Reviewerly/exHarmony huggingface dataset:
Video Streaming Service Identification on Software-Defined Networking

It is well known that video streaming is the major network traffic today. Futhermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, SoftwareDefined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm that takes advantage of Artificial Intelligence (AI) by using Machine Learning approaches, which allows integrating behavioural models to detect patterns, like video streaming traffic identification, in SDN traffic. In our initial proof-of-concept, we derive the relevant information of network traffic in the form of flows statistics. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.
Is the Current Account Imbalance of Korea Sustainable?

Purpose - This research examines the sustainability of the current account imbalance for Korea. On the theoretical basis of the intertemporal optimization approach to the current account, we tested for a long-run dynamic budget constraint of export and import plus net interest payment. Design/Methodology/Approach - Nonlinear asymmetric autoregressive-distributional lag methodology proposed by Shin, Yu and Matthew (2014) was applied as an econometric analytical tool. The Korean balance of payments data was adopted in the periods of 2001Q1~2021Q1. Findings - Our empirical results found that the Korean current account imbalance is sustainable when imports plus net interest payments increase, but unsustainable in the case of imports plus net interest payments decrease. That means the long-run dynamic budget constraint holds in asymmetric form. Research Implications - Asymmetric aspects of intertemporal dynamic budget constraints, e.g, asymmetric sustainability of current accounts is the important differentiation of this research. The current account surplus imbalance of Korea is hard to sustain when it comes to decreasing absorption led by decreasing domestic investment. A current account surplus imbalance could be sustainable at a desirable level, and absorption should be increased with more domestic investment.
TTUHSC El Paso Library: College Colloquium (Textbooks): Readings for the College Colloquium

This guide contains information about accessing the readings for Dr. Pfarr's College Colloquium. This guide will provide you with links or information about how to obtain the readings for the College Colloquium.
Anomaly event detection and localization of video clips using global and local outliers

The automatic detection of anomaly events in video sequence has become a critical issue and essential demand for the extensive deployment of computer vision systems such as video surveillance applications. An anomaly event in video can be denoted as outlier behavior within video frames which formulated by a deviation from the stable scene. In this paper, an anomaly event detection and localization method in video sequence is presented including multilevel strategy as temporal frames differences estimation, modelling of normal and abnormal behavior using regression model and finally density–based clustering to detect the outliers (abnormal event) at clips level. Hence, outlier score is obtained at the segment or clip level along video frames sequences. The proposed method seplits video frames into non-overlapped clips using global outlier detection process. Afterward, at each clip, the local outliers are determined based on density of each clip.  Extensive experiments were conducted upon two public video datasets which include dense and scattered outliers along video sequence. The experiments were performed on two common public datasets (Avenue and UCSD). The experimental results exhibited that the proposed method detects well outlier frames at clip level with lower computational complexity comparing to the state-of-the-art methods.
Constrained Clustering Problems: NewOptimization Algorithms

Constrained clustering problems are often considered in massive data clustering and analysis. They are used in modeling various issues in anomaly detection, classification, systems’ misbehaviour, etc. In this paper, we focus on generalizing the K-Means clustering approach when involving linear constraints on the clusters’ size. Indeed, to avoid local optimum clustering solutions which consists in empty clusters or clusters with few points, we propose linear integer programming approaches based on relaxation and rounding techniques to cope with scalability issues. We show the efficiency of the new proposed approach, and assess its performance using five data-sets from different domains.
Paradigm integration of fine art training for would-be professionals of art at high school at theory and local experience

In accordance with historic analysis, the article considers the valued traditional and innovative fine arts studios of artists of different generations; they help to identify specific artistic features of artists of different generations who create artistic works of the information world. We describe the genesis of digital art practice development and demonstrate its influence on the renovation of classic fine art classification system via the digital works of different types and genres. The limits of artistic amateur field as a factor of professional and profane blurring in an artist’s personality are clarified. The new concept of “digital paradigm of fine art training at higher educational institutions” is proposed in the article as an innovative method of specific subjects studying. Methodology of professional skills development while designing digital works for students of artistic profession at higher education was justified and the results of local experience of its implementation at higher educational institutions of Ukraine were described.
Few-Shot Visual Relationship Co-Localization

In this paper, given a small bag of images, each containing a common but latent predicate, we are interested in localizing visual subject-object pairs connected via the common predicate in each of the images. We refer to this novel problem as visual relationship co-localization or VRC as an abbreviation. VRC is a challenging task, even more so than the well-studied object co-localization task. This becomes further challenging when using just a few images, the model has to learn to co-localize visual subject-object pairs connected via unseen predicates. To solve VRC, we propose an optimization framework to select a common visual relationship in each image of the bag. The goal of the optimization framework is to find the optimal solution by learning visual relationship similarity across images in a few-shot setting. To obtain robust visual relationship representation, we utilize a simple yet effective technique that learns relationship embedding as a translation vector from visual subject to visual object in a shared space. Further, to learn visual relationship similarity, we utilize a proven meta-learning technique commonly used for few-shot classification tasks. Finally, to tackle the combinatorial complexity challenge arising from an exponential number of feasible solutions, we use a greedy approximation inference algorithm that selects approximately the best solution. We extensively evaluate our proposed framework on variations of bag sizes obtained from two challenging public datasets, namely VrR-VG and VG-150, and achieve impressive visual co-localization performance.
Smartphone-based hybrid 3D profilometry with an extended imaging depth range based on binary code and phase-shifting

For the emerging demands of three-dimensional (3D) profile measurement in daily life, a smartphone-based 3D profilometry based on structured light was presented in this paper. To measure 3D profiles within a large depth range, a method combing binary code and phase-shifting was employed. Applying anti-phase complementary binary code patterns successfully overcame the defocusing issue of both the camera and projector. It also helped reduce the influence of objects’ surface reflectivity and the phase unwrapping error. For a depth range of 1100 mm, an average height reconstruction error lower than 2 mm can be achieved with this system.
THE ASSOCIATION BETWEEN SARS-COV-2 INFECTION AND INCREASE IN DISEASE ACTIVITY IN RHEUMATOID ARTHRITIS PATIENTS OF PROJECT REUMACOV IN MANAUS/AMAZONAS

Viral infections can not only be a trigger, but a worsening factor in the context of immune mediated rheumatic diseases (IMRDs).Thus, it is important to measure the impact of the new coronavirus SARS-CoV-2 infection, also known as COVID-19, in IRMDs' activity.This study has the objective of testing the association between SARS-CoV-2 infection and increase in disease activity in rheumatoid arthritis patients at the research center located in Manaus, Amazonas state.
Methods to Perform Opinion Mining and Sentiment Analysis to Detect Factors Affecting Mental Health

Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to remember that the developers are human and might pass the biasness to the algorithms during training. The main target of this paper is to give background knowledge on opinion extraction and sentimental analysis and how factors affecting mental health can be collected. The paper aimed to use interested individuals in knowing some of the algorithms in opinions extraction and sentimental analysis. The paper also provides benefits of using sentiment analysis and some of the challenges of using the algorithms.
A Multimodal Learning to Rank model for Web Pages

“Learning-to-rank” or LTR utilizes machine learning technologies to optimally combine many features to solve the problem of ranking. Web search is one of the prominent applications of LTR. To improve the ranking of webpages, multimodality based Learning to Rank model is proposed and implemented. Multimodality is the fusion or the process of integrating multiple unimodal representations into one compact representation. The main problem with the web search is that the links that appear on the top of the search list may be either irrelevant or less relevant to the user than the one appearing at a lower rank. Researches have proven that a multimodality based search would improve the rank list populated. The multiple modalities considered here are the text on a webpage as well as the images on a webpage. The textual features of the webpages are extracted from the LETOR dataset and the image features of the webpages are extracted from the images inside the webpages using the concept of transfer learning. VGG-16 model, pre-trained on ImageNet is used as the image feature extractor. The baseline model which is trained only using textual features is compared against the multimodal LTR. The multimodal LTR which integrates the visual and textual features shows an improvement of 10-15% in web search accuracy.
THE IMPLEMENTATION OF AUDIO VISUAL MEDIA TO IMPROVE STUDENTSâ€™ SKILL IN WRITING RECOUNT TEXTS TO THE NINTH GRADE STUDENTS OF SMP SWASTA PABA BINJAI IN THE ACADEMIC YEAR OF 2020/2021

The objectives of this Classroom Action Research (henceforth, CAR) are to prove that the audio visual media can improve studentsâ€™ skill in writing recount texts and to describe the studentsâ€™ responses after being taught with audio visual media. Audio visual media were applied to improve the writing skill of the ninth grade students of SMP Swasta PABA Binjai. After six meetings it could be seen that there is a significant improvement of the studentsâ€™ audio visual media in writing recount texts. The result of the test showed that the studentsâ€™ mean score in pre-test is 40,23 with 18 % of students got score â‰¥ 75, in formative test is 59,09 with 36% of students got score â‰¥ 75, and in the post test is 72,73 with 73 % of students got score â‰¥ 75. The percentage students who strongly agree is 60 (66,6%), agree is 30 (33,3%), there is no students choose strongly disagree and disagree. It is suggested that English teachers use audio visual media as an alternative in teaching recount texts to their students.
Tools for Data Governance

This article describes the challenges of data governance in terms of the broader framework of knowledge commons governance, an institutional approach to governing shared knowledge, information, and data resources. Knowledge commons governance highlights the potential for effective community- and collective-based governance of knowledge resources. The article focuses on key concepts within the knowledge commons framework rather than on specific law and public policy questions, directing the attention of researchers and policymakers to critical inquiry regarding relevant social groups and relevant data “things.” Both concepts are key tools for effective data governance.
Exploration via Distributional Reinforcement Learning with Epistemic and Aleatoric Uncertainty Estimation

The problem of exploration remains one of the major challenges in deep reinforcement learning (RL). This paper proposes an approach to improve the exploration efficiency for distributional RL. First, this paper proposes a novel method to estimate the epistemic and aleatoric uncertainty for distributional RL using deep ensembles, which is inspired by Bayesian Deep Learning. Second, This paper presents a method to improve the exploration efficiency for deep distributional RL by using estimated epistemic uncertainty. Experimental results show that the proposed approach outperforms the baseline in Atari games.
Effectiveness of Mobile Assisted Pronunciation Training in the Acquisition of English Vowels, /o/ and /ɔ/

With the wide availability of mobile devices, efforts to accommodate these technological devices have been made in pronunciation training. This study aimed to investigate the effectiveness of mobile apps in improving the perception and production of the English vowels, /o/ and /ɔ/, which are difficult to discriminate and produce for Korean learners of English. The participants were 24 college freshmen enrolled in a general English course. They shadowed the model pronunciation from the application and corrected their production after instant feedback from the automatic speech recognition application. Their perception was evaluated in the preand post-tests, and the production was visually examined on the formant chart. The training was held for 5 minutes each day, for a month. The results showed that the training made positive effects on improving students’ perception of the target vowels. There was also some progress in their production although the improvement was not as noticeable as in perception. A discrepancy in the production was discovered between male and female participants with the latter performing better.
ANALISIS KUALITAS SISTEM INFORMASI AKUNTANSI PADA SKPK DI LHOKSEUMAWE: PENDEKATAN ANALISIS DESKRIPTIF

This study aims to determine the accounting information system on the SKPK in the city of Aceh province. Respondents who became the object of this study were Financial Administration Officers and PPK assistants in the city of Lhokseumawe, this study used a combination method. The results showed that 1) The accounting information system in the Lhokseumawe City SKPK can be used to solve problems in recording and preparing financial reports, 2) Information Technology in Lhokseumawe City SKPK has been implemented properly, 3) E-commerce website can be accessed and used by Lhokseumawe City SKPK, 4) Cloud computing in Lhokseumawe City SKPK has been implemented well.
Соционические типы в управлении коллективами и в различных сферах деятельности

In a sample of 2748 people. - employees of more than 100 enterprises, firms and organizations of various fields of activity studied the distribution of socionic types at different levels of management. A detailed analysis was carried out which of the Jung-Reinin-Augustinavichyute traits can be associated with the achievement of high levels of management. It is considered the relationship between the employee’s TIM belonging to the socionic group: quadra, stimulus group, temperamental group, communication group and the management level occupied by this employee. On the example of the posts “chief engineer”, “accountant”, “economist”, “software engineer”, etc., the correlation between socionic qualities of informational metabolism types and the specifics of professional activity is traced. It is shown that even in one (financial and economic) field of activity there are certain preferences for different types of this activity (in the specific case, for accountants and economists). It is concluded that the features inherent in different types of professional activity can be expressed in terms of socionics, and the distribution of TIMs confirms the objectivity of differences in the information structure of activities.
Students’ Writing Difficulties in Procedure Text: An Analysis Study

Abstract&#x0D; The function of writing skills is the most effective way to show someone’s English proficiency, including the ability and difficulties. The objective of this research was to know the factors and to the dominant factor of students’ writing difficulties in procedure text. The instruments of this research were interview and writing test as the support data. Therefore, the researcher conducted a descriptive qualitative study analysis. The technique in analysed the data of the interview was a descriptive analysis technique (percentage) and descriptive analysis technique without percentage to analyse the writing test. The researcher asked some aspects of students’ writing difficulties in writing procedure text based on the syllabus of class 9 K13 namely social function, structure text, language features, and topic. For the writing test, the students were asked to write a procedure text. So that the students had difficulties in social function, structure text, language features, and topic. This research showed the dominant factor students’ writing difficulties in procedure text was language features, like vocabulary and grammar which made the students difficult when writing the procedure text. Thus, the students still faced difficulties in writing skills like procedure text and the students have to improve their ability in writing skills.&#x0D; &#x0D; Keywords: Difficulty, Writing, Procedure Text&#x0D;
Data Deduplication Using Reminiscent Read cache

Data de-duplication, an efficient technique to eliminate redundant bytes in the data to be stored, is largely used in data backup and disaster recovery. This elimination is achieved by chunking the data and identifying the duplicate chunks. Along with data reduction it also delivers commendable backup and restore speeds. While backup process pertains to write process, the restore process defines the read process of a de-dupe system. With much emphasis and analysis being made to expedite the write process, the read performance of a de-dupe system is still a slower process comparatively. This paper proposed to Reminiscent Read Cache framework process in data de-duplication for secondary storage.
Weighted Measures of Pseudorandom Binary Lattices

In cryptography one needs pseudorandom sequences whose short subsequences are also pseudorandom. To handle this problem, Dartyge, Gyarmati and Sárközy introduced weighted measures of pseudorandomness of binary sequences. In this paper we continue the research in this direction. We introduce weighted pseudorandom measure for multidimensional binary lattices and estimate weighted pseudorandom measure for truly random binary lattices. We also give lower bounds for weighted measures of even order and present an example by using the quadratic character of finite fields.
The Use of Location-Based Augmented Reality as Information Media in The Digital Map Application of Jakabaring Sport City

Jakabaring Sport City (JSC) is an integrated sports facility complex and a public tourist spot in Palembang. Due to its extensive area and numerous facilities, visitors need a map with distinct markers for the landmark to make it easier when walking around. Recent technology advancements and its expanding role in various fields invites the question on how to integrate technology in the tourism industry. This journal aims to create an android-based digital map application with augmented reality (AR) as a guiding medium that will display directions towards the Jakabaring exit gate. This app is built using Maps SDK for Android to build the digital maps, Navigation SDK for Android to run navigation, and Maps for Unity to display AR objects that will show the directions towards the exit. The development focuses on a location-based service using GPS to track the user's whereabouts and provide real-time location data around the user. This application has a location search feature, a guiding direction (route and navigation) to a designed location, and an AR camera feature to display where the JSC exit gate is located. Every feature is working well and able to help users while visiting JSC. However, this application could still be improved in the AR feature because on its current version, it is only able to display directions to the JSC exit gate when users are within a radius of one kilometer or less to the exit.
End-to-End Chinese Dialect Discrimination with Self-Attention

Dialect discrimination has an important practical significance for protecting inheritance of dialects. The traditional dialect discrimination methods pay much attention to the underlying acoustic features, and ignore the meaning of the pronunciation itself, resulting in low performance. This paper systematically explores the validity of the pronunciation features of dialect speech composed of phoneme sequence information for dialect discrimination, and designs an end-to-end dialect discrimination model based on the multi-head self-attention mechanism. Specifically, we first adopt the residual convolution neural network and the multihead self-attention mechanism to effectively extract the phoneme sequence features unique to different dialects to compose the novel phonetic features. Then, we perform dialect discrimination based on the extracted phonetic features using the self-attention mechanism and bi-directional long short-term memory networks. The experimental results on the large-scale benchmark 10-way Chinese dialect corpus released by IFLYTEK 1 show that our model outperforms the state-of-the-art alternatives by large margin.