Reviewerly/exHarmony huggingface dataset:
Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system

Robotics technology is known as a great technology demand to be developed continuesly. One of the important things that need to be considered is the control of the motion of the robot. Movement predictions can be modeled in mathematical equations. Prediction based on learning logic is also very supportive of motion control systems, especially arm motion. In this study, the authors combined the two methods as the main study. The working principle of the arm is to take colored objects detected by the camera. In this study, we made arm four DOFs (Degree of Freedom), but only one DOF is controlled by ANFIS because the other three DOFs only move at two fixed angles. Two methods of determining the arm angle of motion used are inverse kinematics and ANFIS methods. The angle of motion and the position of the red object can be observed in real-time on the monitor with the interface in the MATLAB GUI. The angular output that appears in the MATLAB GUI is sent to Arduino in the form of characters, then, Arduino translates it into servo motion to the coordinates of the object detected by the camera. The results showed that the ANFIS method was more effective than the inverse kinematics model.
Searching for low frequency earthquakes with various durations near Parkfield, California

&amp;lt;p&amp;gt;Previous studies suggest that all LFEs could be roughly the same size; most LFE durations are between 0.2 and 0.5 s, and most LFE moments fall within a 1 to 2-magnitude unit range. These apparently characteristic LFE sizes could imply that LFEs are hosted on asperities of a characteristic size on the plate interface.&amp;amp;#160; However, it is also possible that LFEs with a range of sizes do occur but are not detected. With existing methods, it is usually harder to detect LFEs with shorter or longer durations. In this study, we search for LFEs with various durations near Parkfield, California. We generate synthetic LFE templates with durations&amp;amp;#160; of 0.05 - 1 s by modifying Shelly (2017)&amp;amp;#8217;s template waveforms. We cross-correlate time-shifted versions of the templates with 500 days of seismic data to search for LFEs within 5 km of the original template location. We estimate the duration and location of each detection by associating the detection with the template that it matches best.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our preliminary results are encouraging. We find large numbers of 0.2-s LFEs at the original location, as have been detected previously, but we also appear to detect LFEs with durations of 0.05 - 1 s. These new detections appear to be spread along 1 3-km region on a near-vertical plane that matches the downward extension of regular seismicity. We are currently cautious in interpreting these results, as it remains possible that all the LFEs occur at the original location with the same duration and that our apparent range of detections simply reflects scatter introduced by noisy data. Nevertheless, we note that our initial analysis implies that LFE duration in Parkfield changes minimally with LFE moment, and we are continuing to more rigorously assess the LFEs&amp;amp;#8217; properties and their implications.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;/p&amp;gt;
DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.
Automatic mucous glands segmentation in histological images

Abstract. Mucous glands is an important diagnostic element in digestive pathology. The first step of differential diagnosis of colon polyps in order to assess their malignant potential is gland segmentation. The process of mucous glands segmentation is challenging as the glands not only needed to be separated from a background but also individually identified to obtain reliable morphometric criteria for quantitative diagnostic methods. We propose a new convolutional neural network for mucous gland segmentation that takes into account glands’ contours and can be used for gland instance segmentation. Training and evaluation of the network was performed on a standard Warwick-QU dataset as well as on the collected PATH-DT-MSU dataset of histological images obtained from hematoxylin and eosin staining of paraffin sections of colon biopsy material collected by our Pathology department. The collected PATH-DT-MSU dataset will be available at http://imaging.cs.msu.ru/en/research/histology.
P‐26: IoT Display‐based Non‐Face‐to‐Face Visual Experiment Method for Preferred Color Temperature under Varying Ambient Lights

An entirely wirelessly and remotely controlled experiment and its result on visually preferred color temperature on large IoT display screen under various ambient lights are presented. The remote solutions were implemented for a zero‐contact experimental environment to avoid spreading COVID‐19 between experimenter and subjects. Applications based on Android and Tizen operating system for controlling ambient lights, adjusting TV picture options and collecting dataset in server are implemented based on IoT technologies. Under the proposed experimental environment, a visual recognition experiment to find generally preferred color temperature of a given image set was conducted and the results are presented.
Teaching Histology in the Age of Virtual Microscopy and E-Resources: Is a Tailored Approach to Domestic and International Students Warranted?

Background: Individuals’ learning habits and associated challenges in medical education have rarely been studied in the present digital era. We investigated if a tailored instructional approach is needed for domestic and international students studying histology. Methods: This cross-sectional survey was performed in May 2019 at the Faculty of Medicine and Dentistry of Palacky University in Olomouc, Czech Republic. Census method was applied, and a validated researcher-made questionnaire was distributed among all students attending histology courses. We collected 192 evaluable questionnaires from Czech students and 61 from international students. Differences in opinions between the two groups were tested using Fisher´s exact and chi-square test. Results: Compared to their domestic peers, international students were less reliant on printed textbooks (6.9% vs. 17.3%), while showing greater interest in materials from lectures (35.3% vs. 9.2%) (p <0.0001). They were also more inclined to use virtual microscopy only (p <0.0001), and unlike their Czech counterparts, they preferred to study in small groups rather than alone (35.5% vs. 4.7%) (p <0.0001). As for the theoretical knowledge acquired in high school, international students were less prepared than Czech students (P=0.0218). Most international students (62.3%) welcomed preparatory courses aimed at solidifying their knowledge of basic sciences in advance of medical studies. However, no differences in opinions on the use of resources were found between native and non-native English speakers (P=0.6506). Domestic as well as international students experienced very similar problems when studying histology. All students appreciated taking interactive tests to assess their practical and theoretical knowledge. Conclusion: Despite the introduction of modern digital technologies such as a Virtual Microscopy in histology courses, a tailored approach to different groups of students is needed.
Tirukkural Couplet Classification with Long Short-Term Memory Neural Network

In the modern machine learning world, the text classification is playing major role in organizing the knowledge in a structured way so as to apply the deep analysis over the letters, words, sentences and the structured contextual semantical meanings of the text corpus. In this paper, we have considered the three classes of  Tirukkural corpus labelled as ARAM, PORUL and INBAM with 7 snippet key terms in each couplet. Tirukkural comprises of 1330 couplets of 133 chapters. As determined by the great author of Tirukkuṟaḷ Tiruvalluvar, these documents were categorized into three classes as mentioned earlier. The meaning for the ARAM, PORUL and INBAM is Morality, Materialism and Love respectively. The collections of classification models were built using various algorithms with the help of the training set of the Tirukkural corpus and then these models were compared with the testing set couplets of Tirukkural. The classification performance accuracy and the errors were visualized with plots and numerical values.  A comprehensive experiment is also conducted using bisectional Long Short-Term Memory (LSTM) deep learning networks and the performance is visualized in this study. The proposed work is the first work in Tamil Language with respect to Tirukkuṟaḷ using deep learning technique like LSTM – RNN. In proposed work, the accuracy of the various classifiers like Linear Regression, Random forest, Naive Bayes Multinomial, and LSTM – RNN are compared. The accuracy of linear regression classifier and LSTM – RNN are high when compared to other classifiers like Random forest and Naive Bayes Multinomial. The accuracy value of LSTM – RNN classifier is approximately 65%.
Joint Positions Detection for the Elderly Exercises using Backpropagation Neural Network

The inspiration of this work is based on the limitation of the elderly’s movement. The lack of monitoring while the elderly is doing the exercises may cause the injury. This system can prevent the injury of the elderly by real time detecting and alerting. The video clips of the elderly exercises used in this system were created by the sport science specialists from Thai Health Promotion Foundation (THPF). There are 2 modes of exercise: sit mode and standing mode, the elderly may prompt to see the demonstration of each exercise from the video clip on the GUI. The Kinect detects the whole-body movement, and the system will alert if the wrong posture is detected. Our method used the backpropagation neural network for training and testing models. The 16 exercises of the elderly were recorded and extracted to 8,300 frames as the training data. Our model used 25 hidden layers and 2 outputs. In the real time classification, the system yields 89.37% of accuracy. The user interface is quite simple and easy to use for the elderly.
PERENCANAAN ARSITEKTUR ENTERPRISE SISTEM INFORMASI MENGGUNAKAN TOGAF DI SMA BPK PENABUR HOLIS

BPK PENABUR HOLIS high school is a private school located in the city of Bandung. BPK PENABUR HOLIS senior high school is engaged in education. Information Systems and Information Technology is a basic requirement and has been applied in schools, but not all parts of implementing information systems as a result of data and information needs are not integrated, so that business process activities are not running effectively. To overcome this, by planning an enterprise architecture that can integrate existing systems with systems that will be built. This methodology uses the TOGAF ADM (Architecture Development Method) in collecting data using qualitative methods based on a description of the actual conditions by means of Literature Studies and interviews. The results of this study are the proposed blueprint of information systems and information technology that are needed now and in the future for business activity strategies at BPK PENABUR HOLIS high schools. Keyword: Architecture Enterprise, High School BPK PENABUR HOLIS, TOGAF ADM Framework. DOI :  http://doi.org/10.5281/zenodo.4958211
Complaints Information System for the Polytechnic of South Aceh

The industrial revolution 4.0 is a hot topic discussed only among academics and industry. Of course, this condition is a positive thing that shows that people have started to care about utilizing technology that is developing very rapidly today. South Aceh Polytechnic (POLTAS) as a vocational higher education institution, of course, must be consistent in making innovations in improving services to stakeholders. The quality and quality of service to stakeholders must of course be maintained so that input, criticism, and suggestions from them are one of the most important factors in creating a safe and comfortable academic atmosphere for higher education, especially POLTAS. The formulation of the problem in this study is how to build and simulate the results of an information system that can adapt dynamically in carrying out the political services of South Aceh provided to stakeholders. The purpose of this research is to build a Complaints Information System for the Polytechnic of South Aceh (SIAPA). This research is also part of the POLTAS Internal Quality Assurance System (SPMI) which is managed independently assigns to control and improve quality in the implementation of higher education in a planned manner (with designed) and sustainable (sustainable). This type of research is applied research with case studies at the South Aceh Polytechnic. The research procedure is in several stages, namely planning, analysis, design, development, testing, implementation, simulation of results, and evaluation.
METHODS OF IMPLEMENTATION OF MANIPULATIVE TECHNIQUES AS A BASIS FOR A SPEECH PROFILE OF A POLITICIAN (ON THE EXAMPLE OF G. MELONI’S SPEECH DURING THE ITALIAN CENTER-RIGHT PROTEST)

This article analyzes manipulative strategies and methods of their implementation in the Italian-language political discourse. The study is based on the materials taken from Giorgia Meloni's speeches of recent years, in particular, her speech in St. John's Square in Rome during the wave of demonstrations of supporters of the centre-right politicians. The research material was selected and analyzed in accordance with the method of discourse, conceptual and component analysis. The aim of this paper is to look at the linguistic features of G. Meloni's political discourse in order to analyze her speech behavior in detail and to make up her speech characteristic. In the course of the study it was noted that when communicating with voters, G. Meloni is primarily focused on the value-based concepts of her political party program and appeals to the traditional national values — family, faith, and centuries-old traditions of Italian culture. She is prone to give a tough assessment of the actions of her political opponents via such manipulative techniques as the realization of the concept of own - alien, the strategy of positive self-presentation and the strategy of belittling the opponent. It is worth mentioning the politician's extensive use of a number of lexical, grammatical and syntactical means, such as vivid metaphors and similes, rhetorical questions, lexemes from one and the same semantic field, spatial and temporal deixis and some others to achieve her political goals. The study emphasizes the necessity of analyzing the speech behavior of Italian politicians in view of the lack of coverage of linguistic features of the Italian-language media space and the absence of works on the speech characteristic of Italian politicians in Russian linguistics.
Demonstrating Quantum Communication Under Realistic Conditions

Researchers achieve secure “real-world” quantum communication along 428 km of optical fiber, the longest terrestrial distance outside of a lab setting.
Improve Test Quality by Applying a Clustering-based Test Planning Procedure for Customer Experience Vehicle Functions

The automotive industry is undergoing technological change that is not only shaped by electro mobility, but also by the rising importance of customer experience. It puts the customers first and manages their journeys with the help of software. These software-driven customer experience vehicle functions can promote competitive technological advantages for the automotive company. In the ramp-up phase of new car models, testing of electric and electronic customer experience vehicle functions is conducted on prototypes. The functional availability of these functions depends on various requirements. Thus, test planning quickly becomes difficult to conduct manually. In order to optimize the test planning procedure for these functions, we developed a data-driven procedure that automates test planning on test objects. In this paper we introduce a procedure that comprises interaction of hierarchical clustering, weighted parameters and ranking. We evaluated a proof of concept at an Original Equipment Manufacturer and conclude that the procedure leads to higher quality in testing. This use case is a successful application of unsupervised machine learning in the automotive industry.
A Pragmatic Approach to Regulating Artificial Intelligence: A Technology Regulator's Perspective

Artificial Intelligence (AI) and the regulation thereof is a topic that is increasingly being discussed within various fora. Various proposals have been made in literature for defining regulatory bodies and/or related regulation. In this paper, we present a pragmatic approach for providing a technology assurance regulatory framework. To the best knowledge of the authors this work presents the first national AI technology assurance legal and regulatory framework that has been implemented by a national authority empowered through law to do so. In aim of both providing assurances where required and not stifling innovation yet supporting it, herein it is proposed that such regulation should not be mandated for all AI-based systems and that rather it should primarily provide a voluntary framework and only be mandated in sectors and activities where required and as deemed necessary by other authorities for regulated and critical areas.
The Effectiveness Of Flipchart Media On Students’ Reading Comprehension Of Narrative Text (An Experimental Research of The VIII Grade of MTs KULNI Cikande-Serang)

Siti Nahdhiyyatul Ummah (132301501). The Effectiveness Of Flipchart Media On Students’ Reading Comprehension Of Narrative Text (An Experimental Research of The VIII Grade of MTs KULNI Cikande-Serang).ABSTRACTThe objective of this research is to find the effectiveness of using flipchart towards students’ reading skill for the Second Graders At MTs KULNI Cikande-Serang academic year 2019/2020. To know whether this media is effective, the writer used quasi experimental and the writer collects data from 44 students’ in two classes, first class as the control class and another class as the experiment class, by applying experimental research method.The method of this research is Quantitative, which uses experiment research as follow: Test (pretest and posttest). This model supported by using Flipchart technique. While class VIII A as the control class and VIII B as the experiment class are taken as the samples. Since the research uses experimental method. The data is gathered trough pre-test and post-test.The result of the research shows that the hypothesis, which states the use of a flipchart media to improve students’ reading of narrative text is effective. It means there is significant difference between experiment and control class. The students’ who use a flipchart achieved better performance than those who didn’t use flipchart. The data collect from the result of the statistical calculation, it can seen from the result: to = 13,28, tt 5% = 2,07 and 1% = 2,82, with df = 22. By formula : tt 5 % to tt 1 %. The result is 2,07 13,28 2,82. So, to tt . it means there is significant difference between pre-test and post-test. Finally, the writer concludes that application of flipchart has effective for teaching reading English of narrative text.
Stone 19RUS Map V01.04.20

The map is based on the 19rus map from Spintires 03.03.16 A complex and long map for a single pass. Interesting map intelligence. For the company, ...
Preservation and digitization in modern and heritage libraries of Jammu Province: an analytical study

Twenty-two heritage and modern libraries in the Jammu province of Jammu & Kashmir were studied to understand the digitization status and types of services offered to users. Much of the data were collected from the respective institutes/library websites wherever these were available. For libraries that did not have websites or where the websites were not updated, the librarians of the institutes were interviewed to gather data. The findings show that most of these libraries maintain a unique collection of manuscripts and rare documents of historical importance that need to be preserved for future generations. A few of the libraries have initiated digitization projects to preserve the rare collection of documents. We recommend that the information scientists in Jammu province should continuously make efforts to take up the process of digitization in libraries to serve their users in an effective manner and preserve the knowledge for future generations.
Krill Herd (KH) algorithm for text document clustering using TF–IDF features

The text document clustering is essential for providing navigation and searching methods by managing huge amounts of data into small and meaningful clusters. Due to a large number of text documents, document management tools are termed as a major topic for information retrieval. However, the conventional text clustering methods relate documents that utilize identical terminology and disregard conceptual similarity which may degrade the performance. This paper devises a KH algorithm for clustering a large number of documents. Here, the pre-processing is performed to eliminate noise and artifacts present in the data which is performed by stemming and stop word removal method. Moreover, term frequency–inverse document frequency (TF–IDF) is adapted for extracting the noteworthy features. The clustering of documents is carried out by acquiring features obtained from TF–IDF and applies the KH strategy to cluster text documents. The fitness function is newly devised considering mean square error and Jaccard similarity.
Deterministic Finite-Memory Bias Estimation

In this paper we consider the problem of estimating a Bernoulli parameter using finite memory. Let $X_1,X_2,\ldots$ be a sequence of independent identically distributed Bernoulli random variables with expectation $\theta$, where $\theta \in [0,1]$. Consider a finite-memory deterministic machine with $S$ states, that updates its state $M_n \in \{1,2,\ldots,S\}$ at each time according to the rule $M_n = f(M_{n-1},X_n)$, where $f$ is a deterministic time-invariant function. Assume that the machine outputs an estimate at each time point according to some fixed mapping from the state space to the unit interval. The quality of the estimation procedure is measured by the asymptotic risk, which is the long-term average of the instantaneous quadratic risk. The main contribution of this paper is an upper bound on the smallest worst-case asymptotic risk any such machine can attain. This bound coincides with a lower bound derived by Leighton and Rivest, to imply that $\Theta(1/S)$ is the minimax asymptotic risk for deterministic $S$-state machines. In particular, our result disproves a longstanding $\Theta(\log S/S)$ conjecture for this quantity, also posed by Leighton and Rivest.