Reviewerly/exHarmony huggingface dataset:
Evaluating Free Rides and Observational Advantages in Set Visualizations

Abstract Free rides and observational advantages occur in visualizations when they reveal facts that must be inferred from an alternative representation. Understanding whether these concepts correspond to cognitive advantages is important: do they facilitate information extraction, saving the ‘deductive cost’ of making inferences? This paper presents the first evaluations of free rides and observational advantages in visualizations of sets compared to text. We found that, for Euler and linear diagrams, free rides and observational advantages yielded significant improvements in task performance. For Venn diagrams, whilst their observational advantages yielded significant performance benefits over text, this was not universally true for free rides. The consequences are two-fold: more research is needed to establish when free rides are beneficial, and the results suggest that observational advantages better explain the cognitive advantages of diagrams over text. A take-away message is that visualizations with observational advantages are likely to be cognitively advantageous over competing representations.
Are They Likely to Complain on Phish or Spam? A Prediction Model

Customers are the core of businesses. Specifically, telecommunication companies, customer satisfaction is considered to be a commercial necessity and therefore a priority. High rates of customer satisfaction increase both retention and attraction rates. As a result, telecommunication companies are always seeking new means to achieve these objectives. A large volume of calls is received in a typical call center from customers complaining about phishing or spam attacks daily. It is difficult to identify the purpose of the call manually. In this work, we expand on previous efforts to focus more on impacted phone spam or phish consumers. The study focuses on both mediums of communication, phone calls and messages. A historical sample of customers' complaints dataset was used, and several machine learning classification algorithms were applied to analyze the calls. These are Logistic Regression, XGBoost, Gradient Boosting, Random Forest, CatBoost, KNN, and SVM. The predictive model can identify whether an individual is likely to complain about a spam or phish attack. The performance of the baseline classifier achieves an accuracy of 63.4 % that is based on CatBoost. Moreover, the model identifies consumers' demographics. The findings show that people of age 45 are more likely to complain and that males are less likely to complain.
ARLooper: A Mobile AR Application for Collaborative Sound Recording and Performance

This paper introduces ARLooper, an AR-based iOS application for multi-user sound recording and performance, that aims to explore the possibility of using mobile AR technology in creating novel musical interfaces and collaborative audiovisual experience. ARLooper allows the user to record sound through microphones in mobile devices and, at the same time, visualizes and places recorded sounds as 3D waveforms in an AR space. The user can play, modify, and loop the recorded sounds with several audio filters attached to them. Since ARLooper generates the world map information through iOS ARKit’s tracking technique called visual-inertial odometry which tracks the real world and a correspondence between real and AR spaces, it enables multiple users to connect to the same AR space by sharing and synchronize the world map data. In this shared AR space, the user can see each other’s 3D waveforms and activities, such as selection and manipulation, as a result, having a potential of collaborative AR performance.
Low-Resolution Facial Expression Recognition Based on Texture Mapping-Based GcForest

Low-resolution facial expression recognition is broadly required in the field of video surveillance in various real-world environments, which make it necessary to speed up the algorithm while maintaining high recognition accuracy. Since deep forest has the ability of easily distributable on parallel hardware architectures, we consider it as an efficient approach for low-resolution facial expression recognition. Different from the original multi-Grained Cascade forest (gcForest), which is the first model of deep forest, we propose the texture mapping-based gcForest (TM-gcForest) method. TM-gcForest unifies texture-mapping as the first level of representation learning with gcForest which originally consists of only two steps, namely multi-grained scanning and cascade forest. The added layer is especially designed for low-resolution images, which can extract features focusing on the contour of faces and is also helpful for the classification at the step of multi-grained scanning. Experimental results on three facial expression datasets show that TM-gcForest obtains on par or superior results compared with hand-feature methods, original deep forest model and the DNN model, which validates the effectiveness of TM-gcForest in both lab-controlled and wild environments. Especially, TM-gcForest performs much better than hand-feature methods and original gcForest on low-resolution images, indicating that TM-gcForest is well suited for real-world video surveillance.
An Efficient Approach for the Detection of Abnormalities in Different Cancerous Images Using TFD Techniques

Disease is the conspicuous wellspring of mortality in around the world. The malignant growth which is cancer arises because of unrestrained varieties in a cell. Stringent diagnosis of cancer with explicit procedure is a prerequisite for retaining human lives. The relevant image transforms such as STFT and WT utilize a detection system where the cancerous image features are retained and localize the signal characteristics in different domains. The predetection of malignant growth prompts proper conclusion to either control or fix disease, as at later stages the patient may not react to the treatment. The suspicious tissues are examined and diagnosed with the help of MRI. These filtered pictures can be used to evaluate the destructive cells. The TFD yields appropriate information in the study of non-stationary images. The key objective of TFD representation is to figure out the variations in the frequency substance of images with respect to time. The TFD representation is proposed by STFT and WT to analyze, evaluate and transform the time differing category of images.
Towards Generalization of 3D Human Pose Estimation in the Wild

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images.
Security of Cyber-Physical Monitoring and Warning Systems for Natural and Technological Threats

This work describes the use of cyber-physical architectures for the construction of regional environmental monitoring and alert system for residents to the natural and technological threats. Regional systems are structures covering an area of several km2. The work focused on ensuring the security of collected and processed information as well as the continuity of system operation. The base tool to improve its parameters is the use typical solution for cyber-physical systems, including component of Internet of Things. Nationwide, environmental monitoring in most countries is still based on IT solutions from the early 1980s. In the case of slow-changing global threats covering large parts of country, this solution works well. However, for dynamically changing regional threats, using them for early preparation of accurate warnings is impossible. As a solution for such problem, we are using the data that comes from weather stations owned by the residents of the monitored area, that was designed using Internet of Things technology. This approach creates new, unprecedented threats to information security and system continuity. In addition to threats resulting from the use of the Internet of Things, there are new, resulting from the properties of algorithms for threats forecasting, which require, among others input dataflow continuity. In work the functional organization of environmental monitoring system and their architecture are presented. On their basis the methods and means of counteracting security and accessibility threats are evaluated. This work describes also the implementation of environmental monitoring system in a town adjacent to a larger agglomeration.
Clustering of internet network usage using the K-Medoid method

Abstract The use of the internet network at Wahidin Vocational High School (SMK) Cirebon City is a very significant need in supporting the progress of learning. The ease of using the internet has made the academic community free to use the internet network facilities provided by the school and carry out the activities they want without any control. Causing the academic community to freely access the web that is not related to ongoing education, and certainly this matter will be very disturbing in the teaching and learning process. This matter is in order to identify accurate analysis also using Information Mining as information processing. After that, using the clustering algorithm procedure is K-Medoid. The K-Medoid procedure is able to carry out information grouping on the use of social media access which is often accessed by the academic community throughout the school. Grouping using the K-medoids algorithm can be known that the use of internet network access is on average used to access social media compared to accessing E-learning. It is suitable from the results of research that was tried if the use of WhatsApp reached an average of 126 users, after that accompanied by Instagram, its usage reached an average of 108 users, then accompanied by E-Learning, the average usage reached 90 users, after that it was accompanied by Facebook and YouTube which an average of 71 users. Thus, from the research that has been tried, it is necessary to implement layer 7 protocol firewall security to block social media access so that internet network access at Wahidin Vocational High School (SMK), Cirebon City. Used as a suitable early idea is to facilitate the academic community to maximize E-learning education.
Research on Knowledge Graph in Education Field from the Perspective of Knowledge Graph

Rapid advent of the era of big data, deep integration of education and technology, and multidisciplinary nature of educational research are driving the application of knowledge graphs in educational research. We use CSSCI and CSCD journal articles as data sources and analyze the Knowledge Graph of education using the graph database Neo4j in this article. Result shows that the number of literatures in the research area show a clear upward trend and go from the stages of brewing. Highly cited literatures’ research topics, subject categories, and authors are widely distributed. From the perspective of core institutions and authors, multiple normal universities have strong competitiveness. Citespace, SPSS and Bicomb are the most commonly used data processing tools for scholars. In further study of the area of knowledge graph, we should fully combine with the cutting-edge computer technology, based on big data and artificial intelligence to improve the level and quality of education research.
Quantitative Analysis of Spoken Discourse Using Memoirs of Old-time Moviegoers

We present the first quantitative analysis of spoken discourse for the Turkish language using memoirs of a group of old-time moviegoers of varying age groups whose birth year spreads over a period of four decades ranging from the 1930s to the 1960s. They tell their experiences by answering a set of questions. Their responses are evaluated comprehensively with the expectation that various attributes of the participants are reflected by their everyday speaking language. We also investigate their language characteristics in terms of their vocabulary richness and word usage. The results show that the age and gender of the participants can be inferred to some extent from their speech, as is the case for written text. However, the difference is not significant in the language use of younger and older respondents in terms of vocabulary richness and archaic word usage. With additional data obtained for some participants, it is shown that text can be accurately identified as being either spoken or written; however, the spoken text of a person can only be differentiated from their written text with the accuracy level of a random guess.
Key Points Detection Algorithm of Object Based on Full Convolution Network

This paper proposes a method for detecting key points of an object based on a fully convolutional network. The method for estimating all the key points of the object includes feature extraction without spatial information loss and regression of numerical coordinates. The first step mainly uses KPDA-Net to construct the AI model and output the feature map; the second step is to return the numerical coordinates according to the feature map; finally the algorithm design and experiment are completed. In this paper, we use the key point detection data set of All Tianchi competition to train and test the model. The key point detection accuracy and speed of the algorithm are (PCP mean: 81.3, t: 300ms). The advantage of this algorithm is that the output structure of the algorithm can be flexibly adjusted to be suitable for the key point detection of new object categories, so the algorithm has a strong migration ability.
The Optimum PV Panels Slope Angle for Standalone System: Case Study in Duhok, Iraq

Abstract Due to the high operation and maintenance charges of the photovoltaic (PV) tracking systems, it is better to install the PV panels at a stationary angle which is considered as an optimum slope angle. Usually the optimum annually slop angle equals to latitude. However, Duhok site (latitude 36°50’40.6” N, longitude 43°01’05.3” E) has a special case because it’s located in valley between two mountains where the eastern sides of these mountains block the sunlight at the early morning. This paper aims at finding the optimum annually slop angle that makes the solar panels in this site generate the highest possible amount of electrical energy by capturing largest amount of solar radiation at this period when the sun appears from behind the mountain. HOMER software is used to simulate the suggested standalone PV system at the study site. The optimization outcomes present that the optimum annually slope angle for the PV panels should be equal to 25 °.
The Computer as Game Partner

A computer can be programmed to behave as though it were thinking. A good example of this phenomenon is that of chess computers and programs. In the autumn of 1977, at the Berlin radio and television exhibition, the “Chess Challenger 3,” the first of a series of chess computers, was presented. The two true pioneers of computer chess were the American Claude Shannon and the Englishman Alan Turing. In 1936, Alan Turing, the second pioneer of computer chess, came up with the idea of a theoretical computer, now called a Turing machine, in order to investigate the limits of algorithmic computation. The null window search gives a relatively efficient answer to the question of whether the minimax value exceeds a specified bound. The first computer endgame analyses were carried out in 1970 by Thomas Strohlein in connection with his dissertation at the Munich Technical University.
Modeling cognitive learning spaces in Russian universities in the context of globalization and digitalization

The article is devoted to the modeling cognitive learning spaces in Russian universities for the effective training of students and their preparation for life and future profession. Based on a comparative analysis of theoretical and practical approaches to the problem by Russian and foreign scientists, a conclusion is made about the importance and relevance of the topic for academic community and society as a whole. In modern conditions, the processes of globalization, integration, informatization and digitalization induce universities to change the vector of their activities in the direction of technology and innovation, advanced forms of communication and mental interaction, technological methods of teaching and knowledge transfer, and, as a result, cause them to review their learning spaces as well. From a pedagogical perspective, the design of cognitive learning spaces may equally influence the entire educational landscape of the country as well as the learning climate of a particular institution, learning outcomes of a particular student and the system of professional development of a particular teacher and/ or researcher in multiple ways. The purpose of this work is to study and analyze the current state of the problem, systematize and concretize the conceptual apparatus, identify current positive trends in higher education allowing to model and design the effective tech-based, information-rich and equitable cognitive learning space at university, which will provide a beneficial learning environment with regard to mental needs and expectations of modern students.
Hyper Fuzzy AT-ideals of AT-algebra

Abstract The aim of this paper is to introduce the notion of hyper fuzzy AT-ideals on hyper AT-algebra. Also, hyper fuzzy AT-subalgebras and fuzzy hyper AT-ideal of hyper AT-algebras are studied. We study on the fuzzy theory of hyper AT-subalgebras and hyper AT-ideal of hyper AT-algebras. Furthermore, the fuzzy set theory of the (weak, strong, s-weak) hyper fuzzy ATideals in hyper AT-algebras are applied and the relations among them are obtained.
Digitalization of Education as a Prerequisite for the Implementation of Industry 4.0 Technologies in the Activities of Enterprises

Abstract The article is devoted to the problem of modernization of higher education in the digital economy. The place and role of education in the process of introducing Industry 4.0 technologies into the activities of enterprises have been determined. The directions of modernization of the educational process aiming students at fluency in knowledge and tools of the digital economy for smart production are determined. The modeling of the processes of implementation of technologies "Industry 4.0" based on the products of educational production. The main attention ispaid to solving the problem of implementing the main stages of knowledge transfer and the integration of participants in this process.
Education Teaching and Evaluation Under Data Mining Algorithm

The evaluation of education and teaching effect is an important measure to deepen the education reform. To carry out an education reform, we must first evaluate the feasibility of the reform scheme. In the process of reform, we must strengthen the formative evaluation, and at the end of a certain reform, we must go through the final evaluation. Only in this way can we avoid blindness and ensure the healthy development of education reform. The evaluation of education and teaching effect is an important means to improve teaching quality in an all-round way. Through the evaluation of education and teaching effect, we can get feedback information about the effectiveness of school’s education and teaching work and the development of students in all aspects, summarize the achievements in time and correct the deviation, so as to keep the education and teaching work in a good state all the time, so as to promote the improvement of school education and teaching quality. The evaluation of education and teaching effect is an important content of educational science research.
Multi-step Reasoning for Knowledge Graph Completion

Most Knowledge Graphs (KGs) have the problem of missing relationships, although embedding methods that map entities and relationships to low-dimensional spaces can achieve certain results. But they only focus on the direct relationship between entities and ignore the importance of the semantic features of entity types. In addition, a large amount of unstructured text can effectively supplement the semantic information of the Knowledge Graph (KG). In order to solve the problem of scattered knowledge and lack of relationships in the KG, we propose a neural network model which is based on attention mechanism. The model combines the semantic information and the entity type captured by the attention layer to predict the link of tripe. We performed link prediction tasks on public datasets, and under the premise of sufficient data sources, also confirmed that our method is innovative and has research value.
Tracking and Landing of QUAV under Complex Conditions based on Ground-Air Information Fusion

In recent years, the quadrotor unmanned aerial vehicle (QUAV) and its combination with the mobile ground vehicle have been widely used in various military and civilian scenarios. This paper takes the cooperation system composed of the QUAV and the mobile ground vehicle as the research object, and conducts in-depth research on the tracking and landing of the QUAV on the mobile ground vehicle. For the tracking and landing of the QUAV in the actual environment, two difficult problems that are easy to appear are mainly discussed: when the mobile ground vehicle has acceleration, the QUAV is easy to loss the target due to the strong maneuverability of the ground vehicle; when the mobile ground vehicle moves on non-horizontal roads, changes of the slope will also affect the tracking and landing of the QUAV. In response to the above two problems, the method of ground-air information fusion is adopted. The sensor information of the ground vehicle is used to reflect the movement and driving conditions of the mobile ground vehicle. And then combined with the current visual tracking status of the QUAV to achieve information adaptive fusion and reasonably adjust the control amount of the QUAV. The tracking and landing strategies of the QUAV under two conditions are designed separately. Finally, the experimental scenarios are built in Gazebo and the effectiveness of the designed methods and strategies are verified.
A Topological View of Reed–Solomon Codes

We studied a particular class of well known error-correcting codes known as Reed–Solomon codes. We constructed RS codes as algebraic-geometric codes from the normal rational curve. This approach allowed us to study some algebraic representations of RS codes through the study of the general linear group GL(n,q). We characterized the coefficients that appear in the decompostion of an irreducible representation of the special linear group in terms of Gromov–Witten invariants of the Hilbert scheme of points in the plane. In addition, we classified all the algebraic codes defined over the normal rational curve, thereby providing an algorithm to compute a set of generators of the ideal associated with any algebraic code constructed on the rational normal curve (NRC) over an extension Fqn of Fq.