Reviewerly/exHarmony huggingface dataset:
IT Environment Analysis to Design Smart Campus System for Private University in Surabaya Based on Green Computing

The implementation of education is carried out in a modern way by applying the technology in the concept of smart campus. It is a means to win competition, in terms of providing various facilities for stakeholders and the academic community. In this study, an IT environment analysis was conducted to design a smart campus system at a private university based on green computing. This analysis in both internal and external IT condition. The internal IT environment analysis by using McFarlan Strategic Grid analysis and the external IT environment by using IT trend analysis. These analyzes are made by considering the concept of green computing, in the context of reducing costs and energy saving. The result of the study is a Current Application Portfolio and External IT Trend. This current portfolio shows the role and contribution of applications in university business operations. The current application portfolio divided into four quadrant namely strategic, key performance, support and key operation.
Tfidf and entropy for sports, news and gambling web content classification

The exponential increase in online data or information brought up the issue of information security. Gambling web content is one of the greatest harmful res ources that pollutes children's and adolescents ' minds by disguis ing sports and News Web pages. Gambling can be adopted by any gender or age. Several web content-based analytical approaches were proposed to prevent children from accessing these illegal web content. They are introduced to the Internet at an early age. Most approaches are weak to classify web content of high similarity such as Gambling, Sports and News Web pages. In this paper two existing term weighting schemes namely TFIDF and Entropy are used as feature selection process in filtering website. We examine the performance of both techniques via datasets and compare it with the term weighting schemes. The suitability of these term weighting schemes as the selection of features is measured according to the accuracy of the results obtained using the classification program known as Support Vector Machine (SVM). In this paper the performance of TFIDF and Entropy is judged on the basis of Accuracy. Results showed that TFIDF performed better than Entropy. On average, TFIDF obtained 97% and Entropy 91% accuracy. This study is hoped to give other researchers an insight, especially those who would like to work in the same area.
Preprocessing Unbalanced Data using Support Vector Machine with Method K-Nearest Neighbors for Cerebral Infarction Classification

Abstract Cerebral infarction is focal brain necrosis due to complete and prolonged ischemia that affects all tissue elements, neurons, glia, and vessels. Stroke infarction or known as cerebral infarction is a condition of damage in the brain due to insufficient oxygen supply, due to obstruction of blood flow to the area. Research shows stroke infarction does not only occur in the elderly, but occurs at a young age of around 15-55 years, especially with certain risk factors, such as diabetes, hypertension, heart disease, smoking, and long-term alcohol consumption. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. Therefore, it requires timely detection and more accurate methods of classification. This study aims to use Support Vector Machine (SVM) as preprocessing and K-Nearest Neighbors (KNN) algorithm to classify Infarction Cerebral. In this study, discusses the application of SVM to deal with class imbalances. The first strategy is to balance data using SVM as a preprocessor and the actual target value of the training data is then replaced by trained SVM predictions. Then, the modified training data is used to classify with K-NN method. We use data CT scan result from a Department of Radiology at Dr. Cipto Mangunkusumo Hospital (RSCM). This accuracy in this paper shows around 69,85 %.
Hypersphere Face Uncertainty Learning

An emerging line of research has found that \emph{hyperspherical} spaces better match the underlying geometry of facial images, as evidenced by the state-of-the-art facial recognition methods which benefit empirically from hyperspherical representations. Yet, these approaches rely on deterministic embeddings and hence suffer from the \emph{feature ambiguity dilemma}, whereby ambiguous or noisy images are mapped into poorly learned regions of representation space, leading to inaccuracies~\citep{shi2019probabilistic}. PFE is the first attempt to circumvent this dilemma. However, we theoretically and empirically identify two main failure cases of PFE when it is applied to hyperspherical deterministic embeddings aforementioned. To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space. Mathematically, we extend the \emph{von Mises Fisher} density to its r-radius counterpart and derive an optimization objective in closed form. For feature comparison, we also derive a closed-form mutual likelihood score for latents lying on hypersphere. Extensive experimental results on multiple challenging benchmarks confirm our hypothesis and theory, and showcase the superior performance of our framework against prior probabilistic methods and conventional hyperspherical deterministic embeddings both in risk-controlled recognition tasks and in face verification and identification tasks.
Perbandingan Metode Klasifikasi Data Mining untuk Prediksi Tingkat Kepuasan Pelanggan Telkomsel Prabayar

Algoritma C.45, Support Vector Machine, Naive Bayes, Particle Swarm Optimization, Tingkat kepuasan, Data Mining, Klasifikasi
The Analysis of Code Mixing of English Department Students of Universitas Bandar Lampung

Sociolinguistics is the study about language in a society. In learning about the language in society, there is a phenomenon called code mixing. In this study we focus on the reason of doing code mixing. It is to find the reasons of code mixing occur at English Department Universitas Bandar Lampung. This study uses qualitative research design aiming to analyze the reason of using code mixing. The results were gained from observation and interview. Observation is aimed to find out the words uttered to be classified into reasons. The interview goal is to get more information from the students. The result shows that some respondents tend to mix the language because they feel comfortable in doing this. They cannot express some words in Indonesian. To sum up, the code mixing occurs unintentionally and spontaneously.
Privacy-preserving Data Sharing with Attribute-based Private Matching Based on Edge Computation in the Internet-of-Things

The data sharing is essential for the analytics of specific tasks in internet-of-things (IoT). The availability and the latency of data exchange affect the validity of critical and real-time IoT services. Thus, a new computing model, i.e., edge computing model, is urgently required for data sharing in IoT. However, data sharing based on edge computing model needs to address additional security issues, i.e., the privacy protection of data acquisition and transmission against honest-but-curious edge devices. Thus, this work proposes a privacy-preserving data sharing with attribute-based private matching based on edge computation in IoT. In the proposed scheme, IoT users/devices can acquire/distribute data based on attribute-based private matching on honest-but-curious edge devices without exposing attribute/policy information and exchanged data. Moreover, the proposed scheme guarantees anonymous IoT user/device authentication with the support of handover between the regions covered by two adjacent edge devices. Nonetheless, the data transmission is of secure end-to-end communications between IoT users and devices to reduce the consumption of bandwidth between IoT users/devices and edge devices. Finally, this work implements the system to evaluate the performance and provides the security analysis of the proposed security system.
Unsupervised Gaze Prediction in Egocentric Videos by Energy-based Surprise Modeling

Egocentric perception has grown rapidly with the advent of immersive computing devices. Human gaze prediction is an important problem in analyzing egocentric videos and has largely been tackled through either saliency-based modeling or highly supervised learning. In this work, we tackle the problem of jointly predicting human gaze points and temporal segmentation of egocentric videos, in an unsupervised manner without using any training data. We introduce an unsupervised computational model that draws inspiration from cognitive psychology models of human attention and event perception. We use Grenander's pattern theory formalism to represent spatial-temporal features and model surprise as a mechanism to predict gaze fixation points and temporally segment egocentric videos. Extensive evaluation on two publicly available datasets - GTEA and GTEA+ datasets show that the proposed model is able to outperform all unsupervised baselines and some supervised gaze prediction baselines. Finally, we show that the model can also temporally segment egocentric videos with a performance comparable to more complex, fully supervised deep learning baselines.
C-Learning: Horizon-Aware Cumulative Accessibility Estimation

Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks. In order to address these limitations, we introduce the concept of cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that these functions obey a recurrence relation, which enables learning from offline interactions. We also prove that optimal cumulative accessibility functions are monotonic in the planning horizon. Additionally, our method can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We evaluate our approach on a set of multi-goal discrete and continuous control tasks. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality. Our code is available at https://github.com/layer6ai-labs/CAE, and additional visualizations can be found at https://sites.google.com/view/learning-cae/.
On the Determinants of Environmental Information Disclosure: Evidences from Industrial Saudi Listed Firms

This paper discusses the determinants of corporate environmental disclosure by industrial Saudi listed firms. Using a sample of 63 industrial firms listed in Saudi Arabia between 2016 and 2018 and a fixed effect panel data, we test the effect of corporate governance variables, firms' characteristics and CEOs’ financial education on the level of environmental information disclosure. Two measures of corporate environmental disclosure are used based on Global Reporting Initiative and the content analysis technique. Our results show that the presence of environmental committee and the firm’s age are the most relevant factors that can influence the corporate environmental disclosure in annual reports in the Saudi context. In order to urge Saudi firms to pay more attention to the environment and adopt strategies to conserve it as well as disclose environmental information, it is suggested that decision makers change the nature of the environmental committee from an optional committee to a mandatory committee.
Impacts of Mobile Phones on Our Youth Today

No one can deny the positive effects of mobile phones. Mobile phones have not only given us the power to talk wirelessly, but also provided us with many highly usable functionalities like taking pictures and videos, listening to music, playing games, accessing the Internet and much more. Mobile phones have taken over our modern world. It seems almost every person in every major city has one. The mobile phone users have increased rapidly globally due to the development of the technology. It is now crucial part of majority of the lives of the youth. This is because there are so many applications available on the mobile phone these days. It is now an integral part of adolescents’ daily lives and is for the majority, the most popular form of electronic communication. The current mobile market has a close relationship with the teenagers. As a trend, teenagers are more inclined to the newest gadgets of any sort. Whenever a new multimedia or electronic gadget is launched-just see the queue in any outlet-you will see a large number of youngsters filing the crowd-isn't that? Frankly speaking, youngsters are becoming tech-savvy than ever; It is now crucial part of majority of the lives of the youth. However, researches say that excessive use of mobile phones may cause serious problems to anybody in different aspects. This paper presents the impacts, especially negative impacts of mobile phones on our youth today.
The Mineral Adventures and Collection of Bruce Carter

The sparkling water‐clear cluster of quartz stood on his older sister's bureau for as long as Bruce Carter could remember. Safely in Carol's room, he could escape the wrath of his parents, who ador...
VideoFlow: A Framework for Building Visual Analysis Pipelines

The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks. These frameworks have significantly facilitated algorithm development in multimedia research and production. However, how to easily and efficiently build an end-to-end visual analysis pipeline with these algorithms is still an open issue. In most cases, developers have to spend a huge amount of time tackling data input and output, optimizing computation efficiency, or even debugging exhausting memory leaks together with algorithm development. VideoFlow aims to overcome these challenges by providing a flexible, efficient, extensible, and secure visual analysis framework for both the academia and industry. With VideoFlow, developers can focus on the improvement of algorithms themselves, as well as the construction of a complete visual analysis workflow. VideoFlow has been incubated in the practices of smart city innovation for more than three years. It has been widely used in tens of intelligent visual analysis systems. VideoFlow will be open-sourced at \url{https://github.com/xxx/videoflow}.
Visualización de Imágenes Médicas de Alta Resolución  Mediante una Aplicación Zero Footprint

In the last few years there has been an increasing interest in the acquisition of medical images stored and distributed through PACS (Picture Archiving and Communication Systems). Also, several tools for remote diagnosis have arisen; therefore, Web-PACS have been proposed for image handling. However, most web browsers are unable to visualize DICOM (Digital Imaging and Communication in Medicine) images in its native format, consequently browsers require some application to be installed on the web client.In this work, the development of a web viewer for DICOM images is presented. The viewer can run in any browser that supports HTML5, without any previous installation (zero-footprint viewer). A progressive transmission scheme is considered to minimize the image visualization time, and to assure its diagnostic quality. The performance of the progressive image transmission algorithm has been evaluated by means of the Time-To-Display metric.
Explicit Connection Distillation

One effective way to ease the deployment of deep neural networks on resource constrained devices is Knowledge Distillation (KD), which boosts the accuracy of a low-capacity student model by mimicking the learnt information of a high-capacity teacher (either a single model or a multi-model ensemble). Although great progress has been attained on KD research, existing efforts are primarily invested to design better distillation losses by using soft logits or intermediate feature representations of the teacher as the extra supervision. In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation. ECD has two interdependent modules. In the first module, given a student network, an auxiliary teacher architecture is temporarily generated conditioned on strengthening feature representations of basic convolutions of the student network via replacing them with dynamic additive convolutions and keeping the other layers unchanged in structure. The teacher generated in this way guarantees its superior capacity and makes a perfect feature alignment (both in input and output dimensions) to the student at every convolutional layer. In the second module, dense feature connections between the aligned convolutional layers from the student to its auxiliary teacher are introduced, which allows explicit layer-to-layer gradients propagation from the teacher to the student via the merged model training from scratch. Intriguingly, as feature connection direction is one-way, all feature connections together with the auxiliary teacher merely exist during training phase. Experiments on popular image classification tasks validate the effectiveness of our method. Code will be made publicly available.
Classification of Academic Events from Their Textual Description

The aim of this paper is to compile dictionaries of slang words, abbreviations, contractions and emoticons to help the preprocessing of texts published in social networks. The use of these dictionaries is intended to improve the results of the tasks related to data obtained from these platforms. Therefore, a hypothesis was evaluated in the task of identifying author profiles (author profiling).
Learning to Recover from Failures using Memory

Learning from past mistakes is a quintessential aspect of intelligence. In sequential decision-making, existing meta-learning methods that learn a learning algorithm utilize experience from only a few previous episodes to adapt their policy to new environments and tasks. Such methods must learn to correct their mistakes from highly-correlated sequences of states and actions generated by the same policy's consequent roll-outs during training. Learning from correlated data is known to be problematic and can significantly impact the quality of the learned correction mechanism. We show that this problem can be mitigated by augmenting current systems with an external memory bank that stores a larger and more diverse set of past experiences. Detailed experiments demonstrate that our method outperforms existing meta-learning algorithms on a suite of challenging tasks from raw visual observations.
TO THE EDUCATION OF OPERATIONAL THINKING IN THE HIGHER MATHEMATICS COURSES OF TECHNICAL UNIVERSITIES

The article is devoted to the tasks of operational thinking developing in the process of teaching the higher mathematics courses in technical universities. It considers the questions on the mathematics importance in human activity and notes its role as a useful tool for penetrating into any area of human knowledge in order to turn it into scientific knowledge. A comprehensive comparative discussion of the definitions of mathematical and operational thinking was carried out. The authors analyzed the current social development conditions influencing the teaching methods and the possibilities of upbringing and improving students’ critical thinking skills. It is proposed to form the mental activity of students (including the creative one) from the very beginning of their studies in the higher education institutions, using to that end in accordance with the future specialty, tasks with the need for mathematical modeling and experimental research of issues. The necessity and methods of forming an educational and methodological environment for operational activities with concepts of varying degrees of abstraction are clarified. The authors give examples of a differentiated and structural approach to teaching mathematics in particular to the creation of educational problems in the higher education institutions. They show that already from the 1st year in teaching students it is necessary to take into account their “engineering” (“physical”) or “algorithmic” way of thinking.
InterCriteria Analysis Approach as a Tool for Promising Decision Making in Physiological Rhythms

Recently developed InterCriteria Analysis (ICrA) approach has being intensively gained popularity as quite promising approach to support decision making process in biomedical informatics studies, and in particular—in physiological rhythms. ICrA has been elaborated to discern possible similarities in the behaviour of pairs of criteria when multiple objects are considered. The approach is based on the theories of intuitionistic fuzzy sets and index matrices. Up to now, ICrA has been successfully applied in economics, different industry fields, ecology, artifficial intelligence, e-learning, etc. ICrA has been demonstrated as promising tool also in studies related to medicine and bioinformatics, which are in the focus of this investigation.
Three-dimensional structural characteristics of the Zhaxikang ore-concentration area based on multiple attribute constraints

Abstract The Zhaxikang ore-concentration area (ZOCA) is in the Gangdese metallogenic belt in the southern Tibet Plateau. It has huge prospecting potential. Most of the mineral resources on the Gangdese metallogenic belt are controlled by the dome and fault. The deep structure and the coupling relationship between the Cuonadong dome and the deep structure are of great significance for understanding ore-controlling structures and hydrothermal channels. Existing studies have been controversial in the study of whether there are hidden structures in the deep part of the Zhaxikang deposit, and whether the Cuonadong dome extends to the bottom of the Zhaxikang deposit in the Gangdese metallogenic belt. Therefore, we comprehensively analyzed a large number of gravities, magnetic, and magnetotelluric data in the Zhaxikang area and carried out a fusion display of multiple information in the Zhaxikang ore-concentration area. Understand the relationship between the Cuonadong dome, fault system and deep hydrothermal channel through fusion display to analyze the background of Zhaxikang metallogenesis. The deep structure of the Cuonadong dome was identified, and it was found that there was a hidden structure at the bottom of Zhaxikang that was not connected to the Cuonadong dome. These findings are a great significance for further theoretical research and breakthroughs in ore prospecting.