Reviewerly/exHarmony huggingface dataset:
Gaps in Examination Records Management Procedures: The Reasons for Missing Marks Syndrome in Universities in Kenya

Universities are institutions of higher education which confer undergraduate and postgraduate degrees in a variety of subjects. There is need for greater care in the management of examination records at every stage of the university student’s life cycle so as to avoid the pain of students failing to graduate on time. This study was conceived with the purpose of establishing if examination records management procedures could be the cause of missing examination marks in universities in Meru and Nyeri counties in Kenya. The objective in this paper is to demonstrate the relationship between missing marks and examination records management procedures in universities. Descriptive survey research design was used in the study. Target population of 482 full time lecturers and school examination coordinators with sample size of 214 respondents, and whose response rate was 128(60%) was used. Structured questionnaires were used to collect data. Data was analyzed using descriptive statistics. The findings revealed that the selected universities had documented examination records management procedures that were updated after every five years; unfortunately, the enforcement of the procedure was lacking.  The study concludes that the main cause of missing marks in the universities was lack of central enforcement of examination procedures. The study recommends the training of all lecturers and examination coordinators on basic records management to ensure adherence to examination procedures to check cases of missing marks and scripts.
3 Deep learning approaches in metastatic breast cancer detection

Breast cancer is one of the most common dangerous and fatal diseases in the world. Breast cancer, which is usually observed in women, is also observed in men, although rarely. Nowadays, thanks to developing technology, the early diagnosis and treatment of breast cancer have resulted in a decrease in mortality due to this disease. Although cancer occurs in a particular organ, it can then spread from that organ to other organs. This condition, called metastasis, is defined as the spread of cancerous cells to other areas directly or through blood-lymph vessels outside the tissue in which they are located. Cancerous cells can move to other parts of the body, settle there and quickly spread to new areas. As they spread to new parts of the body, abnormal cells of the same type develop and continue to have the same name as the first tumor. For example, if breast cancer has spread to the lung, the cells herein are essentially breast cancer cells, and the tumor that is formed in the lung is called “metastatic breast cancer” rather than “lung cancer.” The microscopic examination of adjacent lymph nodes is performed for metastatic breast cancer detection. In this procedure performed by pathologists, the detection of lymph nodes especially with very small tumors is quite difficult and time-consuming. Therefore, computerassisted metastasis detection increases the sensitivity and speed of the procedure. The data obtained from medical images can be processed in models using various artificial intelligence algorithms. The results obtained at the end of this procedure provide guidance to specialists in the diagnosis and treatment of the disease. Therefore, the use of computer vision systems in the diagnosis and treatment of the disease is becoming widespread. Furthermore, the deep learning approach, which has become popular recently, has accelerated this field. Convolutional neural network (CNN), which is a deep learning approach especially used to classify images, is a highly successful model. CNN models are insufficient to determine the properties of objects in the image, such as location, orientation, position and angular value. For this reason, capsule neural networks have been designed in order to obtain the image properties. In this section, breast metastases to axillary lymph nodes, a publicly available breast cancer dataset, were classified with CNN and capsule neural networks. Using the dataset consisting of 130 pathological images of 78 patients available, a study was performed to detect metastases on images.
Exploring deep learning with the game of Go

Go is a classic board game with relatively simple rules. However, it is very complex in practice due to the large number of alternatives to consider per move. Before 2015, most Go programs could only attain low amateur ranks. In 2016, the program AlphaGo beat the highly-regarded former world Go champion Lee Sedol 4 games to 1, the first time a player of Sedol’s calibre ever lost to a Go program. AlphaGo achieved its success through a combination of deep learning neural networks and reinforcement learning. The learning phase used thousands of hours of CPU time on Google’s higher-performance TPU clusters. Our project studies the techniques used by AlphaGo and Alpha Zero (a later improvement on AlphaGo), with an emphasis on improving both the learning speed and performance of the program. A key research question is whether the success of AlphaGo can be replicated using fewer computational resources.
A European Perspective on Evolving Standards for Public Reporting on Mineral Exploration Results

Summary Reliable information on mineral exploration, mineral resources and mineral reserves is important for informing decision makers and to protect investors and the public. The PERC Reporting Standard provides a framework for consideration of such aspects from a European perspective, as well as influencing the development of consistent international standards through PERC’s membership of CRIRSCO. The CRIRSCO-family of international codes and standards are continually evolving. In the current update cycle of the PERC Reporting Standard attention is focussed on incorporating additional guidance on the reporting of environmental, social and governance issues, as well as the need to discuss sources of risk and uncertainty.
The Utilization of Instagram Social Media To Improve Students’ Skill In Writing Descriptive Text

The aims of this research is knowing the use of Instagram social media and knowing the improvement of writing skills of seventh grade students of SMP N 1 Semin. This type of research uses classroom action research. The research method used is quantitative descriptive to analyse data using numbers. The subjects in this research were students of class VIIA SMP 1 Semin. The number of students was 20 children, while the object in this research was the process (implementation of the description text in teaching learning process with the help of social media namely Instagram in class VIIA SMP 1 Semin and the results (in the form of scores obtained by students during learning activities to write descriptive text skills with the help of social media Instagram). The results shows that all students, especially class VIIA SMP 1 Semin know the social media Instagram. Most of the students are active users of Instagram. Instagram media is not used properly or is not used for any of the media when they are studying. For pre-action results in skills write a description of the average score of students 55.5. In the first cycle the average value of 68 increased by 12.5, while in the second cycle the average value of 75.25 increased from the first cycle of 7.25.
Optimization of Learning Media to Improve Lecture Outcomes in Phonology Courses

Optimization of instructional media is expected to increase student understanding of lecture material so as to improve lecture results.Therefore, research was conducted on the use of video media as a report on group discussions in phonology courses.So, this article aims to explain the use of video media in optimizing learning media to improve the results of phonology lectures.This research was conducted using qualitative methods.Data was collected by conducting classroom action research on phonology class students who are second semester students in the Indonesian Literature Study Program.After that, the data are analyzed by comparing the results of lectures with the media tested with the results of previous lectures.Thus, the results are obtained that lectures by optimizing learning media especially the use of video as a report of group discussions on weekly learning material can increase student understanding as evidenced by increasing student final grades.
Design and Implementation of Novel Security Approach designed for Cloud Computing With Load Balancing

A cloud computing is a virtualized computing environment to provide the services and storing data as data warehouse and the information stored remotely and accessing with scalable and measured resources. Cloud computing is becoming popular as many enterprise application and valuable information, data are moving into cloud platforms. On the other hand, a main for cloud adoption is being short of security. So cloud computing security is an important thing when we store data into cloud platforms. In this load balancing also should be considered as only minimum level of the algorithms is present for balancing the load. Infrastructure associated with cloud should be protected. Most existing security system consists of classical encryption, decryption technique and proxy signature has been used. In existing load balancing system they were concentrated on the reducing energy consumption and agents were used for balancing the load. In this paper holistic view of cloud computing load balancing with security is proposed. The Quantum Based Security Framework has been developed and fuzzy logic is used for balancing the load. The major security policies are considered in an efficient way and according to the number of conditions selected by the user, service will be provided. The Security Framework technique developed to store the data within the cloud by generating a check bits rather than keys and allowing the user to access their data after verifying the check bits. The check bits generated by the user and cloud service provider should be same then only the user can access the services along with the load balancing. Replication is the major factor as the data may lose due to failure or outage, hence replicated copies are provided. By using this method of security, we can achieve high level of security.
IN SEARCH FOR A HOLISTIC APPROACH TO THE REFORMATION OF POLICING AND SECURITY ARCHITECTURE IN NIGERIA

The pervasive internal insecurity in Nigeria underscores the need to question the effectiveness of the current policing and security system. With increasing prevalence and incidence of insecurity in the country, the Nigeria Police has failed in its major assignment of internal security. Against this backdrop, this study focuses on the search for holistic approach to the reformation of Nigeria Police and Security Architecture in the country. In-depth interviews were conducted with forty-five (45) participants from the NPF. From the findings, major challenges affecting policing and the security architecture in the country include a lack of effective accountability and monitoring the mechanism in the police force, gross misconduct, and mismanagement of funds and resources in the police force. Critical recommendations to enhance the reformation process include improved accountability and monitoring mechanism in the police force, adoption of an effective policing model that addresses crime and insecurity and provision of modern equipment, research, science and technology to enable police and security agencies meet the crime and insecurity challenges. The situation calls for a fundamental reformation of the NPF targeted at vision actualization and achievement of its mission and goals of improved internal security. Key Words: Accountability, Monitoring, Policing System, Reformation, Nigeria, and Security Architecture.
A modified subformula property for the modal logic KC4

The modal logic KC4 is the smallest normal modal logic K with the additional axiom $\Box\Box A \mathbin{\supset} \Box A$ called C4, which claims density of the accessible relation. In this article, a modified subformula property for this logic is shown by devising an appropriate sequent calculus. The finite model property and decidability of this logic are easy corollaries. It is peculiar that a modified subformula for this logic is defined for a set of formulas but not for a single formula.
Fuzzy Logic Algorithm for Solar Tracking System

The purpose of the study was to implement a two-axis solar tracking system, controlled by a fuzzy logic algorithm, optimizing energy collection with respect to fixed orientation solar panels. The diffuse control was performed on the LabVIEW platform, building a prototype to verify that a solar panel located on a two-axis solar tracker uses energy better than that of a fixed orientation. The non-parametric Mann-Whitney U test was performed. In conclusion, the two-axis solar panel prototype allowed validating the fuzzy control algorithm for a two-axis solar tracker.
Introduction to the Minitrack on Organizational Cybersecurity: Advanced Cyber Defense, Cyber Analytics, and Security

Cybersecurity touches all aspects of organizational management, with organizational systems, networks and critical infrastructures increasingly targeted by determined malicious actors with advanced skills and resources.Such attacks have the potential for major disruption of our society including damage to assets, people, and the environment.Organizations, of all sizes and at all levels, are faced with the challenge of continuously, effectively, and efficiently protecting their systems while enabling and ensuring their mission.Organizational cybersecurity aims to advance knowledge on the theory and practice of cybersecurity management.
Switching to a Synchronous Mode of Chinese Calligraphy Teaching During the Period of COVID-19 Pandemic: An Experience Report

The purpose of this report is to describe how a teacher utilized technology to teach elderly learners Chinese calligraphy and how they learnt. Before the incidence of COVID-19, six sessions of Chinese calligraphy were conducted in a community services centre for elderly learners in Hong Kong from 9:00am to 5:00pm on a weekday every week, with each session lasting 75 minutes. There were about 20 learners aged above 50 in each session, and they were naturally grouped in pairs and sat together in a classroom equipped with a data projector. At the very beginning of each session, a teacher of Chinese calligraphy explained the contents of Chinese poems or mottos on the handouts he gave, and he also discussed the Chinese fonts he would use. The model of pedagogy of teaching Chinese calligraphy in a technology-enriched environment was successful with the justifications in accordance with research studies in Bozkurt and Ruthven (2017), Drijvers et al. (2010), Geiger (2014), and Jeong and Hmelo-Silver (2016). In addition, the learners gave positive feedback, and expressed sincere thanks for their teacher's devotion to Chinese calligraphy teaching especially in the period of the epidemic of COVID-19.
Point-of-Interest (POI) Recommendation for Location Based Services

The research focuses on the location data of user to make meaningful and good quality recommendation of Point-of-interest in real world based on geographical influence. Point-of-interest recommendation is an emerging area of research on location-based social networks (LBSNs). Geographical proximity, a unique feature of LBSNs can acts as major factor which significantly affects users’ check-in behaviour. Location-based social networks made it viable for servers to record users’ location periodically over time, extract their mobility patterns and deduce individual precedence. As a crucial unit of LBSNs, recommender systems gained popularity in recent years. Recommender systems acts as a content filtering systems which can automatically present a list of more suitable candidate locations from available corpus of POI for users based on their preferences, which is distinct from traditional search methods in which user explicitly needs to search of a particular poi based on users’ requirement. This paper proposes a solution for POI recommendation incorporating temporal and social influential factor with GA-GMM model.
Multi-Object Tracking in Aerial and Satellite Imagery

Multi-pedestrian and -vehicle tracking in aerial imagery has several critical applications, including event monitoring, disaster management, predictive traffic, and transport efficiency. While some research works already studied vehicle tracking in remote sensing scenarios, pedestrian tracking has not found the necessary attraction caused by the insufficient level of detail in aerial imagery. Recently, the development of better camera systems and the possibility to capture aerial imagery at low-cost paved the way for establishing novel tracking approaches based on remote sensing. However, current state-of-the-art algorithms, including deep learning based methods, perform especially poorly with pedestrians in aerial imagery, incapable of handling severe challenges such as the large number and the tiny size of the pedestrians (e.g., 4 × 4 pixels) with their similar appearances as well as different scales, atmospheric conditions, low frame rates, and moving camera. In contrast to vehicles moving along predetermined paths, such as highways or streets, pedestrians show more difficult motion characteristics posing additional demands on the tracker. Within the scope of this master thesis, we propose AerialMPTNet, a novel regression-based deep neural network able to tackle the challenges of pedestrian and vehicle tracking in geo-referenced aerial imagery. AerialMPTNet fuses appearance features by a Siamese Neural Network with movement prediction of a Long Short-Term Memory and adjacent graphical features of Graph Convolutional Neural Network. In contrast to previous works, we encode the motion model and the adjacent neighbor modeling in an end-to-end fashion as part of the neural network. Consequently, our network can learn motion characteristics directly from the data and additionally learns to weight the influence of surrounding objects. Furthermore, tothe best of our knowledge, we are the first to apply Squeeze-and-Excitation layers and Online Hard Example Mining to a regression-based deep tracker. We evaluate AerialMPNet intensively on two aerial pedestrian datasets, AerialMPT andKIT AIS pedestrian. Both datasets consist of multiple image sequences captured at two frames per second on different flying altitudes, showing different crowd densities and different terrain (e.g., open-air concerts, Munich city areas, BAUMA trade fair). Results indicate that AerialMPTNet outperforms state-of-the-art algorithms such as Tracktor++, SMSOT-CNN or DCFNet on pedestrian tracking in aerial imagery significantly. Compared to the SMSOT-CNN baseline, multiple-object tracker accuracy (MOTA) improves by 18.8 points to -16.2 and by 13.8 points to -23.4 on KIT AIS pedestrian and AerialMPT, respectively. Additionally, we evaluate our approach on the KIT AIS vehicle dataset. AerialMPTNet achieves a competitive MOTA score of 42.0. Since we fitted our method gradually for pedestrian tracking, other trackers achieve better scores here.
Facts and Where to Find Them: Empirical Research on Internet Platforms and Content Moderation

We live in an era of increasing worry that internet platforms like Facebook or Twitter, which mediate our online speech, are also fomenting hatred, spreading misinformation, and distorting political outcomes. The 2016 US presidential election, in particular, unleashed a torrent of concern about platform-borne harms. Policymakers around the world have called for laws requiring platforms to do more to combat illegal and even merely “harmful” content. Reliable information about platforms’ content-removal systems was, for many years, hard to come by, but data and disclosures are steadily emerging as researchers focus on the topic and platforms ramp up their transparency efforts. This chapter reviews major sources of information released by platforms, as well as independent research concerning content-takedown operations.
Research on Innovative Talent Training Mode Based on STEAM TheorySTEM Education, Technological Innovation, Applied Innovation, Innovative Talents, Personnel Training Mode

In order to cultivate more competitive and market-oriented innovative talents, colleges and universities need to reform and innovate in educational concept, evaluation and assessment system and education and teaching. According to the theory of STEAM multidisciplinary comprehensive quality education, this paper analyzes the connotation and main characteristics of innovative talents, studies the existing problems of innovative talents training, the evaluation index and training approaches of innovative talents, and puts forward measures and methods for constructing innovative talents training mode. According to the theoretical basis and practical basis necessary for innovative talents in application-oriented universities, the course system is constructed, the training center is constructed, and the innovation guidance service system is improved through school-enterprise cooperation, project construction and perfect innovation training system. It provides strong support for the cultivation of scientific research innovative undergraduate talents, improves the quality and level of talent cultivation, and is conducive to the multi-disciplinary knowledge reserve of innovative talents, the use of knowledge and skills, the solution of social problems, and technological innovation. It is beneficial to study hard, practice hard and strive for breakthrough application innovation.
Analisa Penjualan Produk Oli Dengan Metode Data Mining Asosiasi Algoritma Apriori Pada PT. Mitra Petra Sejahtera

Competition in the business world makes business people have to think harder in developing strategies to deal with such competition. Marketing strategy requires a reference so that promotion is right on target, for example by looking for similarities between product items. This research was conducted by applying the association rule a priori algorithm method to the sales transaction dataset, to form a candidate combination between product items. The sales transaction dataset was taken in January to December 2019. In this study, a minimum support value of 25% and a minimum confidence value of 60% were determined with data processing using Microsoft Excel and RapidMiner software. A priori algorithm can produce association rules as a reference in product promotion and decision support in providing product recommendations to consumers based on the specified minimum support and confidence values.
GRECO launches the Generation Solar app

The web-version of the citizen science Generation Solar app has just been launched. The GRECO project has developed this tool to research innovative ...
Design and Analysis of Modified S-Box Based AES Security Algorithm for IOT Application

Internet of things (IoT) is the extension of the Internet to connect just about everything on this earth. Security of multimedia data is an imperative issue because of fast evolution of digital data exchanges over unsecured network. Multimedia data security is achieved by methods of cryptography, which deals with encryption of data. Most of the application uses Advanced Encryption Standard (AES) and modify it, to reduce the calculation of algorithm and for improving the encryption performance. In modified AES algorithm used 1-Dimensionla S-box instead of 2-Dimensional S-box. Theoretical analysis and experimental results prove that this technique provides high speed as well as low latency on data encryption and decryption. Modified-AES algorithm is a fast lightweight encryption algorithm for security of multimedia data. Achieved efficiency of proposed MAES is around 23.7% while previously it is 18.35%. The total latency of previous work is 29.983milli sec while this work achieved 38.161ns.