Reviewerly/exHarmony huggingface dataset:
Petrological and structural charateristics of the peddavura schist belt and adjacent rocks in eastern Dharwar craton in parts of Nalgonda district, Telangana state

The NW-SE trending Peddavura Greenstone belt extending over 25 kms with a width of 0.5 to 2 km is flanked by granitoid rocks. The rocks are classified as Basalt, Basaltic Andesite, Andesite, Dacite and Rhyolite. Pillow structures with cherty layers along the margin of the pillows of basaltic rocks were observed in the field. Quartz veins intrude along the fractures of the felsic rocks. South of river Krishna, banded iron formations (BIF) occur interlayered with the volcanic rocks. Basaltic rocks are fine grained and made up of hornblende and calc-plagioclase feldspar. Felsic volcanic rocks show porphyritic texture with quartz occurring as phenocrysts set in a fine-grained matrix of quartz and feldspar. In the study area, sulfide and oxide ore minerals are concentrated within quartz veins that traverse the metavolcanics.
Machine Learning-Based Optimization Technique for Forecasting the Solar Radiation

Abstract Solar radiation measurement determines how often electricity a given area absorbs from the sun. This light is the key source of energy for conversion into solar thermal and photovoltaic plants. The radiation incident is not stable and relies on the temperature records, contributing to intermittent activity and electricity supply changes. This justifies designing a method to forecast and estimate incident radiation to predict improvements in photovoltaic systems’ performance. In this paper, the support vector machine (SVM) based machine learning is proposed to improve solar radiation prediction accuracy. The designed system results are compared with existing models that predicted the radiation and the global solar radiation is predicted accurately with efficient.
The Design and Simulation of Sit-Up Counter Based on MCU

Our country’s economy and people’s material life level rapid ascension, people also pay more and more attention to personal health in today’s busy society. We do not have the time and space to do strenuous exercise. You can squeeze in some sit-ups during the AD breaks on TV in the evenings. To get a better count, this work designs a sit-up counter based on MCU. This design put computer software and hardware technology as one solution, using 51 SCM as the control core, achieve the automatic counting crunches. Specific processes include start, end control, real-time display the count value, tip end of the test, etc. This design has a simple structure, functional, practical, and high reliability.
Large-Scale Volume Rendering as a Vertex-Oriented Graph Processing Task Using Apache Distributed Computing Tools

The article proposes an original idea and a basic algorithm for performing a direct volume rendering procedure using a distributed vertex-oriented graph processing approach. A procedure for constructing the graph of the problem based on a regular partition of the volume is presented. Some improvements to the proposed approach are described by adding techniques for empty space skipping and adaptive sampling. The simple experiments carried out show the fundamental feasibility and prospects of the proposed ideas. #CSOC1120.
ERM-AT Applied to Social Aspects of Everyday Life

Based on the notion of quality characteristics in design and quality characteristics in use in terms of the UX for products and services, authors tried to apply the ERM that was originally developed for measuring (and evaluating) the UX to the new field “social aspects of everyday life” based on the idea that the UX is just a part of the total experience and that we have to make efforts to improve the total experience of human life. The ERM was originally developed to let users write down episodes in terms of some specific product or service with the rating value from 10 to −10 on the rough time scale. We could examine how the positive and negative episodes were experiences. It should serve as a basis for the improvement of the UX. But unfortunately, the original ERM lacked the linkage to the improvement from finding problems. ERM-AT was thus proposed to provide the analytical clues where quality characteristics in design caused the (negative) quality characteristics in use. The challenge in this paper is to expand the scope of the ERM-AT to be applied from just the products and services to the social aspects of everyday life. For that purpose we changed the quality characteristics in design and in use to the independent variables and dependent variables that will include different set of keywords depending on the type of social aspect of everyday life. A trial application to the university life suggested the successful expansion of the method. Finally, a vast map of applicable domain was shown.
Study on the Impact of Learning About Information Security Measures on Mental Models: Applying Cybersecurity Frameworks to Self-learning Materials

The shortage of information-security workers is a problem. Appropriate mental models are considered important to encourage learners to appropriately plan, understand, and act on security measures. We aim to create materials to help security staff members acquire the right mental model in their learning and improve the learning effect. To create such materials, it is important to investigate the current mental model of the staff, how the model changes through learning, and its impact on the learning efficiency. A preliminary experiment was conducted on individuals who had not received such education and individuals who had participated in security-related work for a few to several years. The participants self-studied both standard and revised materials on information security measures, and we conducted tests and semi-structured interviews to examine the changes in their performance and confidence as well as the changes to their mental models before and after self-learning. Four mental models were identified during the experiment: role-based, timeline/phase-based, framework-based, and unstructured models. After learning, for those with security-related work experience, we identified examples of changing to a framework-based model, and for groups with no security education, we identified examples of acquiring a role-based model. Instances when the model did not change were also noted. The test scores and degree of confidence of both groups improved after the self-learning, and a significant difference was shown for questions regarding which security measures contribute to which security function based on a small sample size
A Surveillance System Focused On Approved Blockchains and Computation of Edges

Abstract Video surveillance systems have become a critical tool for urban management in recent decades. Without visiting the scene, managers may grasp the scene information. The monitoring system can improve management and supervision effects and reduce the likelihood of major accidents. With both the invention of the Internet of Things (IoT) era, however, sensor networks face difficulties, like massive access to equipment, massive data, inadequate bandwidth, attack vulnerability and real-time surveillance problems. We provide an overview of the implementations situation of the visual supervision system accordance with the project blockchain and border computers. The framework uses BCs, edge processors, developments from the IPFS and transformed neural networks. Edge computing is used for huge wireless communication collection and analysis. The IPFS storage service can be used and CNN infrastructure is used in legitimate surveillance to do a large video collection.
Text Summarization for Thai Food Reviews using Simplified Sentiment Analysis

The review of a customer who had experienced using the products and services helps others make the purchasing decision. Especially in the food and beverage domain, Thai reviewers frequently write long reviews in multiple dimensions, requiring a longer time to read and understand the reviews' opinions. The Thai sentiment analysis can analyze the review's sentiment in food, environment, and service dimensions and generate the review summary accordingly. Readers can save time in reading only the review summary and perceive the sentiments in the original review. In this paper, we propose a method to analyze the sentiment in Thai food review and generate a summary with the positive and negative sentiments in the review. The results show that our proposed method can generate 1,876 review summaries from 4,000 original reviews, which is equivalent to 46.68%. The total number of words in the review summaries is only 5.13% of the original reviews, with an average accuracy of 65.25% in Type 1 and 85.05% in Type 2.
Image processing method based on aerospace monitoring

The paper considers a digital image processing method that provides efficient transmission, redistribution and storage of aerospace environmental monitoring videographic information. The requirement to increase the monitoring efficiency, if necessary, to carry out regular monitoring of changes in the image field of the observed object is especially relevant when describing spatially aligned sets of images obtained after multi-zone, multi-time and multi-polarization shooting. In many remote sensing applications, the processing efficiency reflects the degree of reduction in the volume of transmitted, described, analyzed and stored videographic information. The computational processing of digital images based on the coordinate survey scheme is presented. The method can be used to perform the procedure for decoding images in order to solve the problem of classifying objects of interest, analyzing images. Such characteristics of binary objects as a spatial boundary and a contour were used as deciphering signs. These features make it possible to describe the shape of an object, its geometric parameters and search for images with specific spatial structures. The processing method is realized by executing efficient algorithms for coding an image on a discrete grid by means of a chain code and spectral coding based on the fast discrete Hartley transform. The result of processing comes down to minimizing the number of such basic computational operations as multiplication, addition, transferring, as well as reducing the time and capacitive complexity of the program, and reducing the redundancy of the initial data. An example of an effective representation and description of a segmented image of an object is given. The proposed method makes it possible to expand the technical capabilities of more efficient transmission and processing of images for solving problems in the field of remote sensing.
Morphology and clustering of galaxies images using transfer learning and unsupervised machine learning

Clustering galaxies based on structural properties reveals important details about their evolution and development. The morphology of a galaxy reveals a lot about its dynamic and merger history. It is closely linked to a number of physical parameters, including mass, star formation background, and mass distribution. Unsupervised machine learning, does not include labeled data, is appropriate for morphological analysis of recent and upcoming surveys, with its approach based on extensive computer vision techniques that measure visual similarities between various forms of morphology. The proposed method perceives similarities between collections of galaxy images automatically. This article makes use of the K-means clustering and threshold functions. The whole dataset is partitioned into K-clusters using the K- mean algorithm.
Sun Closeness/Clearance Estimation for 3 Different Days

With the need for clean and sustainable energy, solar energy use has increased significantly in recent years. However, power fluctuations due to solar energy variability are among the main challenges in using photovoltaic systems. One of the most critical factors that cause these fluctuations is the clouds that block the sun’s radiation. In this study, the motions of clouds are tracked, and the motions they could take in the future are estimated. Thus, in the short term, it has been tried to determine the moments when sudden solar radiation changes may occur by estimating the closeness and clearance of the sun. For this purpose, firstly, sky images are collected at regular intervals with the help of a digital camera placed in the main campus area of Afyon Kocatepe University. The Shi-Tomasi algorithm determines the points to be tracked on the collected sky images. Then, the points determined by using the Lucas-Kanade optical flow algorithm are tracked on sequential images. By analyzing the clouds’ motions, the clouds with the risk of obstructing the sun are detected, and the motion estimations are made using the calculated direction and speed information. Finally, due to the experiments performed on datasets obtained from 3 different days, sun closeness/clearance estimations at the 1-second resolution at 120 seconds time horizon are reached. The obtained results showed that the approach could be used successfully in cloud motion estimation in different cloudy conditions.
Product Classification Using Partially Abbreviated Product Names, Brands and Dimensions

Retail companies are looking for ways to support or automate the data entry process for product master data which is currently often a time-consuming and cost-intensive manual process. The basis which many attributes and business processes in master data management depend on is the classification of articles to a certain product category. In this paper we propose a machine learning approach to classify articles according to the Global Product Classification schema by their product name, brand name and other attributes such as the product weight and dimensions. One of the challenges in our data set is posed by the product names containing a significant amount of abbreviations, for which we implement several preprocessing strategies. Additionally, the data set suffers from class imbalance and missing values that must be considered. Different classification algorithms, data imputation methods and feature combination strategies are evaluated. We show that automatic classification can be performed successfully based on the partly abbreviated product names despite the challenges mentioned. A simple Support Vector Machine model shows to outperform more sophisticated models and the brand names. The product dimensions and other additional attributes did not increase prediction quality.
Research on the Application of Book Locating Technology in Library Shelving Management for Universities and Colleges

Book shelving and locating is a highly useful book management method, which could shorten the book search time of readers and improve the efficiency of book management. Focusing on the application of book locating technology in the library shelving management of Wuhan University of Technology, this paper explores how different book locating methods bring convenience to the book management of library in universities and colleges. The practical experience reveals the improvement of reader satisfaction and management efficiency by selecting a suitable locating method for books in terms of management mode and circulation.
Fault-tolerant control for delayed interval type-2 fuzzy systems with nonlinear fault input

Abstract This paper addresses the stabilization of Takagi-Sugeno (T-S) fuzzy systems with time delay. In particular, the control system with parameter uncertainties are modeled through an interval type-2 (IT2) T-S fuzzy model, in which the uncertainties are handled via lower and upper membership functions. By developing some new techniques, a fault-tolerant controller is designed to ensure that the closed-loop fuzzy time-delay system is asymptotically stable with nonlinear fault input. Finally, a numerical example that demonstrate the effectiveness of the proposed conditions are provided.
The Choice of AI Matters: Alternative Machine Learning Approaches for CPS Anomalies

We compare the pros and cons of two Artificial Intelligence (AI) solutions, addressing the anomaly detection and identification challenge in industrial Cyber-Physical Systems (CPS). We demonstrate how our current approach, Advanced DL, based on Convolutional Neural Networks (CNN) differs from a previous one, Classic ML. Though both workflows prove to result in highly accurate classification of anomalies, Classic ML is superior in this regard with 99.23% accuracy against 94.85%. This comes at a cost, as Classic ML requires total insight and expertise regarding the system under scrutiny and heavy amounts of feature engineering, while Advanced DL treats the data as a black box, minimising the effort. At the same time, we show that finding the best performing CNN model design is not trivial. We present a quantitative comparison of both workflows in terms of elapsed times for training, validation and preprocessing, alongside discussions on qualitative aspects. Such a comparison, involving analysis of workflows for the given use-case, is of independent interest. We find the choice of AI solution to be use-case dependent.
Malicious Webpage Classification

Attacks through the web pages containing malicious content have become an increasingly threat to the web security in the recent years. Thus, detection of the malicious URL is an important task to reduce the security threats. To detect malicious URL or web pages, there are several ways but the most traditional technique is through the Black List detection. The Black list contains the list of malicious web pages that are maintained so that user can be aware about the web pages before accessing any webpage. But, the problem with the black list is that it is not an effective method as malicious web pages change frequently, and also growing numbers of web pages that pose scalability issues. A part from blacklist technique, various research techniques have been proposed that use machine learning technique and some use CNN (Convolution Neural Network) to classify web pages into category: malicious or benign. In the paper, a literature survey on classification of malicious web pages is presented that compares various machine techniques with parameter: precision, recall, and F1 score. This survey shows that the Machine learning techniques are better if the features used are textual but when there are images in web page, CNN performs better for the malware image classification.
Operational Usability Heuristics: A Question-Based Approach for Facilitating the Detection of Usability Problems

Heuristic evaluation (HE) is a method designed to help evaluators detect usability issues in any given system. It has gained popularity since it is a discount method, meaning it does not require much time or resources. However, it has been reported that novice evaluators face difficulties when applying HE, and they produce results of poor quality when compared to the results produced by expert evaluators. For years, researchers have worked on improving HE in multiple ways by producing different heuristics, modifying and extending existing heuristics, or addressing and improving certain parts of the HE process. In this work, we provide a set of questions based on Nielsen’s heuristics for evaluators to ask themselves while examining the system. This list of questions can facilitate the detection of usability problems in any given system. The list is a result of interviews we conducted with 15 usability experts from both academia and industry.
Leveraging Blockchain Technology for Artificial Intelligence

Artificial Intelligence (AI) and blockchain are among the most promising and widely deployed technologies in recent times. On the one hand, blockchain can enable interactions among clients without the presence of any trusted third party and provide access to a blockchain ledger that holds transactions, logs, and data in a decentralized, trusted, and secure manner. On the other hand, AI facilitates making intelligent decisions by observing the environment. This chapter presents an overview of integrating blockchain with AI technology, covering the importance, applications, challenges, and potential open research issues.
On the Іssue of Beauty Perception in Onthogenesis

Developmental specifics of beauty sense in ontogenesis are analyzed in the article. It is demonstrated that mature formation of beauty sense and aesthetic activity relates to early adolescence. Ability to individual aspects of aesthetic activity can be noted on earlier stages of ontogenesis (A.V. Zaporozhets, Z.N. Novlianskaja, N.V. Papucha and others). However attitude to beauty is not realized clearly at the moment. Aesthetic activity in the strictly proper sense of the term can be analyzed when image dynamics shifts from encompassing involuntary changes of ideal images to free image management as well as realization of both freedom and contemplation in order to transform it according to “beauty principles”. Aesthetics exists exceptionally in the individual form of development. Therefore mature forms of aesthetic activity (perception or creation of aesthetic products) can be manifested at earlier ontogeny stages, for example, among artistically gifted children. It is demonstrated that behavioral acts of perception of an artistic work, while being visually similar, are phenomena of different psychological nature for different age groups (for example, infants, preschoolers, younger school children, teenagers). Features of fairy-tales and lullabies perception in early ontogenesis are analysed. Perception of fairy-tales in early ontogenesis originally emerges as external, unfolded activity and supposes participation in events which are directly perceived and experienced. Further on this basis similar activity acquires internal nature of assistance, empathy to fairy-tale characters and becomes true in the domain of imagination (A.V. Zaporozhets). Providing help to a fairy-tale character, a child places him/herself into the situation and further operates within its internal logic.
The information retrieval anthology 2021

The Information Retrieval Anthology, IR Anthology for short, is an endeavor to create a comprehensive collection of metadata and full texts of IR-related publications. We report on its first release, the use cases it can serve, as well as the challenges lying ahead to develop it towards a resource that serves the IR community for years to come. The IR Anthology's metadata browser and full text search engine are available at IR.webis.de.
Model Evasion Attacks Against Partially Encrypted Deep Neural Networks in Isolated Execution Environment

It is important to hide DNN models from adversaries not only for protecting intellectual property but also for preventing attacks. Isolated execution environments (IEEs) are necessary to protect the DNN model in an edge device. Conventional studies on DNN inference in IEEs have focused on model confidentiality and not the threat of model evasion attacks. If some of the model parameters or intermediate results are leaked to adversaries, model evasion attacks may occur even if the confidentiality of the model is secured. In this work, we performed attacks against partially encrypted DNN models that are executed on IEEs. In an existing proposal, a feature extractor of the model is executed in a normal world and a classifier is executed in a secure enclave, but there is still a threat that an adversary may perform a gradient-based model evasion attack against a feature extractor. We performed gradient-based model evasion attacks against the feature extractor more efficiently by preparing multiple guide images. Our results clarified that all parameters on the feature map should be kept secret by the parameter encryption. In addition, we consider another risk case where calculated values on the feature extractor are stored in the unencrypted memory and demonstrated the gradient estimation-based model evasion attack by exploiting the intermediate feature maps. Our results indicate that both DNN model parameters and intermediate feature maps should be concealed not only for protecting intellectual property but also for preventing model evasion attacks.