Reviewerly/exHarmony huggingface dataset:
TensorFlow-based smart home using semi-supervised deep learning with context-awareness

In recent years, Smart homes play a significant role in improving the quality of human life due to the rapid proliferation of the Internet of Things (IoT) technology. The previous research works on the smart home system have adopted the machine learning and deep learning algorithms to predict the sequential activities in the smart home. This work presents a model of SMART home automation with Context-Awareness using Stacked AutoEncoder (SAE) -Long Short-Term Memory (LSTM) in TensorFlow (SMART-CAST). The SMART-CAST approach comprises three main processes, including the integration of internal and external home data, SAE-assisted unsupervised learning, and LSTM with back propagation-assisted supervised learning. By inter-linking the spatial and temporal attribute-values, the SMART-CAST unifies the smart home and weather data for facilitating decision-making. It employs the SAE to generate the compressed representation of the unified smart home data from the unlabeled information. In consequence, the SMART-CAST approach applies the LSTM with the extracted compressed representation for learning the labeled data and updates the weight of the LSTM through backpropagation to predict the sequential activities in the smart home system. To further improve the decision-making performance, the experimental model executes the proposed semi-supervised deep learning algorithm in the TensorFlow deep learning framework.
Arithmetic Software Libraries

In Chapter 9, Arithmetic Software Libraries, Victor Shoup provides a peek under the hood of NTL, a software library for doing number theory, as well as its relation to a few other related software libraries. NTL supports data structures and arithmetic operations manipulating signed, arbitrary-length integers, as well as extensions dealing with vectors, matrices, finite fields and polynomials over the integers. These mathematical objects are essential building blocks for any efficient cryptologic implementation. As Shoup explains, he started development of NTL around 1990 in order to implement novel number-theoretic algorithms and determine at what point theoretical, asymptotic gains turned practical. NTL has become an essential tool for number-theoretic cryptanalysis to answer how well algorithms perform in practice and contains several algorithms' lattice-basis reduction, including LLL. As explained in Chapter 9, NTL might have been initially based on FreeLIP, but it continues to evolve to make best use of GMP and modern hardware to provide cutting-edge performance.
High-risk AI systems in the EU Commission’s Proposal for an AI Act

On April 21, 2021, the EU Commission published a comprehensive proposal for an AI Act, which is intended to be the world s first set of regulations to create the legal framework for the development, use and sale of AI systems. The focus of the proposal is on the regulation of so-called high-risk AI systems. The article elaborates the obligations of providers, importers, traders and users of high-risk AI systems. These are part of IT and product safety law and - even if not explicitly addressed in the proposal - have an impact on contract law and tort law, which the article also illuminates. It is consequent that the Commission holds both providers and users responsible for subjecting high-risk AI systems to monitoring and security obligations throughout their entire life cycle. The requirements and obligations of the Proposed AI Act can ensure an increased level of legal certainty with regard to the use of AI systems in civil law. Nevertheless, the Proposed AI Act also reveals considerable weaknesses. First of all, the fuzzy definition of AI systems, which also includes software that does not pose any AI-specific risks, should be emphasized. Furthermore, the security requirements of the Proposed AI Act are partly too vague or difficult to implement in practice, which is particularly clear from the requirement for complete and error-free data sets and the retailer s inspection obligations. The relief for small and medium-sized companies is still too vague. In its current form, the Proposed AI Act could therefore ensure that Europe as an AI location is extremely cautious in developing and using AI. With a view to the wide range of applications of AI, it can be assumed that the AI Regulation will have just as much of an impact on the European and global market as the GDPR.
Intelligent Scalable Algorithm for Resource Efficiency in Cloud

Computing primarily based on the network sharing assets is known as cloud computing. Cloud computing is the quickest developing science in today’s technology supplying ubiquitous offerings to users. Nowadays not everyone is prepared to buy the units that supply the service. Customers instead buy the offerings supplied by devices at large datacenters. The infrastructure of pay-per-use is distinctly in demand. Cloud computing allows easy and on-demand services with a shared pool of computing sources that need to be managed. Even though the scheduling difficulty is an old topic, nearly all preceding work focuses on maximizing the overall performance through the scheduling process. The algorithms developed this way are now not appropriate for real-time embedded applications, in which the predominant goal is to reduce the electricity consumption of the device under tight overall performance constraints. This chapter showcases an energy-efficient hierarchical task scheduling algorithm for cloud to decrease the electricity consumption of the cellular nodes. The job is rescheduled when the node strikes past the transmission range. The overall performance is estimated primarily based on the network performance and packet shipping ratio based totally on nodes and flows. The overall performance metrics are analyzed with the use of an NS-2 simulator.
Participatory Socio-Environmental Systems Modeling over Knowledge Graphs

By considering the dynamicity and complexity that is present in modern Socio-Environmental Systems along with the misalignment in the usage of terms that are used by scientists in different disciplines, there is a need to support new ways of modelling and analysis based on the establishment of synergies and the collaboration among scientists. To address this challenge, we present a novel paradigm for modeling of Socio-Environmental Systems that aims to enable interdisciplinary scientists to realise participatory, reproducible and easily extensible modelling and analysis, across different temporal and spatial scales. To conceptualize the overall paradigm, emerging technologies for knowledge management and analysis are exploited, such as Knowledge Graphs and Machine Learning techniques. Knowledge Graphs are used as a variant of a semantic network, where constraints, structural elements and characteristics of nodes and links are continuously evolving. Machine Learning techniques are used for populating the Knowledge Graph with different types of data, as well as supporting analysis and inference processes over the produced knowledge. A proof of concept scenario has been implemented, focusing on the tracking of indicators specified in the Sustainable Development Goals.
A STUDY ON THE SENTIMENT ANALYSIS (POSITIVE, NEGATIVE) OF WORDS APPEARING IN KYRGYZ NEWS BY APPLYING THE DEEP LEARNING-BASED NLP (NATURAL LANGUAGE PROCESSING) TECHNIQUES FOR STUDENTS PRACTICE

This study is theoretical on the sentiment analysis field of deep learning-based natural language processing, which is the world's advanced technology, namely data collection and preprocessing stage, tokenizing stage, Sentiment Dictionary construction stage, positive and negative word extraction stage through sentiment analysis, deep learning introduces major contents and related technologies such as model configuration, execution stage, and data visualization stage. In addition, speech processing technology performed in the data collection stage, STT (Speech to Text) and TTS (Text to Speech) technology will be introduced. In this study, a program was written using various open sources (libraries) including ’keras’ 2.0 version used in the deep learning natural language processing field of the python language base. This study is executed to help students who are studying deep learning natural language processing.
Messengers from the High-Energy Universe

Compute the number of crossings that a particle must do in each of the mechanisms to gain a factor 10 on its initial energy assuming:
The Role of E-learning in Improving Education Quality and Reducing Education Fees during the Pandemic of the COVID- 19

E-Learning is not a new phenomenon after the prevalence of Internet networks, not the total alternative to universities and educational institutions before the prevalence of Kovid 19, and after the importance of e-learning has emerged in front of universities and educational institutions. Imam Jaafar Al Sadiq University (P) Salah Eddin Branch is one of the educational institutions that govern ministries and government institutions with professional, medical and accounting cadres, Arabic, English and English and the law faced by high efficiency by applying an e-learning experience during the second semester.
Text Summarization Using Extractive Techniques

Text summarization is an indirect way of contracting the given content into more simple words while preserving its information and importance. It is hard for individuals to sum up huge reports physically. A text summarization strategy mainly consists of removing unwanted repeated matter and making it as precise as possible without losing its core concept. In this venture, we have applied three extractive outline strategies - to be specific: term frequency inward document frequency (TF-IDF), text rank calculation, and latent semantic analysis (LSA). In this work, an exhaustive survey of extractive book synopsis measure strategies has been determined. The ROUGE-N metric has been utilized to break down and assess the resulting synopsis of the first report. Text summarization reduces study time. It can be used in question-answering systems, as it provides personalized information. For certain reports, summarization makes the procedure simpler. Summarizing by the text summarizer is less biased than the human summarizer. It also empowers business theoretical administrations to build the number of content archives they can process. In this chapter, the different methods and difficulties of extractive outline have been surveyed. This chapter interprets extractive text summarization methods with a less redundant summary, highly adhesive, coherent, and depth information. We have found a moderate methodology for the component extraction of sentences.
Impact of Higher Education Expansion on the Urban-Rural Income Gap in China: An Empirical Analysis based on Provincial Panel Data

With selected provincial panel data from 2004-2018 and a fixed-effects model, this paper conducts an empirical study on the impact of higher education expansion on the urban-rural income gap in China, and examines the heterogeneity between the Eastern, Central and Western regions. Those empirical findings show that higher education expansion significantly reduces the urban-rural income gap. In addition, factors including the upgrading industrial structure, increased openness, higher investment in education, and stronger government support for agriculture also significantly reduce the gap. Finally, the results reached by this paper are robust, proven by replacing the explaining variables and robustness tests on those explained variables. Therefore, prioritizing higher education is an important policy option to help reduce the urban-rural income gap and increase awareness of higher education in rural areas.
Aircraft Parameter Estimation Using Gaussian Sum Information Filter with Lyapunov Stability Analysis

Accurate estimation of aerodynamic parameters of an aircraft is a matter of great importance in aviation industry as well in aircraft design and development programs. In this paper, Gaussian sum extended information filter (GSEIF) is studied for aircraft parameter estimation. The convergence condition for the GSEIF has been obtained using the normalized Lyapunov energy functional by proposing a nonlinear observer for the filter and studying its asymptotic behavior, this is a novel approach for deriving stability results for corresponding nonlinear filter. The performance of extended information filter (EIF) and GSEIF is evaluated for aircraft parameter estimation using simulated flight data generated using the MATLAB implementations. The estimation results using GSEIF are better than that obtained using EIF, especially when the initial state is not known (well), which shows that the proposed technique is really feasible for aerodynamic parameter estimation.
Transparent and Explainable ML

The successful deployment of ML methods depends on their transparency (or explainability). We refer to techniques that aim at making ML method transparent (or explainable) as explainable ML. Providing explanations for the predictions of a ML method is particulary important when these predictions inform decision making [1]. Explanations for automated decision making system have become a legal requirement [2].
Administrative Transparency and its Impact on Reducing Levels of Job Alienation: An Analytical Research of the Opinions of a Sample of Workers in Al-Muthanna Governmental Cement Plant

The research aims at testing the relationship between administrative transparency as an independent variable in the level of organizational alienation of the employees of the studied organization as a dependent variable through the study and analysis of the opinions of a sample of workers in the Muthanna Cement Plant. For the purpose of identifying statistical differences between both research variables and reaching a set of conclusions and drafting recommendations, a questionnaire was drafted that included 33 items and collected data from 245 respondents from the various departments representing the study community and then collecting and analyzing the data and test the hypotheses using SPSS software through the use of descriptive statistic methods (percentages, arithmetic means, variance, materiality, simple correlation coefficient, simple linear regression). The research found that the existence of a relationship, an association and an influence with high p-values of administrative transparency in reducing the levels of organizational alienation and creating a climate of work through publicity in procedures and reasons for making decisions and issuing clear instructions will contribute in reducing the culture of confidentiality and monopoly of information. The most important thing recommended by the research is the need for senior management to identify the most important practical concepts that are needed to be clarified for working individuals in order to reduce administrative routine and clarify decisions and instructions and discuss them with all administrative levels in the organization and that could possibly contribute in enhancing the levels of employees’ job satisfaction in a way that can directly affect to reduce their levels of organizational alienation.
Self-paced learning with multigranularity metric for one example person re-identification

Person re-identification (re-ID) is an important topic in computer vision. We study the one-example re-ID task, where each identity has only one labeled example along with many unlabeled examples. In practice, for the unlabeled data, it is difficult to differentiate each person because of many conditions, such as low resolutions, occlusions, and lighting. In previous works, fine-grained information has been proven to be useful for supervised re-ID. To solve choosing a reliable, easy sample for self-paced learning, we exploit fine-grained features to metric the distances between labeled data and unlabeled data, the combination strategy of global and overlapping-part distance selecting more positive data for model training. In addition, the attention mechanism has been introduced to suppress the interruption of background. The training data are split into three parts, i.e., labeled data, pseudolabeled data, and instance-labeled data. First, the model is initialized by one-shot data for each identity. Then, pseudolabels are estimated from the unlabeled data and updating the model iteratively. The self-paced progressive sampling method is adopted to increase the number of the selected pseudolabeled candidates step by step. Notably, with pretrained model, on Market-1501, the rank-1 accuracy of our method is 86.0% which exceeds most other methods, experiment on two image-based datasets demonstrate promising results under one example re-ID setting.
A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers

The wide use of smartphones and later smartwatches equipped with a set of sensors such as location, motion, and direction blaze the trail for researchers to better recognize human activity. However, researches on using inertial or motion sensors (i.e., accelerometer, gyroscope) for human activity recognition (HAR) has intensified and reside a great confrontation to be faced. Lately, many deep learning methods have been suggested to improve the human activity classification and discrimination performance to reach an optimal accuracy. Therefore, this paper applies a Convolutional eXtreme Gradient Boosting (ConvXGBoost), which combines Convolutional Neural Network (CNN) represented by AlexNet to learn the input features automatically, followed by XGBoost decision tree used to predict the class label and thereof recognize the performed activity. Human activities are collected from sensors as time series data. Therefore, we suggested using one-dimensional AlexNet (1D AlexNet) model instead of 2D. The AlexNet model is compiled with two optimizers Adam and Stochastic Gradient Descent (SGD) which are applied consecutively. The suggested architecture was trained and evaluated on the “WISDM Smartphone and Smartwatch Activity and Biometric Dataset” that consists of raw data for eighteen activities recorded from phone and watch. The experiments revealed that using multi optimizer with a convolutional neural network improved the accuracy of recognition by 5%. Moreover, a proposed ConvXGBoost model outperformed the performance of other models works with the dataset as mentioned above with an overall accuracy of 98-99% depends on the device used.
A Study of Moving Vehicle Detection and Tracking Through Smart Surveillance System

The real-time video surveillance system has become an integral part of transportation system. For a transportation system, it also prevents accident along with various challenging issues on highways. Various machine learning- and artificial intelligence-based techniques are evolving with numerous advancement to handle such issues through smart surveillance system. In literature, various researchers have contributes numerous methods for vehicle detection and tracking. These methods are efficient and time consuming. This paper work has focused on the study of various methods, publicly available datasets, and recent AI- and ML-based techniques which may be suitable for future perspectives. This study focuses on various metrics such as speed measure, vehicle counting, feature extraction, detecting driver behavior, traffic flow prediction, surveillance, etc. This paper also demonstrates for existing research for the deep learning-, ML- and AI-based methodologies to resolve more complex problems.
High-speed secure key distribution based on chaos synchronization and optical frequency comb technology

Secure key distribution is one of the most crucial issues for the security of communication systems. However, it is extremely challenging to realize the gigabit key distribution required for high-speed secure communication. Targeting high-speed secure key distribution, we propose a novel scheme based on chaos synchronization and optical frequency comb technology. By the multi-channel multiplexing, final security key rate is greatly extended to gigabit. The feasibility and performance of the proposed high-speed secure key distribution scheme are verified by numerical simulations.
Current Account Balances, External Debts and Exchange Rates in Selected African Countries

Most African countries had been bedeviled by the problem of deficits in their current account balances (World Bank, 2020). In a bid to overcome this challenge, they usually resorted to contracting external debts from institutions and countries that required them to repay these loans in the currencies of the creditors. Thus, the rate of exchange between the creditors and debtors should be of concern. Several studies have been carried out to examine the relationship between current account balances, external debts and the exchange rates. Some studies found a unidirectional relationship among these variables. In some other studies, the relationship knitting these variables was bi-directional. In most of these studies, the methods of analysis ranged from the ordinary least squares, panel data analysis and the vector error correction model. This study fills the gap in the literature by examining the relationship across these variables making use of the generalized methods of moments (GMM). The GMM estimation technique will correct for unobserved heterogeneity, endogeneity and measurement errors which were observed in the data. The study found that a 9.46% increase in external debts leads to a percentage increase in current account balances in the selected African countries. Also, there is a positive and significant relationship between the exchange rates and the current account balances in the group.
Linguistics Abstracts Online

Linguistics Abstracts Online is an indexing and abstracting database that is part of the EBSCO suite of products. It indexes over 800 journals in linguists and related disciplines, with the bulk of coverage beginning in 1985, and currently contains more than 768,000 records.
“Gaisi Takeuti’s Finitist Standpoint” and Its Mathematical Embodiment

Gaisi Takeuti’s mathematical achievements in consistency proofs of subsystems of second order arithmetic were always lined by his thoughts on the finitist standpoint. Takeuti’s insistence on the finitist standpoint originates in the requirement from the consistency proof per se. In its essence, it is to show the termination of a decreasing sequence from an order structure used in a consistency proof as clearly as possible. Takeuti has left many writings on the finitist standpoint. Some of them will be introduced and closely studied. Then a way of giving a mathematical form to Takeuti’s finitist standpoint will be proposed. This is done by means of a semi-formal theory of generalized functionals. One can explicitly present a functional which, given any decreasing sequence from the order structure in concern, evaluates the terminating point of the sequence.
Cache Data with Service Workers

Service workers are used to cache data responses. So far, you have seen how to create a new Angular application, configure the application to be installable, and cache the application so that it is accessible even when offline. This chapter introduces how to cache a data response from an HTTP service.
Geoscan

Managed by the Government of Canada Publications, GEOSCAN is an Open Access database that collates the Earth Sciences publications of the Natural Resources Canada (NRCAN). Included are almost 90,000 records of Mining, Materials and Energy, Geological Survey of Canada publications, Topographic Maps, Remote Sensing, and Geodesy publications. GEOSCAN offers integrated access services to researchers and students while preserving and disseminating knowledge stemming from Natural Resources Canada's published research and information. It classifies, organizes and disseminates knowledge, providing access to NRCan's publications on topics related to sustainable development, mineral and energy resources and landmass. The database is managed by the Information Management Division of Natural Resources Canada. An Open Access service, GEOSCAN continually adds records and full-text publications as they become available.