Reviewerly/exHarmony huggingface dataset:
APLIKASI VERIFIKASI DATA PEMBELIAN GAS LIQUEFIED PETROLEUM GAS MENGGUNAKAN TEKNOLOGI QR CODE BERBASIS ANDROID

Data processing using applications in the trade recording process can make a major contribution to the effectiveness, efficiency and facilitate entrepreneurs in processing sales, purchasing and inventory data. However, at this time there are still many sellers who do the recording / bookkeeping of buying and selling transactions manually, one of which is the sale of LPG Gas at Triple D base, which is still buying and selling LPG Gas by manually recording the Family Card to collect data on residents who will buy LPG gas. The researcher intends to solve this problem by making an application for buying and selling transactions by creating a QR Code for each family card using ROT13 to encrypt the data to be scanned. This method can be applied so that purchases and sales at the Triple D LPG gas base become more effective and efficient and minimize errors in bookkeeping transactions.
Statistical Properties Of Single-mode Displaced Squeezed Vacuum State

In this paper the displaced squeezed vacuum state is produced by application of displaced operator on squeezed vacuum state. With help of density operator we find Q function, with the Q function mean, variance and quadrature variance would be calculated. From this we can determine the system has superpoissonian statics, the squeezed parameter is direct proportion with both mean and variance of photon number, but inversely proportion with quadrature variance. The squeezing occurs in plus quadrature with the maximum squeezing of 99.7% for r=3.
LibGuides: Streaming Video Resources: News

This guide will provide students, faculty, and staff with a number of streaming platforms available through the University of Illinois Libraries, as well as free streaming resources.
LibGuides: Computer Engineering research guide: Home

Lists websites, print books, e-books and other sources to provide research assistance with current information on computer engineering.
The Role and Place of Pedagogical Techniques in Improving the Professional Skills of Teachers

Under the context of growing demand for teachers’ professional training, the issues of the formation and improvement of their pedagogical mastery become of current interest. A teacher who wants to develop his/her pedagogical skills shall constantly learn to apply pedagogical methods in his/her work. After all, the professional competence of a teacher directly depends on the possession of professional technique. The purpose of this work is to clarify the concept of “pedagogical technique” and determine its place and role in improvement of teacher’s professional mastery. In this regard, methods of analysis, synthesis, comparison, generalization, and clarification were used. The narrative of the importance of clarifying the definition of the term is presented for defense and the version proposed is as follows: “Pedagogical technique is an important component within the structure of pedagogical skills, their forms, practical manifestation, through which a high level of pedagogical professionalism can be formed and developed”. The knowledge of structure of pedagogical methods and formation of appropriate behavioral skills increase the teacher’s professionalism. Therefore, in teacher’s activities, pedagogical technique steps forward as a system of social and pedagogical support of the educational process, which performs a number of functions in teaching and educational work.
AI, digital identities, biometrics, blockchain: A primer on the use of technology in migration management. Bertelsmann Stiftung Migration Strategy Group on International Cooperation and Development June 2020.

Digitalization and technological change are rapidly transforming every aspect of our societies and economies, and the migration and refugee policy space is no exception. Technology is already affecting migrants, refugees, and people on the move in many ways, but policymakers have yet to systematically address the different uses of technology in the migration management field. The COVID-19 pandemic is likely to accelerate these digitalization processes, making fast policy adaptation crucial.Technological changes range from broader developments, such as increasing digital connectivity in general — via smart or mobile phones, messaging services and web-based applications, or app-based systems — to more tech-centered applications: Karim the Chatbot X2AI i has provided virtual psychotherapy to Syrians in Zaatari refugee camp; AI-powered Free Robot Lawyers is offering legal help to migrants and refugees; and the non-profit REFUNITE (with more than1 million registered users) helps refugees to find missing family members via mobile phone or a computer. Digital connectivity is providing new options for migrants and refugees to gain access to training or education via online learning platforms and Massive Open Online Courses (MOOCs), or to services delivered virtually by NGOs, international organizations, or governments. Combined with the global spread of social media use, this connectivity has also created new (dis)information ecosystems in the migration space that policy makers must grapple with.
Speech Error of Reporters in Breaking News on CNN

This research was conducted to analyze the speech errors and the type of speech errors carried out by reporters in delivering breaking news via the CNN YouTube video. The researcher tried to answer two research questions, namely, what are the types of speech errors that reporters make on Breaking News CNN and what types of speech errors made by reporters in CNN news are dominant. The current study is based on Clark and Clark's theory concerning speech errors and their types. This research was conducted using descriptive qualitative methods. Data collection procedures involve observing, transcription, identification, classification, analyzing, and reporting processes. The data is taken through the YouTube video braking news CNN. After the analysis was carried out, the results showed that there were five types of speech errors. Namely silent pause, filled pause, repeats, corrections, slip of tongue. After the analysis, the researchers found that slip of tongue was the most dominant type in reporters’ speech with 15 data (75%), corrections with 2 data (10%), repeats 1 datum (5%), filled pause 1 datum (5%), silent pause 1 datum (5%). Finally, the researcher hopes that this research can be useful for the advancement of literature learning in the future, especially regarding speech errors.
IMPLEMENTASI METODE ANALITYCAL HIERARCHY PROCESS (AHP) DAN TOPSIS UNTUK PEMILIHAN KAMERA

In today’s techonologically sophisticated ,camera are an unusual item number one amoung the millennial. Camera are the property of every millennial now, The camera mirrolless is itself used to take video and picture at an excellent resolution and the result will be presented by social media user. The user have option to consider according fit the requirement. The study is conducted to recommend prospective buyer in camera selection with a consideration of photographic ,sutter speed, video resolution, sensitivity and battery resistance. Web-based decision-making system using PHP and MySQL database, by using AHP method and TOPSIS. The AHP is an analytic method for solving flexible quantitative and qualitative. AHP has an advantage on the scale of the measurement given to the structure of the hierarchy, but the AHP method has a boundary in the number of comparison used that requires a TOPSIS method, then the approach of TOPSIS has a simple but regular and logical method of steps that form a system. The result of this camera study which is recommended from the approach, alternative selection has an indirect distance from the desired distance.
SELF-DEVELOPMENT OF THE PERSONALITY OF THE LEADER AS A LEADER OF INNOVATION IN THE CONDITIONS OF SYSTEM CHANGES

&nbsp;The urgency of the work is that globalization and the dynamic development of modern society have led to the emergence of "smart-society" as a smart and high-tech, characterized by continuous development of innovations, the active implementation of which has a great impact on economy, production, culture, education, life. , social sphere, scientific research. Innovation management and interaction with existing innovative technologies become integral components of organization management as a socio-economic system and subsystems in the form of structural units that require creative workers who are able to work in a competitive environment. The aim of the research is the theoretical substantiation of innovative activity as a factor of self-development of the leader's personality. Tasks of research and formation of the purposes of article: to investigate a place and a role of the head as the strategic leader of a team; to find out the development of the leader's personality as a new type of innovative leader; identify areas of innovation as a factor in the self-development of the leader in terms of systemic change and form a program of self-development of the leader's personality; to substantiate innovative technologies in the field of management and the need for their implementation in a smart society. The object of research is the personality of the leader as a leader of innovation and change in the context of systemic change. The task in accordance with the purpose and object of research is to theoretically substantiate the need for innovative leadership as a factor in self-development of the leader, the development of tools for personal effectiveness, their implementation and determine the impact of modern innovative technologies on self-development. Research methods: theoretical and methodological analysis of literature, synthesis of information retrieval concepts, content analysis of scientific works, method of grouping information and generalization of scientific literature, periodicals and sources of information on the Internet, systematization of information; analysis and synthesis, abstraction, systemic, structural, structural-functional. The novelty of the study lies in the justification of the need for the head to perform the functions of an innovative leader and a combination of professional and personal qualities in a digital civilization
THE IMPACT OF THE NEW CORONAVIRUS PANDEMIC ON THE CARNIVAL OF OURO PRETO (MG)

The outbreak of infectious disease caused by the virus known as SARS COVID-19, began in December 2019 in the province of Hubei in China, due to its high levels of contamination spread rapidly around the world becoming a pandemic This scenario has repercussions for all sectors of the world economy, mainly for the tourism and events sector, since social isolation is one of the most effective ways to prevent the transmission of the virus that occurs from person to person Thus, from the qualitative approach and with a descriptive-exploratory objective, this article aims to explore the implications of this pandemic at the Ouro Preto-MG Carnival in 2021 The research brought about the fact that the pandemic is causing changes in the way how the Ouro Preto Carnival and university blocks are organized, and highlighted how the event will be held next year, as the carnival date will be postponed to the second half of 2021
A Hybrid Sentiment Analysis Method of Transformer and Capsule Network for Hotel Reviews

As a significant branch of natural language processing, deep learning-based sentiment analysis has dominated in text sentiment analysis instead of lexicon graph and machine learning methods.To further improve the quality of sentiment analysis, we propose a hybrid sentiment analysis method of transformer and capsule network for hotel reviews.The proposed approach takes advantages of both self-attention mechanism in transformer and detailed representation in capsule network to capture bidirectional semantic features well.Compared with the traditional RNN, CNN and pure transformer, the hybrid sentiment analysis method of transformer and capsule network performs 13.83%, 8.97%, and 8.02% higher accuracy for an open-source dataset of hotel reviews respectively.The comprehensive experiments results demonstrate that our proposed method achieves higher quality of sentiment analysis than latest methods.
LibGuides: Guide to the Library: Library Support Services

This guide is a break down of what the library can offer and how we can help you be your best (student) self!
Proof of No-Work: How to Incentivize Individuals to Stay at Home.

The recent pandemic of novel coronavirus introduces a new challenge to balancing social welfare, the economy, and privacy while reducing contact among individuals. To reduce the reproduction rate without spoiling our economy, we need good incentive mechanisms to reduce the possibility of spreading the virus as well as good privacy enhancing techniques. Unfortunately, some of the recent approaches in contact tracing are not successful due to privacy concerns and a lack of sufficient incentive mechanisms to guide behavior instead of simply tracking infections. In this paper, we provide a design using smart contracts as an incentive mechanism with enhanced privacy of user location information. We utilize encrypted data calculated from a set of network routing information, and a plaintext equality test of a public key cryptosystem to estimate the duration one is present at the same location. By staying at the same location longer, a user can obtain greater rewards. We have implemented a proof concept of this scheme to evaluate its efficiency. We also discuss financial regulation and economic viewpoints.
A Deep Learning Approach for a Source Code Detection Model Using Self-Attention

With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement trees with a deep-first traversal algorithm. Finally, the representation model encodes the sequence of statement vectors via a bidirectional LSTM network, which is a classical deep learning framework, with a self-attention layer and outputs a vector representing the given source code. The discriminative model identifies the code clone depending on the vectors generated by the presentation model. Our proposed model retains both the syntactics and semantics of the source code in the process of encoding, and the self-attention algorithm makes the classifier concentrate on the effect of key statements and improves the classification performance. The contrast experiments on the benchmarks OJClone and BigCloneBench indicate that At-LSTM is effective and outperforms the state-of-art approaches in source code clone detection.
Supervised quality evaluation of binary partition trees for object segmentation

The binary partition tree (BPT) allows for the hierarchical representation of images in a multiscale way, by providing a tree of nodes corresponding to image regions. In particular, cuts of a BPT can be interpreted as segmentations of the associated image. Building the BPT of an image then constitutes a relevant preliminary step for optimization-based segmentation methods. A wide literature has been devoted to the construction of BPTs, and their involvement in such segmentation tasks. Comparatively, there exist few works dedicated to evaluate the quality of BPTs, i.e. their ability to allow further segmentation methods to compute good results. We propose such a framework for evaluating the quality of a BPT with respect to the object segmentation problem, i.e. the segmentation of one or several objects from an image. This framework is supervised, since the notion of segmentation quality is not only depending on the application but also on the user's objectives, expressed via the chosen ground-truth and quality metric. We develop two sides within this framework. First, we propose an intrinsic quality analysis, that relies on the structural coherence of the BPT with respect to ground-truth. More precisely, we evaluate to what extent the BPT structure is well-matching such examples, in a set / combinatorial fashion. Second, we propose an extrinsic analysis, by allowing the user to assess the quality of a BPT based on chosen metrics that correspond to the desired properties of the subsequent segmentation. In particular, we evaluate to what extent a BPT can provide good results with respect to such metrics whereas handling the trade-off with the cardinality of the cuts.
Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based Artificial Algae Algorithm

Recent scientific studies have noted that the seminal quality of males is significantly decreasing due to lifestyle and environmental factors. Clinical diagnosis of sperm quality is one important aspect of identifying the potential of semen for the occurrence of pregnancy. Due to the advances in machine learning algorithms, especially the reliable and high classification accuracy of neural network in health related problems, it is becoming possible to predict seminal quality from lifestyle data. In this respect, a few attempts were made in predicting seminal quality. These studies were conducted using imbalanced datasets, where the performance outcomes tend to be biased towards the majority class. Other studies implemented the gradient descent technique for training the neural network. The gradient descent is a local training technique that is prone to get stuck to local minima. On the contrary, meta-heuristic algorithms enable searching solutions both locally and globally. Therefore, in this study, Artificial Algae Algorithm that is improved using a Learning-Based fitness evaluation method is proposed for training Feed Forward Neural Network (FFNN). In addition, the SMOTE data balancing method was employed to balance normal and abnormal instances. Experimental analyses were carried out to evaluate the predictive accuracy of the FFNN trained using Learning-Based Artificial Algae Algorithm (FFNN-LBAAA). The results were compared with well-known machine learning algorithms, namely: Multi-layer Perceptron Neural Network (MLP), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The proposed approach showed superior performance in discriminating normal and abnormal semen quality instances over the other compared algorithms.
Sentimental prediction model of personality based on CNN-LSTM in a social media environment

The paper first analyzes the correlation between text sentiment values and personality traits, proves that text sentiment can have a good support effect on user personality prediction, then on this basis, a method based on CNN-LSTM is proposed, which can be used to deeply analyze the sentiment analysis capability of the model, hoping to improve the precision of sentiment classification and lay a solid foundation for the next experiment. This experiment proves that the CNN-LSTM constructed in this paper can better predict the emotional tendency of the short text of microblog, has good generalization ability, and has higher precision than other methods.
DefenseChain: Consortium Blockchain for Cyber Threat Intelligence Sharing and Defense

Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require co-operation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain”, to allow organizations to have incentive-based and trustworthy co-operation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform to obtain threat data and select suitable peers to help with attack detection and mitigation. We propose an economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses ‘Quality of Detection’ and ‘Quality of Mitigation’ metrics. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. In addition, we show that our DefenseChain achieves better performance trade-offs in terms of metrics such as detection time, mitigation time and attack reoccurence rate. Lastly, our validation results demonstrate that our DefenseChain can effectively identify rational/irrational service providers.
Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition

Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention. Many efforts have been paid to turn the non-streaming attention-based E2E-ASR system into streaming architecture. In this work, we propose a novel online E2E-ASR system by using Streaming Chunk-Aware Multihead Attention(SCAMA) and a latency control memory equipped self-attention network (LC-SAN-M). LC-SAN-M uses chunk-level input to control the latency of encoder. As to SCAMA, a jointly trained predictor is used to control the output of encoder when feeding to decoder, which enables decoder to generate output in streaming manner. Experimental results on the open 170-hour AISHELL-1 and an industrial-level 20000-hour Mandarin speech recognition tasks show that our approach can significantly outperform the MoChA-based baseline system under comparable setup. On the AISHELL-1 task, our proposed method achieves a character error rate (CER) of 7.39%, to the best of our knowledge, which is the best published performance for online ASR.
A Survey on Crowd Counting Methods and Datasets

Recent successful works of crowding counting are introduced. We summarize several classic achievements of traditional methods: regression methods, detection methods, and density map estimation methods. Some CNN models are categorized according to its function and structure. Specially, we discuss some problems have solved by CNN models like different scale, different background, and lack of label. CNN methods rely highly on the dataset, so several classic and popular datasets and some newly released dataset are presented. At last, we recognized probably the most convincing difficulties and issues which are investigated in crowd counting and density estimation utilizing computer vision and machine learning methods.