Reviewerly/exHarmony huggingface dataset:
Analysis of Corpus Development for Urdu Language

A corpus is a collection of machine-readable linguistic data, gathered as an authenticated written text or a transcription from a recorded speech, with features examined using various methodologies. In the field of Natural Language Processing (NLP), corpus plays a critical function. Urdu is a major language in South Asia, as well as the national language of Pakistan. A lot of NLP related research has been made for various Western languages but Eastern languages, particularly South Asian languages remained deprived of advanced level NLP applications mainly due to unavailability of language and linguistic resources. Same is true for Urdu corpora, which is not widely available to the researchers. The current study focuses on the collection and development of a corpora, consisting of 100k (100,000) Urdu text words and its analysis using several methodologies. The corpus is gathered from 79 different resources such as Urdu articles or columns published in Pakistani newspapers and journals between 2015 and 2021. Each article is over 1000 words long. This corpus is released and is made freely available to the researchers to develop NLP applications for Urdu language.
Comprehensive Analysis of Fused Descriptors for Image Retrieval

Content Based Image Retrieval (CBIR) systems are used to retrieve similar images to the query image from a large database. This paper represents a CBIR model which has been tested with multiple feature descriptors such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and rotated BRIEF (ORB) and combinations of them. Multiple linguistic processing techniques such as Bag of Words and Topic modelling have been used for optimizing the image retrieval and making them meaningful based on human semantics. Using a combination of descriptors and Latent Dirichlet Allocation, our model has proven to yield high precision when tested against standard image retrieval data set.
Multi-Script Video Caption Localization Based on Visual Rhythms

Localization of video caption plays an important role in information retrieval in multimedia applications. In this work, we present and evaluate a novel method for localizing video captions using visual rhythms, which enable the representation and analysis of a specific feature throughout the time. We build visual rhythms from the text location maps produced by general text localization methods that are far more common in the literature than caption-oriented ones. Then, we process the maps properly to keep only the captions, generating caption localization masks. To meet the need for a standardized and large dataset, we constructed a new one, where captions with thirteen different scripts are added to the video frames, generating a total of 221 videos with ground truth. Experiments demonstrate that our method achieves competitive results when compared to other literature approaches.
The Digital Practitioner

Whereas EMFFE was a group of wise men and women reviewing possibilities in the use of extant learning technologies and then designing developmental frameworks to scaffold these possibilities, digital practitioner revealed already existing, transformational digital practice from a bottom-up perspective. Where national education policy is about providing standardised “solutions” to what the future looks like (i.e., around centralised learning management systems), “The Digital Practitioner” survey discovered changing practice on the ground and provided new concepts for describing this work. The critical discovery was that of the use of “personal” technologies (rather than business or “learning” technologies) driving change in learning. The digital practitioner emerges as a craft professional who uses their personal curiosity to redesign learning delivery. This is best described as co-creating artfully crafted, student-centred, learning experiences. This chapter describes the digital craft professional of the future, nascent now.
Latest Trends in Deep Learning for Automatic Speech Recognition System

In the field of Computer Learning and Intelligent Systems research, Deep Learning is one of the latest development projects. It’s also one of the trendiest areas of study right now. Computational vision and pattern recognition have benefited greatly from the dramatic advances made possible by deep learning techniques. New deep learning approaches are already being suggested, offering performance that outperforms current state-of-the-art methods and even surpasses them. There has been much significant advancement in this area in the last few years. Deep learning is developing at an accelerated rate, making it difficult for new investigators to keep pace of its many kinds. We will quickly cover current developments in Deep Learning in the last several years in this article.
Monolithic Implementation of Chaos Modulated VCO-based Random Number Generator

This paper introduces the implementation of a monolithic random number generator. For that purpose, dual oscillator architecture with an irregular sampling of a regular signal is designed and simulated. The irregular sampling clock is generated by voltage controlled oscillator(VCO), which is modulated by a continuous-time chaotic oscillator. The proposed architecture is fully CMOS compatible and suitable for mono-lithic implementation. Therefore, it is robust against tempering attacks and external interference. It is designed in TSMC 180 nm process and requires a chip area of 0.004 mm<sup>2</sup>. To the best of the authors&#x0027; knowledge, this work is the first monolithic implementation of chaos modulated VCO-based random number generator.
Transferring discriminative knowledge via connective momentum clustering on person re-identification

Unsupervised domain adaptation in person re-identification remains a challenge to learning discriminative representations due to the absence of labels in target domain. Clustering could provide pseudo-labels, but the limitation mainly comes from imperfect clustering and noisy pseudo-labels. To address this drawback, we propose Connective Momentum Clustering (CMC) framework to build a connection estimator via graph convolutional networks to transfer rich connection knowledge from the annotation space of source data to target domain. It estimates connections from context to reveal relationship between unlabeled data and helps to discover more reliable clusters. With momentum mechanism, stable pseudo-labels are updated iteratively with confidence and refined consistently to encourage more discriminative networks. Meanwhile, we notice that the huge domain gap between source and target domains results in severe pollution in BatchNorm layers. To tackle this problem, we normalize the data stream separately to decouple different distribution and further boost the performance in target domain. We adopt our CMC framework on mainstream tasks and achieves 80.2% mAP / 91.3% Rank-1 on Duke→Market task and 70.4% mAP / 82.4% Rank-1 on Market→Duke task.
A comparative study between international book exhibitions and book festivals

코로나 19로 인해 오랜 역사와 전통을 지닌 국제도서전과 북페스티벌이 연기하거나 취소하는 등 파행을 거듭하게 되었고, 일부 온라인과 오프라인을 겸하거나 규모를 축소해서 그 명맥을 이어나가고 있다. 전 세계적인 팬데믹으로 인해 국제도서전과 북페스티벌이 위축되었으나 출판 산업에서 두 행사는 산업 전체를 견인하는 빅 이벤트다. 따라서 국제도서전 중 대표성을 띤 프랑크푸르트도서전, 볼로냐아동도서전, 베이징국제도서박람회, 서울국제도서전을 분석하고, 북페스티벌은 헤이페스티벌, 파주북소리, 서울와우북페스티벌을 분석해 본다. 이들 분석을 통해 서울국제도서전의 나아가 방향을 제시한다.COVID-19 has led to a series of disruptions, with international book exhibitions and book festivals with a long history and tradition postponed or canceled, and some online and offline or scaled down to continue their reputation. International book exhibitions and book festivals have shrunk due to the global pandemic, but the two events in the publishing industry are big events that drive the entire industry. Therefore, it analyzes the representative Frankfurt Book Fair, Bologna Children s Book Fair, Beijing International Book Fair, and Seoul International Shenzhen, while the Book Festival analyzes the Hey Festival, Paju Book Sound, and Seoul Wow Book Festival. Through these analyses, the further direction of the Seoul International Book Fair is presented.
A Gini Coefficient Measurement Method of Income Distribution Gap Based on Privacy-preservation

Aiming at the research on individual income distribution gap in the field of Economics, this paper presents a Gini Coefficient measurement method of income distribution gap based on privacy-preserving. A Gini coefficient measurement system of income distribution gap based on privacy-preserving is established which includes cloud server CS, client CR, trusted third-party institution TI, and scientific research institution SRI. Firstly, the Paillier homomorphic encryption algorithm is applied in the scheme to encrypt the client&#x2019;s private data. When the cloud server receives the encrypted data from clients, the cloud server will calculate the ciphertext for calculating Gini Coefficient by aggregating and averaging the encrypted data and will send the ciphertext to the scientific research institution. Finally, the scientific research institution decrypts the ciphertext and calculates the Gini coefficient. Security analysis shows that the proposed scheme achieves integrity and can preserve all users' privacy. Meanwhile, performance analysis demonstrates that the scheme has efficient computing and communication performance for practical applications.
Political Influence Analysis Social Media Text Mining for Public Opinion: Case Study Makassar City

Today, social media as a medium is widely used in communication in society. The existence of social media is very widely used in various community activities such as economics, education, and politics to share information. Social media such as Instagram, Twitter, Facebook, and others are believed to have the potential to increase public participation in politics. Twitter application researchers consider ideal for the public, politicians, political parties, and political institutions to spread political issues, public opinion, and political communication through social media. The process of analyzing the influence of public opinion on social media involves several ways, data search, data collection, data preparation, and analysis. Although there is some literature on the analysis of political influence in presidential elections, governors and mayors use the stages of data discovery, collection, and preparation. In this study, we use different methods to analyze the effect of elections using support vector machines and k-means clustering on social media as proposed solutions. Based on the results of the study, the researcher found another way to analyze the political influence of the general election. This analysis is used to extend the existing social media analysis framework. This research is expected to provide benefits or new knowledge for researchers and practitioners who want to collect and analyze data on the influence of social media in politics.
Facial Action Unit Recognition Using Pseudo-Intensities and their Transformation

Facial action units (AUs) represent facial muscular activities, and our emotions can be expressed through their combinations. Thus, AU recognition is often used in many different applications, including marketing, healthcare, and education. Numerous studies have been conducted on recognizing AUs through several network architectures; however, their performances remain unsatisfactory. One of the difficulties comes from the lack of information regarding a neutral state (i.e., no facial muscular activities) of each person owing to the individuality of a neutral state. This lack of information degrades the recognition performance because the intensities of AUs are derived from a neutral state. In this paper, we propose a novel method using Pseudo-INtensities and their Transformation (PINT) to tackle this problem. To exclude the individuality of a neutral state and accurately capture the changes in facial appearance regarding AUs, we first calculate pseudo-intensities based only on the differences among the intensity states of the same person. We utilize a siamese network architecture and the facial image pairs of the same person to calculate the pseudo-intensities. These pseudo-intensities are then transformed into the actual intensities based on the low pseudo-intensities of the same person, which are considered to correspond to neutral states. We carried out evaluation experiments using two public datasets and found that our method, PINT, achieved a state-of-art performance. The improvements in the average intra-class correlation coefficient score over existing methods were 7.1% on DISFA dataset and 3.1% on FERA2017 dataset.
Correction to: Idealness of k-wise intersecting families

A Correction to this paper has been published: 10.1007/s10107-020-01587-x
Camera Calibration Method Based on Sine Cosine Algorithm

Aiming at the problems of traditional camera calibration method, such as sensitivity to the initial values of camera model parameters and unstable calibration results. This paper proposes a camera calibration method based on sine cosine algorithm. After obtaining a certain initial value by Zhang's camera calibration method, use the sine cosine algorithm (SCA) to form the initial population in the field near the initial value, and perform iterative optimization. The average error between the actual projection point and the calculated projection point is the accuracy criterion. Using the volatility and periodicity of the sine function and cosine function to search and iterate, so that the solution can be oscillating towards the global optimum and achieve the purpose of optimization. Experiments have proved that the adaptive parameters and randomness parameters in the algorithm better balance the exploration and development capabilities of the algorithm. The improved algorithm has fewer parameters, simple structure, easy implementation, and fast convergence speed. The experiment proves that the camera calibration accuracy is effectively improved.
Motion Assisted Video-based Stereo Matching

This paper presents a method to utilize the move-ment relationship between adjacent frames for video-based stereo matching. Unlike existing video-based algorithms that need complicated calculation to fuse the information between frames, the proposed method uses the existing motion vector in video encoder or decoder to reconstruct the initial disparity of the target frame from a reference frame. By disparity plane reconstruction, disparity can be initialized in an easy way, such that unnecessary steps in the stereo matching pipeline can be skipped. By taking advantages of the motion relationship between frames, the proposed scheme can achieve better performance with less computation compared with the original method.
Synthesis of User-Level 3D Head Avatar from Smartphone Video for Virtual Reality

The purpose of an avatar is to intensify the sense of reality in a simulated environment such as a virtual reality system. This is done by creating a replica of a real person in virtual reality, as intricately as possible. Implementation of 3D scanners (i.e., Microsoft Kinect) produced vast opportunities for creating 3D models of celebrities and other people who can afford them. These humanoid 3D models as well as fictional human avatars opened the door of perceiving a more realistic 3D system than before. However, a simplified approach of fully rigged 3D head avatar synthesis may help to expand the application of 3D avatars allowing anyone to feel a personalized experience of their own avatar. In this work, we propose a system of 3D head avatar synthesis at the user-end, using an easily accessible and low-cost sensor (smartphone camera). Our proposed methodology consists of data acquisition of the concerned person as video, feature extraction and matching, image registration, formation of structure form motion and post-processing. Without requiring any training data, our method provides a satisfactory performance based on the experimental results. Additionally, we release a public database for avatar synthesis from smartphone video and introduce a set of attributes to evaluate the synthesized 3D avatars.
Quasi groups and Chaotic maps Cryptosystem based on Image Space

Images used from ages to describe events and memorize worth occasions. Databases such as google, index images for easy access, for special cases images need to be secured, where used for certain purposes, such as shown only for authorized personal. In this paper, we present a revolutionary approach for crypto images. This crypto-images applied on the level of image space components, the suggested cryptosystem employs quasi groups&#x2019; and chaotic maps&#x2019; transpositions, the obtained results were outstanding.
Effect of Hyperparameters on Backpropagation

In machine learning algorithms, parameters and hy-perparameters are important properties in the training process. Parameters are modified through machine learning algorithms while hyperparameters are the parameters that are adjusted manually to achieve the desired accuracy and increase efficiency. In neural networks, weights are parameters while hyper-parameters include layer size, momentum, learning rate, acti-vation function family, weight initialization and normalization scheme for input data. Multi-layer feed-forward Backpropagation neural network (BPN) has been used to solve a variety of classification problems as it can map “any” input-output mapping and classify linearly inseparable data. The research work presented in this paper gives a detailed description of BPN and explains various hyper-mutates for BPN. The paper describes the implementation of BPN, experimentation and analysis with hyperparameters like weight initialization and learning.
ParaX : Bandwidth-Efficient Instance Assignment for DL on Multi-NUMA Many-Core CPUs

Commercial clouds now heavily use CPUs in DL (deep learning) because there are large numbers of CPUs which would otherwise sit idle during off-peak periods. Following the trend, CPU vendors have not only released high-performance many-core CPUs but also developed efficient math kernel libraries. However, current DL platforms cannot scale well to a large number of CPU cores, making many-core CPUs inefficient in DL computation. We analyze the memory access patterns of various layers and identify the root cause of the low scalability, i.e., the per-layer barriers that are implicitly imposed by current platforms which assign one single instance (i.e., one batch of input data) to a CPU. The barriers cause severe memory bandwidth contention and CPU starvation in the access-intensive layers (like activation and BN). This paper presents a novel approach called ParaX, which boosts the performance of DL on multi-NUMA (non-uniform memory access) many-core CPUs by effectively alleviating bandwidth contention and CPU starvation. Our key idea is to assign one instance to each CPU core instead of to the entire CPU, so as to remove the per-layer barriers on the executions of the many cores. ParaX designs an ultralight scheduling policy which sufficiently overlaps the access-intensive layers with the compute-intensive ones to avoid contention, and proposes a NUMA-aware gradient server mechanism for training which leverages shared memory to substantially reduce the overhead of per-iteration parameter synchronization. We have implemented ParaX on MXNet. Extensive evaluation on a two-NUMA Intel 8280 CPU shows that ParaX significantly improves the training/inference throughput for all tested models (for image recognition and natural language processing) by <inline-formula><tex-math notation="LaTeX">$1.73\times \sim 2.93{\times}$</tex-math></inline-formula> .
SamE: Sampling-based Embedding for Learning Representations of the Internet

We have developed SamE, a novel sampling-based embedding technique for learning representations of the Internet. SamE can classify Internet hosts in a scalable and cost effective manner without sacrificing the classification performance. Machine learning has been applied to Internet traffic analysis for a variety of purposes, including botnet detection and application identification. For example, as a major threat on the Internet, a botnet is a group of computers that collaborate together to launch cyberattacks. To analyze related hosts such as the collaborating constituents of a botnet, graph embedding techniques seem to be promising. However, when applying existing graph embedding techniques to Internet-scale traffic data, the time and space complexities become prohibitively high for practical use. To make graph embedding applicable to Internet-scale problems, SamE only samples a subset of nodes to learn elemental representations and aggregates learned elemental representations to generate synthetic representations for all nodes. We have applied SamE to real-world Internet-scale traffic data, and the experimental results show that SamE outperforms existing methods by reducing the data samples required for representation learning by 99% while achieving the same level of classification performance in botnet detection and application identification.
Sharing is Caring: Optimized Threat Visualization for a Cybersecurity Data Sharing Platform

Cyberattacks are increasingly costing organizations billions of dollars annually. To protect against them, cybersecurity information sharing and cyberthreat visualization have become crucial research topics. Our platform, CYBersecurity information EXchange with Privacy (CYBEX-P), implements developments in both areas as an approachable collaborative security tool. CYBEX-P's threat-intelligence graph displays indicators of compromise and their crowd-reported threat levels. Intuitive and efficient data interaction is key for adoption of such a contributor-driven system and is the focus of this work. A user study was conducted with participants from cybersecurity backgrounds to test different visualization configurations. Measurements pertaining to dependent variables such as task accuracy and threat-detection time were recorded. Subsequent analysis revealed that relying on localized color to represent threat comes with serious limitations. Likewise, information density must be carefully considered. We conclude that the misuse of simple visual properties can lead to perilous reductions in accuracy and response-time and provide recommendations for avoiding these pitfalls.
Analysis of the Psychological Characteristics of the Juvenile Delinquency Preventive Center Subjects and the Suggestions for the Development of the Educational Program

This study aimed to lay the groundwork for the operation of specialized programs for each requesting institution and delinquencies type conducted by the Juvenile Delinquency Preventive Center(JDPC). For this purpose, 683 data including the psychological and legal attitude variables of the subjects those who participated in the education programs conducted by Juvenile Delinquency Preventive Center in nationwide were analyzed. The results are as follow. First, number of participation in education at the JDPC were counted. 21.6% among subjects requested for special education from school requests were attending twice, 7.5% were their third. 12% of subjects to suspension of prosecution due to conditional education requested by the prosecution, 13% of subject to a court-requested consultation investigation, 31.6% of subjects to the court''s request to take classes, and 35.7% of subject to additional disposition requested by the court were their second participation. Second, significant differences of psychological and law attitude variables were not found between subjects for each institution requested for education. In delinquency behavior, the difference between educational request institutions was significant, but the post-analysis results were not significant. Finally, no significant difference could be found between delinquency types on psychological and law attitude variables. Based on the results of this study, implications for the development and operation of the JDPC curriculum were presented.