Reviewerly/exHarmony huggingface dataset:
CNN based Gesture Recognition for ISL

Abstract Gesture recognition has always been at a peak when it comes to computer vision and human-computer interaction (HCI). Hand gestures can be used for interaction with systems like managing UAVs, medical devices, videogames, etc. In this paper, the main focus is on handicapped people, who are physically unable to speak or listen. With the help of computer interaction, gestures will be recognized. As, the same gesture by another people differs, recognizing it is the main task. The Convolution Neural Network (CNN) is used, to the extraction of features and gesture recognition, RGB dynamic gesture recognition method is used, the average accuracy received is around 97.44%, which is higher comparatively. The dataset used is the original dataset of Indian Sign Language (ISL), for learning features and the gestures of the images. The method sets the feature concatenation layer of Canny Edge and RGB images in the CNN structure, to give better performance of gesture recognition. Keywords: Convolution Neural Network, Indian Sign Language, hand gesture recognition, Human-Computer Interaction.
R_ML_STATA: Stata module to implement machine learning regression in Stata

r_ml_stata is a command for implementing machine learning regression algorithms in Stata 16. It uses the Stata/Python integration (sfi) capability of Stata 16 and allows to implement the following regression algorithms: elastic net, tree, boosting, random forest, neural network, nearest neighbor, support vector machine. It provides hyper-parameters' optimal tuning via K-fold cross-validation using greed search. This command makes use of the Python Scikit-learn API to carry out both cross-validation and prediction.
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL

Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks. BYOL works like a charm despite the fact that it discards the negative samples completely and there is no measure to prevent collapse in its training objective. In this paper, we suggest understanding BYOL from the view of our newly proposed interpretable self-supervised learning framework, Run Away From your Teacher (RAFT). RAFT optimizes two objectives at the same time: (i) aligning two views of the same data to similar representations and (ii) running away from the model's Mean Teacher (MT, the exponential moving average of the history models) instead of BYOL's running towards it. The second term of RAFT explicitly prevents the representation collapse and thus makes RAFT a more conceptually reliable framework. We provide basic benchmarks of RAFT on CIFAR10 to validate the effectiveness of our method. Furthermore, we prove that BYOL is equivalent to RAFT under certain conditions, providing solid reasoning for BYOL's counter-intuitive success.
A Literature Review on the Impact of Games on Learning English Vocabulary to Children

Digital games play a significant role in the life of the new generation. Although there are many criticisms, many studies focus on the importance of digital games in improving learner’s vocabulary in the target language. Researchers have begun conducting several researches on how using games in the class can foster vocabulary learning. The aim of this paper is to investigate the impacts of digital games on children’s vocabulary learning depending on a literature review. Many studies focus on the impacts of digital games on different aspects of education. This conceptual paper aims to shed light on some games' benefits, and challenges which educators and children face in the use of digital games. The findings of this paper show that Games are used not only for making children successful in EFL classes but more importantly, for motivating them and increasing the cooperation among children. In addition, the findings state that there are advantages as well as disadvantages in using games for learning English vocabulary.
ANALISA MIKROTIK SEBAGAI FILTER JARINGAN DAN PENGATURAN BANDWIDTH MANAJEMEN HOTSPOT PADA UNIVERSITAS ISLAM KEBANGSAAN INDONESIA(UNIKI)

The internet that is stable and safe from interference in one network has become a necessity for every user of the internet network both through mobile phones and computers. Hotspot and LAN need bandwidth management, so when accessingthe internet simultaneously it is no longer a problem. In addition, network security can be filtered through proxy so that online sites and games that do not need to be accessed during lecture hours from 8:00 a.m. until 5:00 p.m. can be blocked and reactivated after finishing college. By implementing network filters, the internet on the campus in Universitas Islam Kebangsaan Indonesia (UNIKI) will be stable and safe during lecture hours as expected.Keywords: MikroTik, HotSpot, Bandwidth,UNIKI
LibGuides: Winter Reading Challenge: Home

Are you up for a challenge? Come broaden your book horizons and challenge yourself to complete our Book BINGO and activities!
Analysis of the influence of the ambient temperature on the energy efficiency of solar modules by application of empirical correlations for natural convection

In this paper, the effect of the ambient temperature on photovoltaic (PV) modules for different angles of inclinations and different intensities of the solar radiation on the surface of PV module is considered using empirical correlations for natural convection. The analysis used an analytical model based on the energy balance equilibrium between PV module and the environment. It has been shown that in real conditions of exploitation, the value of the solar conversion coefficient of the solar energy to be determined by the manufacturer, valid for the standard test conditions (STC) for PV module (25 °C -1000 W/m2). The results obtained indicates that in the case a smaller number of PV modules corresponding to the required number for average household. The proposed procedure can be applied in the techno-economic analysis for PV system with uniaxial monitoring of the sun position as well as static PV systems.
Blockchains: Less Government, More Market

In this paper we provide a brief survey of the potential of blockchain technology to propel a process of private entrepreneurial discovery of institutions that challenge state hegemony. Entrepreneurs now have access to new tools to develop decentralised private governance across areas which have traditionally been the domain of government action. This may see a radical reshaping of the boundaries of ‘public’ versus ‘private’. Blockchain may result in less government, and more market based interactions. We introduce institutional cryptoeconomics, and blockchain as a technology which increases the opportunity set of entrepreneurial action. We then survey the potential of blockchain technology to challenge state hegemony in five socioeconomic areas: monetary institutions, the governance of contracting, civil society and social welfare, collective choice and voting, and the verification of identity. We also discuss some challenges and implications of blockchain-based economic infrastructure for public policy and economic regulation. Together these contributions suggest an increasing scope for entrepreneurial action using blockchain that challenges state hegemony, and a necessary shift in the scope of the provision of public goods and government regulatory control.
LOUVAIN: Stata module to find communities in weighted graph

wraps itself around the Python module python-louvain to find clusters in a weighted graph. It assumes you have loaded your network's weighted edges in stata in the form of three variables. So if you have string variables (the source node) and (the target node) and some numerical variable then you feed the weighted edges by running: snode tnode weight which creates a frame called louvain in which it saves two variables: one called holding the unique values of snode and tnode combined and one called cluster_1 indicating the cluster each node belongs to.
Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink

Wireless network sensors based on the mobile sink are regarded to be a common network and used in various fields in the last few years, they are thought to be easy to use, but contain the problem of energy loss and are affected by an energy hole problem, as it depends on batteries. This paper proposes a solution to this problem by using an innovative objective function for a consistent distributing of cluster heads, the enhanced harmony search based routing protocols based on energy equilibrated node clustering protocol. In order to route the data packet among the sink and cluster heads, an enhanced modified deep echo state neural network is suggested. The efficiency of a projected integrated clustering and routing protocol has been investigated at 500 nodes, and the 96 per cent success data for the proposed algorithm is given using the average energy consumption, send and receive packaged and optimum numbers of CH.
A Prospect on an Intelligent Recommender System

User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies the areas of the future for the development of an intelligent system.
BIMBINGAN TEKNIS PENGOLAHAN LIMBAH CAIR INDUSTRI TAHU DI SENTRA INDUSTRI TAHU TEGALPASANGAN – PAKIS – MALANG

The tofu industrial center of Tegalpasangan - Pakis - Malang is one of the large tofu industrial centers in Malang Regency, where in the tofu industrial center there are no less than 20 tofu industries, most of which are Micro, Small and Medium Enterprises (MSMEs) with a workforce of 2 - 4 people with an average production capacity of 2-5 quintals of soybeans per day. Like other cottage industries, tofu is produced manually without mechanical equipment, and there is little attention to good, healthy production methods. Likewise, without the existence of a wastewater treatment plant (IPAL) to treat waste water generated from the production process, which results in pollution of the aquatic environment. Community service activities aim to increase the knowledge, understanding and skills of partners (tofu industry owners) in producing tofu and treating wastewater properly and healthily. Technical guidance is provided in the form of GMP (Good Manufacturing Product) training, HACCP (Hazard Analitycal and Critical Control Point), waste treatment and provision (grant) of WWTP equipment. The results of PkM activities show that the provision of training and IPAL grants provides significant benefits for the knowledge and skills of partners, especially in handling waste water produced from the tofu industry so that it can reduce environmental pollution.
Machine Learning-Driven Language Assessment

We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguistic models to estimate item difficulty directly for computer-adaptive testing. This alleviates the need for expensive pilot testing with human subjects. We used these methods to develop an online proficiency exam called the Duolingo English Test, and demonstrate that its scores align significantly with other high-stakes English assessments. Furthermore, our approach produces test scores that are highly reliable, while generating item banks large enough to satisfy security requirements.
Blockchain as a Confidence Machine: The Problem of Trusts & Challenges of Governance

Blockchain technology was created as a response to the trust crisis that swept the world in the wake of the 2008 financial crisis. Bitcoin and other blockchain-based systems were presented as a “trustless” alternative to existing financial institutions and even governments. Yet, while the trustless nature of blockchain technology has been heavily questioned, little research has been done as to what blockchain technologies actually bring to the table in place of trust. This article draws from the extensive academic discussion on the concepts of “trust” and “confidence” to argue that blockchain technology is not a ‘trustless technology’ but rather a ‘confidence machine’. First, the article provides a review of the multifaceted conceptualisations of trust and confidence, and the relationship between these two concepts. Second, the claim is made that blockchain technology relies on cryptographic rules, mathematics, and game-theoretical incentives in order to increase confidence in the operations of a computational system. Yet, such an increase in confidence ultimately relies on the proper operation and governance of the underlying blockchain-based network, which requires trusting a variety of actors. Third, the article turns to legal, constitutional and polycentric governance theory to explore the governance challenges of blockchain-based systems, in light of the tension between procedural confidence and trust.
Impact of Architecture Heterogeneity on Confidence in STOIIP Estimation

Summary The aim of this research is finding a metric of reservoir architecture complexity provided by different depositional environments and further study of its relationship with confidence in STOIIP estimation. As a result, the linear dependence between architecture uncertainty in its numerical expression (information entropy) and confidence in STOIIP calculation is established that makes information entropy a qualitative indicator for assessment of system exploration level. In addition, it was found that information entropy has a logarithmic relationship with amount of information (number of wells) that allows predicting the potential contribution to the removal of uncertainty when drilling a new well and statistical assessment the value and justification of exploration work which can increase the efficiency of decision-making.
System Design of Web-Based Medical Record Content in Fulfilling Medical Record and Information Management 13.4 SNARS Edition 1.1 in Santosa Hospital Bandung Central

This research aims to create a medical record review information system design at Santosa Bandung Cetral. Methods of data collection are carried out by observation, interviews, and literature study. While the research method used is qualitative research methods. And the system modeling method uses the Watefall software development method with the Blackbox system testing method. Then from the research conducted, it was found that the problems were: (1) the review method that still used paper media and the process was still manual. (2) the data recording method still uses Microsoft Excel which is sourced from the form of review of the contents of medical records (review of medical records).Suggestions that can be given are: 1) there needs to be a change from the review system that still uses paper media to paperless and the processing method makes it easier for officers to review the contents of medical records (review medical records); 2) immediately create an information system to review the contents of medical records (review medical records) in order to support the review, starting from the review process to reporting the results of the review.
Effect of Semantic Content Generalization on Pointer Generator Network in Text Summarization

Semantic content generalization is a method for text summarization that reduces the difficulty of training of neural networks by replacing some phrases such as named entities with generalized terms. The semantic content generalization has achieved remarkable results in enhancing the performance of the sequence to sequence attention model. Besides that, the pointer generator network could ease the training of the summarization based on a mechanism that copies words from the original text, which shares a similar idea with semantic content generalization. The purpose of this work is to test and verify the effect of semantic content generalization on the pointer generator network. Therefore, we use the preprocessing of semantic content generalization and then combine it with the pointer generator network. We examine the performance through an experiment using CNN/DailyMail dataset. From the experiment, we found that the semantic content generalization can improve the performance of the pointer generator network.
Regulating Online Hate Speech: A U.S. Perspective

In the early 2010s, Terry Jones of Florida became known for his threats to burn, and for eventually burning, the Koran, the holy book of Muslims. The actions and message of the pastor eventually attracted the attention of figures as prominent as U.S. President Barack Obama and, with transmission around the world via the Internet, spread to countries far from the United States like Afghanistan and Indonesia. The response was explosive. The burning of sacred texts such as the Koran provides particularly rich opportunities for study by academics in a variety of fields. Topics like religion, politics, marginalization, nonverbal communication, intercultural communication, and hate speech come together. Digital dissemination adds an element of contemporary technology to the mix. Drawing upon the Florida Koran-burning case, this paper briefly examines the constitutional regulation of online hate speech in the United States, illustrating how limited the punishment and thus regulation of such hate speech generally are. Hate speech is discourse that aims to promote hatred based on categories such as ethnicity, race, national origin, class, and similar categories. The paper proceeds with a summary of the Florida Koran-burning case, continues with a discussion of relevant constitutional principles, and then moves to constitutional analyses of the Florida case.
FACIAL DETECTION IMPLEMENTATION USING PRINCIPAL COMPONENT ANALYSIS (PCA)

A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is used in security features and can be compared to other biometrics, fingerprint or iris recognition systems. Face Recognition is helpful in many ways like checking criminal records, enhancement of security, pattern recognition etc. This project mainly focuses on recognizing faces using Principal Component Analysis(PCA). It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences and also compress data. In this method, facial features are transformed in eigenspace using Principal Component Analysis, which is able to classify individual facial representations. Thus, trained faces have some Eigen values and weight. In order to identify faces, test images are compared with trained images. So, by comparing the weight (w) of an image with test images, we can recognize the actual face. The images are compared on the basis of eigenvalues and images with unique values are stored else they are discarded.
SISTEM PAKAR MENDETEKSI KERUSAKAN POMPA MORITA FIRE TRUCK MENGGUNAKAN METODE FORWARD CHAINING BERBASIS WEB

Detection pump damage Morita Fire Truck without requiring the help of an expert or technician, had to be considered that the presence of expert systems research is expected to be very useful and can be utilized to assist in pump repair Morita Fire Truck which if there is a damage can be repaired immediately with this expert systemThis method is a method with the concept of transferring the knowledge of an expert who has special expertise and knows well about pump damage and how to fix existing symptoms of damage, this knowledge is made into a system that can be accessed by people who do not know about the symptomsThe results of this study area device expert system that will shortly damage detection pump  Morita Fire Truck with the method of web-based expert system to detect damage to the pump Morita Fire Truck that has been applied into a website which can in per use in the repair process or treatment to overcome damage to the pump  Morita Fire Truck.
CT-SCAN KEPALA DENGAN KLINIS TRAUMA KAPITIS POST KECELAKAAN LALU LINTAS

Head injuries are mechanical trauma that occurs in the head. Head injuries are the most clinical cases that can be found in emergency units. Head injuries contribute to death events that are most compared to other types of trauma. Computerized tomography (CT) scans are radiogiagnostic methods that become the gold standard for diagnosing head injuries. The purpose of this examination is to be able to know the presence of bleeding or fracture, using the CT-Scan method of non-contrast head inspection can display the picture of the brain window/tissue overview on the Head, Bone window/bone image of the head and a 3-dimensional picture that displays a full view of the head from various angles.
Tools of the Trade : En granskning av sambandet mellan grafisk design a Adobes digitala verktyg

This bachelor thesis project treats the relationshipbetween graphic design and the digital tools and software offered by Adobe. The first part of the paper presents an investigation which through i ...
UWM Libraries Research and Course Guides: MLA Citation Guide (MLA 8th edition): Home

This guide is to help you in correctly creating in-text citations and a Works Cited page using the latest edition of the MLA Citation Guide
LibGuides: Online Safety, Security, and Privacy: Phishing

This guide looks at the concept of general online safety and security. A look at phishing attempts and how to avoid them.
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks

Over-smoothing has emerged as a severe problem for node classification with graph convolutional networks (GCNs). In the view of message passing, the over-smoothing issue is caused by the observed noisy graph topology that would propagate information along inter-class edges, and consequently, over-mix the features of nodes in different classes. In this paper, we propose a novel architecture, namely VEM-GCN, to address this problem by employing the variational EM algorithm to jointly optimize the graph topology and learn desirable node representations for classification. Specifically, variational EM approaches a latent adjacency matrix parameterized by the assortative-constrained stochastic block model (SBM) to enhance intra-class connection and suppress inter-class interaction of the observed noisy graph. In the variational E-step, graph topology is optimized by approximating the posterior probability distribution of the latent adjacency matrix with a neural network learned from node embeddings. In the M-step, node representations are learned using the graph convolutional network based on the refined graph topology for the downstream task of classification. VEM-GCN is demonstrated to outperform existing strategies for tackling over-smoothing and optimizing graph topology in node classification on seven benchmark datasets.
How accuracy of estimated glottal flow waveforms affects spoofed speech detection performance

In the domain of automatic speaker verification,  one of the challenges is to keep the malevolent people out of the system.  One way to do this is to create algorithms that are supposed to detect s ...