Reviewerly/exHarmony huggingface dataset:
Performance Acceleration of Secure Machine Learning Computations for Edge Applications

Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. That is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend with the original communication backend with the CrypTen framework. The experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.
Indonesia Covid-19 Pandemic Social Media Analysis With Text Mining

This research aimed to analyze public opinion regarding Indonesia's situation in facing community restrictions (PPKM) during the Covid-19 pandemic. This research used the text mining approach to classify public opinion into two classes of the Pro and Cons classes regarding the policies, along with comparing the accuracy, precision, and recall values using two text classification methods of Naive Bayes and Support Vector Machine (SVM). The data collected were 217 tweets from Indonesia in November 2020. The Naive Bayes method showed 64% accuracy, 72% precision, and 53% recall, while the SVM method showed 63% accuracy, 70% precision, and 53 % recall. Based on these classification text methods results, researchers concluded that SVM's accuracy, precision, and recall values were not higher than Naive Bayes.
Object Recognition Using Semantic Segmentation

Object recognition from images or videos is important for detecting the presence of object in them. The analysis of detection/recognition has been increasingly important for security, surveillance, personal archiving or autonomous driving, etc. This paper, presents a method to recognize the scene. In this method, the DeepLab v3+ network with initialized weight using Resnet18 has been used. The CamVid database has been used for detecting various scene form video frame. This database, basically have 701 total RGB frames corresponding to RGB values labeled frames identifies as pixel-wise segmentation labeling. The performance of the model has been proved by using three parameters, accuracy, IoU, and score. Based on these parameters, the model has been analyzed and found out to be ideal for scene recognition from videos frames.
Physiotherapy in the Monitoring of Elderly Diagnosed with Parkinson's Disease

Introduction: Parkinson's disease (PD) is a chronic, progressive and idiopathic condition of the central nervous system, resulting in tonus disturbances, abnormal postures and involuntary movements, where increasing age is the most prevalent factor. Objective: consisted of discussing the role of Physiotherapy in the treatment of elderly people with Parkinson's Disease. Method: narrative literature review, carried out through a bibliographic survey in the health area databases: Latin American and Caribbean Literature on Health Sciences (LILACS), Virtual Health Library (BVS) and Scientific Electronic Library Online (SCIELO). As inclusion criteria for the study, complete scientific articles were collected, published in the health area between 2012 and 2021, in English and Portuguese, covering the proposed topic. Results: it was found that the role of Physiotherapy with Parkinson's patients occurs through physiotherapeutic exercises, such as proprioceptive neuromuscular facilitation, muscle strengthening, exercises on a vibrating platform, physiotherapeutic protocol, dual task, rhythmic training, aquatic physiotherapy, pilates and breathing exercises. Conclusion: the role of Physiotherapy becomes relevant with the elderly with PD, as it makes it possible to delay the onset of functional impairment, improving balance, gait, breathing, body posture, maintaining muscle activity, therefore, reducing the evolution of the disease and its symptomatology, promoting the performance of daily tasks that provide the autonomy of the parkinsonian elderly.
STEM IN THE CONTEXT OF COMPETENCY-BASED APPROACH IN EDUCATION

One of the contemporary approaches in education is related to integration of knowledge from different subject areas. The current article highlights STEM competency – its essence, main components and structure, based on global research and reports from UNESCO, EC and others. Using the results from an inquiry among Bulgarian teachers, an analysis of competencies developed through STEAM, is derived, and presented. There is a growing need for the implementation of such an approach in Bulgarian schools in order to increase students’ interest toward natural sciences and math, while combining theoretical knowledge and practice in the process of development of key competencies.
Towards a comprehensive repair framework for human-chatbot interaction

Chatbots are becoming a regular part of service offerings. However, failures in human-chatbot interactions are common and mitigating them with appropriate strategies is an integral part of the dialogue. For instance, chatbots can be designed to prompt a rephrase, although, due to the complexity of user reactions, this is not always successful. Research has called for taxonomies to categorize user reactions to compute meaningful responses that encourage dialogue continuation. We suggest a framework of strategies based on prior research and test its validity, focusing on how users rephrase across dialogue turns. We find that users rephrase problems formally (49%), by changing the number of words, altering syntax, or using synonyms and to a lesser extent by altering informational value (25%). We suggest training chatbots along this behavior and designing better prompts that guide users' next actions.
Finding appropriate user feedback analysis techniques for multiple data domains

Software products now have more users than ever. This means more people to please, more use-cases to consider, and more requirements to fulfill. These users can then write feedback on software in any number of public or private online repositories. Many tools have been proposed for classifying, embedding, clustering, and characterizing this feedback in aid of generating requirements from it. I am investigating which techniques and machine learning models are most appropriate for enabling these analyses across multiple feedback platforms and data domains.
The Importance of Anomalies and Anomaly Detection

Before we set off on building an anomaly detector, it is important to understand what, specifically, an anomaly is. The first part of this chapter provides us with a basic understanding of anomalies. Then, in the second part of the chapter, we will look at use cases for anomaly detection across a variety of industries. In the third part of the chapter, we enumerate the three classes of anomaly detection that we will return to throughout the book, gaining a high-level understanding of each in preparation for deeper dives later. Finally, we will end this chapter with a few notes on what we should consider when building an anomaly detector of our own.
End-to-End Graph-Constrained Vectorized Floorplan Generation with Panoptic Refinement

The automatic generation of floorplans given user inputs has great potential in architectural design and has recently been explored in the computer vision community. However, the majority of existing methods synthesize floorplans in the format of rasterized images, which are difficult to edit or customize. In this paper, we aim to synthesize floorplans as sequences of 1-D vectors, which eases user interaction and design customization. To generate high fidelity vectorized floorplans, we propose a novel two-stage framework, including a draft stage and a multi-round refining stage. In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence. To polish the initial design and generate more visually appealing floorplans, we further propose a novel panoptic refinement network (PRN) composed of a GCN and a transformer network. The PRN takes the initial generated sequence as input and refines the floorplan design while encouraging the correct room connectivity with our proposed geometric loss. We have conducted extensive experiments on a real-world floorplan dataset, and the results show that our method achieves state-of-the-art performance under different settings and evaluation metrics.
Real-Time Facial Emotion Recognition for Visualization Systems

This project aims to review the most popular deep learning algorithms and their performances in camera systems based on real-time facial emotion recognition and suggest a new model for future applications. Firstly, convolutional neural network (CNN) algorithms that recognize human emotions, such as AlexNet, GoogleNet, and VGG19, are investigated according to their performances. Then, the CNN algorithm with the best numerical performance is chosen for enhancement. After, the new hybrid model is constructed via chosen CNN and long short-term memory (LSTM). Lastly, the proposed model and face images achieved from the camera are combined to simulate real-time application.
A Study on the Effect of Safety Leadership of Team Leader of Special Warfare Forces on Organizational Effectiveness: Focusing on the Mediating Effect of Resilience Capability

The purpose of this study is to ensure that the safety leadership of the Special Warfare Forces is affects organizational effectiveness and the mediating effect of Resilience capability. The results of this study are as follows; First, as studying the effect of safety leadership on organizational effectiveness, it was found that inducing interest and participation in safety among sub-variables of safety leadership had an effect on organizational efficiency. Second, as studying the effect of safety leadership on resilience capability, it was found that safety communication, safety will, and attitudes, safety behavior, and activity, which are sub-variables of safety leadership, affect resilience capability. Third, as studying the effect of resilience capability on organizational effectiveness, it was found that the respond capability among the sub-variables of resilience capability had the most influence on organizational effectiveness. Fourth, as a result of analyzing the mediating effect of Resilience capability in the influence of safety leadership on organizational effectiveness, it was confirmed that all of the variables had mediating effects.
Static Web App CLI

We’ve used the Azure Static Web App CLI (SWA CLI) in an earlier chapter to get started with debugging when running Visual Studio. But there is a lot more that the Static Web App CLI can do!
Copula-Based Outlier Detection (COPOD)

So far, we have looked at clustering-based models for multivariate outlier detection. In this chapter, we will review a novel nonclustering technique that uses a concept called copulas. First, we will define what a copula is and how we can perform outlier detection using it. Then, we will implement application changes to integrate the new technique. After that, we will update our tests and website. Finally, we will wrap up Part III with some concluding notes.
Adaptive MLELM-AE model for efficient prediction of stock market data

AbstractThe stock market makes a mention of public markets that contains buying, issuing, and selling shares which trade on a stock exchange. The aim of stock market is to confer capital to companies that they can utilize for funding and spreading their businesses also to serve investors. But it is elusive to prepare right decision for the companies in particular trading of stocks because of dynamic and intermediate nature of the share price. The charge of funding and commercial enterprise possibilities within the inventory market can boom if an efficient algorithm could be developed to predict the price of an individual stock. There are many deep learning algorithms available in which Extreme learning machine (ELM) is one of the most efficient technique for training single layer feed-forward neural networks (SLFNs). Integrating ELM with auto encoder has gotten another viewpoint for extracting features using unlabeled data. This paper attempts to focus on predicting stock market five days ahead by using a new variant of deep neural network i.e multilayer extreme learning machine with auto encoder (MLELMAE). This model is applied on YES, SBI, and BOI datasets there by the performance of the proposed model is measured and compared with other Deep Learning (DL) techniques like Radial Basis Function Neural Network (RBF), Back Propagation Neural Network (BPNN), and ELM in terms of Mean Absolute error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results also show that the proposed model outperforms best over other DL techniques.Subject Classification: 60G25Keywords: Extreme learning machineDeep learningRadial basis functionBack propagation neural networkMean absolute errorMean square errorRoot mean square error
Text Keyword Extraction Method Combined with Dependent Syntax Analysis

In order to improve the accuracy of key word extraction, an improved TF-IDF method was proposed to solve the problems that traditional TF-IDF keyword extraction algorithm could not recognize new words and polysemous words. This method first TF - IDF values, part of speech of words and position characteristics is calculated respectively, build candidate keywords weighted formula comprehensive score, then stored pretreatment before interdependence syntactic relations from the text, using the established syntactic relations to score higher candidate keywords for combinatorial optimization or reducing weight, the last order form formal extracting keywords. Experimental results show that compared with the traditional TF-IDF method, the multi-feature method based on dependency parsing improves the F value of the dataset containing new words by 10.4%, reaching 60.6%, and the F value of the dataset containing polysemous words by 17.9%, reaching 66.5%, which effectively verifies that the proposed method can improve the accuracy of keyword extraction.
A matching method of oral text to instruction based on word vector

Short oral texts have the characteristics of sparse features and vague expressions, which lead to poor performance when applying matching methods to them. Aiming at these problems, this paper proposes a matching method based on word vector text representation, which comprehensively considers part-of-speech, semantics and word order to map oral text to operation instructions. First, this paper uses the Skip-gram model to train the selected corpus to obtain the word vector representation of the corpus; then uses the cosine similarity to calculate the similarity of the feature words; then, combined with the text characteristics in this scene, use the WMR(Word Matching Rate) to calculate the similarity between short texts; finally, the method in this paper is evaluated on the test set. The results show that the method described in this paper has better precision and recall rate than other methods, and effectively improves the matching of oral text to instruction text.
Basic Statistics: Hypothesis Testing

In Exercises 3.13, 3.17, we have already discussed hypothesis testing in the framework of probability theory. Let us now go deeper into this topic considering the testing (acceptance or rejection) of one hypothesis, called null hypothesis H 0 or, more simply, hypothesis.
Fenites Naberezhne village (Kalmius river, Azov region)

In Eastern Azov (the village of Naberezhne, the left bank of the Kalmiusr river), the rocks, which mainly consists of minerals of the silica group (quartz, chalcedony), oxides and carbonates of Fe, Mn, Ca were found. They contain fluorite, as well as a small amount of acmite and ribeckite and an increased content of REE. These rocks are localized in the field of typical apogranitoid fenites, which are widely distributed in the Azov region. Oxide-carbonate-siliceous rocks are considered as the formation of the upper part of the fenitization column, where SiO2 was removed from fenitized granitoids and Fe, Mn, Ca, CO2, and REЕ, which are characteristic of carbonatites and related fenitizing fluids, were introduced. These oxide-carbonate-siliceous rocks, together with typical apogranitoid fenites, are the criteria for the search for carbonatites with apatite and rare earth mineralization.
Ethical, Social, and Psychological Issues

In this chapter, we discuss the ethical, social, and psychological implications of playing with a robot, in the physical world. Issues like attachment, bullying, and improper use of the device to which some animacy is attributed are reported in literature and in the authors’ experience: they give the opportunity to reflect about social, psychological, and ethical consequences of this new play setting. Measures to limit undesired issues are discussed, together with their implications on design and readiness for the market.
Organisational learning through implementation of knowledge management techniques

Our society is becoming knowledge-based and effective knowledge management is the key to success of any organisation. The purpose of the study is to understand the importance and effectiveness of knowledge management initiatives of academic institutions and future scope of its development by integrating artificial intelligence (AI) taking 'keep learning' of IFIM institutions as a case for reference. Data were collected from the developers of keep learning, IFIM institution to understand the flowchart of the system. The usage feedback/review was assessed across levels of management and users of the system. This case study presents knowledge management not only as a coordinating mechanism but also provides requisite information as how the academic institutions can transform the learning and teaching ecosystem through in the future through effective knowledge management system. The study also considers the role played by knowledge management system of IFIM institution in the current pandemic situation.
Cross-Site Requests

This chapter is about the threats that occur when your site accesses other sites and when your site is accessed from another site. We saw one example in the last chapter, in the exercise “Exploiting a Stored XSS Vulnerability.” Here, you as the attacker were able to exploit a vulnerability and upload malicious JavaScript that sent victims’ cookies to your site.
EDUCATIONAL POTENTIAL OF DIGITAL TECHNOLOGIES IN IMPROVING THE TEACHING PROCESS AT THE UNIVERSITY

The article reveals the essence of the digital transformation of higher education in the Russian Federation, analyzes the positive and problematic aspects of digitalization, the main components of the digitalization strategy, taking into account world and best Russian experience.
Perancangan Sistem Point Of Sale Pada Jelly Potter Ciapus Bogor Berbasis Android

Technology can make work more efficient, data collection of product stocks and transactions at Jelly Potter Ciapus Bogor requires good management. Processing of product stock data and transactions still uses manual methods which causes slow processing of unstructured data. Overcoming the problem of slowness and data accuracy, the Point Of Sale system design for Jelly Potter Ciapus Bogor was based on android, software development aims to facilitate the data collection process. The research method used by the author in making this system is Research and Development (R&amp;D) because this method is considered more effective in reviewing ongoing business processes. In the process of collecting data the author uses several methods of data collection, including: field studies and library methods. The design of this system is done by creating a model that explains each activity in detail using the Unified Modeling Language (UML). The results of the research are expected to be better in the data collection process, data storage is neater, and reporting is easily accessible because it is via mobile.
Digital Literacy in Indonesian Language Learning in Elementary Schools

This researh entitled Digital Literacy in Indonesian Language Learning at Elementary School. This Study aims to analyze how to use of digital litracy in learning Indonesian language at elementary school. This research uses literature study research by reviewing several jurnal articles relate to the title. The result of the research conducted show that digital literacy can be used using several digital media in which learning can be carried out, especially learning Indonesian language at elementray school. So it can be concluded that digital literacy can be done in elementary schools using digital media with the assistance of a teacher or parent.
Research on the Framework of Classroom Interaction Behavior Analysis Based on Artificial Intelligence

Classroom teaching is a system of social activities. As the subject of the activities, teachers, and students are engaged in various interactions, which are called interactions in classroom teaching. Classroom interaction is one of the important factors affecting the teaching effect. It shows in every part of the classroom teaching process and forms a good interaction between each other around the realization of education and teaching objectives. Under the background of educational informatization, using information technology to observe and analyze classroom teaching behavior can promote the development of teaching activities, improve the efficiency of classroom evaluation and teaching quality. This paper starts with the interaction subject and the interactive media to carry out the analysis framework of classroom interaction behavior. In classroom teaching, there are two main bodies directly involved, namely teachers and students. The medium of interaction between subjects is audio or action. Artificial intelligence technology is combined with classroom interaction analysis to classify classroom interaction types by using audio and video information in classroom teaching. The basic interactive classification of classroom teaching can be obtained by combining the two types of subjects with different audio or action.
An Introduction to Multi-series Anomaly Detection

Chapters 13 and 14 dealt with one form of time series anomaly detection: the single time series. With that understanding in place, we will extend our focus to situations in which there are multiple series of data that we want to analyze in conjunction rather than independently. In this chapter, we will formalize our understanding of multi-series time series, focusing on how it differs from the single series approach we took earlier. Then, we will look at four common techniques for analyzing multi-series data. Before we wrap up this chapter, we will review some common problems when trying to analyze multi-series data before we implement one algorithm in Chapter 16 .