Reviewerly/exHarmony huggingface dataset:
Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale

Fake news, hate speech, crude language, ethnic and racial slurs and more have been spreading widely every day, yet in Sri Lanka, there is no definite solution to save our society from such profanities. The method we propose detects racist, sexist and cursing objectionable content of Sinhala, Tamil and English languages. To selectively filter out the potentially objectionable audio content, the input audio is first preprocessed, converted into text format, and then such objectionable content is detected with a machine learning filtering mechanism. In order to validate its offensive nature, a preliminary filtering model was created which takes the converted sentences as input and classifies them through a binary classification. When the text is classified as offensive, then secondary filtering is carried out with a separate multi-class text classification model which classifies each word in the sentence into sexist, racist, cursing, and non-offensive categories. The models in preliminary filtering involve the Term Frequency–Inverse Document Frequency (TF-IDF) vectorizer and Support Vector Machine algorithm with varying hyperparameters. As for the multi-class classification model for Sinhala language, the combination of Logistic Regression (LR) and Countvectorizer was used while the Multinomial Naive Bayes and TF-IDF vectorizer model was found suitable for Tamil. For English, LR with Countvectorizer model was chosen to proceed. The system has an 89% and 77% accuracy of detection for Sinhala and Tamil respectively. Finally, the detected objectionable content is replaced in the audio with a predetermined audio input.
Scene graph generation via multi-relation classification and cross-modal attention coordinator

Scene graph generation intends to build graph-based representation from images, where nodes and edges respectively represent objects and relationships between them. However, scene graph generation today is heavily limited by imbalanced class prediction. Specifically, most of existing work achieves satisfying performance on simple and frequent relation classes (e.g. on), yet leaving poor performance with fine-grained and infrequent ones (e.g. walk on, stand on). To tackle this problem, in this paper, we redesign the framework as two branches, representation learning branch and classifier learning branch, for a more balanced scene graph generator. Furthermore, for representation learning branch, we propose Cross-modal Attention Coordinator (CAC) to gather consistent features from multi-modal using dynamic attention. For classifier learning branch, we first transfer relation classes' knowledge from large scale corpus, then we leverage Multi-Relationship classifier via Graph Attention neTworks (MR-GAT) to bridge the gap between frequent relations and infrequent ones. The comprehensive experimental results on VG200, a challenge dataset, indicate the competitiveness and the significant superiority of our proposed approach.
A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration

Effective variational models exist for either image segmentation or image registration for a given class of problems, though robustness is a longstanding issue. This paper proposes a new and effective variational model that aims to segment a pair of images through a joint model with registration, with the advantage of only requiring geometric prior information on one image (instead of two images) and obtaining selective segmentation on both images. Moreover we develop a deep image prior based learning algorithm to achieve the same segmentation and registration results by dropping the regularisation terms from the loss function. Numerical experiments show quality results obtained from the new approach.
Secure Data Propagation in Edge-Cloud Orchestrated Computing Paradigms: Statistcs and Taxonomy

Despite the importance of the secure data propagation in the edge-cloud orchestrated computing paradigms, to the best of our knowledge, there is lack of systematic and comprehensive review on this research topic. The purpose of this article is to provide a systematic literature survey on secure data propagation in edge-cloud orchestrated computing environment to examine the current technical achievements published between 2010 and February 2020. The brief contributions of the paper are as follows: (1)providing a comprehensive literature review of the secure data propagation approaches in the edge-cloud orchestrated computing paradigms, (2) designing a methodology for document retrieval and selection, (3) presenting analysis and comparison for main technique methods of secure data propagation in hybrid edge-cloud environment.
Freehand Sketching Portrait Recognition with Least Square CycleGAN

Abstract Freehand sketching portrait recognition refers to the recognition of sketched portraits and face photos drawn by artists. Existing research mainly involves inputting a given real portrait and converting it into a similar sketch portrait, and then matching the face with the sketch portrait in the real portrait database. This paper starts from the idea of inputting a sketch image to identify the real portrait in the database, focuses on the research based on the method of portrait synthesis, and introduces the existing methods of sketch portrait identification. We use the CycleGAN improved by least squares to achieve the translation from sketch to portrait, and finally PCA was used to complete face matching. The results show that LS_CycleGAN has certain advantages. Compared with other methods, its synthesis results are most close to the portrait. The average score of SSIM and PSNR is 0.844 and 17.331, and the average success rate of recognition is 88.2% for Rank10.
Text Summarization Application for Indonesian Twitter Document by Using Top-N Feature Selection Algorithm

The rapid development of information technology has changed many things in our lives. One of the most influential technological developments in our lives is the emergence of the internet. One of the phenomenal examples in the world of information technology is the existence of social media services. This service has replaced the habit of people looking for information and writing down its expressions. With a very high data growth rate, it has raised new problems for Twitter users. The large number of tweets sometimes causes difficulties in understanding the information. To solve this problem, one solution that can be applied is to summarize the information circulating on Twitter. Text summarization is a field of study in natural language processing (NLP). This algorithm aims to reduce the number of words in a document so that the information is easier to understand. NLP can be used to help us summarize tweet documents so that they are easier to understand. This study aims to develop tweet summarization software using the top-n feature selection algorithm. Based on the experiment that has been done, the application has succeeded in summarizing the tweet document by displaying the terms that have the highest weight. Hence, users can more quickly understand information from Twitter without having to read the entire tweet document.
Hardware Architecture of Embedded Inference Accelerator and Analysis of Algorithms for Depthwise and Large-Kernel Convolutions

In order to handle modern convolutional neural networks (CNNs) efficiently, a hardware architecture of CNN inference accelerator is proposed to handle depthwise convolutions and regular convolutions, which are both essential building blocks for embedded-computer-vision algorithms. Different from related works, the proposed architecture can support filter kernels with different sizes with high flexibility since it does not require extra costs for intra-kernel parallelism, and it can generate convolution results faster than the architecture of the related works. The experimental results show the importance of supporting depthwise convolutions and dilated convolutions with the proposed hardware architecture. In addition to depthwise convolutions with large-kernels, a new structure called DDC layer, which includes the combination of depthwise convolutions and dilated convolutions, is also analyzed in this paper. For face detection, the computational costs decrease by 30%, and the model size decreases by 20% when the DDC layers are applied to the network. For image classification, the accuracy is increased by 1% by simply replacing $$3 \times 3$$ filters with $$5 \times 5$$ filters in depthwise convolutions.
PITEM: Permutations-Based Instruction Tracking Via Electromagnetic Side-Channel Signal Analysis

The emergence of cyber-physical systems (CPS) and internet of things (IoT) devices impose significant security and privacy concerns that necessitate robust monitoring and malware detection systems. This paper proposes PITEM, a framework for instruction-level monitoring and malware detection using electromagnetic (EM) side-channels. PITEM identifies <i>instruction types</i> with similar EM emanations using hierarchical clustering. To track all combinations of these <i>instruction types</i>, we generate EM signatures for all permutations of them. In testing, we predict the permutation class of testing traces by a matched-filter-like predictor. We test the performance on two devices (FPGA-based and ARM-based) with 50 MHz and 1 GHz clock frequencies. We achieve 95.67 and 87.35 percent accuracies for these devices for single execution of permutations. We note that the accuracy increases to 100 percent when permutation blocks are repeated. Furthermore, we test the limits of the system by tracking permutations of instructions of the same type. With sufficient bandwidth and number of repetitions, individual instructions can be resolved with 87.5 and 95.78 percent accuracies for these devices. The performance is evaluated for different relative signal-to-noise ratio (SNR) levels and performance is stable for relative SNR values <inline-formula><tex-math notation="LaTeX">$&#x003E;15$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>&#x003E;</mml:mo><mml:mn>15</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="ugurlu-ieq1-3076354.gif"/></alternatives></inline-formula> dB. Finally, we demonstrate PITEM&#x0027;s ability to detect <i>fine-grained</i> malware with 99.89 percent accuracy.
A Study On Sentiment Analysis Combine Matrix Factorization For Ratting Product In Vietnamese

In recent years, hybrid system using different kinds of input data into a unified model become an interesting research. According to it, traditional rating data (user x item) can be re-structured by setting additional dimensions (mood, time, user's location...of textual data). Meanwhile, Vietnamese sentiment analysis problem is also restricted by non-expansion feature of data input. Stem from these issues, this research proposed a featured combine model by integrating matrix factorization in collaborative filtering and sentiment analysis in text mining to predict rating user. With experimental results, the author proves that proposed model achieve better results in accuracy and is feasible to develop as the future direction research.
Comparing Recommendation Algorithms in Session-based E-commerce Sites

Recommender systems have become widely used in various website applications. With the integration of deep learning and recommender systems, the classic Session-based Recommender System (SRS) appears, which can obtain implicit feedback from explicit interactions. Some scholars have pro-posed many effective recommendation algorithms to provide better recommendation service in SRS. In order to compare the performance of these session-based recommendation algorithms, we consider a simple E-commerce SRS scenario and choose four representative session-based recommendation algorithms in this paper. Then we do some evaluation experiments. The experimental results show that the combination of local preferences and global preferences will improve the recommendation performance significantly, and for GNNs and RNN s in session recommendation based on deep learning, we also conclude that the prediction effect of GNN s is slightly superior to RNN s on long sessions.
Defining the role of the educational program in the educational and methodological support of the study of disciplines in higher education institutions (Review)

At the moment, the creation of methodological support for academic disciplines is an urgent problem of organizing the educational process in higher education institutions. As you know, the methodological support for the study of an academic discipline consists of four independent methodological documents: the program of the academic discipline, the working program of the academic discipline, the syllabus and the electronic educational and methodological complex of the discipline. The purpose of the study is a comparative analysis of these documents to determine their role in the organization of the educational process. The program of the academic discipline is the main normative document that determines the place and significance of the discipline in the implementation of the educational and professional training of the student, its content, the amount of knowledge, skills and abilities determined by the relevant competencies, as well as the sequence and organizational forms of studying the academic discipline. It is developed by one of the highly educated teachers of the department, discussed at a meeting of the department and approved by it, after which it becomes a working methodological document of the department. Based on a comparative analysis of the structure and content of the program, work program, syllabus and educational-methodological complex of the discipline, which are the main components of educational and methodological support for the study of the discipline, it has been established that all components of the work program in one form or another are repeated in the syllabus and educational-methodological complex discipline. At the same time, in contrast to the work program of the discipline, in the last two methodological components, these components have a large semantic content that is necessary in the educational work of students. In addition, the information contained in the syllabus and the educational-methodical complex of the discipline, the student can use at any time, since these documents are contained in electronic form on the websites of libraries of higher educational institutions and departments, which own the discipline, and the work program of the discipline is stored in paper form at the department and students' access to it is limited. The conducted comparative analysis leads to the conclusion that the work program of the academic discipline is not a necessary link in the methodological support of the discipline study.
The Effect of Cognitive Styles on the Effectiveness of Visual Search Tasks with Different Familiarity

The study explored the differences in visual search tasks for people with different cognitive styles of field independence and field dependence when browsing the different sequence of application layout on computer desktops. This experiment is a 2 (Cognitive styles: field independence vs. field dependence) * 2 (Familiarity: familiar vs. un-familiar) * 2 (Sequence of application layout: name vs type) design of mixed factors. The response time and fixation times of searching computer application were the dependent variables. The experiment meth-od is: All participants took the test with the Group Embedded Figures Test (GEFT), the top 25% and bottom 25% of the test scores are divided into styles of field-independent and field-dependent. They per-formed visual search tasks in different sequence of application layout on computer desktops with eye tracking devices. Each participant per-formed a total of 4 search tasks.It was found that there was a significant difference in the response time of the participants searching for familiar and unfamiliar applications. When the participants searched for familiar applications, their reaction time and number of fixations were significant differences. The difference between this research and previous research on people with different cognitive styles is that the previous research for field independent people can quickly find the target in an unfamiliar environment, but in this research, field independents did not find this feature in the search task for computer applications.
Sentiment Analysis for Movie Ratings Using Deep Learning

With the rapid development of network and technology, an enormous amount of data is produced and shared every day across the Internet over Facebook, Google, Twitter, IMDB, shopping sites, and online review sites. This growing availability of opinion-rich resources leads to a new technology that is concerned with what other people think and their emotions. This textual data may have rich information with various labels such as sentiment, age, country, gender, and so forth. Using these labels may bring benefits to some industrial fields such as product quality and market research. The technology used to analyze people’s sentiments, opinions, evaluations, attitudes, and emotions from their written review is known as opinion mining and sentiment analysis. This work introduces a recurrent neural network (RNN) and long short-term memory (LSTM)-based method to predict movie ratings based on movie reviews. Sentiment analysis is a natural language processing approach to identify the emotional tone behind the opinion text from negative to a positive level. This technique uses fine-grained sentiment analysis that provides a more precise level of polarity by breaking it from very negative to very positive on the scale of the five-star range. A user-friendly web application has been made that provides users to write a review and predict sentiments. Maximum accuracy of around 75% has been achieved on this fine-grained system as text classification is very challenging when it comes to sarcasm, double negation, use of emojis, tone, and subjectivity.
Quality-Blind Compressed Color Image Enhancement with Convolutional Neural Networks

Lossy compressed images and videos suffer from visible compression artifacts, especially when the bit-rate is low. To improve the quality of the compressed image while keeping the same bit-rate, decoder-side compression artifacts reduction (CAR) becomes important. Recently, convolutional neural networks are adopted for CAR tasks and achieve the state-of-the-art performance. However, most CAR algorithms only focus on the reconstruction of the luminance channel. Also, a separate model usually needs to be trained for each quality factor (QF), which makes these approaches not practical in existing codecs. In this paper, we analyze a quality-blind training strategy and compare it with training separate models for each QF. The testing results with three representative CAR algorithms show the superiority of the quality-blind training compared to separate training. The results for pseudo and real quality-blind CAR tests further prove the generalizability of the quality-blind training for practical CAR tasks.
Multi-Directional Multi-Label Learning

In multi-label learning, the key problem is to capture the relationships between multiple labels, including proximities and unconformities. In this paper, we consider the relationships among multiple labels from multi-directions, including utilizing discriminative classifier, proposing a general hierarchical constraint and proximity correlation, meanwhile combining low-rank constraint, to infer a novel Multi-Directional Multi-Label learning (MDML) model. To optimize the problems involved in to the proposed models, we develop an iterative algorithms based on the alternating direction method of multipliers (ADMM) algorithm. In the simulations, the experimental results on 4 popular benchmark datasets demonstrate the superiorities of MDML model.
Feature based Speaker Embedding on conversational speeches

Identifying the speaker of a specific speech by examining the speech features of the voice is called speaker identification. The task of speaker identification consists of three main phases which are feature extraction, feature embedding and voice classification. Speaker embedding is the process of modeling the voice of a person where the model of the utterance can uniquely represent the speaker of that voice. Speaker embedding is a commonly used method in Automatic Speaker Recognition systems to identify the voice of the speaker. Currently, Deep Neural Networks based approaches are used in these systems for speech feature extraction and speech embedding. The performances of different approaches heavily depend on the noise factor and suitability of selected features of the audio data. MFCC, LPC, Dimensional filter banks are some of the frequently used speech features in speaker recognition. This speaker recognition research focuses on the usage of speech features for speaker embedding that are fitting for the speaker identification in conversational environment using a Convolutional Neural Network based approach.
Appearance-Invariant Entry-Exit Matching Using Visual Soft Biometric Traits

The problem of appearance invariant subject re-identification for Entry-Exit surveillance applications is addressed. A novel Semantic Entry-Exit matching model that makes use of ancillary information about subjects such as height, build, complexion, and clothing color to endorse exit of every subject who had entered private area is proposed in this paper. The proposed method is robust to variations in appearances such as clothing, carrying, and head masking. Each describing attribute is given equal weight while computing the matching score, and hence the proposed model achieves high rank-k accuracy on benchmark datasets. The soft biometric traits used as a combination though, cannot achieve high rank-1 accuracy, it helps to narrow down the search to match using reliable biometric traits such as gait and face whose learning and matching time is costlier when compared to the soft biometrics.
Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use

Multivariate latent class profile analysis (MLCPA) is a useful tool for exploring the stage-sequential process of multiple latent class variables, but the inference can be challenging due to the high-dimensional latent structure of the model. In this paper, a Bayesian approach via Markov chain Monte Carlo (MCMC) is proposed for MLCPA as an alternative to the maximum-likelihood (ML) method. Compared to the ML solution, Bayesian estimates are less sensitive to the set of initial values as well as easier to obtain standard errors. We also address issues in MCMC such as label-switching problem with a dynamic data-dependent prior and computational complexity with a recursive formula. Simulation studies revealed the validity and efficiency of the proposed algorithm. An empirical analysis of MLCPA using the National Longitudinal Survey of Youth 97 (NLSY97) identified a small number of representative developmental progressions of adolescent depression and substance use.
Interactive Grounding and Inference in Learning by Instruction

Learning by instruction is one of the most common forms of learning, and a number of research efforts have modeled the cognitive process of instruction following, with many successes. However, most computational models remain brittle with respect to the given instructions, and they lack the ability to adapt dynamically to variants of the instructions. This paper aims to illustrate modeling constructs designed to make instruction following more robust, including (1) more flexible grounding of language to execution, (2) processing of instructions that allows for inference of implicit instruction knowledge, and (3) dynamic, interactive clarification of instructions during both the learning and execution stages. Examples in the context of a paired-associates task and a visual-search task are discussed.