Reviewerly/exHarmony huggingface dataset:
Hybrid Feature Selection for High-Dimensional Manufacturing Data

In manufacturing environment, hundreds of input parameters are related to product quality. To build an accurate machine learning model for quality prediction, it is necessary to find major input parameters which have a big influence in quality prediction. The procedure of identifying major factors out of original high-dimensional input parameters is called to be feature selection. This paper proposes a hybrid method for feature selection, which effectively reduces the searching space by leveraging feature subset chosen by Fast Correlation Based Filter (FCBF) and Relief-based feature selection. The computational complexity is proved to be quadratic in feature number, while most of the existing methods suffer from exponential computation complexity. This improvement is crucial especially when we deal with high-dimensional input parameters because it dramatically reduces the computational time. Further, the proposed method outperforms in prediction accuracy as well when it compares with the benchmarking method. It has been demonstrated by the implementation of our method into real-world manufacturing data sets and open source benchmarking data set.
Study the development of 5G applications in Mixed Reality

With the further development of virtual reality (VR) technology, mixed reality (MR) technology has also received more and more attention, and MR technology will surely form a revolutionary change in display technology. At present, the application services targeted by the 5th Generation Mobile Networks (5G) include smart medical care, smart education, and gaming services, all of which require the construction of infrastructure and social welfare. It means that 5G technology is the core of future social development and industrial competition in advanced countries. This study uses mixed reality combined with the development of 5G technology as the object, through consulting related literature. First, explore the current developments of mixed reality and 5G technologies, and analyze their advantages and disadvantages. Second, study the current application of the combination of the two to find out the existing problems. Explore the future trend of combining 5G and MR again. Finally, research and explore possible technical and security issues, and propose reasonable and effective solutions.
Performance Analysis Between Cloud Storage and NAS to Improve Company's Performance: A Literature Review

Storage systems are becoming increasingly important in framework for storing and accessing information with less retrieval time and a low budget. It is common for every small organization or large company to use storage for storing their data. The data that they store later will boost their value as an organization or company. The problem is that many companies do not know the best option for their storage system. Most companies pick one storage type without knowing the best type for their companies. This paper aims to know and compare what kinds of storage a company uses nowadays and what kind of storage suits its needs. In this paper, we proposed to look for and compare two storages, namely Cloud Storage and Network Attached Storage (NAS) because these two systems are the most commonly used. Based on our research, both storages are good depending on their needs. Even so, for the small companies, Cloud Storage is the best choice as it cost lower, has easier configuration and data back-up, takes up a little space, scalability, and many more.
Voice Controlled Guided System for Wheelchair with Collision Detection and Avoidance

Abstract Many people are suffering from temporary or permanent disabilities due to accidents or any pre-illness that may introduce a wheelchair for the person as essential. The use of a wheelchair independently can be derived from the severity of the disability. But in the case of severe situations when a person relies on somebody else to handle the movement of a wheelchair. This paper aims to provide a solution to this problem by using voice command to guide the wheelchair eliminating the reliability of another person.
Development of “Digital Humanities” as a Liberal Arts Class at University

The purpose of this study is to explore the curriculum and educational contents of four Korean universities that are educating or plan to educate their students in the Digital Humanities, as well as to find and develop Digital Humanities classes suitable for one semester within their liberal arts education classes. As a result of the survey, the four universities were classified according to the affiliation of the students’ subject at each school and the level of programming related to data processing. A and B universities’ Digital Humanities major students were predominantly from liberal arts majors, C universities students consisted both of liberal arts and science and engineering majors, and D universities students’ majors were science and engineering. Universities from group A used basic Google apps and Excel, but D universities used higher-level programming languages. However, Digital Humanities classes at these universities are based on a three-step course: First, education involving basic concepts for Digital Humanities and basic tools for data analysis. Second, search for applicability to Digital Humanities through existing majors. Third, the practical application and creation of the Digital Humanities project. Accordingly, this study sought ways to effectively convey this three-step process in the short period of one semester and devised a detailed lecture outline.
Nigeria’s Gender Country Profile

This paper provides a detailed update on the gender profile of Nigeria over the last ten years and currently. It identified the key women’s rights protection frameworks that have been ratified by Nigeria and analyses the Country’s level of commitment to implementation. It identifies the key themes for profiling the country such as education, economic engagement, health, violence against women and the environment. I draw attention to the key issues in these areas faced by Nigerian women and what the government and NGO’s are doing to fix it, considering cultural and environmental factors such as the COVID-19 Pandemic. The key findings in this report indicates that Nigerian women are marginalised and they experience a lot of hardship, while they have fewer opportunities when compared to their male counterparts. Finally, the report has also made recommendations on each of the key areas highlighted.
Development of the digital competence of students through the electronic course

The article is concerned with the role of an electronic course in higher mathematics in the development of digital competence of students of technical faculties. The principle of organizing the educational process with the help of an electronic course in higher mathematics, focused on the development of students’ digital competence, is considered. The experience of testing an electronic notebook in higher mathematics, which is part of an electronic course, is presented. The experience of the implementation of an electronic notebook in higher mathematics in teaching students at all its stages is considered: the examples of problems solved in seminars are given; the design of classroom lessons is described taking into account the specifics of the academic subject “Mathematical Analysis”. The structure of the electronic notebook, including test tasks and cases for intermediate certification of students based on the results of the seminar, is given. The effectiveness of the organization of the educational process using an electronic course in higher mathematics to develop the digital competence of students in a technical direction is shown. The work examines the attitude of students to teaching higher mathematics using an electronic course, their self-assessment of involvement in the educational process. Moreover, the educational results of the development of digital competence were demonstrated. A reliable relationship was confirmed between the level of development of digital competence and the positive attitude of students to a new format of teaching mathematics: an interactive electronic course with a component base in the form of an electronic notebook. Based on the results of the experimental work, it can be argued that the students positively assessed the use of the electronic course in teaching, because have constant access to training materials and to information about the individual trajectory of the course. This interactive development is convenient to use when preparing yourself for classes.
Arabic Documents Classification by a Radial Basis Hybridization

In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes.
Pengembangan Bahan Ajar Morfologi Berbasis Pendekatan Komunikatif Pada Mahasiswa Jurusan Bahasa Inggris FBS UNM

Abs trak. The problem in this research is how to develop teaching materials of Morphological learning based on a Communicative Approach to  the students of the English Department of FBS UNM, the factors that influence the implementation of materials, students worksheets, exercises and lecture program plans which are expected to improve students' knowledge and language skills. .This research is a development research that aims to develop teaching materials of morphological learning based on communicative approach to the students of the English Department of FBS UNM to improve students' knowledge and language skills, especially the English Department of FBS UNM. The development of teaching materials and learning that are planned will follow the research and development model designed by Borg & Gall (1983). This research is an extensive process in examining the needs of the students and then a product is developed specifically designed to the needs of these students in an effort to improve knowledge and language skills through morphological learning based on  communicative approach. The population of this research were students of the English Department of FBS UNM. The research sample was the students of the English Education and literature study program. The result of this research showed that there are a morphological learning material, syllabus, and design of morphologilal material of English. K ata Kunci : Learning morphological material, and communicative approach
Improved Unscented Kalman Filter Algorithm Adapted to the State under Non-Common View

In the actual tracking environment, when multi-station tracks motion targets, due to the limitation of the observation range, there may be a situation where one or some observation stations cannot detect the radiation source signal. This paper proposes an improved passive filter tracking algorithm that improves positioning and tracking performance under non-common view condition. First, we establish an observation and tracking model in a non-common view environment; we then use joint TDOA(Time Differences of Arrival) and AOA(Angle of Arrival) localization algorithm to estimate the location of the observation and tracking model in a non-common view environment, and finally we use the improved adaptive state UKF(Unscented Kalman Filter) algorithm to make prediction updates. The simulation results show that the proposed algorithm in this paper can effectively improve the target tracking effect under non-common view condition, effectively delay the decline in tracking accuracy when the target cannot be located, and make full use of the previous estimation results.
LighTx: A Lightweight Proof-of-Bandwidth Transactions Transfer System

The race to solve the so-called Blockchain trilemma (i.e., decentralization, scalability, and security) has resulted in a multitude of solutions, each providing at most two of the three features. Moreover, existing blockchain systems still represent several technical hurdles in terms of computation effectiveness and energy consumption, especially in large-scale networks. In this paper, we design LighTx a cost-effective and scalable transaction transfer system that aims to reach agreement in public peer-to-peer networks at a low cost.LighTx leverages a Byzantine Reliable Broadcast (BRB) primitive to transmit and validate transactions in a logarithmic communication cost with respect to the number of nodes. We additionally deploy a Proof-of-Bandwidth-based reputation system to mitigate the threats enforced by Sybil attack in public networks. We assess the performance of our system and demonstrate a considerable transaction rate of hundreds of transactions per second and low latency in the order of few seconds while providing defense against Byzantine adversaries.
Dynamical Systems based Obstacle Avoidance with Workspace Constraint for Manipulators

In this paper, based on Dynamical Systems (DS), we present an obstacle avoidance method that take into account workspace constraint for serial manipulators. Two modulation matrices that consider the effect of an obstacle and the workspace of a manipulator are determined when the obstacle does not intersect the workspace boundary and when the obstacle intersects the workspace boundary respectively. Using the modulation matrices, an original DS is deformed. The proposed approach can ensure that the trajectory of the manipulator computed according to the deformed DS neither penetrate the obstacle nor go out of the workspace. We validate the effectiveness of the approach in the simulations and experiments on the left arm of the UBTECH humanoid robot.
Optimized Ensemble Prediction Model for Breast Cancer

Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.
Knowledge Extraction from Auto-Encoders on Anomaly Detection Tasks Using Co-activation Graphs

Deep neural networks have exploded in popularity and different types of networks are used to solve a multitude of complex tasks. One such task is anomaly detection, that a type of deep neural network called auto-encoder has become extremely proficient at solving. The low level neural activity, produced by such a network, generates extremely rich representations of the data, which can be used to extract task specific knowledge. In this paper, we built upon previous work and used co-activation graph analysis to extract knowledge from auto-encoders, that were trained for the specific task of anomaly detection. First, we outlined a method for extracting co-activation graphs from auto-encoders. Then, we performed graph analysis to discover that task specific knowledge from the auto-encoder was being encoded into the co-activation graph, and that the extracted knowledge could be used to reveal the role of individual neurons in the network.
A Migration System of Database for Virtual Machine in Cloud Computing

Data storage technology has been developing for many years. There are many different ways to store data. So far, database is still one of the main techniques for storing structured data. With the need of big data, application system faces many problems in management, divided into two aspects. First, most data migration methods are inefficient and insecure. As a result, a single host data storage and service capabilities cannot meet the entire application system, so it is necessary to build a cloud database system. In order to ensure the reliability of multi-cloud host operations, it is necessary to carry out frequent data migration operations and dynamic migration of cloud databases between different hosts. Secondly, there are problems of low maintenance efficiency and high cost. Because the traditional database management is in the cloud platform, it is not conducive to remote management operations. Therefore, this paper develops a system to manage multiple host databases in a unified manner, which can not only simplify the operation steps, but also improve the security and operational efficiency of the data. In method, there are manual, automatic, and advanced migration database, and so on. Experimental results show that in the environment of virtual machines, the advanced method of migrating database is superior to the other two methods in terms of security and efficiency.
Analysis of Bitstreamed Cryptographic Conversion Algorithms from the International Project eSTREAM

The winner of the international project for discovering of new stream ciphers eSTREAM, organized by the European Union, are determined: software-oriented SSC: HC-128; Rabbit; Salsa20/12; SOSEMANUK; hardware-oriented SSC: Grain; MICKEY; Trivium. Let us analyze the respective stream cryptoconversions more in details. The keystreams generator Rabbit was analyzed earlier, because of this it is not discussed further. To the analysis was involved also the SSC CryptMT, which was presented to the contest eSTREAM as well, but was not recognized as winner.
Study on Go Programming Language

When developing software today, we still use old tools and ideas. Maybe it is time to start from scratch and try tools and languages that are more in line with how we actually want to develop software. The Go Programming Language was created at Google by a rather famous trio: Rob Pike, Ken Thompson and Robert Griesemer. Before introducing Go, the company suffered from their development process not scaling well due to slow builds, uncontrolled dependencies, hard to read code, poor documentation and so on. Go is set out to provide a solution for these issues. The purpose of this master’s thesis was to review the current state of the language. This is not only a study of the language itself but an investigation of the whole software development process using Go. The study was carried out from an embedded development perspective which includes an investigation of compilers and cross-compilation. We found that Go is exciting, fun to use and fulfills what is promised in many cases. However, we think the tools need some more time to mature.
A high-resolution study of data center traffic at its origin

High-resolution studies of data center traffic at the network core uncover short-term bursty traffic patterns, periods of high buffer utilization that last for tens of microseconds and lead to packet loss and longer flow completion time tails. While recent attention has been directed towards studying the bursty traffic at the network core, less heed has been given to the origins of bursty traffic, e.g., host machines. In this study, we try to perform high-resolution traffic measurements in the host networking stack to quantify the impact of system software components on traffic burstiness. We enforce per-packet timestamping on the datapath using various techniques like NIC timestamping, eBPF, and direct kernel source modification and measure the gaps between egress packets under various configurations. We provide preliminary findings on how process scheduling can affect traffic burstiness.
HEp-2 Specimen Cell Detection and Classification Using Very Deep Convolutional Neural Networks-Based Cell Shape

The accurate detection and classification of HEp-2 specimen staining plays a key role in autoimmune disease diagnosis and transplantation assessment. Such detection and classification is challenging due to the abundant presence of highly overlapped cells, variations in cell densities, the variety of staining patterns, large numbers of cells per image, large data volumes and overfitting of features. In this paper, a robust technique is proposed to deal with images of all staining patterns and address these challenges. Very deep convolutional neural networks with a layer structure inspired by the standard architecture of the VGG-16 image is proposed for classification of HEp-2 staining cells based on cell shape and adapted to consider overfitting. Level set method using geometric active contours with morphological opening and Delaunay triangulation is used for cell segmentation and splitting. The cell segmentation method also considers overlapped cells. The proposed method has been tested and compared with other methods using Task-2 training dataset from competitions held on the ICPR2014 and ICPR2016. A extensive study demonstrates that the proposed method outperforms all other methods and promises to support the diagnosis of autoimmune diseases and allograft rejection prediction in future pathology practice, except for one method in this study which is slightly better than our proposed method.
Selective and Hierarchical Allocation of Sensing Resources for Anomalous Target Identification in Exploratory Missions

We present an approach for selective, hierarchical allocation of sensing resources that aims to maximize information gain in exploratory missions such as search and rescue (SAR) or surveillance in an efficient manner. Specifically, we propose a methodology for perception-enabled SAR or crowd surveillance driven by anomaly detection based on low-level statistical assessment of a region. The characterizations of previously-observed regions are used to populate a window of observations that serves as “short-term memory,” providing a contextually-appropriate characterization of proximate regions in the scene. Currently-observed regions are compared with this short-term memory window, and if sufficiently dissimilar, can be considered as candidates for the presence of a SAR target or unexpected event. We adaptively allocate additional sensing resources for subsequent exploration of anomalous regions through a novel utility function that balances varied mission objectives and constraints including exploratory sensing actions, maintaining situational awareness, or ensuring some degree of confidence in self-localization. Simulation results validate the proposed approach and demonstrate its benefits with regards to efficiency in exploration while maximizing potential information gain and balancing other mission requirements and objectives.