Reviewerly/exHarmony huggingface dataset:
A Novel Deep Parallel Time-Series Relation Network for Fault Diagnosis

Currently, deep learning-based methods are widely used in the fault diagnosis of time-series data for their high precision. However, the application of traditional deep learning fault diagnosis models is limited by their calculational efficiency and poor interpretation ability. To address the problems, a fault diagnosis model named the deep parallel time-series relation network (DPTRN) is proposed in this article. There are three main advantages of the DPTRN. First, our proposed time relationship unit can perform feature extraction on each time node of a time-series sample simultaneously; therefore, the DPTRN performs fault diagnosis in a parallel way and improves the computing efficiency significantly. Second, by improving the absolute position embedding, our novel decoupling position embedding unit can be directly applied for fault diagnosis and can learn contextual information. Third, our proposed DPTRN has an obvious advantage in feature interpretability compared with traditional deep learning-based models. Applying the DPTRN model on four datasets, we achieved higher diagnosis performance with much lower cost, which indicates the effectiveness, efficiency, and interpretability of the proposed DPTRN model.
EASDisco: Toward a Novel Framework for Web Service Discovery Using Ontology Matching and Genetic Algorithm

Web services are gradually elevating as a fundamental aspect of Web applications in the era of Web 3.0. A Web service can be termed as a strategic model curated for reinforcing concordant machine-to-machine interactivity over a network. As there is a gradual transfer toward service-oriented architecture, the importance of service-based computing has turned out to be exceptionally popular. It has become a major asset in an aspect of communication within the Internet. This paper proposes an ontology-based Web service recommendation system that uses ontology matching and collective crowdsourced ontology along with a genetic algorithm for optimization. The dataset is used for training followed with classification and computing semantic similarity using the genetic algorithm which recommends the services in increasing order of similarity. The proposed approach is superior in terms of performance and recorded precision and accuracy of 96.79 and 95.39% which is found to be better than existing approaches.
Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds.The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultralow latency video streaming.However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications.As a result, monitoring and evaluation of the performance of these network-supported services is critical.Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users.This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network.The MOS is the most commonly used metric to assess the quality of experience.The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates.It was examined and evaluated using several test data sizes with various performance metrics.Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network.The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.
Hate Speech Detection Using Machine Learning Techniques

Hate speech is an issue to most governments and the public's concern due to the increased emergence of different social media applications and the increasing use of such media to disseminate hate speech to individuals or groups of persons, communities, or race. Therefore, without detection and hate speech analysis, it is impossible to believe that there is not any malicious information on social media. This paper strives to provide a survey of hate speech detection using different approaches and their comparisons while focusing on aspects like machine learning models, different features put to use, and datasets.
A Multi-Level Optimization Framework for End-to-End Text Augmentation

Abstract Text augmentation is an effective technique in alleviating overfitting in NLP tasks. In existing methods, text augmentation and downstream tasks are mostly performed separately. As a result, the augmented texts may not be optimal to train the downstream model. To address this problem, we propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end. The augmentation model is trained in a way tailored to the downstream task. Our framework consists of three learning stages. A text summarization model is trained to perform data augmentation at the first stage. Each summarization example is associated with a weight to account for its domain difference with the text classification data. At the second stage, we use the model trained at the first stage to perform text augmentation and train a text classification model on the augmented texts. At the third stage, we evaluate the text classification model trained at the second stage and update weights of summarization examples by minimizing the validation loss. These three stages are performed end-to-end. We evaluate our method on several text classification datasets where the results demonstrate the effectiveness of our method. Code is available at https://github.com/Sai-Ashish/End-to-End-Text-Augmentation.
Recognition Algorithm Models Based on the Selection of Two-Dimensional Preference Threshold Functions

The paper deals with the construction of a model of recognition algorithm (RA) based on the selection of two-dimensional preference threshold functions. The proposed RA model is based on the concept of forming a set of groups formed from tightly coupled features and determining a set of two-dimensional preference threshold functions when constructing an extreme RA within the proposed model. A structural description of the developed RA model is given, presented in the form of a sequence of computational procedures, the main of which are: 1) the definition of support pseudo-objects (SP) in a two-dimensional subspace of representative features; 2) determination of two-dimensional threshold proximity function between the pseudo-object and the object; 3) identification of groups of tightly coupled two-dimensional threshold proximity functions; 4) determination of basic two-dimensional threshold proximity functions. The results of the experimental study showed that the use of the developed RA model provides the possibility of a significant reduction in the computational complexity of the algorithm, which implements the test set in the process of object recognition; tosolve the problem of object recognition, given in the high dimensionality features space, with relatively high accuracy.
Detection of sickle cell anemia using SVM classifier

Analysis of Red blood cell and sickle cell detected from the blood samples of sickle cell anemic person is a crucial process in medical diagnosis. In this paper, automated diagnosis model, developed usisng machine learning techniques are deployed to differentiate normal RBC cells from abnormal RBC (SC). The image collected from the subjects to necessary image processing technique for better enhancements and the sample is applied to image thresholding, the methods we used is global thresholding and subjects to morphological operation to lessen the noise in image and feature is extracted and later it is applied to Support Vector Machine (SVM) classifier method of machine learning through which the algorithm analyses the area, perimeter of cell which determines the type of cells for classification.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge

Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by triples whose forms are inconsistent with the corresponding language expressions. For commonsense mining tasks, this inconsistency raises a challenge for the prevailing methods using pre-trained language models that learn the expression of language. However, there are few studies which focus on this inconsistency issue. To fill this empty, in this paper, we term the commonsense knowledge whose triple form is heavily inconsistent with the language expression as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep commonsense knowledge</i> and first conduct extensive exploratory experiments to study deep commonsense knowledge. We show that deep commonsense knowledge occupies a significant part of commonsense knowledge, while the conventional methods based on pre-trained language models fail to capture it effectively. We further propose a novel method to mine the deep commonsense knowledge from raw text that is exactly language expression, alleviating the reliance of conventional methods on the triple representation form. Experiments demonstrate that our proposed method substantially improves the performance in mining deep commonsense knowledge.
Joint Local-Global Discriminative Subspace Transfer Learning for Facial Expression Recognition

Traditional facial expression recognition (FER) has achieved satisfactory results to some extent, and most of the current methods are trained and evaluated on a single database. However, in real applications, the training and testing images are often collected in different scenarios, which will lead to performance degeneration. To tackle this problem, in this paper, we propose a novel transfer learning approach, named joint local-global discriminative subspace transfer learning (LGDSTL), for cross-database FER. In LGDSTL, first, we develop a joint local-global graph as the distance metric, in which we not only consider the local discriminative geometric structure for each database, but also consider a global graph to transfer knowledge. In this way, the discrepancy between the two databases will be significantly reduced. Then, we present a pairwise regression function to guide the discriminative subspace transfer learning. Additionally, a data reconstruction constraint is introduced to preserve the main discriminative information. Finally, comparative studies on six popular benchmarks demonstrate the effectiveness of the proposed approach.
Interval Type-3 Fuzzy Sets

In this chapter, an provide an overview of type-3 fuzzy sets and their operations. This overview is intended to offer the basic concepts required for understanding the methods and algorithms presented later in this book.
A Practical Usability Study Framework Using the SUS and the Affinity Diagram: A Case Study on the Online Roadshow Website

Online Roadshow has many benefits in promoting a concept or a product to the public. It is a new model with the same purpose as a physical roadshow but with higher scalability and flexibility in terms of time, location, target audience, resource utilization, and data collection capability. However, the prototype implementation of the new model has not been evaluated for its usability. As ISO standards defined, usability has three key elements: effectiveness, efficiency, and satisfaction. The effectiveness and efficiency are highly dependent on the different systems, while satisfaction is measurable by System Usability Scale (SUS). SUS is a quick and easy technique where the usability of a system can be assessed in a short time. However, the result of SUS is only a grading scale, with no information on the problematic areas. Hence, this paper introduces a practical framework that combines the SUS with the Affinity Diagram. With the intention of maintaining the simplicity and elegance of the SUS, an additional open-ended question is asked to assess the usability problem of the website. The proposed practical SUS plus one open-ended-question usability-testing framework was applied to the Online Roadshow website. Since the average SUS score for the Online Roadshow website from the test was relatively low, the Affinity Diagram was used to analyze the open-ended comments from the user. As a result, the practical usability framework identified the usability problems on the Online Roadshow website to assist the developers in improving the usability.
Learning Two-View Correspondences and Geometry Using Neighbor-Aware Network

Finding reliable correspondences between two images is a fundamental problem in remote-sensing image registration. In the face of sparse and unordered correspondences, the previous learning-based works often focus on global information and ignore valuable local information. To gather rich local information, we propose a simple and effective approach, the principle of which is to establish a local neighborhood structure for putative correspondences and then extract and aggregate neighborhood context. Specifically, we design a residual block with an innovative normalization operation and then we construct a sub-net, by introducing a competition mechanism in the local neighborhood to enhance the expression ability of features. Finally, we propose a learning-based network, to improve the performance of outlier rejection by extracting neighborhood context and global context. Extensive experiments on relative pose estimation demonstrate that the proposed network surpasses current state-of-the-art approaches on both challenging indoor and outdoor datasets.
Domain adaptive extreme learning machine for epileptic EEG classification

Epilepsy is a common brain disease, caused by abnormal discharge of human brain neurons, resulting in brain dysfunction syndrome. Although epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can lead to physical and mental impact of patients. At present, the method used to detect epilepsy is to make a comprehensive judgment by EEG examination combined with clinical symptoms. With the application of AI technology, some advanced algorithms have been used to assist medical diagnosis. In this trend, we use extreme learning machine to observe and detect patients with epilepsy. ELM has the characteristics of high efficiency and high precision, so it is often used in regression and classification problems. However, in the face of different data sets, ELM structure is not enough to achieve good performance. This is caused by the uneven distribution of data in different data sets. To solve this problem, we add the transfer learning module to the basic ELM structure. The purpose of adding transfer learning is to divide the disordered data in the domain space and construct a data set suitable for ELM learning. Specifically, the raw data are mapped to high-dimensional space by kernel method through domain adaptive method. Secondly, in high-dimensional space, the distance between different domains should be reduced appropriately. Finally, ELM method is used to analyze and predict the changed data set. In the whole algorithm process, due to the characteristics of ELM updating weight, only a certain amount of hidden nodes are needed, and the training process is very fast. At the same time, after adding the transfer learning function module, the accuracy of ELM is also satisfactory. In this paper, the epilepsy data of patients were used for comparative experiments. The experimental results show that the method can maintain high efficiency and satisfactory accuracy.
Image analysis-assisted nuclear morphometric study of benign and malignant breast aspirates

Fineneedle aspiration cytology of the breast is well accepted and accurate for the diagnosis of benign and malignant lesions, however, it remains a subjective evaluation.The present study was carried out to assess the utility of nuclear morphometry in differentiating benign and malignant breast aspirates. Importantly, we wanted to evaluate the utility of nuclear density parameters using Image J software.Nuclear morphometry was carried out using image analysis software Image J 1.44 on 20 selected benign and malignant breast aspirates. Assessment was carried out on a total of 1000 cells in benign and 1000 cells in malignant aspirates counting 50 intact nuclei in nonoverlapping cells for each case. Six parameters including three size parameters, namely nuclear area, nuclear diameter, and nuclear perimeter; one shape parameter, i.e., axis ratio; and two nuclear chromasia parameters, namely density (integrated and raw), were measured.There were significant differences between nuclear area, perimeter, diameter, integrated density, and raw integrated density of benign and malignant lesions. No significant difference was found for axis ratio. Receiver operating characteristic curve analysis revealed that nuclear area, perimeter, diameter, integrated density, and raw integrated density are helpful in discriminating benign and malignant aspirates.Thus, Image J is helpful in the evaluation of nuclear size as well as chromasia. We conclude that nuclear size and density parameters can be used to derive cutoff values of various parameters to differentiate between benign and malignant cells in breast cytology.
Efficiency in Human Actions Recognition in Video Surveillance Using 3D CNN and DenseNet

The human actions recognition in video is a topic of growing interest in the scientific community in computing, due to its application in real problems and different domains such as video surveillance, medicine, psychiatry, among others, and on the other hand, due to the overcrowding of video capture devices all over the planet. Processing video to extract characteristics and subsequent classification or recognition is a complex task, as it involves processing data in a spatial dimension (video dimensions) and a temporal dimension, causing the input data to increase abundantly and become in a challenging task. There are two approaches to the recognition of human actions on video; handcrafted approaches based on optical flow and approaches based on Deep Learning, the latter has achieved many achievements in terms of accuracy; however, it has the problem of high computational cost, making its application almost impossible in specific domains, much less in a real-time scenario. In this way, we propose an architecture based on Deep Learning, for human actions recognition in video, oriented to the domain of video surveillance and in a real-time scenario; For this, the proposal is based on an architecture that combines 3D CNN and DenseNet techniques. The results show that the proposal is efficient and can be used in the domain of real-time video surveillance. Likewise, general representations are proposed referring to the resolution and minimum frames per second that guarantee recognition.
Heterogeneous Defect Prediction through Correlation-Based Selection of Multiple Source Projects and Ensemble Learning

Heterogeneous defect prediction (HDP) predicts defect-prone modules when the source and target data have heterogeneous metric sets. Although several researchers have tried to improve the performance of HDP, many of them did not suggest selection guidelines of source projects nor handle the class imbalance problem. In this paper, we propose a novel approach to improve the performance further by selecting proper source projects for the given target project and considering imbalanced data, called CorrelAtion-based selection of Multiple source projects and Ensemble Learning (CAMEL) for HDP. Specifically, CAMEL first matches metrics through the Kolmogorov-Smirnov test. Second, it calculates fitness scores based on correlation analysis and selects multiple projects. Third, it predicts target labels using each selected source project and integrates the results with ensemble learning. The experiments show that CAMEL produces better results against existing methods. Consequently, CAMEL enhances reliability in the early development phase by providing proper source selection guidelines.
ENCIDER: Detecting Timing and Cache Side Channels in SGX Enclaves and Cryptographic APIs

Confidential computing aims to secure the code and data in use by providing a Trusted Execution Environment (TEE) for applications using hardware features such as Intel SGX. Timing and cache side-channel attacks, however, are often outside the scope of the threat model, although once exploited they are able to break all the default security guarantees enforced by hardware. Unfortunately, tools detecting potential side-channel vulnerabilities within applications are limited and usually ignore the strong attack model and the unique programming model imposed by Intel SGX. This article proposes a precise side-channel analysis tool, ENCIDER, detecting both timing and cache side-channel vulnerabilities within SGX applications via inferring potential timing observation points and incorporating the SGX programming model into analysis. ENCIDER uses dynamic symbolic execution to decompose the side-channel requirement based on the bounded non-interference property and implements byte-level information flow tracking via API modeling. We have applied ENCIDER to 4 real-world SGX applications, 2 SGX crypto libraries, and 3 widely-used crypto libraries, and found 29 timing side channels and 73 code and data cache side channels. We also compare ENCIDER with three state-of-the-art side channel analysis tools using their benchmarks. ENCIDER does not only report most of the bugs with 20%-50% run time improvement and 65%-92% memory usage improvement, but also detects 9 missing bugs from these tools. We have reported our findings to the corresponding parties, e.g., Intel and ARM, who have confirmed most of the vulnerabilities detected.
AI-Based Syntactic Complexity Metrics and Sight Interpreting Performance

Complex syntax may lead to increased cognitive effort during translation. However, it is unclear what kinds of syntactic complexity have a stronger impact on translation performance. In this paper, we employ several syntactic metrics which enable us to explore the impact of syntactic complexity on the quality in English-to-Chinese sight interpreting. We have operationalized syntactic complexity by six metrics, namely, Incomplete Dependency Theory metric (IDT), Dependency Locality Theory metric (DLT), Combined IDT and DLT metric (IDT+DLT), Left Embeddedness metric (LE), Nested Nouns Distancemetric (NND), and Bilingual Complexity Ratio metric (BRC). Three professional translators have manually annotated translation errors using MQM-derived error taxonomies, which includes accuracy, fluency, and style errors, each as critical or minor errors. We assessed inter-rater agreement by adopting weighted Fleiss’ Kappa scores. We found that there are strong correlations between the IDT and IDT+DLT metrics and sight interpreting errors. We also found that language-specific syntactic differences between English and Chinese such as directions of branching and noun modifiers can have a strong influence on accuracy and critical errors.
An Iterative Nonlinear Filter Based on Posterior Distribution Approximation via Penalized Kullback–Leibler Divergence Minimization

This letter deals with Gaussian approximation of complicated posterior distribution involved in the Bayesian paradigm for nonlinear dynamic systems. A general formulation for Gaussian approximation is first provided by equivalently representing posterior distribution as a Gaussian one with some constraint via embedding technique. In this work, it is specified as a penalized Kullback–Leibler divergence minimization problem. This minimization is solved for the expected Gaussian approximation by utilizing a pre-selected cubature rule and the conditional gradient method. Then, a novel iterative filter is developed for nonlinear dynamic systems. In addition, it is also proved to be optimal in linear cases and demonstrated to be effective through simulations.