Reviewerly/exHarmony huggingface dataset:
Privacy protection with heavy-tailed noise for linear dynamical systems

Privacy protection in linear dynamical systems is investigated in this paper. A standard mechanism employed in systems and control literature is to mask private data by adding Gaussian noise . A shortcoming of this mechanism is that the occurrence of outliers is vulnerable. The goal of this paper is to present a novel mechanism that can hide outliers. The key idea is to utilize stably distributed noise , which has the following two preferable properties for this purpose; One is its heavy-tailed distribution that is beneficial to hide extreme values including scale-free data. The other is its closedness with respect to addition similarly to Gaussian, which enables us to design filter/controller under privacy requirements based on linear control theory. From a theoretical point of view, we quantify the privacy level of the proposed mechanism in terms of differential privacy . The derivation is nontrivial because the density function of the stable distribution has no analytic expression.
Investigation on Self-efficacy of Rural Primary School Teachers — Takes X Village Primary School in Sang Zhi County as an example

Sang Zhi county; Primary school teachers; Self-efficacy
Preparation of future teachers for the development of students’ critical thinking

The problems of developing critical thinking in the training process of future teachers are considered in the article. In introduction the relevance of this study in the regulatory documents adopted in the Republic of Kazakhstan are analyzed. The structure of critical thinking of future teachers is determined and its components are analyzed. The concepts of “criticism” and “critical thinking” are analyzed and refined in the studies of scientific researchers. The basic model of critical thinking development technology is given. The development stages of the of critical thinking technology, approaches and methods are considered that can be used at each stage. A model for the development of critical thinking of future teachers is given that are created on the basis of experience that conducted in a pedagogical university. The article notes that the technology of developing critical thinking gives effective results, if take account the specific features of the specialty.
A Studyon Impact of Job Satisfaction on Performance at Abn Infotech

When employees feel dissatisfied with the nature of job they do, their level of commitment could be deliberately reduced and since employees are the engine room of an organization, their dissatisfaction with the nature of job they do could also pose a threat to the overall performance of the organization. The main objective of this study is to examine the impact of job satisfaction on employees performance, with ABN INFOTECH. In view of the above cause, data was collected from both primary and secondary sources. which was used as the bases for the research analysis. Ordinary least square regression was the statistical tool used in analyzing the data. Also, personal interviews and general observations were part of the source on information for this study. The research findings revealed that there is a linear relationship between job satisfaction (nature of job, job reward and job security) and employees performance proxy which is employees morale. It was concluded on the note that employees are dissatisfied with the working conditions of the organization, it is evident in their responses. It was recommended that the management of the company should provide good working conditions for its employees, so as to boost their morale.
Cloud Interaction with SBCs

SBCs working in remote locations some time have to communicate with the cloud environment for data sharing and remote command sharing. This chapter mainly deals with the cloud-based interaction such as Google sheets, Firebase integration of rpi and other similar Linux operating boards with the cloud environment. The authentication and data porting from firebase to spreadsheet and vice versa is also discussed in detail.
An Analysis on the Relationship Between Content Topics of Social Media and Customer Engagement Using Machine Learning Methodology

Customer engagement is regarded as a performance indicator of social media marketing, and previous studies have reported that the characteristics of content to increase customer engagement. However, the topic of content has not been sufficiently studied. This study analyzes the relationship between the topic of social media content and customer engagement and suggests an analysis procedure that can apply a machine learning model, a key tool for recent digital transformation. For empirical analysis, 154,705 social media data of 51 global brands were collected, and content topics were classified using a topic modeling method. And the relationship between content topic and customer engagement was analyzed using zero-inflated negative binomial regression analysis and machine learning model. As a result of the analysis, contents of 51 brands were classified into 18 contents topics, and there was a difference in the impact on customer engagement according to the topic. In addition, using a machine learning model, it was possible to predict the customer engagement performance of the content with an accuracy of about 90%. This study contributed to the marketing literature by analyzing the relationship between social media content topics and customer engagement through machine learning methodology.
Digital Health Technologies and Artificial Intelligence in the Islamic World: Embracing the Evolution in Healthcare

Health and Healthcare have been highly impacted by the global technological revolution resulting in more intelligent systems, optimised hospital workflows, disease prediction and analytics, precision medicine and personalised patient experience. Digital health offers a wide spectrum with artificial intelligence at the far-end having a significant influence on the healthcare systems and medical practice. Artificial intelligence makes sense of the large volumes of medical data through efficient algorithms, thus transforming healthcare systems. But, has the Muslim world been up to date with the advancements in healthcare? This detailed study presents the importance of adopting and adapting to the new technologies in the field of medicine, taking into consideration that Muslim scientists were, in fact, pioneers in the field, carving new medical techniques. The significance of digital health technologies and artificial intelligence in healthcare is also supported by various issues in Muslim societies related to cross-gender patient-doctor interactions, lack of resources, inefficient healthcare systems, and poor socio-economic status. This study concludes with examples of research studies being carried out in the fields of cardiology, haematology, and radiology where algorithms and systems are developed that enable physicians to perform their job with more ease and higher precision.
Detecting Spammer Groups From Product Reviews.

As the trend towards online shopping increases day by day, More people are interested in buying their goods and more people The online stores need it. Does not allow this sort of shopping It takes a lot of a client's time. Customers go to the shop online, Check for the item you need and put the order. But the, the, the, Stuff that make it impossible for individuals to purchase goods The low product quality is from an online retailer. User Place the order by looking only at the rating and reading Reviews were linked to the specific product. Comments of this nature Other individuals are the source of happiness for the new Material Purchaser. Here, it could be possible for the single negative to be negative Review shifts the client's angle of not buying the Product. Product. It is a specific quality of the method proposed thatIt works with the three websites for e-commerce and not only Analyze the English reviews, but also the reviews published in Urdu and Urdu Roman. Previous studies on counterfeit reviews does No function to evaluate feedback written in languages is supported. Like Urdu and Roman Urdu, and they are unable to manage the feedback of Different Websites for e-commerce. The planned job accomplished 87 percent accuracy in the identification of fake reviews published in Using intelligent learning methods, English Better than the precision of the preceding systems.
High energy flash X‐ray image restoration using region extrema and kernel optimization

The quality of high energy flash X-ray images is crucial to the high-precision diagnosis of object density. High energy flash X-ray radiography is susceptible to the system blur, which usually causes the poor quality of static images. In response to this, a novel restoration algorithm using region extrema and kernel optimization (REKO) is presented. Based on the observation that the region extrema distribution of blurred high energy flash X-ray images deviates from opposite ends of image grey domain, the sparseness-inducing prior for regularizing image region extrema is applied to construct the restoration model. Considering the sparse characteristics of blur kernels, the sparseness-inducing regularization is incorporated to constrain blur kernels in the restoration model. The non-convex and non-linear objective function is gradually minimized through energy alternating minimization and dually linear approximation. Furthermore, a continuity enforced kernel optimization algorithm is proposed to estimate more accurate blur kernels. The discontinuous kernel elements are suppressed by extracting the main structure of blur kernels and constructing kernel continuity function in cross windows. Experimental results demonstrate that our algorithm can more accurately estimate blur kernels and achieve restoration results with sharper edges on high energy flash X-ray images.
LibGuides: Sport : Home

A comprehensive guide to the resources provided by the Library service to support you with your course.
The Reality Of Applying Management Engineering In College Of Education For Pure Sciences / Ibn Al-Haitham

The main objective of the research is to identify the reality of applying management engineering at Ibn al-Haitham College of Education at Baghdad University, as well as the significant differences from the perspective of the targeted sample according to gender and years of service. The researcher addressed the problem of the search as well as its importance. The researcher adopted the analytical descriptive approach by identifying the research community which consists of 55 members with senior administrative positions from Dean to an manager unit official at the college for the academic year (2019-2020). The research sample consists of 39 membrs that cover 71% of the community. The researcher has used a special search technique for the (questionnaire) consists of four fields to ensure its reliability. The theoretical aspect and previous studies on the subject were presented, besides, the researcher analyzed, discussed  then submitted the results, the most important of which is that the application of management engineering in the college was of a moderate and near-weak level, while no significant differences appeared in the service variable. Differences in gender and female-favor appeared. The researcher then drew up several conclusions, recommendations, and proposals.
METHODOLOGICAL PRINCIPLES OF THE CONCEPTUAL APPARATUS IN PEDAGOGICAL RESEARCH

The article substantiates the methodological principles of the conceptual apparatus in pedagogical research; a retrospective analysis of the conceptual and terminological system of pedagogy is given: methodological seminars, monographs, scientific articles. It is proved that the conceptual apparatus of pedagogy allows one to judge the degree of development of pedagogical theory, to determine the various facets of real pedagogical objects and the variety of cognitive tasks that arise in the process of education . The dialectical principles of knowledge underlying the theoretical definition of the methodological principles of the conceptual apparatus of pedagogical research are presented; the definition of the term concepts, its volume and content, the evolution of the system of concepts in pedagogy. The methodological principles of the conceptual apparatus in pedagogical research are substantiated and disclosed: systematic (determining the place of the key concept in the conceptual research system), valency (the ability of pedagogical concepts to enter terminological phrases with concepts from other fields of science), adequacy (the need to study scientific concepts in pedagogical research in differences from normative), historical conditionality (evolution of concepts in different historical eras), impulsiveness (unevenness l the development of conceptual theories, their ability to evolutionary bursts), subjectivity (argumentation of the subjective definition of concepts in pedagogical research).
A Particle Swarm and Ant Colony Optimization based Load Balancing and Virtual Machine Scheduling Algorithm for Cloud Computing Environment

In cloud computing balancing the load of the Virtual Machine (VM) is very much essential. Load balancing efficient utilization of resource and fairly balance the resource usage. In the real time scenario the request for the Virtual Machine (VM)s and tasks submission could be dynamic, whereas system creates the Virtual Machine (VM) according to the customer demand and map it to suitable Physical Machine (PM). These Virtual Machine (VM) could be created without knowing the detailed information about the task.Hence, the scheduling of these taskscould not be optimised using traditional task scheduling algorithms. In this paper a hybrid meta heuristic approach for scheduling these tasks is proposed. Two different optimization techniques for Virtual Machine (VM) scheduling has been used in this paper. We combine Particle Swarm optimization and Ant Colony Optimization approaches called (PSACO). The PSACO uses the historical information regarding the Virtual Machine (VM)s and task submission to predict The workload of new task submission and resource request in dynamic environment without extra information. The proposed approach also rejects the computing request which does not satisfied the current resource constraints. It reduces the computation time for scheduling. the experiment results shows that the proposed metaheuristic algorithm balance the load with the dynamic environment and outperformed the existing algorithms.
Expert Opinion at Crime Scene: An Overview

The crime scene is an important place for crime investigation. It often becomes victim of police officers inparticular and general public at large as they are not fully aware of the importance of scientific clue materialswhich will have important impact on the whole process of investigation for successful detection of factsrelating to the offence. Sometimes, it may also so happen that the interested persons knowingly disturbthe crime scene to mislead the investigators. Considering and the evolution of the scientific aspects offorensic science, more attention must be awarded at crime scene to recovering and maintaining the integrityof evidence which will be eventually examined by forensic scientists in the laboratory. As every crimescene is always unique in nature, accordingly a different approach is required for one crime to another. Theopportunity to examine the scene is available only once. If the same is not fully exploited the wealth ofinformation is lost forever. This article focuses on the crime scene management and role of forensic scienceand the laws relating to expert opinion.
A COMPREHENSIVE FRAMEWORK FOR VOICE BIOMETRICS

Recognizing someone by his/her voice is called Automatic Speaker Recognition (ASR). Biometric protection comes under Speaker ID biometric is concerned with individual characteristics Voiceprint or biometric authentication is used to identify a person. It depends on an individual's voice activity or physiology. Behavioral biometrics includes speech, signature, keystroke, and typing while physiological biometrics includes face, retina, fingerprints, and DNA now biometric authentication is cutting-edge.Several forms of speech recognition and feature extraction systems were employed in this paper. A novel feature extraction method was proposed using Sub-band based SBC, and energy calculation was also covered. For a specific combination of 350 users, our approach achieves recognition rate above 94% with EER less than 6%. The output is measured on two separate sets of data, clean and degraded.
Theoretical foundations of the quality management of the didactic process in school

The theoretical foundations of quality management of the didactic process in school are defined. The urgency of the problem of quality management of didactic process in the context of humanization and the technologization of education, reveals the essence of basic categories and concepts in the field of quality management of didactic process. The correlation of the concepts: goal, result and quality is defined, and the concept of «quality of the didactic process»is widely considered. The distinctive feature of the technology «three-Dimensional methodical system of training» from other pedagogical technologies and the effectiveness of its implementation in the educational process is shown. Based on the requirements of this technology, the quality of learning of educational material by each of the students increases, and an objective assessment of the level of personal development is guaranteed. The main feature of this technology is that it is mandatory to organize a feedback stage. It consists of multi-level measurement tasks for each topic. The goals of the learning levels are gradually becoming more complex and are set clearly with a forecast of the expected intermediate results.
Review of Data Mining for Twitter Data Analysis

In today’s life Twitter, Facebook, Google are well known social sites that many uses for different purposes. Social sites are the fastest medium which delivers news to user as compared to the new paper and television. One among the online social networks like Twitter, has quickly gained fame as it provides people with the opportunity to communicate and share messages known as “tweets”. Tremendous value lies in automated analysis and data mining of such vast and diverse data to derive meaningful insights, which carries potential opportunities for businesses, consumers, product survey and political survey. In the proposed system of analysis of the tweets, a search query for topic is provided to extract required data using ‘clustering algorithm’ in machine learning. The unique advantage of using machine learning is that once an algorithm knows what to do with the information, it can do its job automatically. To deduce for a search query, the proposed system extracts feature from the tweets like keywords in tweets, number of words in tweets. The output tweet list can further be used for analysis for business improvement or for surveys.
Innovación pedagógica en el área de Matemáticas: una experiencia participativa con docentes y estudiantes de la provincia María Trinidad Sánchez, República Dominicana

The article approaches the integration of ICT in the area of Mathematics in educational centers in the Dominican Republic, taking as reference the Aristides Fiallo Cabral Elementary School. In this center, there are many situations that limit the effective use of ICT: shortage of technological resources; impairment in the domain of digital and techno-pedagogical skills; lack of motivation towards innovation; limited teacher training, among others. Based on this, the objective of the research presented (carried out between 2017-2018), was to promote the integration of ICT in the pedagogical practices of mathematics teachers. Action research was assumed to achieve the stated objectives. It is concluded that, despite the difficulties for the integration of ICTs, strategies adapted to our reality can be developed through which it is possible to integrate technologies into the teaching-learning process of mathematics.
An adaptive cache switching based video streaming using Buffer seeded multi checkpoint peer responder to improve the quality of service in WSN

The widespread of online video access by the various user become huge in recent days by direct streaming through the internet. However, video streaming in wireless networks consumes more delay due to response from the service provider. Due to high bandwidth consideration, the quality of streaming video consumes more buffer state to load the data. By this mitigation, the loss quality becomes problematic to access the service continually. To resolve this problem, we propose an On-Demand Service-Based Peer Switching (ODSBPS) for video streaming using Buffer seeded multi checkpoint peer responded to improve the quality of service in WSN. Initially, these methods verify the service request load based on the energy consumption in multi-channel service responder. Based on the Energy-Aware Traffic Flow Estimation (EATFE) verifies the buffer state from the nearest peer to create a checkpoint. This nearest checkpoint is estimated based on Streaming Service Intense Access Rate (SSIAR). Through this SSIAR checkpoint, the data is loaded on reserved cache points to create buffer points. Simultaneously the nearest peer responder checks the previous checkpoint to load the seed into the peer end. The peer end verifies the last buffer checkpoint to stream continuous service to make low energy level streaming provide the best video streaming service.
LibGuides: Virtual Book Display: April 2020: Libraries in Books

Explore virtual collections of books and ebooks by monthly themes curated by University librarians!
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

The increasing size of neural network models has been critical for improvements in their accuracy, but device memory is not growing at the same rate. This creates fundamental challenges for training neural networks within limited memory environments. In this work, we propose ActNN, a memory-efficient training framework that stores randomly quantized activations for back propagation. We prove the convergence of ActNN for general network architectures, and we characterize the impact of quantization on the convergence via an exact expression for the gradient variance. Using our theory, we propose novel mixed-precision quantization strategies that exploit the activation's heterogeneity across feature dimensions, samples, and layers. These techniques can be readily applied to existing dynamic graph frameworks, such as PyTorch, simply by substituting the layers. We evaluate ActNN on mainstream computer vision models for classification, detection, and segmentation tasks. On all these tasks, ActNN compresses the activation to 2 bits on average, with negligible accuracy loss. ActNN reduces the memory footprint of the activation by 12x, and it enables training with a 6.6x to 14x larger batch size.