Reviewerly/exHarmony huggingface dataset:
PSİKOTROP İLAÇLARIN BRUKSİZM ETİYOLOJİSİ VE TEDAVİSİNDEKİ ETKİSİ: GÜNCEL BİLGİLER IŞIĞINDA BRUKSİZME GENEL BAKIŞ

Bruxism, also known as teeth grinding, is an unusual activity that affects people's quality of life to a certain extent.Bruxism that occurs during the day and / or during sleep is thought to have a multifactorial etiology.There are many studies on the etiology of bruxism, but certain issues have not been fully clarified in these studies.Various methods are used in the treatment, but the main factor must be found in order to apply effective treatment of bruxism.As well as psychotropic drugs used in the treatment of bruxism, there are also psychotropic drugs that initiate bruxism or induce bruxism.The effects of these drugs at different doses and durations vary.In this review, we investigated the etiology of bruxism and the effects of psychotropic drugs on initiation, induction and treatment of bruxism.Thus, we tried to emphasize the effect of drugs used in evaluating or treating bruxism.
INNOVATIVE COMPUTER TECHNOLOGIES IN FOREIGN LANGUAGES INSTRUCTION AT UKRAINIAN HIGHER SCHOOLS

The global process of transition to the information society, as well as economic, political and social changes that accompany it, accelerate the reform of the Ukrainian education system. First of all, it concerns providing access to education and training for all people who have the necessary skills and knowledge. The effective solution of these problems is facilitated by distance and blended learning, carried out on the basis of modern pedagogical, information and telecommunication technologies. In the article the ways of organizing blended teaching/learning of foreign languages in Ukrainian higher education institutions (HEI) are analyzed; types of software used for creating educational materials by means of innovative computer technologies to organize blended language learning are revealed; the prospects of using electronic materials of known platforms of online courses for teaching languages as supplements to distance courses compiled by the lecturers of the Department of Foreign Languages at Lviv Polytechnic National University on the basis of Lviv Polytechnic virtual learning environment (VLE) are considered. Materials of research are as follows: dissertations, scientific papers, materials of Ukrainian and international scientific and practical conferences, published by lecturers of foreign languages at Ukrainian higher education institutions, as well as web pages of these institutions and software for compiling educational materials. On the basis of the analyzed materials, partially presented in the article, it was revealed that for organization of blended learning the lecturers of Foreign Language Departments at HEI, in addition to traditional educational materials for full-time study (manuals, dictionaries, audio and video recordings, etc.), actively use: electronic materials compiled by means of different types of tools in open access; portal materials for language learning; distance courses or their elements compiled by means of the VLE of their educational institutions; educational materials of the platforms of mass open online courses (MOOC). Lecturers of the Department of Foreign Languages at Lviv Polytechnic National University have used MOOC for their students teaching since January 2019. English language practical classes, held at Lviv Polytechic for the first year students, demonstrated that the students using successfully the materials of distance course, compiled by the lecturers of the Department of Foreign Languages for their students, can improve better their knowledge of the language than the rest students. The further researches will be concerned to description of the techniques for organization of foreign languages blended learning by incorporating the materials of the MOOC platforms in the course of classes with full-time and part-time students.
Integrating KNN and Gradient Boosting Decision Tree for Recommendation

As an efficient information filtering tool, the recommendation system can help users obtain valuable information quickly and efficiently while effectively alleviating the information load problem. However, the classic collaborative filtering algorithm has data sparsity problems in practical applications, which affects the accuracy of recommendation. To solve this problem, this paper took movie recommendation as the research object and proposed a hybrid recommendation algorithm named K-GBDT which combined KNN and gradient boosting decision tree. Firstly, it used KNN to obtain the similar information of the target user and item for preliminary prediction. At the same time, it mined users' basic information features and potential features of users and items from the original data. Then, it tried to adopt XGBoost, LightGBM and CatBoost algorithm which were proposed base the idea of gradient boosting decision tree to build regression model. Finally, the target user's rating for the movie was predicted with the trained model, and the recommendation list was generated based on the predicted results. Compared with some classic recommendation algorithms such as NMF, Slope One, Co-Clustering and etc. Based on MovieLens datasets, results show that the mean absolute error and root mean square error of this algorithm are both low, and its recommendation accuracy is higher.
Privacy Preserving Inference with Convolutional Neural Network Ensemble

Machine Learning as a Service on cloud not only provides a solution to scale demanding workloads, but also allows broader accessibility for the utilization of trained deep neural networks. For example, in the medical field, cloud-based deep-learning assisted diagnoses can be life-saving, especially in developing areas where experienced doctors and domain expertise are lacking. However, preserving end-users' data privacy while using cloud service for deep learning is a challenge. Some recent works based on fully homomorphic encryption have enabled neural-network predictions on encrypted input data. In this paper, we further extend the capability of privacy preserving deep neural network inference, through a joint decision made by multiple deep neural network models on encrypted data, to address bias caused by unbalanced local training datasets. In particular, we design and implement a privacy preserving prediction method through an ensemble of convolutional neural networks. The extensive experiment results show that our method can achieve higher accuracy compared to individual models, and preserve the user data privacy at the same level. We also verify the time efficiency of our implementation.
Open data validation of a classification method of eye movement by a convolutional neural network

We analyze fixation and saccade using an extension of a convolutional neural network (CNN) model and compare the results to a conventional modeling method (gazeNet). Unlike the conventional method, ours can easily be applied to realtime analysis of eye movements during data acquisition since it can feed the results back to trainees for image interpretation in real time. Eye movement data was divided into “fixation” and “saccade” sections using the open data sets, “Lund2013” and “GazeCom,” that are available via the internet and which can be used as validation data for interpreting eye movements while viewing medical images. Images to be input into our Deep CNN model, DCNN, were created by drawing path lines from 12 consecutive gaze points over a period of 0.1 s assuming 120 Hz measurements with appropriate downsampling of the data (500 Hz for Lund2013 and 250 Hz for GazeCom). Our DCNN model was shown to be largely superior to gazeNet, yielding high sensitivity (97.7% for Lund2013 and 98.2% for GazeCom) and specificity (86.4% and 93.8%, respectively). Our findings show that eye movement data classification was generally more accurate for our DCNN model than for the previously reported model.
Increased Autonomy and Situation Awareness for ROV Operations

This paper proposes a semi-autonomous mission planning and management architecture for a Remotely Operated Vehicle (ROV). The work has focused on three main aspects to increase the autonomy of ROV operations: developing a plan-based mission management architecture for ROV global navigation and local intervention, integrating path planning as a refinement of actions, and real-time communication with visual SLAM (VSLAM) systems for obstacle detection and motion estimation. The architecture is inspired by the hybrid agent architecture [1] using a deliberative and a reactive layer to perform planned tasks and handle contingencies. Hardware-in-the-loop (HIL) simulations are carried out to test and verify the capability and feasibility of the proposed layered mission management architecture. The results demonstrate that the mission planning and management system can automatically generate the correct plan and guide the ROV to achieve its mission goals.
A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms

Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train’s internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults.
Multi-Scale Context Enhanced Network for Monocular Depth Estimation

Abstract Monocular depth estimation is a classical computer vision task. At present, most CNN methods cannot effectively combine high-level and low-level features, leading to the loss of details and blurring of boundaries. To solve the problem, we propose a Multi-Scale Context Enhanced Network (MCEN) to learn more abundant context and expand its receptive field for high-accuracy estimation. Our method employs CRE-HRNet (Context and Receptive Enhanced High-Resolution Network) with four branches ranging from low-dimension to high-dimension features to obtain richer contextual information and extract multi-scale features. It then uses RM (Refinement Module) adopting the residual dilated convolution to retains detailed information and improve the receptive field. Finally, non-local block enables our network to capture the longdistance context through its special non-local operation. Experiments with the NYU Depth V2 dataset show its outstanding performance.
On Producing Events Timeline for Memory Forensics: An Experimental Study

Cybercrimes have risen dramatically recently as a result of the widespread usage of the various digital devices. Digital forensic science has been founded to address cybercrimes aspects. It follows a standard procedure to extract digital evidence and finally get it admitted to courtrooms. Fortunately, many artifacts can be extracted from such devices in convicting cybercriminals and inhibiting their actions. Identifying what a criminal had been doing during a certain time frame can be very informative to the investigative process. Identifying the timestamped events and putting them in a timeline helps achieving the investigative purposes. In this paper, we intend to extract various events out of the system memory and present them in chronological order to be utilized by the investigators. We design and investigate several scenarios and show that user activities that happen during a specific time frame can be correlated and summarized in a useful timeline.
A defensible role-based case management system for remote forensic investigation

The COVID-19 scenario has necessitated various activities to be performed remotely. All kinds of businesses are in resuming mode and they are restarting their jobs in new perspectives all around the world. Corporate and internal investigations also have been impacted because of the lockdown imposed on businesses. Organizations are now increasingly considering remote forensic investigation on the target environment. A novel defensible, role-based case management system is presented in this paper for efficient remote forensic investigation. The proposed method facilitates the remote forensic acquisition of volatile data without making accidental changes. By the use of high-speed networks, the process of acquisition of evidence can be achieved with durability. The system enables evidence collection, preservation and analysis to be performed with minimal disruption to regular operations. The paper presents experimental observation and evaluation of results on remote routing to devices for volatile data collection, providing additional insights to devices, preserving data in the cloud. The system proceeds with virtual machine snapshots based crime scene recreation for cloud forensic investigation of evidence. The proposed system is also compared against the state-of-art techniques for proving its defensible, role-based case management efficiency.
Morphological Features of Nigerian Pidgin in the Football Commentary

The aim of this paper is to analyze and describe the various morphological features that embody commentary in Nigerian Pidgin (NP).The data for this study were drawn from 2018 FIFA World Cup that was hosted in Russia.Pidgin commentary was run and aired on a channel on Digital Satellite Television (DSTV).The pidgin commentaries of the two Semi-Finals between France and Belgium as well as Croatia and England and the Final between Croatia and France were tape-recorded for analysis.The theoretical orientation relies largely on the Item and Process theory (IP) of descriptive morphological analysis popularized.Findings show that reduplication,
SYNTHESIS OF BIOMEDICAL IMAGES BASED ON GENERATIVE ADVERSARIAL NETWORKS

Mo­dern da­ta­ba­ses of bi­ome­di­cal ima­ges ha­ve be­en in­ves­ti­ga­ted. Bi­ome­di­cal ima­ging has be­en shown to be ex­pen­si­ve and ti­me con­su­ming. A da­ta­ba­se of ima­ges of pre­can­ce­ro­us and can­ce­ro­us bre­asts "BPCI2100" was de­ve­lo­ped. The da­ta­ba­se con­sists of 2,100 ima­ge fi­les and a MySQL da­ta­ba­se of me­di­cal re­se­arch in­for­ma­ti­on (pa­ti­ent in­for­ma­ti­on and ima­ge fe­atu­res). Ge­ne­ra­ti­ve ad­ver­sa­ri­al net­works (GAN) ha­ve be­en fo­und to be an ef­fec­ti­ve me­ans of ima­ge ge­ne­ra­ti­on. The archi­tec­tu­re of the ge­ne­ra­ti­ve ad­ver­sa­ri­al net­work con­sis­ting of a ge­ne­ra­tor and a discri­mi­na­tor has be­en de­ve­lo­ped.The discri­mi­na­tor is a de­ep con­vo­lu­ti­onal neu­ral net­work with co­lor ima­ges of 128×128 pi­xels. This net­work con­sists of six con­vo­lu­ti­onal la­yers with a win­dow si­ze of 5×5 pi­xels. Le­aky Re­LU type ac­ti­va­ti­on functi­on for con­vo­lu­ti­onal la­yers is used. The last la­yer used a sig­mo­id ac­ti­va­ti­on functi­on. The ge­ne­ra­tor is a neu­ral net­work con­sis­ting of a fully con­nec­ted la­yer and se­ven de­con­vo­lu­ti­on la­yers with a 5×5 pi­xel win­dow si­ze. Le­aky Re­LU ac­ti­va­ti­on functi­on is used for all la­yers. The last la­yer uses the hyper­bo­lic tan­gent ac­ti­va­ti­on functi­on. Go­og­le Clo­ud Com­pu­te Instan­ce to­ols ha­ve be­en used to tra­in the the ge­ne­ra­ti­ve ad­ver­sa­ri­al net­work. Ge­ne­ra­ti­on of his­to­lo­gi­cal and cyto­lo­gi­cal ima­ges on the ba­sis of the ge­ne­ra­ti­ve ad­ver­sa­ri­al net­work is con­duc­ted. As a re­sult, the tra­ining sample for clas­si­fi­ers has be­en sig­ni­fi­cantly incre­ased.
Identifying Offensive Content in Social Media Posts

The identification of offensive language on social media has been a widely studied problem in recent years owing to the volume of data generated by these platforms and its consequences. In this paper, we present the results of our experiments on the OLID dataset from the OffensEval shared from SemEval 2019. We use both traditional machine learning methods and state of the art transformer models like BERT to set a baseline for our experiments. Following this, we propose the use of fine-tuning Distilled Bert using both OLID and an additional hate speech and offensive language dataset. Then, we evaluate our model on the test set, yielding a macro f1 score of 78.8.
LSTM-based Anomal Motor Vibration Detection

Recently, drones are being used in various work sites and the scope of use thereof is gradually expanding. However, since there is no special abnormality detection system to predict the fall of a drone, it is always exposed to the risk of a fall. Therefore, in this paper, we proposed an algorithm that predicts abnormal behavior through Long Short Term Memory (LSTM) based on the motor vibration data collected from the drone to predict the abnormal motion of the drone.
Correcting Block Attack on the 32-bit Reduced NEEVA

NEEVA is a hash function algorithm with a sponge function structure. The algorithm is designed for sensor nodes on the Wireless Sensor Network (WSN). In this study, we employed the correcting block attack on the 32-bit Reduced NEEVA which is a simplification of the NEEVA algorithm. The attack aimed to find internal collisions of message sets and the combinations of message sets. The attack was carried out on 1 message block and 2 message blocks from the 32-bit Reduced NEEVA. The results showed that internal collisions and combination collisions were found in the 32-bit Reduced NEEVA algorithm. The search is performed by looking at each process in the f function of the first round in each correcting block and the f function of the last round. Collision arises due to the unbalanced Feistel process in the last round of function f on the 32-bit Reduced NEEVA. In addition, the modular addition process on the 32-bit Reduced NEEVA does not affect the collision found.
Development of the Data Interaction Structure for the Production Expert System Focused on Support of Measurement Channels Design

The work considers the interaction schemes of components of an intelligent information system (expert system), focused on the measurement channels design support. The paper presents a generalized structural scheme of a production model, an inference engine generalized structure, as well as the system's data interaction structure.
A Study on the Curriculum Design of Personalized English Teaching in the Context of Internet

This paper focuses on the new practice and new development of learning science, makes a comprehensive analysis of the theoretical basis, basic connotation and educational significance of the personalized learning by using the method of literature review, and deeply analyzes the current status of university curriculum design and the existing problems from the perspective of personalized learning.On this basis, this paper puts forward the construction strategy of English courses curriculum design based on personalized learning, and hoping to have the students the active learner in the classroom and develop harmoniously as well as make the curriculum construction in a virtuous ecological circle.
Electric Powered Wheelchair Trajectory Planning on Artificial Potential Field Method

Abstract An addendum of People with Disabilities (PWDs) worldwide quite worries the World Health Organization in which they are consist of people that incapability of doing their own daily routine without the aid of a third party. The third party may consist of a person or a specific equipment or tools that could aid for their daily mobility. This paper tends to help PWDs with their daily routine by introducing a path planning method that recognize the trajectory of the route by using the Artificial Potential Field Method (APF) that will act as an algorithm in a control system of an Electric Powered Wheelchair (EPW). The EPW will be modelled into the MATLAB and Simulink according to the parameters such as inductance, resistance, and driving torque that is exactly as an actual model. Artificial Potential Field Method (APF) are consist of two main force field, Repulsive Force Field (RFF) and Attractive Force Field (AFF). The Repulsive Force Field (RFF) is designated as obstacle on a path whereas influencing the EPW to navigate away from the obstacle. While the Attractive Force Field (AFF) act as goal or the destination that designated for the EPW in which the EPW will be navigated in a way by approaching towards the AFF. The RFF consists of spacing headway that defines the distance from the obstacle to the EPW and while the limit distance is the maximum distance for the EPW to navigate away from the obstacle before the EPW collides with the obstacle. The strategy is to control the trajectory of the EPW during the obstacle avoidance with yaw angular velocity of the EPW. With spacing headway, limit distance and yaw angular velocity results, the trajectory of the EPW during the obstacle avoidance could be determined.
Towards a Benchmark for Software Resource Efficiency

Data centers already account for over 250TWh of energy usage every year and their energy demand will grow above 1PWh until 2030 even in the best-case scenarios of some studies. As this demand cannot be met with renewable sources as of today, this growth will lead to a further increase of CO2 emissions. The data center growth is mainly driven by software resource usage but most of the energy efficiency improvements are nowadays done on hardware level that cannot compensate the demand. To reduce the resource demand of software in data centers one needs to be able to quantify its resource usage. Therefore, we propose a benchmark to assess the resource consumption of data center software (i.e., cloud applications) and make the resource usage of standard application types comparable between vendors. This benchmark aims to support three main goals (i) software vendors should be able to get an understanding of the resource consumption of their software; (ii) software buyers should be able to compare the software of different vendors; and (iii) spark competition between the software vendors to make their software more efficient and thus, in the long term, reduce the data center growth as the software systems require less resources.
Breast Cancer Prediction using Deep Learning Techniques

Breast cancer is one of the fundamental drivers for the disease spread across the globe. Early diagnostics builds the odds of right treatment and endurance, however this cycle is dreary and regularly prompts a contradiction between pathologists. PC helps the determination frameworks with the indicated potential for improving the symptomatic exactness. In any case, early recognition and anticipation can altogether reduce the fatality. Henceforth, it is foremost to detect breast cancer as early as possible. Furthermore, the proposed research work has developed a machine learning model, wherein the model gets trained by considering the parameters such as: radius, texture, perimeter, area, smoothness, concavity, concaveness, and compactness. Here, all these parameters are taken in mean and overall values are considered. Moreover, the model is trained by using auto AI service in IBM cloud and that can be deployed either in web or mobile applications.