Reviewerly/exHarmony huggingface dataset:
UNIVERSITY PROFESSORS’ RESISTANCE TO CHANGE: REASONS AND FACTORS

The main idea of this case study paper is to introduce the practice of monitoring and analysis of the university professors’ resistance to changes, and to describe the possible solutions for the resistance management using the example of the Institute of Economics and Entrepreneurship of Lobachevsky University. The methodological background of the research was based on the basic understanding of the resistance to change, its negative role, its causes and circumstances. Different reasons for resistance were described based on the psychological approach. The idea of the questionnaire is the qualitative description of the dependence between the university teachers’ resistance to changes and the factors of their resistance. The basis of the questionnaire is the authors’ description of psychological reasons and organizational factors of resistance. The 170 members of the pedagogical staff of the Institute were questioned, which is more than 50 % of the personnel. The following conclusions were made: 1) the professors’ resistance to change has modest strength and, mainly, the passive form; 2) the main reasons of their resistance is the fear of negative results of changes and change of academic values; 3) the crucial factor of professors’ resistance is inefficient system of communication. The next step for the research is to improve the methodology of the measurement of the resistance to change and to extend that methodology for all the departments of the university. It is quite important to notice that the resistance is usually presented in the passive form, and it could create the illusion that there is no resistance at all. In order to have the real picture of the situation, it is essential to measure the quantitative characteristics of the resistance and to use different methods for different directions. The fear of the negative effect of changes is one of the problems to be solved by the explaining of the positive circumstances of changes. One possibility for that is to give the teachers the role of actors of the new policy, to describe the possible effects of changes. Another possible way is the substitution of the traditional academic university culture by the entrepreneurial culture; this can be achieved by the introduction of the new values. Also it is worth mentioning that one of the main organizational barriers for the resistance of change is the inefficient communication among the university staff, it slows down the distribution of the information about the changes. The results of the research are unique, as they lay in the suggested hierarchy of the causes and organizational factors of the resistance to change at the universities.
VOLUNTEER ACTIVITY CHANGES AFTER MIDDLE YEARS OLD AND THE INFLUENCES OF VOLUNTEERING ON WELL-BEING

Abstract The objectives of this paper are to examine volunteer activity changes over the life courses and the long-term influences of volunteer activity on the well-being. The paper analyzes the KLoSA data from wave 1 to wave 5, and selects 10,254 persons over 45 years old as study samples in the base year. For the statistical analysis, the two-way connected line plot and transition probability analysis techniques are employed and the GEE method is used for the multivariate regression estimation of the coefficients. The results show that the volunteer activities increase highest in one’s late 40s and decrease abruptly from the age, and slow down 50s to one’s late 70s, showing a repeating rise and fall pattern. Entering one’s 80s, the volunteer activities come to be stable relatively than the previous years. And, the probability of continuing the volunteer status after 2 years is about 31.2 percent and the probability of stopping volunteer activity comes to be around 68.9 percent. As a last, the regression analysis results show that there is a positive relationship between volunteerism and well-being variables. Expecially the interaction term is statistically significant, showing a negative sign of the coefficient. This implies that the volunteerism contributes to increasing the well-being but decreasing by engaging excessively. The results support the previous study outcomes that excessive volunteer activities are not good for the health.
A Self-Learning Immune Co-Evolutionary Network for Multiple Escaping Targets Search With Random Observable Conditions

The search for multiple escaping targets is a significant issue of cooperative control in multi-agent systems since targets consciously seek to avoid being captured. Moreover, the assumption of continuous observations in existing works is not always suitable due to the limit of measuring equipment and uncertain movement of targets. Therefore, the problem with searching for escaping targets, which can be more aptly labeled “multiple escaping-targets search with random observation conditions” (MESROC), is difficult to address by conventional methods. Inspired by machine learning and the immune response mechanism of human bodies, a self-learning immune co-evolutionary network (SLICEN) is proposed. The SLICEN consists mainly of an immune cellular network (ICN) and an immune learning algorithm (ILA). The ICN provides feasible solutions to MESROC. Different kinds of network models are introduced to work as an ICN, such as convolutional neural networks, extreme learning machines, and support vector machines. The ILA evaluates the performance of feasible solutions and selects the optimal ones to further strengthen ICN reversely. Solutions are repeatedly improved through the co-evolution of ICN and ILA. An essential distinction to conventional machine learning approaches is that SLICEN works well without training samples. Simulations and comparisons demonstrate that patterns of advanced cooperative behavior among searchers function properly. SLICEN is an efficient method for solving MESROC.
Design and Implementation of Low-Cost, 3-D Printed, Humanoid Robotic Arm

In today's world, with the development of advanced actuators and other precise dynamic motors, the field of robotics has seen a major boom in its growth, with applications ranging from in the International Space Station to common household homes. However, bionic, humanoid robots, that mimic human features, are still under development due to their high costs and design hindrances, which when perfected could open wide a whole new field of robotics and their applications. This paper aims to describe the design and process for the development of a low cost robotic humanoid arm, using an additive manufacturing process, commonly known as 3-D printing. The implementation of control software using high efficiency motors and microcontroller technology is also discussed. The result is a dexterous humanoid hand capable of mimicking common human-like gestures with speed, and accuracy.
Stain independent nuclei segmentation of cytopathology images: A case study in pleural effusion

Digital pathology (DP) is a technology that makes it possible to apply image analysis techniques to cytopathology images for cancer diagnosis. Color variation is a common problem in cytopathology as a result of a number of factors, including variable chemical coloring of different stain manufacturers, and inconsistencies in the staining procedure. In this study, a new stain normalization method is proposed using the residual learning approach. The performance of the proposed method was measured by applying the problem of nuclei segmentation in pleural effusion (PE) cytopathology images. The proposed method has significantly improved the performance of image analysis techniques that are sensitive to color variations.
Data Embedding Method for Printed Images using Deep Neural Networks

Data embedding techniques for images, also known as watermarking and steganography, are used in various applications such as copyright protection and security. In recent years, data embedding methods for images is applied not only to electronic media but also to printed textures and other patterns for information presentation. In some of them, a digital watermarking method using DCT (Discrete Cosine Transform) has been used. In this paper, we propose a novel data embedding method for printed images using deep neural networks. In the proposed method, the process of embedding and restoring data is represented by one deep neural network, and the embedding process into DCT coefficients is optimized by learning of the deep neural network. In the experiment, we apply the proposed method to the printed images have various textures. The results showed the effectiveness of proposed method.
Unsupervised Neural Machine Translation with Future Rewarding

In this paper, we alleviate the local optimality of back-translation by learning a policy (takes the form of an encoder-decoder and is defined by its parameters) with future rewarding under the reinforcement learning framework, which aims to optimize the global word predictions for unsupervised neural machine translation.To this end, we design a novel reward function to characterize high-quality translations from two aspects: n-gram matching and semantic adequacy.The n-gram matching is defined as an alternative for the discrete BLEU metric, and the semantic adequacy is used to measure the adequacy of conveying the meaning of the source sentence to the target.During training, our model strives for earning higher rewards by learning to produce grammatically more accurate and semantically more adequate translations.Besides, a variational inference network (VIN) is proposed to constrain the corresponding sentences in two languages have the same or similar latent semantic code.On the widely used WMT'14 English-French, WMT'16 English-German and NIST Chineseto-English benchmarks, our models respectively obtain 27.59/27.15,19.65/23.42 and 22.40 BLEU points without using any labeled data, demonstrating consistent improvements over previous unsupervised NMT models.
Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference)

The Special Issue contains the selected articles presented at the 15th International Conference on the Simulation of Adaptive Behavior (SAB 2018). The conference took place during August 2018 in Frankfurt, Germany. The articles introduce different aspects of closed-loop dynamic computations for adaptive behavior in artificial agents. The aspects cover a range of adaptive behavior research from morphological computation to brain-body-environment interactions, nature-inspired special perception, and closed-loop online learning. SAB is a biennial conference; its next incarnation will be during September 2020, in Paris. If the papers in this issue inspire you, please consider submitting your work to the 2020 conference—who knows, next time it may be your paper in the Special Issue.
A comparison between Entity-Centric Knowledge Base and Knowledge Graph to Represent Semantic Relationships for Searching as Learning Situations

Searching the web with learning intent, known as Searching as Learning (SaL), consists on learners to use Web search engines as a technology to drive their learning process. However, it may be difficult to users to find out relevant information online due to an inability to accurately specify their information need, a situation known as Anomalous State of Knowledge (ASK). To minimize the ASK situation, the continuous flow of data gathering and interaction between user and the search results could be used by search engines to tailor learning-intent search experience. It requires Web search engines to identify such intent and they may use linked data, Knowledge Bases and Graph Databases in order to recognize the meaning of query terms and keywords and use them to predict learning intent. In order to explore the possibility of semantic data structures to represent knowledge that could aid a learning-driven Web search engine to recognize learning intention from user's queries, the present paper compared the performance of two different types of data structures based on entity-centric indexing to identify properties and semantic relationships. One was a knowledge base that used a entity-centric mapping of Wikipedia categories and the other was the KBpedia Knowledge Graph. The entity ranking and linking of both were analyzed and we discovered that the knowledge graph could identify about three times more properties and relationships.
Blind scanner Server and Batch Programming Implementation in the Process of Automatically Scan Documents

Abstract The purpose of this study is to streamline the time in the process of archiving and scanning documents at government or private offices and to implement batch programming and blind scanner server applications in the scan process automatically. The research method used in this study is to use descriptive analysis methodology, which has two stages, namely the stage of data collection and software development stage. Based on the results of testing for this study, this research can make it easier for government or private offices to search for archives that have been scanned. This research can speed up the process of scanning document archives in government or private offices and integrating all scanning documents on one server. The lack of this research has not been able to convert letter images to ASCII characters that can be recognized by computers. The impact of this study is to provide benefits to government and private offices to accelerate the process of scanning the archives and integrating all scanning documents on one hardware.
ANALISIS DAN PENILAIAN TEKNOLOGI INFORMASI PADA PROSES TATA KELOLA DENGAN COBIT 5 PADA DOMAIN EDM STUDI KASUS UNIVERSITAS PERADABAN

Information technology governance (IT Governance) in companies has an important role in maximizing information technology that has been implemented at the University. However, IT governance can have several problems due to IT being only a concern of the technical team because it does not get the attention of top management. Without good IT governance various problems will arise. The EDM domain (Evaluate, Direct, Monitor) is one of the COBIT 5 tools, which is to measure the level of capability of the governance process. This study aims to analyze and assess the IT governance process using COBIT 5 tools on the EDM domain (evaluate, direct, monitor) with the Self Assessment method with a case study of the Peradaban University. The results of the assessment using the capability level COBIT 5 approach indicate that PUKSI of Peradaban University has a capability level at Level 1 for the EDM01 process because process performance is not planned and not monitored, nor are the resources and information needed to carry out processes not identified, provided, allocated, and used. So that IT governance is not maximally used.
PENERAPAN ALGORITMA K-MEDOIDS UNTUK MENGELOMPOKKAN PENDUDUK 15 TAHUN KEATAS MENURUT LAPANGAN PEKERJAAN UTAMA

The main occupations in 15 years and above are on average not suitable for their age, which is done by adults but is done by people 15 years and older. Therefore, I do grouping data on population 15 years and above so that we know what their main jobs are. Here I use data mining with the K-Medoids method to classify the population of 15 years according to the main occupation, this research was conducted in Indonesia. The K-Medoids method is a method of collecting data with the classic clustering partitioning technique that groups datasets from n objects into K groups known a priori. A useful tool for determining k is a silhouette. It is stronger to be agreed upon and bigger than K-Means because it must add to the difference in the difference in the square of the euclidean distance. Attractions that can be determined as cluster objects are average differences for all objects in the cluster are minimal. That is the easiest point in the cluster. K-Medoids uses objects in a collection of objects to represent a cluster. The object chosen to represent a cluster is called medoid. Clusters are built by calculating the proximity they have between a medoid and non-medoid objects.Keywords: Data mining, K-Medoid, Main Job Fields 15 years and above
A Near Real-Time Approach for Sentiment Analysis Approach Using Arabic Tweets

Big data storage and real time data analysis are major challenges for IT researchers.The recent massive increase in data has not been accompanied by adequate storage technology and data processing algorithms.Understanding what people think about an idea, a product, a service or a policy is important for individuals, companies and governments.Sentiment analysis process can be used to identify opinions expressed in text on certain subjects.The result accuracy has a direct effect on decision making in both business and government.Our focus in this paper is first to identify the critical issues associated with real-time big data analysis and then to develop a new paradigm on Hadoop Ecosystem with real-time stream data processing to analyze Arabic tweet sentiment on Twitter.To perform real-time analytics, data collection should be performed using Apache Flume in order to move and aggregate all tweets received online (near real-time) to pre-defined locations through a channel called Sinks to the Hadoop distributed file system (HDFS).In addition, due to the serious challenges in Arabic text and speech and the high speed with which tweets arrive, we designed a complex sentiment analysis (SA) module to process each incoming tweet in such a way that no tweets are lost without being analyzed.Also, a sentiment analysis approach to Arabic text was developed using multiple Hive User Defended Functions (UDF).Finally, to guarantee a varied data collection, we proposed a Java MapReduce program for lexicon-based Arabic sentiment analysis, which supports n-gram search in the lexicon.Our approach was applied to determining opinions about MERS virus in the Kingdom of Saudi Arabia on Twitter Public Stream API and the results are discussed.
Evaluation of Heavy Metals and Physicochemical Parameters of Oil-contaminated Soil and Water Samples from Bonny, South-South, Nigeria

This study analyzed physical, chemical and selected heavy metals concentration of oil-polluted soil and water samples from Bonny, South-South, Nigeria. Unpolluted soil and water samples were used as controls. The pH of the test soil sample was acidic (4.24±0.03) when compared with control (7.6±0.05). Chloride, Phosphorus, Magnesium and Potassium concentrations of the test soil samples were 1151.614±0.37 mg/g, 1.23±0.02 mg/g, 11.27±0.34 mg/g and 11.52±0.5 mg/g respectively. Selected heavy metals such as Iron (Fe), Cadmium (Cd), Mercury (Hg), Arsenic (As), Lead (Pb) and Copper (Cu) showed that Hg and As were below detectable limits in test and control samples while Cd and Pb were below detectable limit in the control sample while test sample had 0.015±0.01 mg/g and 1.73±0.04 mg/g respectively. Fe and Cu contents were high at 38.7±0.13 mg/g and 5.49±0.05 mg/g when compared with the control at 3.52±0.02 mg/g and 1.08±0.03 mg/g. Total Petroleum Hydrocarbon (TPH), Total Organic Carbon (TOC) and Total Organic Matter (TOM) were at 5.93±0.13 mg/g, 3.64±0.1 mg/g and 4.23±0.1 mg/g respectively. Water pH, Temperature, Conductance, Dissolved Oxygen, Biological Oxygen Demand (BOD5) and Turbidity were 5.21±0.03, 25.1±0.01ºC, 610±0.04 µS/cm, 5.0±0.03 mg/l, 1.8±0.02 mg/l, and 53.50±0.06 NTU respectively. Chloride, Phosphorus, Magnesium, Potassium, Calcium and Bicarbonate concentrations of the water were 609.82±0.37 mg/ml, 1.03±0.02 mg/ml, 13.41±0.42 mg/ml, 7.68±0.3 mg/ml, 21.22±0.12 mg/ml and 4.3±0.07 mg/ml in that order. Arsenic and Mercury were below detectable limit in both test and control samples while Fe, Cd, Pb, and Cu were 42.2±0.6 mg/ml, 0.016±0.001 mg/ml, 1.9±0.04 mg/ml and 5.2±0.03 mg/ml respectively. Total Petroleum Hydrocarbon, Total Suspended Solids, Total Dissolved Solids, and Total Solids were 3.87±0.11 mg/ml, 23036±0.51 mg/ml, 396.5±0.3 mg/ml and 23433±0.4 respectively. The levels of detected heavy metals were higher than the World Health Organization (WHO) permissible limits and Nigerian standard set by Federal Environmental Protection Agency (FEPA). This indicates a moderate level of pollution in both soil and water samples, as a result, poses a serious threat to ecological and species survivability as well as growth and reproduction of aquatic and plant life.
Dialogue Analysis for Clinical Data Query Mediation.

Efficient access to clinical data by investigators is critical for data-driven biomedical research. Mediated query is commonly adopted to facilitate data access for investigators. However, the query mediation process remains a black-box. This study analyzed the email-based dialogues between investigators and clinical data query mediators from three institutions. We identify discussion topics and their frequencies, model task flows, and analyze user needs for query mediation support revealed from the dialogues. While the datasets between different institutions are distinct in some notable respects, we find that together they provide common insights for streamlining data access. From our findings, we conclude an intelligent dialogue-based query support model is feasible to automate human-mediated clinical data access for investigators and stakeholders.
Stepwise Local Synthetic Pseudo-CT Imaging Based on Anatomical Semantic Guidance

In this study, an anatomic semantic guided neural style transfer (ASGNST) algorithm was developed and pseudo-computed tomography (CT) images synthesized in steps. CT images and ultrasound (US) images of 20 cervical cancer patients to be treated were selected. The foreground (FG) and background (BG) regions of the US images were segmented by the region growth method, and three objective functions for content, style, and contour loss were defined. Based on the two types of regions, a local pseudo-CT image synthesis model based on a convolution neural network was established. Then, global 2D pseudo-CT images were obtained using the weighted average fusing algorithm, and the final pseudo-CT images were obtained through 3D reconstruction. US phantom and data of five additional cervical cancer patients were used for prediction. Furthermore, three image synthesis algorithms-global deformation field (GDF), stepwise local deformation field (SLDF), and neural style transfer (NST)-were selected for comparative verification. The pseudo-CT images synthesized by the four algorithms were compared with the ground-truth CT images obtained during treatment. The structural similarity index between the ground-truth CT and pseudo-CT synthesized by the improved algorithm significantly differed from those synthesized by the other three algorithms (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GDF_bg</sub> = 7.175, t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLDF_bg</sub> = 4.513, t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NST_bg</sub> = 3.228, t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GDF_fg</sub> = 10.518, t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLDF_fg</sub> = 5.522, t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NST_fg</sub> = 2.869, p <; 0.05). Further, the mean absolute error and peak signal-to-noise ratio values prove that the pseudo-CT synthesized by the ASGNST algorithm is similar to the ground-truth CT. The improved algorithm can obtain pseudo-CT images with high precision and provides a novel direction for image guidance in cervical cancer brachytherapy.