Reviewerly/exHarmony huggingface dataset:
Template Based Pose and Illumination Invariant Face Recognition

This article presents a method “Template based pose and illumination invariant face recognition”. We know that pose and Illumination are important variants where we cannot find proper face images for a given query image. As per the literature, previous methods are also not accurately calculating the pose and Illumination variants of a person face image. So we concentrated on pose and Illumination. Our System firstly calculates the face inclination or the pose of the head of a person with various mathematical methods. Then Our System removes the Illumination from the image using a Gabor phase based illumination invariant extraction strategy. In this strategy, the system normalizes changing light on face images, which can decrease the impact of fluctuating Illumination somewhat. Furthermore, a lot of 2D genuine Gabor wavelet with various orientations is utilized for image change, and numerous Gabor coefficients are consolidated into one entire in thinking about spectrum and phase. Finally, the light invariant is acquired by separating the phase feature from the consolidated coefficients. Then after that, the obtained Pose and illumination invariant images are convolved with Gabor filters to obtain Gabor images. Then templates will be extracted from these Gabor images and one template average is generated. Then similarity measure will be performed between query image template average and database images template averages. Finally the most similar images will be displayed to the user. Exploratory results on PubFig database, Yale B and CMU PIE face databases show that our technique got a critical improvement over other related strategies for face recognition under enormous pose and light variation conditions.
in brief

PAGE 1103 Researchers from the Republic of Korea present a self-capacitance sensing technique for touch screen panels with alternating panel charge sharing, which effectively suppresses low-frequency noise and achieves better signal-to-noise ratio. SNR is improved by 7.65 dB compared to conventional panel charge sharing based techniques. PAGE 1148 A deep reinforcement learning-based approach for the delay-constrained buffer-aided relay selection in a cooperative cognitive network is proposed by Chinese researchers. The proposed algorithm can solve the complicated relay selection problem and achieves the optimal throughput when the buffer size and number of relays is large. Schematic of proposed analog front-end circuit. PAGE 1111 Brazilian researchers present an alternative analysis method to derive the voltage-current transmission matrix of transmission line sections. The method efficiently computes the relationship between the voltages and currents at the input and output of a circuit modelled through N identical cascaded two-port networks. The overall framework of the proposed Q-Learning algorithm. PAGE 1116 Researchers from Daejeon, Republic of Korea, present a new method for training convolutional neural networks that are designed to detect targets of interest in hyperspectral images. Using synthetic training data only, a Siamese CNN has been trained to learn robust features for the detection task. Experimental results on real hyperspectral images show the effectiveness of the proposed method. Circuit model composed of N identical cascaded two-ports. PAGE 1130 Indian researchers have designed and presented a photonic configurable logic block with a 1×1 Mach-Zehnder modulator as a universal photonic gate, capable of performing logical operations with the help of a field-programmable gate array as a configuration controller. The device exhibits neglibile transmission loss and a flexible switching control when compared to existing 2×2 MZI-basd photonic gates. A band image from the hyperspectral data cube (l) and ground truth for targets of interest (r). Cross-section details of the designed 1×1 Mach-Zehnder modulator.
Emotion Detection on live video using Deep Learning

In modern days, feeling exposure is a ground of curiosity and is used in fields such as cross-examining prisoners and teenagers observing human-computer relations. The anticipated work designates the exposure of mortal sentiments from an instantaneous video or stationary video with the help of a convolution neural network (CNN) and haar cascade algorithm. The foremost part of the announcement constitutes field appearance. The suggested work aims to categorize a given video or a live video into one of the emotions (natural, angry, happy, fearful, disgusted, sad, surprise). Our work also distinguishes multiple faces from live video and organize their emotions. Our recommended work also imprisonments the pictures from the video every second, hoard them into a file, and generates a video from those pictures along with their respective.
In the News

Welcome to the June 2020 issue of C&amp;RL News. Every two years, ACRL’s Research Planning and Review Committee produces their “Top trends in academic libraries.” The 2020 edition discusses change management; evolving integrated library systems; learning analytics; machine learning and AI; the state of open access and research data services; social justice, critical librarianship, and critical digital pedagogy; streaming media; and student wellbeing. Many thanks to the committee for pulling together this important survey of the current landscape of academic and research librarianship.
Classifying Streaming Data

This chapter is concerned with the classification of streaming data, i.e. data which arrives (generally in large quantities) from some automatic process over a period of days, months, years or potentially forever. Generating a classification tree for streaming data requires a different approach from the TDIDT algorithm described earlier in this book. The algorithm given here, H-Tree, is a variant of the popular VFDT algorithm which generates a type of decision tree called a Hoeffding Tree. The algorithm is described and explained in detailed with accompanying pseudocode for the benefit of readers who may be interested in developing their own implementations. An example is given to illustrate a way of comparing the rules generated by H-Tree with those from TDIDT.
More Exploration to Composable Infrastructure: The Application and Analysis of Composable Memory

Memory-Driven Computing facilitates real-world applications like in-memory database to benefit from substantial memory resources; however, these workloads perform dramatically downgraded if the memory capacity cannot reach to a threshold. In this paper, we present a novel remote memory system, which can handle memory-intensive workloads up to the scale of 1 Terabyte, by leveraging sophisticated tmpfs, virtual memory system, and Remote Direct Memory Access (RDMA) over Ethernet. To the best of our knowledge, this is the first design that provides (1) the most significant memory extension without any software modification and (2) the large-scale performance evaluations comparing to other state-of-the-art works about network-based remote memory. Based on the evaluation results, we believe the proposed composable memory is valuable for readers to re-visit wildly developed computing models (e.g., Apache Spark) and facilitate research work which can potentially reshape the data center architecture/deployment further.
Analysis of Dangerous Mistakes in Discussion Activities

This study aims to describe the analysis of language errors of Baubau 3 High School students in class discussion activities. The research design is descriptive qualitative. Research data analyzes language errors in class discussion activities in the form of use of intonation / tone, diction (choice of words), and sentence structure.The results showed that the form of language error analysis in class X of SMA Negeri 3 Baubau in class discussion activities was the use of intonation consisting of: high intonation, moderate intonation and low intonation. The use of diction (word choice) which consists of: the use of synonymous words, the use of denotation and connotation words, the use of foreign words or terms, the use of popular words and study words, and the use of words of conversation and slang. The use of sentence structure consists of: the use of compulsory sentence structures and the use of sentence structures are not mandatory (where they like). Based on the data obtained regarding the analysis of language errors in class X students in class discussion activities, it can be said that in the use of intonation there are errors in the form of high intonation of 11, intonation is not there and intonation is low as many as 16 errors. In the use of diction there are errors in the use of 40 synonymous words, use of denotations and connotations of 41, use of foreign words or terms as many as 14, use of popular words and study words as many as 3, and the use of 120 words of conversation and slang. Furthermore, the use of sentence structures has errors in the form of mandatory sentence structures that do not exist, and in the sentence structure is not mandatory as many as 156 errors.
On blockchains, cryptos, and media of exchange. Not there (yet)

Ever since the inception of the blockchain technology and bitcoin (Nakamoto, 2009), the first and foremost of so-called cryptocurrencies, the prospect within the 'crypto' community has been one of growing decentralisation and an impending threat to traditional monetary policy and its legal monopoly. Here we discuss the potential use of bitcoin and other competing 'cryptos' as media of exchange and argue that we are still far from seeing a dethroning of major currencies, in spite of the undermining effect that cryptos might have on monetary policy and central banking.
Development of Information Security Competency in Students

This article describes methodological aspects of developing students’ information security competencies. In the article, the author clarifies the essence of the concept of “information security”, its various aspects, the relationship between the development of information security competency and information-analytical competence, as well as the methodological conditions of information-analytical function. In addition, he substantiates the importance of vitagenic education technology in the development of students’ information-analytical competency, and, on the basis of experimental materials, the correlation between the components of development of students’ information security competency.
Software Konsultasi Seleksi Karir Siswa menggunakan Metode Certainy Factor

&lt;p class="Abstrak"&gt;Model &lt;em&gt;softwar&lt;/em&gt;e konsultasi seleksi karir sebagai alternatif pengambilan keputusan dimana konsultasi meliputi pemahaman karir, perencanaan karir, alternatif pilihan karir yang berupa alat penelusuran minat dan bakat siswa terhadap keputusan karir siswa menggunakan metode &lt;em&gt;certainty factor&lt;/em&gt; berbasis sistem pakar. Penelitian ini menghasilkan model &lt;em&gt;software&lt;/em&gt; konseling dalam memperoleh informasi penting tentang pengembangan karir siswa dan membantu memfasilitasi perkembangan individu siswa melalui bentuk layanan agar mampu merencanakan karirnya berdasarkan jurusan, minat, bakat, pengetahuan, kepribadian, kompetensi dan faktor-faktor lain yang mendukung kemajuan dirinya dalam menentukan pilihan dan keputusan yang sesuai dengan dunia kerja pilihan siswa.Sistem diuji menggunakan&lt;em&gt;white box&lt;/em&gt; dan &lt;em&gt;black box&lt;/em&gt;dengan menunjukan hasil dimana sistem dapat digunakan sesuai kebutuhan.&lt;/p&gt;&lt;p class="Abstrak"&gt; &lt;/p&gt;&lt;p class="Abstrak"&gt;&lt;em&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;&lt;p class="Judul2"&gt;&lt;em&gt;Career selection consulting software model as an alternative decision making where consultation that includes career understanding,&lt;/em&gt;&lt;em&gt;career understanding, career planning, alternative career choices in the form of a tool to trace students' interests and talents towards student career decisions using expert system based certainty factor method. &lt;/em&gt;&lt;em&gt;This research produces a counseling software model in obtaining important information about student career development and helps facilitate individual students development through a form of service in order to be able to plan his career based on majors, interests, talents, knowledge, personality, competencies and other factors that support his progress in determining choices and decisions that are &lt;/em&gt;&lt;em&gt;appropriate to the world of work choice.&lt;/em&gt;&lt;em&gt; The system is tested using white boxes and black boxes by showing the results where the system can be used as needed&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p class="Abstrak"&gt;&lt;em&gt;&lt;strong&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
An efficient Task Scheduling and Load Balancing in Cloud Computing using KD-Tree Algorithm

Cloud Computing provides the sharing ability and access for available cloud host and various distributed environments, namely Load Balancing (LB), virtualization technologies and scheduling techniques. The satisfaction of both users and cloud providers are the major issues for effective LB and task scheduling algorithms in cloud resource management, where the requirements namely high resource utilization, low monetary costs and minimum makespan. Many researchers tried to develop various heuristic and meta-heuristic algorithms to attain the aforementioned user requirements. But, when the number of tasks grows exponentially, these algorithms failed to achieve LB, lower running time, and it faces the high time complexity. In this research work, a KD-Tree algorithm is developed to address the issues of heuristic algorithms and provide efficient LB by partitioning the environments into several tasks. According to the deadline of task execution, the remaining tasks are adjusted dynamically by the proposed KD-tree algorithm in the virtual environment. The experiments are conducted to evaluate the efficiency of KD-Tree algorithm with existing heuristic techniques by using makespan, energy consumption and task migrations. When the number of tasks is 20, the proposed KD-Tree algorithm achieved 71.33% makespan and 5% task migrations.
Pengaruh efikasi diri dan dukungan sosial teman sebaya terhadap prestasi belajar

Learning achievement is influenced by several factors such as internal factors or external factors. The purpose of this study is to determine the effect of self-efficacy and social support of peers on learning achievement simultaneously or together. The sampling technique uses simple random sampling in one of the existing SMA N in Woha District, Bima Regency, West Nusa Tenggara. The subjects in this study were 150 high school students in class X. The method of data collection in this study uses a scale of self-efficacy and social support of peers and reporr cards. The results of the multiple regression test analysis showed that self-efficacy and social support of peers simultaneously or simultaneously affect the learning achievement with an influence contribution of 25,0%.
Secure E Donation System u sing Blockchain Technology

It is highly common for organizations to receive anonymous donations. Financial resources with centralized leadership are far easily prone to corruption. There is a need to decentralize the distribution of resources. This can be achieved using blockchain. It is a transparent, decentralized and distributed technology which operates without a central control organ. The main purpose of this system is to eliminate the corruption in organizations by means of which, black money is legalized or resources are manhandled. The system will be decentralized, not owned or operated by a single person or organization but rather shared among users. We have developed a blockchain-based Donation System capable of decentralizing the resources used by a given organization. The user may create any new organization and others will be able to donate money in the organization in the form of ether. This resource can only be used by the organization after getting consent from the users in network. This makes the people the actual owners of the resources and brings transparency to the entire process of charity fund system. Thus, making wrong and illegal use of money donated to organizations will be minimized to a great extent and thereby, corruption will be reduced with the use of this blockchain system, solving problems in many nations.
A Persuasive Resource-Aware Allocation Scheduler for Enhancing Task Scheduling

The deployment of Map Reduce has been built to grant enhancements to total system objectives such as job throughput. Hence, the support for user-specific objectives and resource allocation management has been least regarded and addressed. Schedulers enable users to assign jobs to queues that fulfil shared of specific resource. Existing work mainly focus on scheduling glitch occurring on the master’s side where the scheduler on the master node tries to allocate same work across all the worker nodes. The proposed scheduler focus on enhancing resource allocation when various kinds of workloads execute on the clusters. In order to evaluate the performance on the proposed scheduler which enhances resource utilization, an accomplishing time goal with each job is created.
Pengaruh Interaksi Edukatif dan Disiplin Belajar Terhadap Hasil Belajar Ekonomi Siswa Kelas X Di SMA Negeri 4 Sumbar

This study was conducted to find out (1) the effect of educative interaction and learning discipline to ward student economic learning outcomes, (2) the effect of educative interaction to ward student economic learning outcomes, (3) the effec of learning discipline to ward economic learning outcomes. This research is descriptive and associative. The population in this study were specialy for sports school students of class X MIPA and IIS in West Sumatra 4 High School (Sports Giftedness) with 97 students. The total sample is determined by Slovin formula and propotional random sampling. From this formula, 80 students were obtained as research samples. Data collection techniques used in this study were questionnaires and documentation. The instrument of validity test uses Collected Item Total Colleration and for reliability testing using alpha coefficient formula. The prerequisite test is done by normality test, heterocedasticity test, and multicollinearity test. The technique used to analyze data is multiple linear regression analysis. Based on this study, it was found that (1) educative interaction and learning discipline had a positive and significant effect on the economic learning outcomes of class X students West Sumatra 4 High School (2) educational interaction had a positive and significant effect on the economic learning outcomes of class X students in West Sumatra 4 High School (3) learning discipline has a positive and significant effect on the economic learning outcomes of West Sumatra 4 High School. Keyword: educative interaction, student discipline, sport school, learning outcomes
Secured Health Protective Services in Social Media Network using K-Anonymity

Healthcare media provides a capable model in order to fascinate consumer to discuss their health information and access health protection facilities from connected caretakers. Since the traditional health care service is time consuming process, we are switching to online health care practice, which can reduce the gap between care takers and patients. Due to the frankness of the social network the trust between the patients and care takers is a challenging issue and there is a chance of revealing personal information of the patients. Here, we intended to propose a reliable health protection service to facilitate user in social media networks, we deploy Bloom Filter technique for suitable personalized caretakers, in order to assure trustworthy rankings and critiques for caretakers, we include Sybil attack detection technique to identify users’ fake rankings and critiques using various false name. It incorporates generalization and suppression techniques to protect individual’s private data. For this purpose, k-anonymity Technique is implemented to anonymize the data.
Comparing simulation model for objective QoE video evaluation with real IPTV test scenario during appearance of packet losses

The aim of this article is to explain benefits of the simulation model for objective QoE (Quality of Experience) video evaluation in order to find similarities with real IPTV (Internet Protocol TeleVision) system distribution in a DSL (Digital Subscriber Line) network. In DSL networks, it is known that packet losses appear suddenly and might have a "burst" character, at any time during IPTV service delivery. In addition, these packet losses usually appear in groups and lead to huge degradation of the IPTV video service, which decreases a customer's QoE guaranteed level. Hence, estimation of this degradation in an access network is crucial and this paper explains a simulation model based on SSIM (Structural Similarity Index) analysis, which can be used as one perceptive video quality assessment by imitating a real environment with packet losses. To check this, we compared simulation model cases with the real ones in IPTV video distributed over DSL and exposed to different packet loss appearances.
SIM Forensics: Extraction and Preparation of Digital Evidence using Sim Xtractor

In each and every mobile phone the SIM card plays a major role in communicating the information. In a crime if a mobile phone is been taken as the evidence, the first and the foremost thing is to investigate the SIM card. Though the evolution of smart phone is very rapid without the SIM card the smart phone is uncertain in communication. The SIM card stores some valuable information like call logs, messages, contacts etc…. . For extracting those information we need a tool. In this experiment we are using a tool known as SIMXtractor from CDAC. It is not a open source tool. By using this tool we are able extract those information from the SIM card.
Classification and generation of grammatical errors

The grammatical structure of natural language shapes and defines nearly every mode of communication, especially in the digital and written form; the misuse of grammar is a common and natural nuisance, and a strategy for automatically detecting mistakes in grammatical syntax presents a challenge worth solving. This thesis research seeks to address the challenge, and in doing so, defines and implements a unique approach that combines machine-learning and statistical natural language processing techniques. Several important methods are established by this research: (1) the automated and systematic generation of grammatical errors and parallel error corpora; (2) the definition and extraction of over 150 features of a sentence; and (3) the application of various machine-learning classification algorithms on extracted feature data, in order to classify and predict the grammaticality of a sentence.
Retraction

This article refers to:RETRACTED ARTICLE: A Robust Face Emotion Recognition Approach through Optimized SIFT Features and Adaptive Deep Belief Neural Network
Improve OCR Accuracy with Advanced Image Preprocessing using Machine Learning with Python

Optical Character Recognition or Optical Character Reader (OCR) is a pattern-based method consciousness that transforms the concept of electronic conversion of images of handwritten text or printed text in a text compiled. Equipment or tools used for that purpose are cameras and apartment scanners. Handwritten text is scanned using a scanner. The image of the scrutinized document is processed using the program. Identification of manuscripts is difficult compared to other western language texts. In our proposed work we will accept the challenge of identifying letters and letters and working to achieve the same. Image Preprocessing techniques can effectively improve the accuracy of an OCR engine. The goal is to design and implement a machine with a learning machine and Python that is best to work with more accurate than OCR's pre-built machines with unique technologies such as MatLab, Artificial Intelligence, Neural networks, etc.