Reviewerly/exHarmony huggingface dataset:
The Use of Artificial Intelligence in Modern Educational Technologies in the Transition to a Smart Society

The innovative model of activity management in educational institutions ensuring transition to Smart society is described in this article. It sets before higher education institutions and all interested persons (employers, customers) such tasks as orientation of educational process to the results of education and changing forms of its results representation into the competencies acquired by the graduate (“has to know” and “be able to do”). A technology is being developed where at the request of the economic sector, using the ontological technologies, the training process is restructured to reduce the time required to obtain the estimated number of personnel with the competencies required by employers. The ontology contains competencies from different fields of knowledge which makes it possible to describe the level of today graduate readiness. That allows carrying out the effective formalized search of candidates with the necessary profile and necessary qualification level. It is proposed to develop and implement a modern open educational platform that would help to eliminate the isolation of the educational process from the requirements of modern professional standards and to accelerate the interaction of all participants of the development and updating of federal state educational standards process. It will not only improve the speed and efficiency of training a specialist of any level of education according to competencies requirements, but also provide operational information and methodical support of the preparation process. The ontology of the educational process constructed on the base of the master's degree training allows to develop general requirements. The professional competency model of the specialist needed by the employers is designed in accordance with these requirements. The curriculum is also dynamically formed or reorganized on the basis considering the most modern employers requirements. Moreover, the innovative approach to educational and methodical materials formation is described taking into account any changes of requirements of federal standards to “working programs” and “funds of estimated means”. Such an open system for educational institutions meets the requirements of Industry 5.0, one of the paradigms of which is high quality products to order, and allows to get talented students who will study the most relevant and innovative technologies and will acquire the most modern competencies in order to be active at the worlds level and that will accelerate the creation of the Super Smart Society.
Lightweight Traffic Sign Recognition Algorithm based on Cascaded CNN

Autonomous vehicle technology is evolving with deep learning. Traffic sign recognition informs the driver of necessary information when the driver does not recognize the traffic sign while driving or when the traffic sign information is missing from the GPS database. In this paper, we collected Traffic signs in South Korea and we proposed a light-weight traffic sign recognition (TSR) algorithm based on cascaded CNN. This algorithm is hardware-friendly and reduces the computational complexity and the number of computations compared to the previously announced YOLO v2-tiny. Our Korean traffic sign dataset was used to learn the network and verify the algorithm. Through this process, we have studied the future improvement plan to make algorithm that can be used for actual road driving.
OCR for Drawing Images Using Bidirectional LSTM with CTC

Nowadays, the system is changing with the Optical Character Recognition (OCR) technology in various industrial. It is technology that allows computers detect and convert form handwritten or scanned images into searchable machine encoded data. Searchable data can save a huge amount of time and effort. Therefore, the machine industry has also improved portability and accessibility by converting existing dealer customers' mechanical parts drawing books into mobile. Dealers can easily order mechanical parts without looking for drawing, just search a serial number of mechanical parts on the database. In this paper, we propose the OCR on the drawing images to convert images into searchable data. Proposed OCR consist of three parts: pre-processing, deep learning and postprocessing. The pre-processing part removed the guide lines and shape for improving the precision. The guide lines and shape usually cause false recognition as `1' or `7'. The deep learning part extract features and classify the images. We adopt Shi, et al.'s CRNN architecture [1]. The post-processing, drop the low probability of object. Most of left lines and shapes are seem to the low probability. As result of proposed OCR, it shows 98.52% of average recall rate and 92.25% average precision.
Enhanced MORE Algorithm for Fully Homomorphic Encryption Based on Secret Information Moduli Set

Cloud Computing has offers incredible benefits in terms of data storage and data processing. However, data protection and privacy issues when it comes to data processing on the cloud has triggered several challenges for the existing crypto-systems. In this paper, we propose symmetric based Fully Homomorphic Encryption (FHE) scheme with Matrix Operation for Randomization and Encryption (MORE) that allows arbitrary computation on an encrypted data. The proposed scheme uses Secret Information Moduli Set (SIMS) to preserved data privacy and enhanced the existing MORE scheme. The result shows that the proposed scheme (MORESIMS) tradeoff 2% execution time compared to MORE scheme with high level of security and better storage overhead of n x n based on presented security analysis.
Where Does LDA Sit for GitHub?

A lot of research in Software Engineering (SE) automatically extract topics of the text data and use the results directly or as a feature for a machine learning method. Research has shown that the majority of studies in SE use Latent Dirichlet Allocation (LDA) as the topic modeling approach. Similarly, there is a lot of work that apply LDA on GitHub data. However, there is no study that explores whether LDA is a good choice compared to other algorithms, nor is there any to investigate the effects of specific pre-processing steps on its performance. In this paper, we explore a large dataset of GitHub repositories and apply two main topic modeling algorithms, LDA (3 variants) and Non-Negative Matrix Factorization (NMF), in several experiments with different experimental settings. The results show that LDA results in a higher coherence score compared to NMF. However, care should be taken in the choice of LDA algorithm, setting its parameters, and the text pre-processing steps. The results of this paper benefit SE researchers who apply intelligent techniques using LDA.
High-dimensional Motion Segmentation by Variational Autoencoder and Gaussian Processes

Humans perceive continuous high-dimensional information by dividing it into significant segments such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion. To this end, we previously proposed a hierarchical Dirichlet process-Gaussian process-hidden semi-Markov model (HDP-GP-HSMM). However, an important drawback to this model is that it cannot divide high-dimensional time-series data. Further, low-dimensional features must be extracted in advance. Segmentation largely depends on the design of features, and it is difficult to design effective features, especially in the case of high-dimensional data. To overcome this problem, this paper proposes a hierarchical Dirichlet process-variational autoencoder-Gaussian process-hidden semi-Markov model (HVGH). The parameters of the proposed HVGH are estimated through a mutual learning loop of the variational autoencoder and our previously proposed HDP-GP-HSMM. Hence, HVGH can extract features from high-dimensional time-series data, while simultaneously dividing it into segments in an unsupervised manner. In an experiment, we used various motion-capture data to show that our proposed model estimates the correct number of classes and more accurate segments than baseline methods. Moreover, we show that the proposed method can learn latent space suitable for segmentation.
Another Look at a Proposed Quartic Chaotic Mapping

In this paper, we take another look at a quartic chaotic mapping proposed before, in which two cases were left unconcerned. For these two cases, we could form a quartic mapping easily almost as same as we did before. Analysis illustrates that the mapping formed for the two cases demonstrates different chaotic properties: one with vast chaotic area, the other with broad periodic area. That is to say, they could be applied to anti-control of chaos and control of chaos, respectively. Both cases are quite efficient, and easy to use.
On the Dynamics of Structured Argumentation: Modeling Changes in Default Justification Logic

This paper studies information changes in default justification logic with argumentation semantics. We introduce dynamic operators that combine belief revision and default theory tools to define both prioritized and non-prioritized operations of contraction, expansion and revision for justification logic-based default theories. This combination enriches both default logics and belief revision techniques. We argue that the kind of attack called “undermining” amounts to those operations that contract a knowledge base by an attacked formula.
Probabilistic Computing with Binary Stochastic Neurons

Deterministic bits that represent "0" or "1" form the basis of all digital computing. At the other end of the spectrum, quantum bits represent a superposition of "0" and "1" and form the basis of the emerging field of quantum computing. Recently, it has been shown that probabilistic bits or "p-bits" which fluctuate between "0" and "1" can be correlated using techniques borrowed from neural networks and can be used to address a broad class of problems relevant to machine learning and quantum computing. It has been shown that scaled p-bit implementations can be realized in hardware by making slight modifications to existing MRAM technology that are nearing production in Gb level densities. Even though there are no fundamental obstacles, implementing MTJ-based p-bits in gigabit scales can be challenging as they require a careful design of low- barrier nanomagnets which, unlike high-barrier nanomagnets, remain relatively unexplored. In this paper, we propose and experimentally demonstrate the operation of a compact, low-level p-bit emulator that retains much of the physics of the MRAM- based p-bit. The emulator uses most of the components of the original mixed-signal design and simply replaces the low-barrier nanomagnet with a fluctuating resistor circuit. Our emulation allows us to build asynchronously operating rudimentary p- computers to explore potential difficulties that could arise in scaled nanodevice based p-computers.
Predicting the Trajectory Tracking Control of Unmanned Surface Vehicle Based on Deep Learning

The waypoint behavior is a method of trajectory tracking control for unmanned surface vehicle (USV). We take the waypoint behavior as the research object, choose speed, steering angle, capture radius, slip radius, lead distance, lead damper as features, and establish a prediction model for trajectory tracking control of USV based on deep neural network. The model effectively predicts the navigation effect and provides assistance and reference for the maneuvering decision of the USV. Test results show that the predicted results and the reference samples have same tendency. The proposed model can improve the performance of trajectory tracking control for the USV.
Design and Implementation of Bangla Sign Language Translator

Human being has an Inherent capability to communicate with each other. But, for deaf and dumb people it's not an easy task to interact with other. They use sign language for their communication with physical world. But this language is not understandable to all because maximum normal people have no knowledge of sign language. This is the main puzzle for speech impaired people to communicate with others, especially in their social and economic environments. To minimize this problem between normal and speech impaired people is the main objectives of this paper. Here designed and implemented a wearable smart sensing glove by using flex sensor and gyroscope. This glove translates sign language to the Bangla speech by recognizing hand gesture.
Learning to Hash for Efficient Search Over Incomplete Knowledge Graphs

Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional, continuous vectors, and thus enables machine learning models to be easily adapted to KG completion and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to learn compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring Hamming distance instead of Euclidean distance or dot-product. Unfortunately, most of learning-to-hash methods cannot be directly applied to KG structure encoding. In this paper, we introduce a novel framework for encoding incomplete KGs and graph queries in Hamming space. To preserve KG structure information from embeddings to hash codes and address the ill-posed gradient issue in optimization, we utilize a continuation method with convergence guarantees to jointly encode queries and KG entities with geometric operations. The hashed embedding of a query can be utilized to discover target answers from incomplete KGs whilst the efficiency has been greatly improved.We compared our model with state-of-the-art methods on real-world KGs. Experimental results show that our framework not only significantly speeds up the searching process, but also provides good results for unanswerable queries caused by incomplete information.
Capturing Micro Deformations from Pooling Layers for Offline Signature Verification

In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets.
Predicting Defects with Latent and Semantic Features from Commit Logs in an Industrial Setting

Software defect prediction is still a challenging task in industrial settings. Noisy data and/or lack of data make it hard to build successful prediction models. In this study, we aim to build a change-level defect prediction model for a software project in an industrial setting. We combine various probabilistic models, namely matrix factorization and topic modeling, with the expectation of overcoming the noisy and limited nature of industrial settings by extracting hidden features from multiple resources. Commit level process metrics, latent features from commits, and semantic features from commit messages are combined to build the defect predictors with the use of Log Filtering and feature selection techniques, and two machine learning algorithms Naive Bayes and Extreme Gradient Boosting (XGBoost). Collecting data from various sources and applying data pre-processing techniques show a statistically significant improvement in terms of probability of detection by up to 24% when compared to a base model with process metrics only.
The Relation between Student Behaviours in Group Presentations and their Teamwork Modalities Using Belbin and MBTI Analysis

This research paper aims to investigate the relationship between learners’ profiles as analysed according to MBTI and Belbin and their behaviour during group presentations while not being the active presenter. This paper is part of a research study in the field of video tagging as a mechanism for analysing individual behaviour in learning activities. As part of this study, the authors analysed videos of student group presentation and associated behavioural patterns to individual student profiles as produced by the Belbin and MBTI analysis models. The aim of the study was to identify how social and teamwork characteristics of individual learners can relate to certain behaviours. Such relations would help to better assess video content of learning activities including meetings and presentations. A primary aim of the study is to identify associations between human behaviour and individual’s teamwork characteristics. Such associations could facilitate the judgment of learners’ ability to work in a team. The study included quantitative research methods for analysing videos in combination with personality profiling analysis, with emphasis on social and teamwork activities. The Belbin and Myers–Briggs Type Indicator (MBTI) models were used for the purpose of this study. Furthermore, the behavioural patterns monitored during the video analysis included ‘eye contact with presenter’ and ‘eye focus out’ as the most prominent observable behaviours. The paper’s conclusion is that based on the analysis results, learners classified as Coordinators (Belbin) or Virtuosos (MBTI) are less likely to lose focus while they are not presenting, and students classified as Monitor Evaluators (Belbin) or Logisticians (MBTI) are more likely to look and take care of their team members while presenting.
Delivery of High Definition Video Content over Bandwidth Constrained Links in Heterogeneous Wireless Networks

In order to deal with the fluctuations in available bandwidth that mobile users experience, modern video streaming employs adaptive bitrate (ABR) algorithms. MPEGDASH enabled clients adapt the bitrate of requested video segments to match as closely as possible the available bandwidth of the wireless link in use. If link conditions are poor or the link is congested, the ABR algorithm will select the lowest available bitrate supported by the video stream. This approach works well for standard video content since there is no restriction on using low bitrates.However, for users wishing to view High Definition (HD) content, there is a lower bound to the bitrate below which they cannot go if the content is to be considered HD. ABR algorithms, forced to maintain HD bitrates over wireless links with constrained bandwidth, experience significant numbers of stalling events. High numbers of stalling events, particularly towards the end of a video, have a serious negative impact on end-user QoE.This paper proposes Adaptive Interface Selection (AIS) to address this issue. AIS leverages the multi-homed nature of modern mobile devices and the segmented nature of video content prepared for DASH-enabled clients to overcome the bandwidth constraints that hobble ABR algorithms. AIS uses multiple wireless interfaces in parallel to download content to maintain the playout buffer level to prevent stalling events. By minimizing the number of stalling events and streaming at HD compatible bitrates end-user QoE is maintained. Downloading as little content as possible over mobile networks helps protect the end-user's mobile data-cap.
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient

Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model. However, the estimation and classifier learning are normally conducted in a pipeline instead of being jointly optimized. In this paper, we propose to alternatively train the two steps using reinforcement learning. Our proposal adopts a policy network to adaptively make assumptions on the labels of unlabeled data, while a classifier is built upon the output of the policy network and provides rewards to learn a better strategy. The dynamic and interactive training between the policy maker and the classifier can exploit the unlabeled data in a more effective manner and yield a significant improvement on the classification performance. Furthermore, we present two different approaches to represent the actions sampled from the policy. The first approach considers continuous actions as soft labels, while the other uses discrete actions as hard assignment of labels for unlabeled examples. We validate the effectiveness of the proposed method on two benchmark datasets as well as one e-commerce dataset. The result shows the proposed method is able to consistently outperform state-of-the-art methods in various settings.
Performance Analysis of Data Parallelism Technique in Machine Learning for Human Activity Recognition Using LSTM

Human activity recognition (HAR), driven by large deep learning models, has received a lot of attention in recent years due to its high applicability in diverse application domains, manipulate time-series data to speculate on activities. Meanwhile, the cloud term "as-a-service" has essentially revolutionized the information technology industry market over the last ten years. These two trends somehow are incorporating to inspire a new model for the assistive living application: HAR as a service in the cloud. However, with frequently updates deep learning frameworks in open source communities as well as various new hardware features release, which make a significant software management challenge for deep learning model developers. To address this problem, container techniques are widely employed to facilitate the deep learning software development cycle. In addition, models and the available datasets are being larger and more complicated, and so, an expanding amount of computing resources is desired so that these models are trained in a feasible amount of time. This requires an emerging distributed training approach, called data parallelism, to achieve low resource utilization and faster execution in training time. Therefore, in this paper, we apply the data parallelism to build an assistive living HAR application using LSTM model, deploying in containers within a Kubernetes cluster to enable the real-time recognition as well as prediction of changes in human activity patterns. We then systematically measure the influence of this technique on the performance of the HAR application. Firstly, we evaluate our system performance with regard to CPU and GPU when deployed in containers and host environment, then analyze the outcomes to verify the difference in terms of the model learning performance. Through the experiments, we figure out that data parallelism strategy is efficient for improving model learning performance. In addition, this technique helps to increase the scaling efficiency in our system.