Reviewerly/exHarmony huggingface dataset:
Rakshak—Prototype Application to Depict Crime Hotspot and Safest Path Between Locations

With every passing day, there is a rise in crime that adversely affects the growth of society. The density and intensity of crimes vary with the region in any city. Technological advancement can be made useful to make people aware of dangerous locations. In this work, we have built a model to visualize the crime-prone regions between the two locations provided by the user and plot the safe route between them. An algorithm to identify the crime hotspots and the safe route is proposed. The real-world datasets of Toronto in Canada and Indore in India are considered. The proposed model comprises data collection of the crime of these cities, data analysis to delineate the crime hotspots in the region, and the safest route between places. Visualizing the outcomes using a heatmap makes people aware of risky locations and further helps in reducing the crime count of the city.
Exploring the use of virtual reality in co-reviewing designs

Virtual reality (VR) is an upcoming technology that is increasingly used in design environments. In a design process, designers often work together through co-creation. The next step, one that is often overlooked, is co-reviewing in VR. Virtual reality has potential as a valuable decisive step in the creation process, replacing a traditional tool. One tool in the traditional design reviewing processes is the trade-off analysis. In VR, concepts can be reviewed in different sizes and through various perspectives, the products are perceived as more tangible and true-to-scale. To obtain a better view on the use of VR in co-reviewing, a comparison of a traditional method with an immersive method is made in this paper. The participants are eight industrial design master students which did the experiment in pairs of two. The results show that VR offers advantages for reviewing ergonomics of a design, in which traditional 2D screen-based software is more limited in comparison.
Fault Diagnosis of Sewage Treatment Equipment Based on Feature Selection

There are many factors that affect the operation state in the wastewater treatment process. Generally, the probability of failure is much less than the probability of normal operation. Fault diagnosis of wastewater treatment is a high-dimensional unbalanced data classification. In this study, we propose a feature selection-based method to improve the classification performance of wastewater treatment fault diagnosis. Two filter-based feature selection methods and one wrapper-based feature selection method were used for experiments. Three classifiers of C4.5, Naive Bayes, and RBF-SVM were used to evaluate the proposed method. Experimental results show that the proposed method can significantly improve the overall classification accuracy and AUC value on the wastewater treatment fault diagnosis dataset.
A Comprehensive Study on Multi Document Text Summarization for Bengali Language

Automatic text summarization is a useful and needed approach in which a small subset of text is extracted concisely and pertinently from large text documents where the extracted sentences may have significant and notable meaning com-pared to other sentences in the document. Although there have been a lot of approaches to English text summarization, very few works have been found in the literature on automatic Bengali text summarization. Our work focuses on multi-text summarization tasks based on data mining and some statistical approaches which primarily employ the method on Bengali text documents as a basis for summarization. We used a hybrid approach for extracting the most significant word during tokenization and used some statistical methods to rearrange the sentences. The TextRank algorithm is used to pick the top few sentences from the processed text as the summary and finally we compared and evaluated our model with benchmark standard summary text generated by a group of human contributors. Our proposed hybrid model generates an average of 0.66 Precision, 0.59 Recall and 0.62 F-Score which indicates that our model can be used as an alternative system to address multi-text summarization problems of Bengali text documents.
Oral examinations of the student during distance physics education

Many unusual situations have been brought to schools because of distance education. Teachers face now an even more challenging task of assessing and evaluating their students. According to the methodical instructions for the assessment and evaluation of primary and secondary school students, the student is examined orally, in writing or in practice. Due to the fact that correcting students` written outputs is more difficult for teachers during distance education than usual, the student’s oral output could be chosen for assessment or evaluation. However, the results of the survey, which was conducted with physics teachers, showed that even during non-distance education, the oral examination is used by most of the teachers only once every term. The most common reason given by teachers was the time required for the oral examination. We believe that a silent video task might be a suitable mean of carrying out and assessing oral examination. It is a short video that lasts approximately one to two minutes and the task for student is to prepare and record the voice-over to the video. The paper contains a definition of the silent video task, the possibilities of its use in distance education of physics and the results of a survey focused on oral examination conducted with physics teachers.
A Computerised Goal Model of Quality of Life of People Living with Dementia

Maintaining quality of life is important to people living with dementia, however few digital technologies are available to support planning in order to maintain quality of life. This paper reports new research that draws on existing social care frameworks to model common quality of life goal types with sufficient precision to enable algorithms to reason about these goal types as part of one new digital technology. A set of types of goal for quality of life extracted from these frameworks was modelled using a formal language from software engineering to produce a preliminary version of a model of quality of life with dementia. A physical version of the model was then validated and refined in eight workshops by experienced professional care workers. The validated model was expressed using IBM Controlled English language and computerised in a reasoning engine called the CE-store. This computerised model was a central element of a digital tool that was demonstrated to support older people, including people with dementia, to make decisions about activities that contribute to their preferences for quality of life.
Dimensionality Reduction Using Convolutional Autoencoders

A dataset may consist of hundreds, thousands, or millions of features which represent the whole data. Greater the number of features in the dataset, higher will be the complexity of data analysis process, thereby increasing the time and space complexity of the algorithm. One of the possible solutions to reduce the complexity of analysis process is to use dimensionality reduction technique which helps in minimizing the complexity of an algorithm. Dimensionality reduction is an essential activity performed prior to any data analysis process to reduce number of features from the dataset. In this experimental study, convolutional autoencoder has been implemented to study the impact of kernel size and activation function on the accuracy of algorithm. From the experimental results, it can be concluded that (3 * 3) is the best choice for kernel size and PReLU is best suitable for activation function used in the convolutional layers.
Detection of Seismic Events by Combined Horizontal and Vertical Permanent Das Arrays at Curtin University Campus

Summary Curtin University installed a DAS array consisting of an instrumented 900 m deep borehole along with 1.2 km of dark fibre and 300 m of helically wound cable buried in a shallow trench both along the surface. This array was used to characterise the seismic signature of both natural events as well as human activity. The data showed sufficient signal-to-noise ratio to detect many events, including earthquakes, traffic noise, ocean swell, thunderstorms and anthropogenic signals.
Trust-Aware Location Recommendation in Location-Based Social Networks: A Graph-Based Approach

With the increase in the use of mobile devices having location-related capabilities, the use of Location-Based Social Networks (LBSN) has also increased, allowing users to share location-embedded information with other users in the social network. By leveraging check-in activities provided by LBSNs, personalized recommendation systems can be built.Trust is an important concept in social networks to improve recommendation accuracy. In this work, we develop a method to predict trust scores of LBSN users and propose a recommendation technique, TLoRW, to recommend locations to users based on their previous check-ins, the social network, and predicted trust scores of users. In the proposed model, global trust scores of users are generated on the basis of their check-in histories. Spatial context lies in the hearth of TLoRW to generate location recommendations based on the current location of a user. The proposed algorithm runs on a contextual subgraph rather full graph, relaxing the computing resource requirement. We represent a given LBSN with a undirected graph model. Recommendation scores of the locations are generated as the result of the random walk performed on the generated graph. A comprehensive evaluation of TLoRW is conducted by comparing its recommendation performance to baseline techniques as well as state-of-the-art trust-aware recommendation approaches in the literature based on benchmark datasets. The experiments reveal that the trust information incorporated into random-walk-based approach improves the accuracy of the recommended locations @5 by minimum 5%.
Personal Data Circulation Model Based on Blockchain Technology

At present, major technology companies are applying user data for different commercial purposes. These companies use users’ personal data to obtain revenue, but individuals who own the data cannot obtain any revenue from it, even in the circulation of data. Blockchain technology benefits from the use of asymmetric cryptography key pairs in cryptography to prevent data from being copied at will, protect user privacy, and enable the circulation of personal data among various commercial entities. Individuals can obtain benefits in the process of their own data circulation. With the promulgation of the Personal Information Protection Law, the author proposes a path to realize the value of personal data based on blockchain technology. Big data holders, personal data account banks, personal data regulatory agencies and other parties are supported by blockchain technology, which can be chained to share the economic results of data together.
Usability Evaluation of MOSES (Monitoring and Operating System for Emergency Services Tablet)

MOSES Tablet is a device specifically made for Filipinos to help prepare them for climactic disasters through its software applications. Currently in prototype stage, the tablet is not yet tested for usability. This study focused on the usability evaluation of the tablet before its deployment to different barangays all over the Philippines. Five officials from different barangays were asked to undergo the usability test. MOSES applications received high ratings on effectiveness and overall look and impression, but varied ratings on learnability and ease of use. NOAH and Arko are the top 2 desired applications by most test subjects, but they received the lowest ratings on ease of use. For NOAH, 3 of 5 test subjects had difficulty choosing the right option when checking a specific type of weather information. Most users agreed that performing tasks with Arko was difficult and more complicated than NOAH. Distinguishing between the functions of the two applications is quite difficult for one test subject. For other applications, issues about unrecognizable icons, illegible font size and hard to perceive message alerts were observed, among others.Given the findings from the study, it can be concluded that usability evaluation is indeed necessary in the development of MOSES tablet. Issues and problems that users encountered were revealed and thus could help in improving its design for the users.
Blockchain Based Peer-to-Peer Power Trading Platforms

This electronic document is about Peer to Peer Power Trading Platform built on top of Blockchain technology.
YouTube Data Analysis &amp;amp; Prediction of Views and Categories

There's enormous growth and fashionability of YouTube. It has all implicit to move billion of lives encyclopedically as the number of observers is growing day by day. Nearly billions of vids are watched on YouTube every single day, generating a huge quantum of data daily. YouTube data is actually in unshaped form, so there's great demand to store the data, process the data and assaying the data. This analysis will help in discovering how people are performing on YouTube, one can fluently identify what content works best on YouTube. The primary purpose of this design is to find how real time data can be anatomized to get the rearmost analysis and trends in YouTube. The analysis is done using stoner features similar as views, commentary, markers, likes, and dislikes. Analysis can be performed using algorithms like direct retrogression, bracket and other machine literacy models and python libraries like pandas, Matplotlib library to classify the YouTube data and gain useful information.
Multi-authority Decentralized Attribute-Based Authorization Framework

The advancement in collaborative work among multiple institutions results in tremendous data sharing and, at the same time raises concerns about data security and privacy. Current systems are mostly built upon a centralized model, so the system requires one authority to concede another authority or mitigate trust to a trusted third party. In this paper, we propose a multi-authority decentralized attribute-based authorization framework illustrated in an eHealth scenario that provides resilience and transparency, enforces data privacy, and allows each authority to enforce their access policy on the shared data. The framework operates on Blockchain technology and multi-authority attribute-based encryption. Our framework protects against malicious single authority who aims to grant permission to users to the shared data. Moreover, we optimize the user key structure that could reduce the key size of the key set by a factor of up to n! where n is the number of attributes in the user key to eliminate brute-force attacks. We implement the framework, show the system performance, and prove the efficiency of our proposed framework.
Software Architecture during Release Planning

This research paper will throw light on the design and implementation of how software architects are involved in release planning of industry and how issues are tackled during this time. Release planning basically deals with the inclusion of products in future release. The intricacy of investors guarantees the application of results. The prescribed method of release planning is referred to redirect versatile categories and it will help in an organized way. Moreover, difficulties related to danger, personal controls, structures, money or technical needs can be easily implemented into the release planning. Release planning is also referred to as new embodiment of a growing product. Though, the idea of a release is not limited to this but can be functional to any kind of intruded progress where a release planning relates a time period. An experienced based planning procedure is not able to justify size, complication, ambiguity, problems and such plans leaves a customer with discontentment which can result in the loss of time, budget and market share.
Using Events and Forms

In this chapter, I continue describing the basic Angular functionality, focusing on features that respond to user interaction. I explain how to create event bindings and how to use two-way bindings to manage the flow of data between the model and the template. One of the main forms of user interaction in a web application is the use of HTML forms, and I explain how event bindings and two-way data bindings are used to support them and validate the content that the user provides. Table 12-1 puts events and forms in context.
APPLICATION OF THE CHECK-IN SYSTEM IN THE EDUCATIONAL PROCESS

The practical use of rapid response QR codes and mobile devices in the educational process is considered on the example of a Financial University under the Government of the Russian Federation. The model of the organization of accounting for students' attendance at educational events (lectures, seminars, exams, tests, etc.) using the Check-in system created by the authors based on Google tools and Microsoft OneDrive cloud access tools is presented. The requirements are considered and methodological approaches to the formation of this system and its functioning within the framework of digitalization of the educational process and optimization of the process of student registration are given. The proposed application of the Check-in system allows you to quickly and conveniently record the presence or absence of a student at a particular educational process by scanning a QR code and displaying the necessary data in the appropriate electronic journals. By applying this system of accounting for students, the teacher significantly reduces or does not waste time on conducting a survey at all, if this is required by the procedure of an educational event. Some economic indicators characterizing the use of the Check-in system are given.
Comparative Analysis of Environmental Internet of Things (IoT) and Its Techniques to Improve Profit Margin in a Small Business

The Internet of Things (IoT) is a computer concept in which common items are enhanced with computational and wireless communication capabilities, generally through the inclusion of resource-constrained components such as sensors and actuators that allow them to access the internet. The Internet of Things (IoT) is regarded as a critical component in the implementation of intelligent environments. Nonetheless, the present IoT ecosystem provides a plethora of different connectivity options with varying performance characteristics. This circumstance makes determining the best IoT connectivity solution for a certain intelligent environment extremely difficult. In this article, we look at the specific requirements of major smart settings, such as the home automation, smart healthcare, smart urbanization, and advanced manufacturing, and compare them to modern Iot communication solutions. We define the key features of these smart settings before providing a detailed assessment of applicable IoT communication technologies and systems. The Internet of Things (IoT) has the potential to change organizations by automating operations ranging from inventory management to robotics and automation, therefore resulting in cost savings. Can, meanwhile, tiny businesses profit from IoT? This study investigated the increasing importance of the Internet of Things (IoT) in small companies, its influence on their capacity to engage in a fast-changing digital world, and their awareness, attitudes, opinions, and desire to embrace it. An initial exploratory method is used in the research, which is predicated on a study of instance studies in the literature, interviews with many economic development employees, and several small and medium-sized business executives. The adoption of IoT can lead to greater operational efficiency and cost savings in enterprises. These advantages have been confirmed by the medium-sized firms surveyed.
Analysis on the Application of Computer Intelligence System in College Physical Education

In the process of physical education teaching in Colleges and universities, teachers should actively introduce new teaching methods, and constantly improve the quality of curriculum education on the basis of enhancing students’ interest in learning. At present, distance teaching and multimedia teaching have been widely used in the teaching of various disciplines, intelligent teaching method is generally recognized by teachers and students, and can improve students’ interest in learning, but it is still in the development stage. In view of the shortcomings of current physical education and computer intelligent system, according to the dynamic characteristics of physical education, this paper puts forward a design of intelligent physical education teaching system. In the application, we can find that the design of knowledge-based intelligent physical education teaching system is flexible, teaching is not limited by time and place, and can meet the needs of students in different situations.
Pinnhypo: Hypocenter Localization Using Physics Informed Neural Networks

Summary Many industrial activities needed to sustain human society have the potential to induce earthquakes. With the increasing availability of data and computational resources, researchers have started to exploit the capabilities of machine learning algorithms to detect, locate, and interpret seismic events. For hypocenter localization, typically a convolutional neural network (CNN) is trained in a supervised manner using a historical or synthetically generated dataset. However, this approach often requires a huge amount of labeled data that may not be readily available. Therefore, we propose a hypocenter location method based on the emerging paradigm of physics-informed neural networks (PINNs). Using observed P-wave arrival times for an event, we train a neural network by minimizing a loss function given by the misfit of observed and predicted traveltimes, and the residual of the eikonal equation. The hypocenter location is then obtained by finding the location of the minimum traveltime in the computational domain. Through synthetic tests, we show the efficacy of the proposed method in obtaining robust hypocenter locations, even in the presence of sparse traveltime observations. This is due to the use of the eikonal residual term in the loss function that acts as a physics-informed regularizer.
Deep Autoencoders for Anomaly Detection in Textured Images Using CW-SSIM

Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects introduce changes in the normal pattern. We address the anomaly detection problem by training a deep autoencoder, and we show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images compared to traditional autoencoder loss functions. Our experiments on well-known anomaly detection benchmarks show that a simple model trained with this loss function can achieve comparable or superior performance to state-of-the-art methods leveraging deeper, larger and more computationally demanding neural networks.