Reviewerly/exHarmony huggingface dataset:
Exploring Solar Energy Potential using Solar Radiation GIS Toolset - A Case of College of Natural Resources, Lobesa, Punakha

This study aimed to explore solar energy potential on a small landscape (campus of the College of Natural Resources) using topography of fine spatial resolution that can detect number of rooftops feasible for harnessing solar energy. Digital elevation model was derived by extracting elevation values from the Google Earth Pro which was then used as input for solar radiation calculation. The total radiation that the campus received was 2,100 kWh over 259 days and the result was validated using power access viewer and global solar atlas databases. The monthly average radiation indicated that October, November, December and January are the radiation peaking months at the college campus. Twenty two rooftops were identified to receive more than or equal to 3.08 kWh/m2/day, while the remaining rooftops receiving less than or equal to 3.08 kWh/m2/day. The college has potential of supplementing power shortage during lean hydro season using the rooftop solar panels.
Vehicle Detection in a Video Frame Using Machine Learning Technology

Motion tracking is one of the most active research titles of the computer vision concepts. Vehicle detection process on road are used for vehicle tracking, counts, traffic analysis and vehicle categorizing objectives and it can be implemented under different environments changes. Vehicle counting process provides appropriate information about traffic flow, and the vehicle crash occurrences and also traffic peak times in roadways[2]. Analysis of traffic may account traffic and also streamline the process, Analysis of traffic may account for the number of vehicles in an area per some time period[1]. Most of the design involve use of sensors to detect the vehicles. As it is a detection of vehicle in a video frame it uses the algorithms like Gaussian Mixture Model background subtraction it is a widely used approach for foreground detection. By the digital image processing methods which including object detection, edge detection, frame differentiation and kalman filter using the different library and algorithm with real time image[2]. This paper describes the detection of vehicle in a video frame, tracking in the video frame, counts the number of vehicle passes through the indicative line in roadways.
Spoken digit recognition utilizing a reservoir computing system based on mutually coupled VCSELs under optical injection

In this work, we propose a reservoir computing (RC) system based on mutually delay-coupled vertical-cavity surface-emitting lasers (MDC-VCSELs) under optical injection for processing a spoken digit recognition task, and the performances have been numerically investigated. In such a system, two MDC-VCSELs are taken as two nonlinear nodes of the reservoir to perform non-linearly mapping of the input information. Each spoken digit is preprocessed by two different masks to form two masked matrices, whose subsequent column vectors are connected to the preceding one to form two time-dependent series. Then, they are injected into the main polarization of two VCSELs, respectively. The transient states of two VCSELs distributed in the whole coupling loop are sampled for post processing. Through analyzing the influences of some key parameters on the system performance, the optimized parameter regions for processing a spoken digit recognition task with high speed and low word error rate are determined. The simulation results show that, for processing a spoken digit recognition task with a rate of 1.1×10 7 words per second, the word error rate ( WER ) can achieve 0.02% under adopting a dataset consisting of 5000 samples.
Reducing EFL Learners’ Speaking Anxiety through Selective Error Correction and Group Work

This quantitative study aimed to examine the level of speaking anxiety experienced by second-year high-school Acehnese EFL learners in their oral performance in L2 classroom and to investigate the attempt to lower their anxiety through the implementation of selective error correction and group work. The Foreign Language Speaking Anxiety Scale (Horwitz et al., 1986) was used as a means to survey and evaluate the learners’ speaking anxiety, and their oral performance was assessed through speaking performance rubric as adopted from Ismailia (2021) before and after the learners’ exposure to selective error correction and group work. The findings showed that a high level of speaking anxiety was found in the majority of the learners, and that the use of selective error correction and group work for the treatment of speaking anxiety overall resulted in the learners’ improved performance in their oral task. The findings highlighted the needs to create a positive and supportive educational environment that stimulates learners to freely experiment with L2 and to establish close-knit learning communities that promote authentic interactions in L2 and provide opportunities to be more engaged in oral language use.
Comparison of Semantic Segmentation Methods on Renal Ultrasounds Images

There are many people with chronic kidney disease in China, so manual segmentation can not meet the huge social needs. Due to the ability to accurately segment the images, deep learning methods can be used for the detection of kidney diseases. In this paper, a total of 881 renal ultrasound images were collected and labelled. Four semantic segmentation networks, including FCN, U-Net, SegNet and Deeplab were used to segment renal ultrasound images. In order to measure the segmentation effect of different networks, two common indicators, PA and IoU, were used to evaluate the results. The results showed that all the four semantic segmentation networks achieved good results in renal ultrasound image segmentation, among which Deeplab had the best effect on the test set, with PA reaching 99.14% and IoU reaching 0.8219.
Faktor-Faktor yang Memengaruhi Keberhasilan Studi Mahasiswa IPB Jalur Ketua OSIS dengan Metode Pohon Regresi

The success of IPB student's study can be seen from the achievement index obtained at the end of each semester. Meanwhile, the success rate of one's study is generally influenced by two factors, internal factors and external factors. Internal factors consist of intelligence (intellectual), physical, attitudes, interests, talents, and motivations, while external factors consist of family circumstances, school conditions, and the community environment. Therefore, this study uses the analysis method of classification and regression trees (CART) to find out what factors influenced the success of the Student Council (OSIS) university students.&#x0D; Regression tree it is one of the methods of classification and regression trees (CART) to perform classification analysis on both categorical and continuous response variables. Continuous response variables will produce a regression tree or hierarchical data group that starts at the root and ends with a relatively homogeneous small group. The response variable used in this study is the Achievement Index of first semester students. The results obtained from the analysis showed that there are several different variables in each class in influencing the success of the student council (OSIS) university students, but if we look further, there are two variables that are the same in influencing the success of the student council (OSIS) university students, which are variables from high school province and student study programs.&#x0D; This study uses secondary data from 493 IPB students track the chairman of the student council of the year 2018-2020 which is still active until now. Furthermore, the analysis of the regression tree is performed against four different models, for each of the force and the overall force by adjusting the variables available. The formation of tree regression performed 10 repetitions and the results of regression trees is taken from a tree which has the approximate value of the smallest risk. Then, the final results obtained from the analysis showed that there are several different variables in each class in influencing the success of the student council (OSIS) university students, but if we look further, there are two variables that are the same in influencing the success of the student council (OSIS) university students, which are variables from high school province and student study programs.
Research on equipment performance index evaluation system for HPLC

Since signal transmission is performed on a 220V/330V low voltage power line, there is a series of problems such as distribution transformer blocking the carrier signal, more load access leading to significant signal reduction, inherent pulse interference of power line. Therefore, the channel transmission characteristics of power network must be considered when power lines are used to transmit carrier communication signals with high frequency and low energy. On the premise of analyzing the necessity of power line carrier communication performance index evaluation, the channel input impedance characteristics, channel noise and interference characteristics, channel attenuation characteristics are studied. On this basis, the power line carrier communication equipment performance index evaluation system based on fuzzy comprehensive evaluation method is studied.
Dataset of discourses about COVID-19 and financial markets from Twitter

In this data article, a collection of 11,625,887 tweets on the topic of the COVID-19 pandemic are provided. The data from Twitter were collected through Twitter API from January 2020 to June 2020. In addition, we also provided subsets of tweets containing discourses on both COVID-19 and financial topics. In order to facilitate the research on sentiment analysis, the Sentiment140 dataset containing 1,600,000 tweets that were annotated as positive or negative sentiment was also provided (Go et al., 2009) We used Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to transform documents to numeric vectors and used logistic regression classifier to train and predict sentiments of tweets. These datasets may garner interest from data science, economists, social science, natural language processing, epidemiology, and public health groups.
Artificial Intelligence in Military Systems and their Influence on Sense of Security of Citizens

This paper will give us an overview of the future and present development of artificial military applications, especially in artificial intelligence algorithms, and conducted research regarding applications in the area of civilians. This paper mainly focuses on AI algorithms in robotics, cybersecurity, and object detection. It discusses the problems related to the present solutions and how artificial intelligence and its algorithm can help solve them. Expectation-Maximization and Gaussian Mixture Model algorithms are present that are used in solving discussed problems. In the application of artificial intelligence, the problem bound to ethics is responsibility issues for errors that occur due to autonomous systems are discussed in this paper.
BMI Prediction using Kinect and Data Mining Techniques for Healthcare System

Body mass index (BMI) is a person’s weight in kilograms divided by the square of height in meters. Body mass index is a measurement of obesity based on measured height and weight. Traditional method of calculating BMI is inconvenient and requires physical measuring of a person and particular instruments. A proposed healthcare system to predict BMI using Kinect and data mining techniques so that everybody can easily predict their BMI values using Facial images. Face detection and feature extraction component using haar cascade to detect useful face information. Framework by using facial images that uses machine learning algorithms for data mining namely, Data Preprocessing, Data Extraction, data evaluation and presentation to train models that would help predict obesity levels (Classification), Bodyweight, and fat percentage levels (Regression) using various parameters. System helps to advance the study aspect based on body weights and patients that are paralyzed or severely ill patient who unable to undergo basic measurement for emergency medical service.
A Formal Model for the FAIR4Health Information Architecture

In the EU project FAIR4Health, a ETL pipeline for the FAIRification of structured health data as well as an agent-based, distributed query platform for the analysis of research hypotheses and the training of machine learning models were developed. The system has been successfully tested in two clinical use cases with patient data from five university hospitals. Currently, the solution is also being considered for use in other hospitals. However, configuring the system and deploying it in the local IT architecture is non-trivial and meets with understandable concerns about security. This paper presents a model for describing the information architecture based on a formal approach, the 3LGM metamodel. The model was evaluated by the developers. As a result, the clear separation of tasks and the software components that implement them as well as the rich description of interactions via interfaces were positively emphasized.
Application and network architecture model construction analysis based on key electronic communication technologies

Today, my country's economy is developing rapidly, and it has basically entered the information age in an all-round way. The application of information technology is more and more extensive, and it has effectively improved the efficiency of life and work. As one of the important parts of information technology, electronic communication system can provide greater convenience for communication between people. Therefore, contemporary people's dependence on electronic communication equipment is becoming stronger and stronger, and the development of key electronic communication technologies can also promote the sound development of my country's electronic communication system. This paper analyzes the application of key technologies in electronic communication and the measures for building network architecture models for reference.
Floating Departures: Developing Quarantine Dance Technique as an Artistic Practice Beyond the Pandemic

We describe our process of quarantine dance technique in making the dance film and meditation, Floating Departures. This work, created during lockdown in 2021, brings together dance movement, poetry, painterly styles, and sound to explore cyclical patterns and points of departure in movement and life. To create Floating Departures we used a broad range of technologies–from everyday objects to smartphones to AI art systems. We experiment with various techniques to record ourselves and bring our movement together in a shared digital space with post-production video editing techniques. Using a bricolage approach, we incorporate materials, such as bubble wrap and balloons, to transform our spaces and explore our personal experiences during lockdown. We construct multiple layers of reality that are further transformed in unanticipated directions with AI technologies. Through our creation process, we develop a collective physical body to explore a new realm, unbound by reason or logic, that was only made possible through our remote collaborative processes and technologically-mediated interactions.
Logical Diagrams, Visualization Criteria and Boolean Algebras

This paper presents logical diagrams as attempts to visualize facts about logical/linguistic/conceptual systems. It introduces four criteria for assessing visualization: 1) completeness, 2) correctness, 3) lack of distortion, and 4) legibility. It then studies presents well-known families of diagrams, based on the geometrical figures of a) the hexagon, and b) the tetrakis hexahedron. These are usually presented as exemplary diagrams. To understand better why they succeed so well at visualizing logical information, they are presented as visualizations of complete (finite) Boolean algebras. This also establishes the connection between the combinatorial concept of partition and the logical concept of opposition (i.e. contradiction, contrariness, and subcontrariness). Finally, the paper suggests that the two geometrical figures in question are part of a larger family of polytopes with deep connections to Boolean algebras.
A Framework for Semantic Clustering of News Articles Based on Fuzzy

Text mining is a process that uses data mining approaches to extract valuable information held in the hidden form in textual data. In this paper, we are proposing a framework for fuzzy clustering of news articles. These news articles originate on different news portals on the web. The data obtained need to be stored in a central database and then pre-processing reduces the noise. The keyword extraction is used to extract keywords from the text and then word-frequency vector is generated. On these vectors, distance measure or similarity measure function is used to find the similarity between articles. One article may belong to more than one cluster so fuzzy context vector must be generated. Mutual Information can be used to find fuzzy membership values. The threshold values are required for the identification of clusters. The proposed framework shows that fuzzy clustering does not restrict each news article to belong exactly to one cluster. Therefore this framework when applied to information retrieval systems or other application systems, system gives better performance and relevance to the users.
TEXT SUMMARIZATION USING PYTHON NLTK

Text summarization is basically summarizing of the given paragraph with the use of natural language processing and machine learning. There has been an explosion in the quantity of textual content records from lot of sources. This quantity of textual content is a useful supply of facts and information which needs to be efficiently summarized to be useful. In this paper, the primary tactics to computerized textual content summarization were described. The distinctive approaches for summarization and the effectiveness and shortcomings of the distinctive methods were described. The machine works through assigning rankings to sentences withinside the document to be summarized, and the use of the maximum scoring sentences in the summary. Score values are primarily based totally on functions extracted from the sentence. A linear mixture of function rankings was used. Almost all the mappings from function to score and the coefficient values withinside the linear mixture were derived from a training corpus. Some anaphor decision was performed. In addition to primary summarization, a strive was made to address the issue of targeting the text at the user. The meant user was taken into consideration to have little history information or analyzing ability. The machine enabled through simplifying the individual words or phrases used in the summary and through drawing the pre-needful history facts from the web.
Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.
An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

Due to large current applications on digital images in these recent years, outlier suppression is one of the primary stages for modern computer vision implementations thereupon there are tremendously invented for creating an efficient and practical outlier suppression, which ordinarily are composed of outlier recognition stage and outlier rebuilt stage. The localised rank ordered difference (LROAD) approach, which is progressed from rank-ordered absolute differences (ROAD), has been invented since 2016. Later, the LROAD approach evolved to be one of the efficient outlier recognition stages from its eminent effectiveness. The paper focus to propose the innovative vigorous outlier recognition placed on localised rank-ordered logarithmic differences (LROLD) approach, which is progressed from LROAD and rank-ordered logarithmic differences (ROLD), which is higher effectiveness than the ordinary LROAD, for applying on FAIN. From the computer experiments, which are examined on many depictions such as Girl, Pepper F16 and Lena, the innovative vigorous outlier recognition placed on LROLD approach has higher eminent effectiveness then the stage-of-art approach such as LROAD and ROAD approaches at numerous consistencies of FAIN.
Statics

This advanced undergraduate physics textbook presents an accessible treatment of classical mechanics using plain language and clear examples. While comprehensive, the book can be tailored to a one-semester course. An early introduction of the Lagrangian and Hamiltonian formalisms gives students an opportunity to utilize these important techniques in the easily visualized context of classical mechanics. The inclusion of 321 simple in-chapter exercises, 82 worked examples, 550 more challenging end-of-chapter problems, and 65 computational projects reinforce students' understanding of key physical concepts and give instructors freedom to choose from a wide variety of assessment and support materials. This new edition has been reorganized. Numerous sections were rewritten. New problems, a chapter on fluid dynamics, and brief optional studies of advanced topics such as general relativity and orbital mechanics have been incorporated. Online resources include a solutions manual for instructors, lecture slides, and a set of student-oriented video lectures.
Top websites allow bad passwords

Analysis of popular English-language sites finds many don't follow best security practices
Optimal ternary sequence sets rigorously achieving the Welch bound

Sequences are finite-dimensional discrete signals in the complex domain, which are widely used in communication, radar, sonar, and information security to complete the requirements for synchronization, multiple random access, channel estimation, ranging, anti-interference, and digital watermarking due to their anti-interference, stability, and easy implementation. There are close relations between sequences and mathematical objects such as cyclic Hadamard matrices, difference sets (families), tight frames, unbiased bases, and cyclic codes. Constructing sequences reaching or approximating the theoretical bounds has always been a core topic in sequence design and coding theory. In this paper, based on the Semi-Bent function and difference set, we propose a class of optimal ternary sequence sets (the elements of sequences belong to $\{0,\pm~1\}$), which is the first class of optimal sequence set strictly achieving the Welch bound since this bound was established in 1974. Compared with the binary sequence sets (the elements of sequences take values $\pm~1$), the new sequence set is more suitable for ultra-wideband communication, digital watermarking, and spectrum constrained scenarios. While compared with known ternary sequence sets asymptotically approaching two times the Welch bound, the proposed ternary sequence sets have better correlation properties.