, including things like word reading, reading comprehension, and academic performance in English. This study investigated the levels of word reading and reading comprehension of Grade IV students as measured by the Phil-IRI assessments, and the relationship between these levels and their academic performance. This research was conducted among 201 Grade 4 students enrolled in schools in the Northern District of Nueva Vizcaya, who were identified using the G*power calculator as being from the study's target population. The data came from the Phil-IRI assessment tools, which focused on the students' word reading and reading comprehension skills, together with the students' overall academic performance in English, which was collected through interviews. Independent, instructional, and frustration reading levels were identified in the students' reading abilities. The researcher used descriptive and inferential approaches to examine the data, which included weighted mean, frequency, and percentage. In order to show the students' levels of word reading and reading comprehension, as well as their academic performance in English, descriptive approaches were chosen. The correlational methods were used to investigate the relationships between the factor variables. The findings revealed that the majority of students chose to read independently and comprehend the texts they were given. Only a few of them experienced feelings of frustration. The vast majority of them performed admirably. Furthermore, there were statistically significant positive relationships between the variables. Given the fact that the variables were positively related to one another, an intervention program to close the gap was proposed.
Rancang Bangun “Siwani” Media Pembelajaran Perakitan Komputer Di Masa Covid 19

Currently the world is facing a big problem, namely the emergence of corona virus disease (covid-19), the entry of this virus inhibits all human activities, including in the field of education. Based on interviews with teachers of computer subjects and basic networks, the subject of computer assembly in the learning process is still hampered by time when learning theory during the Covid-19 period, the application of current learning is using whatsapp groups, where the teacher provides material files and assignments. Online learning with this whatsapp group is considered less than optimal from both teachers and students resulting in students having difficulty understanding the material, based on these problems a solution is given by developing learning media with the name "SIWANI". The objectives of this research are: 1) to build SIWANI as a learning media for computer assembly; 2) determine the feasibility of SIWANI as a learning media for computer assembly; 3) knowing students' responses to SIWANI as a learning media for computer assembly. The method used is Research and Development (R&amp;D) with the ADDIE development model. The results showed that the material expert feasibility test obtained an average of 85% with a very feasible category. While the feasibility test by media experts obtained an average percentage of 81% with a very feasible category. While student responses obtained an average percentage of 83% with a very high category.
ON THE ESSENCE AND MEANING OF LIFE

The authors make an attempt to determine the fundamental property of living matter: the presence of a special "living field" that changes the coordinates of space and time in living matter. They emphasize the impossibility of creating any living element without the participation of this field (“primer”). Living things have the properties of a thermodynamically open system in a metastable ordered non-equilibrium state with minimal entropy. The fundamental quality of living matter is the ability to reproduce by transferring the "living field" with genetic material. They analyze the special properties of the living – consciousness and the ”anticipatory reflection” of the world. They emphasize the amazing coincidence of the meaning inherent in the religious dogma about the “trinity” with the properties of living matter. Express an opinion about the meaning of life.
DEVELOPMENT OF PHOTOCATALYTIC AND ULTRASONIC EQUIPMENT TO PREVENT THE SPREAD OF CORONAVIRUS SARS-COV-2. PHOTOCATALYTIC SAMPLE OF SEPARATION STAGE

The Full-scale experimental photocatalytic sample of separator consists of 5 equal purification modules. Each of the modules consists of separation elements for increasing of purification level. The equipment is designed for air volume G = 50… 150 m3 / hour, should reduce the degree of microbial contamination of the air to the required level (capture particles of 0.1 μm) and help reduce the risk of airborne diseases. Project considers solving an important scientific and technical problem of creating and development of photocatalytic and ultrasonic heat and mass transfer separation equipment for air clean from dust and viruses (coronavirus SARS-COV-2). Filtration is a quite economical and efficient method of improving air-conditioning system, the air filtration systems represent a good solution for the improvement of Indoor Air Quality (IAQ), and the Antimicrobial treatments (coronavirus SARS-COV-2 prevention) of filters may be a solution to these problems. It is possible to prevent the accumulation and dispersion of microorganisms by adding anti-microbial agents on the surfaces of filter, which contributes to the improvement of air quality. Purification from dust and viruses and liquid media smaller than 1 μm requires development of air-purifying separation equipment able to capture particles of this size with implementation of resource-saving features base on photocatalytic and ultrasonic equipment. Development of photocatalytic and ultrasonic heat and mass transfer separation equipment for air clean from dust and viruses (coronavirus SARS-COV-2 prevention) is based on multilevel gradient aerosol technologies, as well as research of methods of their control, is their joint use in the presence of substantial gradients of the hydrodynamic and thermophysical parameters (temperature, pressure, velocity, density, etc.).
Adaptive neural network PID controller for nonlinear systems

In this paper, we are interested in adaptive neural PID control with a reference model for nonlinear systems. A recurrent neural network architecture is studied, and its parameters are computed to mimic a conventional PID controller. Two neural networks architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are used for modeling nonlinear systems. To avoid the errors of the Jacobian system approximation, it is best to model the system with a neural network called an emulator. By adding an emulator in parallel with the dynamic system model, the emulator will be trained to learn the dynamics of the system.The back-propagation method is used as the basis for developing algorithms capable of modeling and controlling our nonlinear systems.