Reviewerly/exHarmony huggingface dataset:
Accelerating Learning Bayesian Network Structures by Reducing Redundant CI Tests

The type of constraint-based methods is one of the most important approaches to learn Bayesian network (BN) structures from observational data with conditional independence (CI) tests. In this paper, we find that existing constraint-based methods often perform many redundant CI tests, which significantly reduces the learning efficiency of those algorithms. To tackle this issue, we propose a novel framework to accelerate BN structure learning by reducing redundant CI tests without sacrificing accuracy. Specifically, we first design a CI test cache table to store CI tests. If a CI test has been computed before, the result of the CI test is obtained from the table instead of computing the CI test again. If not, the CI test is computed and stored in the table. Then based on the table, we propose two CI test cache table based PC (CTPC) learning frameworks for reducing redundant CI tests for BN structure learning. Finally, we instantiate the proposed frameworks with existing well-established local and global BN structure learning algorithms. Using twelve benchmark BNs, the extensive experiments have demonstrated that the proposed frameworks can significantly accelerate existing BN structure learning algorithms without sacrificing accuracy.
Multi-Round Parsing-based Multiword Rules for Scientific Knowledge Extraction

Information extraction (IE) in scientific literature has facilitated many down-stream knowledge-driven tasks. Ope-nIE, which does not require any relation schema but identifies a relational phrase to describe the relationship between a subject and an object, is being a trending topic of IE in sciences. The subjects, objects, and relations are often multiword expressions, which brings challenges for methods to identify the boundaries of the expressions given very limited or even no training data. In this work, we present a set of rules for extracting structured information based on dependency parsing that can be applied to any scientific dataset requiring no expert's annotation. Results on novel datasets show the effectiveness of the proposed method. We discuss negative results as well.
Research on Pedestrian Detection Based on Improved SSD Algorithm

Pedestrian detection is an important part of assisted driving and automatic driving. This paper proposes a pedestrian detection method based on improved SSD (Single Shot MultiBox Dectector) algorithm for the problem of the detection accuracy in pedestrian test tasks. We reconstructed the feature extraction network of the SSD model. The MobileNet and ResNet (Residual Network) were used as the basic network of SSD to build the SSD model and improve the detection capability of the algorithm. We conducted experiments on public pedestrian detection data sets. The results were evaluated using mAP and FPS. The experimental results show that the mAP of the SSD algorithm after introducing the residual network reaches 85.00%, which is 11.92% higher than the original SSD algorithm, and the FPS reaches 39.31%, which effectively improves the accuracy and speed of pedestrian detection.
Capture Codes for Better Geology

Abstract Automated core logging technology is starting to replace geologists in the core shed. Often-cited justifications for this include increased speed, multivariate sensors, and the perception that manual logging can be subjective and produces inconsistent data. An alternative is to keep the geologists and replace subjectivity with objectivity. The common practice of selecting lithology from a predetermined list of rock types can force subjective decisions. These lead to data inconsistencies that tend to increase as the rocks become progressively hydrothermally altered. A system of capture codes following the maxim, “first observe, then interpret,” is proposed as a tool to improve coding consistency and collect geologic data with greater resolution. The codes capture empirical geologic observations in a systematic and comprehensive fashion to produce a compact, computer-friendly format that facilitates data synthesis, analysis, and 3-D visualization. Capture codes do not replace any existing project or standardized company summary codes for rock types, alteration facies, or degree and style of mineralization. They capture the underlying, specific geologic observations required to make correct and consistent summary code categorizations. In other words, capture codes are empirical data, while summary codes are often subjective labels. Utilization of the codes improves the understanding of the project geology and consistency in coding between geologists, while simultaneously strengthening their field skills. After reading this article, a geologist should be able to pick up any rock and capture code the lithology and, if applicable, its alteration and mineralization as well.
Partial Occlusion Face Recognition Based on CNN and HOG Feature Fusion

Although the present studies of face recognition have relatively been mature, in some complex scene environments, the efficiency of face recognition needs to be improved due to the influence of uncertain factors such as changes in illumination, changes in facial expressions, and partial facial occlusion. In order to improve the efficiency of face recognition, this paper proposes a feature fusion method based on convolutional neural networks (CNN) model and hog model. The model extracts rich implicit features from the original image by using convolutional neural network (CNN), and uses Dropout technology in the convolutional layer and the fully connected layer to randomly inhibit the activation of some neurons, so as to better solve the problem of overfitting. Moreover, this method also gives full play to the stability and robustness of Histogram of Oriented Gradients (HOG) Feature Enhancement Model. After extracting the CNN features and HOG features of the face, the method combines CNN SoftMax and HOG-SVM classifiers. The experimental results show that the recognition rate of this method is higher than that of single convolution neural network, which can reach 96.1%.
Multi-sensor Data Fusion Algorithm Based on Dempster-Shafer Theory

Aiming at the problem of uncertainty in multi-sensor data collection, a multi-sensor data fusion algorithm (MSDF) based on D-S theory is proposed. By calculating the distance between the data, the trust function is obtained to eliminate the abnormal data, calculate the basic probability distribution function for the correct data obtained as the original evidence body. The improved Deng entropy is used to quantify the uncertainty of the evidence body and form new evidence, which is fused according to the D-S theory. Computer simulation result shows that the algorithm effectively solves the high conflict problem in D-S theory and improves the accuracy of data fusion.
High Throughput Folded Architecture of AES

Advanced Encryption Standard (AES) is the most widely used crypto algorithms in various secured data commu-nications such as SSL, and so on. This paper proposes a high throughput folded architecture to perform 128-bit AES encryption/decryption, where four AES operations can be performed in parallel. The hardware utilization and throughput of this proposed folded AES design is lying in between the conventional folded and parallel designs. All the existing and proposed AES designs are implemented with Virtex-6 FPGA (XC6VLX760) using Xilinx. The synthesis results show that our proposed design achieves 84.65% of improvement in the throughput than the existing folded design with looping parameter-2 (Fig. 4 of [7]).
Replay Attack Detection in Automatic Speaker Verification Based on ResNeWt18 with Linear Frequency Cepstral Coefficients

This paper proposes, effective method for replay attack detection used in an automatic speaker verification system. The replay attack is of interest because it is the most straightforward and effective attack and is challenging to detect. It is a playback of the recording of the voice of a target speaker. From the literature, no speech features work well with all classifiers, and there is no investigation of using ResNet-based model, called ResNeWt, with linear frequency cepstral coefficient (LFCC). Therefore, a replay attack detection model based on 18-layer ResNeWt that takes LFCCs as the input, was constructed in this paper. The proposes method was tested on a dataset provided by ASVspoof 2019 competition. In terms of the equal error rate (EER), the proposed method is the best in all existing methods, with an EER of 0.29%. The comparison in terms of replay attack detection was also made in detail. The performance of the proposed method in terms of the balanced accuracy, precision, recall, and F1-score was considerably better than existing methods.
Marketplaces and the Internet of Value

This chapter focuses on the importance of e-commerce marketplaces and how the properties of trust, transparency and immutable data, which are brought forth by blockchain, can lead to a fully functioning decentralised marketplace.
Efficient HLS Implementation for Convolutional Neural Networks Accelerator on an SoC

Convolutional Neural Networks (CNNs) have achieved high accuracy in many applications such as image recognition and classification. However, due to their large amount of parameters and intensive required operations, general purpose processors cannot achieve the desired inference performance levels. Recently, various hardware accelerators for deep CNNs have been carried out to enhance the throughput of CNNs. Among these accelerators, field programmable gate array (FPGA)-based ones have gained a lot of interest due to their high performance, low power consumption, high reconfigurability, and fast development cycle. Furthermore, the availability of high-level synthesis (HLS) tools lowers the programming burden and increases the productivity of the FPGA-based accelerator designers. In this paper, a C++ HLS implementation for FPGA-based accelerator for the convolutional layers of CNNs is proposed. As a case study, we evaluate the proposed accelerator using Resnet50 CNN on Xilinx Zynq UltraScale+ MPSoC ZCU104 evaluation board using SDSoC development environment, achieving up to 339x inference speedup.
SynEvaRec: A Framework for Evaluating Recommender Systems on Synthetic Data Classes

This study proposes a novel method for evaluating and comparing recommender systems using synthetic user and item data and parametric synthetic user-item response (rating) functions. The method compares recommender systems on classes of synthetic data, oppositely to how it is usually done on particular real or synthetic datasets. The usage of classes particularly allows for managing the effects of the No Free Lunch theorem for recommender systems. Furthermore, we implement the method in the form of a flexible framework (that we call SynEvaRec) for conducting comparison experiments under different scenarios of synthetic data behaviour. Our experimental study shows that SynEvaRec helps to determine scenarios (e.g. in terms of data classes) where one recommender system is more preferable than another by means of recommendation quality. Moreover, the results turn to be rather stable over several synthetic dataset instances based on the same real-world dataset indicating the robustness of our method. The datasets, the framework implementation and the results related to our study are publicly available on GitHub.
International Experience in the Organisation of a Multimedia-Based Distance Learning System

In the leading countries of the world has become a trend organization of networking for distance education. The intensity of this process is due not only to market needs for skilled workers, the merits of distance learning, innovation in the sector of IT, but also the fact that the development of training programs have actively joined the world's largest computer company, indicating the importance of market distance education for national economies.
Images thresholding via within-cluster weighting variance

As one of the most popular thresholding methods, Otsu method is known to produce biased threshold when variances among data classes are significantly different, yet most researches point out this deviation mainly by experiments. In this paper, the essential cause of the deviation of Otsu algorithm is investigated theoretically, then starting from the analysis of two improved versions of Otsu algorithms, a thresholding method based on within cluster weighting variance is proposed, in which two parameters are optimized to adapt to images with all kind of distribution. Experimental results on both synthetic and real images demonstrated that the proposed method is effective and robust. Furthermore, the proposed method is especially suitable for segmenting serialized images which is common in real application.
No-Reference Stereoscopic Image Quality Assessment Considering Binocular Disparity and Fusion Compensation

In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.
Self-Sovereign Identity and Blockchain applications for the automotive sector

The possible applications of the blockchain to the automotive industry are countless to date. Where there are issues of incomplete or inconsistent or repudiated information, or data privacy, it comes into play by not making it possible to add, delete or modify data that has already been validated within a blockchain. The paper presents a possible application of blockchain technologies to the automotive sector. The authors analyze the main critical issues that a driver and his respective insurance company face following an accident, a situation in which an abundant amount of data is exchanged by all parties involved. The objective of the paper is to find a way to manage the situation after a car accident in the fastest and most effective way possible, recording all sensitive data and ensuring an efficient transfer to the insurance companies, without the possibility of fraud, using the blockchain. The main result obtained was to design a software application that, running on smartphones, would allow the driver to fill in the CID (Amicable statement of accident, also known as knock-for-knock agreement). In order to solve the typical criticalities mentioned before, the application will exploit Self-Sovereign Identity (SSI) credentials and the car's digital passport, both blockchain-based technologies that will be exposed in this paper.
A Novel Minimization Approximation Cost Classification Method to Minimize Misclassification Rate for Dichotomous and Homogeneous Classes

Dependence of the linear discriminant analysis on location and scale weakens its performance when predicting class under the presence of homogeneous covariance matrices for the candidate classes. Further, outlying samples render the method to suffer from higher rates of misclassification. In this study, we propose the minimization approximation cost classification (MACC) method that accounts for some specific cost function 23.9. The theoretical derivation is made to find an optimal linear hyperplane θ, which yields maximum separation between the dichotomous groups. Real-life data and simulations were used to validate the method against the standard classifiers. Results show that the proposed method is more efficient and outperforms the standard methods when the data are crowded at the class boundaries.
Nearpod: An Effective Interactive ICT Tool for Teaching and Learning Through Google Meet

In the digital era of education, the use of ICT tools is important for all human activities. School children, college students, graduates of various streams, and teachers are dependent on digital technology for teaching and learning. Student interaction and engagement is one of the challenging tasks in an online classroom for teachers of various streams to deliver the knowledge efficiently. An interactive Web-based platform improves the learning experiences of students and reduces the teacher effort. The transition from traditional lecture-based teaching methods to interactive learning can be accomplished by using various interactive tools such as Nearpod, Kahoot, and Socrative. Nearpod is a Web-based tool that allows the teacher to engage students during the online virtual sessions by creating contents with the addition of video, slide, Web content, Nearpod 3D, slideshow, audio, PhET Simulation School-level mathematics concepts, VR field trip, and Sway and also to create interactive lessons by including various activities such as Time to Climb, Quiz, Flipgrid, Poll, Fill in the Blanks, Memory Test, Matching Pairs, Collaborate Board, Open-Ended Question, and Draw It. This paper describes the various features of Nearpod and content creation, using Nearpod for effective teaching and learning.
Enhancing the Decentralized Application (Dapp) for E-commerce by Using the Ethereum Blockchain

Blockchain is the upcoming technology in creating the Dapp for different types of use cases. The transactions in the blockchain technology are transparent to all the nodes in the decentralized network. Most of the applications were built and trying to implement it with the Ethereum platform in blockchain for the security purpose. The nodes in the distributed network shares all the data to the other nodes without any modification of their data in the blockchain technology. The transactions done by the nodes are trusted through the digital signatures. In this paper, we built a decentralized web application for e-commerce by using the blockchain Ethereum platform. Our application is deployed by using the smart contracts, which are written in the solidity language and the front-end and back-end process are explained. All these data about the e-commerce details are stored into the blocks in blockchain. The Web application for online shopping will be secured and trust worthy for the nodes in the decentralized network. The other online shopping platforms like amazon are trying to deploy the blockchain for security of data and user’s convenient. The issues like storage, scalability, and others can be considered for the future work.
Dual-quality map based no reference image quality assessment using deformable convolution

Traditional feature-based no-reference image quality assessment (NR-IQA) technologies rely heavily on the correlation between the handcrafted features and distortions, and often fail to predict perceptual quality because of the diversity of image distortion types. End-to-end deep learning-based metrics are driven mainly by human-labeled data, which are prohibitively labor-expensive to collect. An NR-IQA method that imitates the hierarchical and inferential perceptual behavior of the human vision system (HVS) is proposed to address the challenges of distortion diversity and lack of labeled data. In the proposed method, first, a dual-quality map is generated for use as an intermediate target, which contains multiscale features that are highly related to image quality. To capture human eye-sensitive distortion information in distorted images, a dual attention mechanism is utilized in the quality map generation network. Then, a regression network is designed for mapping the intermediate target to a quality score. The deformable convolution which has adaptive receptive field is introduced to extract multiple scale features from the dual-quality map. Experiments on eight benchmark databases show the superiority of the proposed method over other state-of-the-art NR-IQA metrics.
DSA-PR: Discrete Soft Biometric Attribute-Based Person Retrieval in Surveillance Videos

Physical characteristics or soft biometrics are visually perceptible aspects of a human body. Noticeable attributes like build, height, complexion, clothes help with the development of a human surveillance system. The paper proposes Discrete Soft biometric Attribute-based Person Retrieval (DSA-PR) from a video using height, gender, torso (clothes) color-1, torso color-2, and torso (clothes) type given in a textual query. The DSA-PR uses Mask R-CNN for semantic segmentation and ResNet-50 for attribute classification. Height is estimated using the Tsai camera calibration method. DSA-PR weighs attributes and fuses their probability to generate a final score for each detected person. The proposed approach achieves an average Intersection-over-Union (IoU) of 0.602 and retrieval with IoU $\ge$ 0.4 is 0.808 over the AVSS challenge II dataset which works out to 5.8% and 2.02% above the state-of-the-art techniques respectively.
The Reduction of Fully Connected Neural Network Parameters Using the Pre-training Technique

This paper proposes an approach to the parameters reduction of fully connected neural networks using the conventional method of pre-training deep neural networks. It is shown that this approach makes it possible to reduce the number of trainable parameters without significant loss the generalization ability. The capabilities of the proposed technique are demonstrated using the standard dataset of handwritten digits MNIST.
Resource Rationing for Federated Learning with Reinforcement Learning

Federated learning (FL) aims to complete certain machine learning tasks by multiple rounds of progressive learning. Because of this unique characteristic, the importance of each round towards the final model performance varies as the global model converges. Motivated by this, this paper studies how to allocate the quantization bits across rounds. In particular, we propose a new quantization bits allocation mechanism based on deep Q-learning to realize the full potential of “later-is-better” principle. Unlike the existing allocation mechanism, this mechanism learns resources allocation on the fly to maximize the validation accuracy of the global model in the FL while satisfying the resource constraints. Through extended experiments, we concluded that allocating quantization bits by the proposed approaches can indeed improve the training time and final performance of FL compared with the existing resource allocation strategies. We also pointed out the available number of quantization bits and the transmission mode of FL have a significant impact on the performance of quantization bits allocation. The allocation of quantization bits across rounds based on deep Q-learning shows the great potential of resource allocation in improving the performance of FL. Besides, the method is quite general and it can be well extended to other resource rationing problems in FL.