Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Modulation Detector for an MIMO System

Deep learning approaches applied in diverse fields further the expectations and demands of 5G wireless networks and necessitates leveraging the intelligence to network with widespread renewed interest of big data. The integration of machine learning (ML) notion across core and edge infrastructure of wireless networks imparts intelligence. ML can enable mobile devices to intelligently monitor their environment by learning the features of a wireless channel—its dynamics, traffic pattern, requests, and user context, among other aspects. ML enables the infrastructure to learn from the wireless networking environment and adaptively makes decision, which provides optimized solutions for best connectivity, resource allocation, quality-of-service parameters, etc. ML can redefine how the physical layer functions, namely, coding/decoding, channel estimation, channel equalization, and modulation/demodulation at the transmitter and receiver sides. The majority of the communication system is modeled as a linear system, but practically, ML captures the nonlinear effects of hardware and quantization and provides a better solution. A combined approach is possible through ML for the solutions provided in the literature for individual blocks, say, coding, resource allocation block. Also parallel processing ability and distributed memory aid to complexity reduction at the respective block. The proliferation of graphics processing unit–enabled hardware provides speedy action. These are the benefits of deep learning in physical layer design. This chapter intends to provide a deep learning model addressing the physical layer of the network. The authors propose an intelligent ML detector at the multiple-input multiple-output receiver able to classify a signal constellation and the transmitted antenna.
THE INFLUENCE OF STUDENT MOTIVATION, FAMILY AND PEARL ENVIRONMENT ON STUDENT LEARNING DISCIPLINE, JAKARTA STATE UNIVERSITY.

TThis study aims to determine whether there is an influence between learning motivation, family, and peer environment on the learning discipline of Jakarta State University students. The research design is quantitative. The population in this study were active students at the State University of Jakarta. The sample in this study was 100 students. Collecting data in this study using a questionnaire. The data analysis technique used in this research is using the classical assumption test, multiple regression analysis, and hypothesis testing with the IBM SPSS Statistics 26 data processing application. The results show that there is a significant effect of Learning Motivation on Learning Discipline with a tcount of 2.891 > t table of 1.985. There is a significant influence between the Family Environment on Learning Discipline with tcount value of 4.018 > t table of 1.985. There is no significant negative effect between Peers on Learning Discipline with values tcount of -0.001 < t table is 1.985.
The value of blood cell morphology test in the diagnosis of pediatric diseases

In order to improve the accuracy of pediatric disease diagnosis, the value of blood cell morphology test in the diagnosis of pediatric diseases is studied.Through the analysis of blood cell morphology test methods, it is proposed that the value of blood cell morphology test can screen the types of pediatric diseases.Blood cells can be divided into red blood cells, platelets and white blood cells according to their morphology In a normal operation state, the appearance of blood cells is round and smooth without significant change.However, once it is found that the morphology of blood cells or their peripheral fibrous tissue changes, it can be considered that there is a certain degree of body disease in the human body.At the same time, the analysis of blood cell content can provide the basis for disease diagnosis.After the classification of blood cell morphological changes, the number or content of deformed blood cells can be screened.According to the proportion of blood cell morphological changes, the final diagnosis decision of pediatric diseases can be provided.
Homicide investigation in the digital era: The development and evaluation of a case-specific elements library (C-SEL)

The homicide rate dropped globally in the last decades, while in the same period the percentage of unsolved homicide cases increased. At the same time technological developments have drastically changed our lives and little is known about how this influenced the process of decision-making within homicide investigations. Therefore, the aim of this research is to develop a methodology that supports homicide investigators in the collection, prioritizing and elimination of persons of interest using pieces of evidence, in the new digital era, in such a way that the methodology is effective, reduces tunnel vision and obeys the laws for privacy.Currently used methodologies that use pieces of evidence to manage persons of interest were applied to three recent real-world homicide cases to evaluate whether these methodologies effectively collect, prioritize and eliminate persons of interest. The potential of a general approach, relying on big data and data science, to limit the number of persons of interest to be incorporated in homicide investigation, was explored. Subsequently, a methodology to incorporate and prioritize persons of interest was developed based on literature and the knowledge and expertise of experts in criminal investigation. This case specific elements library (C-SEL) consists of twenty-four elements and twelve underlying factors that can be used for the incorporation and prioritization of persons of interest. This new developed methodology was evaluated and compared to the currently used methodologies, not only on the performance measures of effectiveness, but also on tunnel vision reducing properties and the obedience to the laws of privacy.C-SEL effectively collects and prioritizes the perpetrator in all three real-world homicide cases and showed better results compared to the currently used methodologies on the tunnel vision reducing character and on the obedience to the laws of privacy. These results provide sufficient leads to further validate the new developed methodology.
DEVELOPMENT & ANALYSIS OF IMAGE FORGERY DETECTION USING SVM CLASSIFIER IN IMAGE PROCESSING.

The fraud of legitimate records gets comfortable and this made a ton of issues and troubles to the official establishments. One of these issues is the improvement of advanced picture preparing programming and altering devices. with the new the advanced incredible computerized printers and a ton of programming instruments it become easy to alter examined archive and make new one with various data that is hard to recognize from the first and the phony one. This paper utilized picture handling strategies to recognition phony in authentic examined report. The point of this paper is plan a brisk and most proficient framework for distinguishing imitation in authentic reports. the outcome for location rely upon eliminating commotion if the framework applied on examined archive with clamor Increases the presence of fashioning proportion.
Disruptive Technologies in Human Resources Management- An Overview

Are we harnessing today's state-of-art technologies to manage our workforce efficiently and effectively? If the answer is no, this is the right time to buckle up. These days, it is believed that technology can assist human being in automating and streamlining several processes and procedures in human resources management ranging from recruitment, orientation, training and development, employee benefits, employee engagement, retention, and attrition as well. Nowadays, almost all the practices of human resources management is positively impacted by the technology which in turn results in augmentation and enhancement of workforce capabilities. To attain a competitive edge in the industry and organization has to be up-to-date with the latest technological advancements that significantly influence the industry. In this article, latest disruptive technologies and practices that are revolutionizing the HR functioning are focused.
A Deep Non-Parametric Learning Architecture for High Dimensional Data Clustering- Deep Learning Paradigm

Data Clustering is a fundamental research focus in many data-driven high dimensionality application domains and clustering performance of data with high dimensionality is highly depends on the quality of data representation in terms of effectiveness and efficiency. Machine learning is a classical approach to provide soft partition of data but faces many difficulties due to increasing sparsity of data and increasing difficulties in distinguishing distance between data points, especially it is high complex in handling high dimensional data with complex latent distributions. In order to tackle those issues, a new deep learning paradigm has been presented in this paper. The proposed model is termed as deep non parametric learning architecture for high dimensional data clustering. The Proposed model leverages unsupervised feature learning along autoencoder for high cluster friendly representation. It first map the data into embedding objects and embedded objects has been  taken for clustering on generation of objective function to produce maximum margin cluster. Those cluster further fine tuned to refine parameter of different layers of convolution neural network to ensure the minimum reconstruction error between feature max pooling layer and ReLu activation layer. Softmax layer minimizes the intra cluster compactness and inter cluster seperability in the feature space. Non parametric tuning has been enabled in the output layer to make the data instance in the cluster to be close to each other by determining the affinity of the data on new representation. It results significant increase in the clustering performance on the discriminative information’s. Extensive experiments have been conducted on real datasets to compare proposed model with several state-of-the-art approaches. The experimental results show that Deep Non-Parametric learning architecture can achieve both effectiveness and good scalability on high dimensional data.
ผลการพัฒนาโปรแกรม Tracking ต่อคุณภาพการติดตามเวชระเบียนผู้ป่วยใน โรงพยาบาลแพร่

Background: The completeness and timeliness of medical records are the important key for patient care. Documentation of medical Records which use in management planning, academic education and healthcare reimbursement must be complete rapidly to improve the quality of patient care. Management planning and healthcare reimbursement require the medical record documents within 30 days after the patient discharge.Objective: To study the result of Tracking program to reduce the duration of patient medical records tracing in Phrae Hospital.Study Design: Interrupted time design. The sample was inpatient medical records at PhraeHospital, before using the Tracking program 1053 files and the used Tracking program group 1053 files. Compare the difference of average duration of inpatient medical records tracing after discharge, and compare the proportion of timeliness at 15 days and 20 days after discharge. Data were analyzed by using t-test, exact probability test, gaussian regression and logistic regression.Results: The used Tracking program group had a shorter duration of inpatient medicalrecords tracing average 2.25 days with statistically significant (95% CI -2.66 - 1.84, p <0.001). The timeliness at 15 days is 2.55 times (95% CI 2.13-3.05, p <0.001), at 20 days is 3.43 times of the group that did not use the Tracking program (95% CI 2.41-4.87, p <0.001).Conclusions: The Tracking program improve the quality of inpatient medical records tracing. It reduce the average duration of medical record tracing from discharge to the disease/ procedure record in the HOSxP program, and increase the timeliness at 15 days and 20 days. This advantage of being able to use the information to take care of patients, management planning and academic education timely. Documentation of medical records charge medical fees within 30 days after discharge. Hospital receives the full amount of healthcare reimbursement, and no deductions if delayed data.Keywords: Tracking program, inpatient medical records, duration of medical records tracing, quality of medical record tracing
What Works? Measuring the Efficacy of Cyber Policy Interventions with Quasi-Experiments

What works? How would we know? As states pass new cybersecurity and privacy legislation, natural experiments present themselves that allow us to start measuring policy efficacy. One measure of this efficacy is the number of reported state data breaches. More and more states have modified their data breach notification legislation to publicly report this data. Yet, datasets like the Data Breach Clearinghouse don't retain state level dummy variables. Without these variables, researchers cannot identify non-equivalent control groups for interrupted time series experiments. To this end, this research presents the data and a methodology to integrate 21 state level datasets of breach reports into a national dataset that retains state level metadata. Supplementing those states which publicly report breach incidents are state level data sources acquired from open records requests. This methodological progress is necessary to begin to address the research question, do state level cybersecurity policy interventions reduce the frequency of data breaches in the target population?The data for this kind of analysis has, until now, been limited to private sector firms like Advisen. Consequently, this paper leverages its data source to produce descriptive statistics on the characteristics of data breach incidents similar to findings in industry reports. Further findings include the rate of breach incident frequency and breaches per-capita over time in reporting states. Evidence demonstrates that breaches have historically been rising by ~20% per year, however, incidents plateaued starting in 2016. Annually, breach incidents per-capita are shown to be quite similar in states with shared reporting requirements. This per-capita normalization enables state level rankings of breach likelihood. However, while industry breach reports have historically limited themselves to descriptively characterizing breach activity, this methodology is also intended to enable traditional policy evaluation. Quasi-experiments of state level regulatory interventions, like the Massachusetts Data Security Law, present a case study for further policy evaluation studies. Monthly time series analysis comparing pre and post treatment with a relevant control group, presents the best means for these evaluation studies. This research consequently provides tangible code, data sources, and lessons learned for future researchers to employ to identify which regulatory interventions work. If we can start to learn from this laboratory of democracy, perhaps new regulatory interventions can be designed to protect customer data and reduce incidents of identity theft.
Circuits and Systems in Turkey [CAS in the World]

Reports on the activities of CASS chapters.
Virtual Pathology Education in Pandemic Times: Challenges, Solutions and Future Implications

In view of the highly contagious nature of SARS-CoV-2, slide reviews with multi-headed microscopes can no longer be a safe approach in pathology instruction. The outbreak has disrupted the routines of pathology education and interactive didactic conferences. The emerging challenge here is to develop practical laboratory sessions in virtual environments. This has given rise to multiple other challenges in the development of virtual pathology education, some of which are highlighted in this commentary. The main difficulties center around the integration of virtual learning technologies into a pathology curriculum and incorporation of digital pathology infrastructure into an educational platform. Maintaining the competency standards in virtual pathology education (VPE) during a pandemic may prove to be another significant challenge. The quality of virtual learning depends on several factors including infrastructure provisions, deployment of modern equipment, faculty knowledge and learner engagement. In response to the educational challenges in pandemic times, VPE has utilized some invaluable digital platforms that provide access to various virtual pathology cases and slide reviews. This commentary examined the impact of COVID-19 on pathology education at undergraduate and postgraduate levels. It also explored the potential solutions to overcome the present challenges and future implications for virtual pathology education and practice.
A Study on the Objectivity of China-related reports of the New York Times

This paper includes the definition, history, and practice of the journalistic objectivity in the US.Taking COVID-19 reporting as examples, this paper explores the reasons why The New York Times has failed to be absolute objective in China-related reports.Based on the above research, it discusses significance of the role which the public plays in creating professional environment of journalism.
Research and Development of Simulation System for Industrial Robot Stamping Automation Production Line Based on Virtual Reality

With the increasing demand for automation of production lines, robots are increasingly used in industrial production, making robot production line simulation technology a hot research topic. The virtual test run system of the robot is based on the layout plan of the robot production line to carry out three-dimensional visual simulation of the production line, to verify the rationality of the layout plan, and to avoid unnecessary losses caused by blind production. However, the current research on simulation software is lacking, so the research of robot virtual trial operation system has important significance for domestic industrial development. The purpose of this paper is to use the virtual prototype to solve the problems in the design stage before manufacturing the physical prototype of the stamped product, and contribute to the research of the robotic stamping automation production line. This paper is mainly based on the advantages of high flexibility of robot stamping production line, low mold requirements, simple programming of new workpieces, virtual prototyping technology, computer science and other disciplines, physical simulation, full simulation, and finally the same physical prototype test production method.
The Book by E. A. Makarova “Siberian Literary and Artistic Miscellanies of the End of the 18 th – First Third of the 20 th Century” (Tomsk, TSU Press, 2020, 236 p.)

The review is devoted to the book by E. A. Makarova “Siberian literary and artistic miscellanies of the end of the 18 th – first third of the 20 th century” in the aspect of regional book publishing formation» (Tomsk, 2020). This monographic study continues and develops the best traditions of previous years, devoted researches on the literary process and book business in Siberia and the Far East. The work is distinguished by a huge volume of reviewed material and a modern high level of scientific reflection. Historical-literary and regional approaches to the description of the Siberian literary process are successfully combined.
Penerapan Home Learning Akibat Pandemi COVID 19 di Sekolah Dasar Alam Al Izzah Junwangi Krian Sidoarjo Tahun Ajaran 2019/2020

This study is intended to determine the application of home learning due to the COVID 19 pandemic in Al Izzah Nature Elementary School Krian Sidoarjo. The formulation of the problem to be answered from this research is about the concepts and processes in the preparation of home learning, the process of implementing home learning, supporters and obstacles in the application of home learning at Al Izzah Nature Elementary School Krian Sidoarjo. This research is a descriptive qualitative research that is library research and phenomenological studies. The subjects in this study were the principal, educators, parents and students at Al Izzah Nature Elementary School Krian Sidoarjo. Data collection carried out in this study is to collect literature on home learning, retrieve data through interviews and observations related to the application of home learning in Al Izzah Nature Elementary School Krian SidoarjoThe results of this study indicate that Al Izzah Nature Elementary School Krian Sidoarjo has made thorough preparations regarding the concept and arrangement of home learning during the COVID-19 pandemic. The inhibiting factors in the application of home learning are the lack of parental intensity, the lack of enthusiasm of students in running the home. learning and unstable signal fluency.The learning process during the COVID-19 pandemic at Alam Al Izzah Krian Sidoarjo Elementary School only changed places, which was carried out at home without changing the concept of school learning. The house must be able to become a place for students to learn. The learning process is carried out at home and without burdening parents in assisting students in learning.  Keywords: home learning, education, pandemic, study from home
ВИКОРИСТАННЯ В ПІДГОТОВЦІ ПЕДАГОГІВ ПРОФЕСІЙНОГО НАВЧАННЯ СОЦІАЛЬНИХ МЕРЕЖ ТА ОСВІТНІХ ПЛАТФОРМ

The article presents an analysis of the results of pedagogical research on the level of introduction of social networks and educational platforms in the educational process, trust in information disseminated by social networks, critical analysis, penetration of communication tools into everyday life of modern youth development of domestic distance learning tools.During February-December 2020, among students and teachers of Vinnytsia Mykhailo Kotsyubynsky Pedagogical University, on the eve of the introduction of quarantine in Ukraine, an anonymous survey was conducted. The purpose of the survey was to obtain information about the level of involvement in the daily lives of students and teachers of modern digital technologies, to identify priority social platforms that are most often used, the effectiveness of modern Internet technologies in the educational process.In our opinion, distance education cannot replace the traditional, classical education system. However, this does not mean that we should neglect the opportunities offered by the development of various technologies. This survey once again showed the extent of interaction of survey participants with various social networks, platforms (49% of young people said they are registered in four different social networks), that each of its participants personally owns a variety of devices that allow you to work with the Internet, interact with a variety of online information platforms. The survey revealed a high level of trust among young people in information that comes from various social networks and information sites. Identified the threat that young people do not check whether the information they receive from the Internet is insufficiently verified. In conclusion, it is suggested to introduce a special course, which will reveal certain dangerous aspects of the online environment, reveal the technology of manipulating human consciousness, and so on.In our opinion, distance education cannot replace the traditional, classical education system. However, this does not mean that we should neglect the opportunities offered by the development of various technologies. This survey once again showed the extent of interaction of respondents with various social networks, platforms (49% of young people said they are registered in four different social networks), that each of its participants has a personal ownership of various devices that allow you to work with the Internet, interact with a variety of online information platforms. The survey revealed a high level of trust among young people in information that comes from various social networks and information sites. Identified the threat that young people do not check whether the information they receive from the Internet is insufficiently verified.