Reviewerly/exHarmony huggingface dataset:
Domain-Based Ranking of Software Test—Effort Estimation Techniques for Academic Projects

The significant segments of software project development are requirement engineering, designing, coding, testing, deployment, and maintenance. These phases are implemented irrespective of type of software methodology such as traditional or agile followed during the establishment of software as well as effectually documented. Also this practice is executed in all technical environment, i.e., software companies as well as academic courses were software projects are developed. Software project development is having utmost prominence and credit in educational prospectus for computer science and engineering. Apart from fundamental stages such as requirement engineering, designing, coding, testing, deployment, and maintenance it is found that IT industries also strictly and mandatory focuses on issues such as software risk management, software scheduling, and tracking as well as effort estimation and distribution related to time, budget, and testing. Also these IT industries consider and execute various testing techniques during software project development. For this proposed research framework 122 large software projects documentation were taken into consideration. For simplicity and better comprehensive research domain-based classification was used to classify these software projects into four main heads termed as—(a) Desktop Application, (b) Web Desktop, (c) Mobile Application, and (d) Portal. Similarly, the test effort estimation such as (a) Delphi technique, (b) Analogy-Based Estimation, (c) Software Size-Based Estimation, (d) Test Case Enumeration-Based Estimation, (e) Task or Activity-Based Estimation, and (f) Use Case Test Points are considered for this research purpose. We found that these testing techniques were not considered by students while developing academic software projects as well as no due weightage is given to these testing techniques and test effort by academic courses of computer science and engineering. The main objective behind ranking these techniques was to indicate that which technique is more suitable for software projects carried out by postgraduate courses in computer science. To achieve this purpose a survey is conducted as a part of research by considering 31 Computer Science Academicians to rank these software test effort estimation techniques. The result of experimentation shows that highest rank is allotted to Use Case Points-Based Estimation whereas second highest rank is assigned to Task-Based Estimation while Test Case-Based Estimation gained third maximum rank.
Quantifying Deformation in Aegean Sealing Practices

In Bronze Aegean society, seals played an important role by authenticating, securing and marking. The study of the seals and their engraved motifs provides valuable insight into the social and political organization and administration of Aegean societies. A key research question is the determination of authorship and origin. Given several sets of similar impressions with a wide geographical distribution on Crete, and even beyond the island, the question arises as to whether all of them originated from the same seal and thus the same seal user. Current archaeological practice focuses on manually and qualitatively distinguishing visual features. In this work, we quantitatively evaluate and highlight visual differences between sets of seal impressions, enabling archaeological research to focus on measurable differences. Our data are plasticine and latex casts of original seal impressions acquired with a structured-light 3D scanner. Surface curvature of 3D meshes is computed with Multi-Scale Integral Invariants (MSII) and rendered into 2D images. Then, visual feature descriptors are extracted and used in a two-stage registration process. A rough rigid fit is followed by non-rigid fine-tuning on basis of thin-plate splines (TPS). We compute and visualize all pairwise differences in a set of seal impressions, making outliers easily visible showing significantly different impressions. To validate our approach, we construct a-priori synthetic deformations between impressions that our method reverses. Our method and its parameters is evaluated on the resulting difference. For testing real-world applicability, we manufactured two sets of physical seal impressions, with a-priori known manufactured differences, against which our method is tested.
Efficient Face Detection And Identification In Networked Video Surveillance Systems

Applications for face detection use algorithms that rely on identifying human faces in broader photos that may include environments, artifacts, and other sections of a person's physique. This work proposes a real-time identification system, based on modern image processing capabilities of open source API like OpenCV and due to the solution requirements, a study on the performance analysis of such solution compared to available commercial framework like SPID from NEC is intended. However, here, the study is available with the results of various experiments on the developed system. A systematic approach is followed to produce such outputs and have been measured using software codes. By using IP camera and a Raspberry Pi, the solution developed is simple in nature. This study relies on face detection and identification functionalities for human faces but not limited to live faces only but mix of faces from still images as well.
Research and Development of TPM Virtualization

Combination of Cloud Computing and Trusted Computing is an important method to build a trusted cloud environment, and the most critical problem is the virtualization of TPM (Trusted Platform Module, TPM). But in view of the current research, TPM virtualization still not only does not meet the whole TCG specification, but also has a lot of security issues, and it is becoming the bottleneck of building a trusted cloud environment by combination of Cloud Computing and Trusted Computing. This paper introduces the basic concepts, types and basic requirements of TPM virtualization. The classification model of TPM virtualization is put forward by the I/O device virtualization technology. The main research work of the key technologies of TPM virtualization, such as architecture, key management, certification trust extension, migration and so on, are described in detail, moreover taking time as the clue, we can display a panoramic view of the evolution of related key technologies. Combined with the existing research results, the research direction and challenges of TPM virtualization under TCG architecture are discussed.
A Method for Handwriting Analysis of Signature Using Mahalanobis Distance

The handwriting analysis of the signature on the mobile device was made using the Mahalanobis distance obtained from the Gabor response vector. Factors of variation in the Mahalanobis distance are the position and direction of the line due to the signature, and the shading caused by the pen pressure change. A 95% confidence interval in the Mahalanobis distance data was calculated using both 20 images signed by the subject and 20 images signed by different people as the test images, respectively. Comparing the obtained statistical values, handwriting analyses were carried out. As a result, it was found that a method for the handwriting analysis by the study is useful.
Algorithms for analyzing secondary resources in physicochemical biology

The significance of secondary sources for scientific research is substantiated. The long-term Information demand monitoring accomplished by the Library reveals the need for improving the processes of planning, coordination and assessment of science and research work. The library is able to facilitate this through providing access to science resources and databases within the subject scope of user inquiries. Based on the Russian and foreign publications, the current state of secondary information resources is analyzed. The recommendations on information analysis and preservation are given. The accumulated materials enabled to generate universal selection and reviewing criteria for the secondary information sources based on their completeness, relevancy, profile compliance, urgency, user friendliness. It is important that scientists and researchers are involved in building the system as well as the librarians providing and updating access to the resources. The researches at Physicochemical Biology Center are provided with the access to the secondary resources to meet their subject inquiries in prompt and efficient manner and to support scientometrical and patent researches.
Design for Adaptive Wheel with Sliding Rim and Control System

In this paper, an automatic wheel diameter control process for mobile robotic complexes is proposed. The proposed design features an adaptive wheel with a sliding rim, strain gauges and a microprocessor. Specifically this is achieved by equipping the adaptive wheel with a microprocessor, which inputs data from the strain gauges located on the inside of the elastic tyre, and the outputs of the microprocessor are connected to the actuator with the ability to control is movement. The obtained reading from the strain gauge is translated using approximating functions. The software and algorithm for determining the need to change the driving characteristics (in particular, the diameter) of the adaptive wheels has been developed. The program checks whether the wheel can negotiate the obstacle, based on the defined height of the wheel. The cut-off is set at half the height of the wheel, and if this is exceeded, the program will report a need to increase the wheel diameter and a new diameter is displayed. This program has application in robotics, namely for use of adaptive wheels on devices such as recon robots, lunar rovers, rescue robots, and robots for exploration of geological reserves. This creates the potential for combined modes of motion and self-diagnosis of the wheels.
Two Problems in Knowledge Graph Embedding: Non-Exclusive Relation Categories and Zero Gradients

Knowledge graph embedding (KGE) learns latent vector representations of named entities (i.e., vertices) and relations (i.e., edge labels) of knowledge graphs. Herein, we address two problems in KGE. First, relations may belong to one or multiple categories, such as functional, symmetric, transitive, reflexive, and so forth; thus, relation categories are not exclusive. Some relation categories cause non-trivial challenges for KGE. Second, we found that zero gradients happen frequently in many translation based embedding methods such as TransE and its variations. To solve these problems, we propose i) converting a knowledge graph into a bipartite graph, although we do not physically convert the graph but rather use an equivalent trick; ii) using multiple vector representations for a relation; and iii) using a new hinge loss based on energy ratio(rather than energy gap) that does not cause zero gradients. We show that our method significantly improves the quality of embedding.
Cloud Task Scheduling Algorithm Based on Squid Operator and Nonlinear Inertia Weight

In order to better meet the user's Quality of Service(QoS) requirements in cloud computing, a SNW-PSO (Squid operator and Nonlinear inertia Weight PSO) algorithm is proposed. The algorithm tasks execution time and execution cost as the target of optimization. On the one hand, the algorithm introduces nonlinear inertia weights to ensure that the algorithm jumps out of local optimum in the process of optimizing. On the other hand, the squid operator is introduced to ensure the diversity of particle swarms and make the particles converge to the global optimal position more quickly. It makes up for the shortcomings of the traditional PSO algorithm, such as easy premature convergence, easy to fall into local optimum and low search accuracy. The simulation results show that the SNW-PSO algorithm not only converges faster but also decreases task completion time and cost as compared to the standard particle swarm optimization algorithm and the particle swarm optimization algorithm with linearly decreasing of inertia weight(LDIW-PSO).
Exploring passenger train occupation rate influencing factors using association rules: A case study in China

Passenger train occupation rate directly affects Railway Company’s revenue and capacity utilization of railway infrastructure, and measures to improve passenger train occupation rate is always the focus of railway companies’ attention. However, researches on what, and how factors influence passenge r train occupation rate remain sparse. Based on the collective data of 363 passenger trains on the Beijing-Shanghai High-speed Railway in November 2015, this paper establishes a data-driven analysis framework to explore the influencing factors of passenger train occupation rate. Specifically, we first analyze the possible factors that influence train occupation rate from the perspective of train service planning. Then, the approach of association rules is applied to analyze the potential relationship between train occupation rate and its influencing factors. And a total of 6711 and 8133 association rules were generated for high and low train occupation rate of passenger trains, respectively. Further analysis found that train departure and arrival times, train departure and arrival station class and the type of trains are the main factors influencing the level of train occupation rate. These findings can provide reference for train service planning.
Malaria Cell Identification from Microscopic Blood Smear Images

This paper is about classifying blood smear images into malaria cell and uninfected cell. In this research, we have used two datasets which contains microscopic blood smear images and through deep learning techniques such as CNN, LeNet, ResNet we have created a model that can classify these images. We have applied these techniques individually on both datasets and on the combined data as well and have shown that when we gave different type of blood smear images to the deep learning model even in that scenario, model is able to identify patterns and learn features with an accuracy up to 94%.
Rancang Bangun Sistem Informasi Akademik Berbasis Web Pada Madrasah Ibtidaiyah Attaqwa 52 Bekasi

Academic Information System at Attaqwa 52 Bekasi Madrasah Ibtidaiyah still done manually and the method is still less effective. So it is less fast and efficient in delivering information to teachers and students. Academic information system is a system designed to meet the needs of managing academic data such as student data, teacher data, scheduling and student grades. The development method used is the waterfall method, which is one of the methods of developing information systems that are systematic and quantitative, meaning that each stage in this method is carried out sequentially and continuously. With the availability of a system like this makes it easier for teachers and students to more quickly and easily see the class schedule and student grades
Network Traffic Optimization in Hadoop MapReduce through Pre-shuffling

MapReduce is a popular programming model used for handling Big Data in a Distributed computing Environment. Hadoop is popularly used for short jobs and desires a comparatively low response time. During shuffling phase, large volume of data generated by the map task needs to be accessed to perform shuffling and sorted before being transferred across the network to the reducers. It may generate huge network traffic and consume high bandwidth. Hence a Pre-shuffling scheme is proposed to shuffle intensive applications in this paper, in order to minimize the network traffic because network connects is likely to become a limited resource when many applications subscribe the cluster. Here a push model and two stage pipeline is used for the shuffling phase of Hadoop MapReduce. Experiments have also been conducted based on the web server log file of NASA and rnsit.ac.in. Experimental results show that the proposed Pre-shuffling scheme substantially reduces the network traffic and it remains slightly faster, when compared to the conventional Hadoop. The proposed push model in the pre-shuffling scheme with 2-stage pipeline reduces the run time by an average of 17.3% for click count application.
Evaluation and Analysis of Performances of Different Heuristics for Optimal Tuning Learning on Mamdani Based Neuro-Fuzzy System

The optimization algorithms based on gradients have a series of hyperparameters that will allow among other things to reach a global minimum of the error function, convergence, and at an adequate velocity, trying to avoid overtraining, divergent solutions and overload of computation to execute an exaggerated number of iterations for its process, hence, greater attention is paid to the learning rate as the main parameter to be adjusted during training to achieve a better performance in learning, for this reason, the contribution of this paper is to show initially the positive and negative impact that would have to use a suitable or not learning rate, the benefits of its adjustment during the learning phase, as well as the evaluation and analysis of the performance of different heuristics implemented on a gradient-based algorithm with momentum and adaptive learning rate, with the purpose of being able to have knowledge of their performance in different contexts and scenarios, since we know that this parameter will depend greatly on the complexity of the data to be treated, so that a fixed number cannot be determined for all cases, but if you can try to identify different scenarios, which can be managed from different heuristics for the optimal adjustment of the parameter during its learning process.
Detection of Devanagari Text from Wild Images Through Image Processing Techniques

Multilingual society makes it difficult for a common man to understand the text written in various languages. Being the national language of India, Hindi is widely understood by the masses. Detecting text from randomly available images and translating it into a local choice of language is one of the peculiar and less explored research areas in image processing and is very popular among the scientific community. Moreover, it is useful for removing the language barrier present on earth. In this paper, we propose a complete solution to detect text from natural scene images as in wild images and converting it into a textual format. The proposed work follows a combined approach of stroke width transform (SWT) and maximally stable extremal regions (MSER) to reduce the computational time. There are three steps involved in our proposed approach. In the first step, we are first segmenting out the text by using the binarization and SWT algorithm. Then, the second step implements the MSER to detect the text regions and bounding boxes. The regions detected in the second step acts as letter candidates, which are then recognized in the third step using the optical character recognition approach (OCR). The experimental results show that the characters are being recognized at an accuracy of 77% approximately, outperforming the existing approaches in the literature.
Optimal Rejection Function Meets Character Recognition Tasks

In this paper, we propose an optimal rejection method for rejecting ambiguous samples by a rejection function. This rejection function is trained together with a classification function under the framework of Learning-with-Rejection (LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification. The latter suggests we can choose a feature space which is more suitable for rejection. Although the past research on LwR focused only its theoretical aspect, we propose to utilize LwR for practical pattern classification tasks. Moreover, we propose to use features from different CNN layers for classification and rejection. Our extensive experiments of notMNIST classification and character/non-character classification demonstrate that the proposed method achieves better performance than traditional rejection strategies.
Text Summarization by Hybridization of Hypergraphs and Hill Climbing Technique

Automatic text summarization (ATS) is an application of naturalSiranjeevi, Hemamalini languageVenkatraman, Swaminathan processing (NLP). It is the process of compressingKrithivasan, Kannan the given text to create a summary. The challenge is creating a concise, non-redundant, coherent and inclusive summary that features all the significant points of the given text. The two approaches of summarization are extractive and abstractive. Extractive summarization works by choosing important sentences of the original text. It relies on the statistical relationship between the sentences. Since the sentences of the text are related by n-ary relationships, the authors have used these relationships to constitute the hyperedges of a hypergraph, which is called the sentence hypergraph. Hill climbing is an optimization technique that the authors have chosen to construct the sentence hypergraph. They succeeded in using the Helly property to select the significant sentences as summary. They have evaluated the performance of the system against the Gold summary using the ROUGE evaluation system.
СУЧАСНІ МУЗИЧНО-КОМП’ЮТЕРНІ ТЕХНОЛОГІЇ: СУТЬ, РОЛЬ ТА ЗНАЧЕННЯ В СУЧАСНІЙ ПРОФЕСІЙНІЙ МУЗИЧНІЙ ОСВІТІ

It was defined in the article that in recent years, the computer technology has been used in all areas of the profession, including the music industry. In light of this, there is a relevant need to study the problem of using music and the computer technology in the training of future musicians. Thus, some issues of the implementation of these technologies in the educational process were studied by I. Haydenko, I. Zabolotskaya, A. Markov, M. Podrazhanskaya, Sanyu Juan, M. Owl, Chen Zhuin, and others. However, the analysis of the scientific literature shows that the scientists interpret a concept of the computer and music technology ambiguously. Therefore, the research of the problem of the use of music and computer technology in the training of future musicians requires the clarification of the author’s point of view in understanding the meaning of the concept mentioned above. The purpose and the task of the article: to analyze the essence of the music and computer technologies, to reveal their role and meaning in the professional music education, based on the analysis of scientific literature. During the scientific research have been used the following research methods: general scientific methods (analysis, comparison, generalization) in order to study the scientific literature on the outlined problem and to determine the author’s point of view regarding the interpretation of the key concepts of the study, the disclosure of the role and importance of the music-computer technology in the music professional education. Based on the work of modern scientific works of Ukrainian and Chinese scientists, it has been established that the music-computer technologies are modern information technologies used in the field of music and, in particular, in the system of music education. These technologies play an important role in professional music education today, as they make it possible to significantly improve its effectiveness and to update a communication model between all participants in the educational process. It has also been found that the value of these technologies in the music industry is increasing year by year.