Reviewerly/exHarmony huggingface dataset:
Aspect-Level Sentiment Classification Based on Graph Attention Network with BERT

With accelerated evolution of the internet, people can express their sentiments towards organizations, politics, products, events, etc. Analyzing these sentiments becomes very beneficial for businesses, government and individuals. To some extent, text sentiment analysis involves Internet content security. Aspect level sentiment classification aims to recognize the sentiment expressed towards a special target given a context sentence, which is more fine-grained than sentence level or document level sentiment analysis. Most previous works concentrate on the semantic information between the contexts and the aspect terms, but they ignore the structural information of the sentences. In this paper, we propose a new method based on Graph Attention Network (GAT) to deal with the complex connections among words in sentences through structural features. A lot of experiments are conducted on two datasets: Laptop and Restaurant from SemEval2014, the results show that our model outperforms other previous works, which confirms the effectiveness of taking sentence structural information into account.
Wangiri Fraud: Pattern Analysis and Machine-Learning-Based Detection

The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such networks. In this article, Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled. In fact, Wangiri fraud has significant negative financial and reputation consequences for the mobile service providers and also has a bad psychological impact on the victims. In order to identify this fraudulent behavior, three Wangiri fraud patterns are defined by analyzing call records of over a year. Then, the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set. In the context of Wangiri fraud detection, classification algorithms outperformed the others based on the chosen security and performance metrics. Finally, the performance evaluation of these algorithms is extended in detecting the other two real-world Wangiri fraud patterns. This article provides a detailed definition of the Wangiri fraud patterns and outlines the implementation and evaluation of ML algorithms in the context of detecting Wangiri fraud. The security analysis and experimental results demonstrate that depending on fraud patterns the best ML algorithm to detect Wangiri fraud may also vary.
Research on Power Big Data Analysis Technology and Application Based on Cloud Computing

In order to solve the problem that the current electricity service strategy can not provide accurate marketing services for electricity customers, this paper carries out exploration and research in three aspects. One is to study the technical principles of energy substitution, build the potential model for electricity substitution, and evaluate the potential of electricity substitution, The third is to use big data mining technology, combined with customers’ electricity consumption and electricity cost, to analyze their potential to participate in electricity market transaction. By analyzing the characteristics of customers’ electricity consumption behavior contained in the data, we can mine the characteristics of different types of customers’ electricity consumption behavior in an all-round, multi angle and multi-level way.
Design and Applications of Improved Metaheuristic Algorithms for Neural Network Training

The success of nature-inspired evolving metaheuristic algorithms can be attributed to the seemingly balanced arrangement of operators used aimed at seamless exploration and exploitation. The lack of such an amalgamation causes an algorithm to converge late, while simultaneously failing to achieve global optimal by stocking in local optimum. The salp swarm algorithm (SSA) and the spotted hyena optimizer (SHO) are two newly developed optimization techniques that aim to handle continuous, nonlinear and complex actual optimization glitches. The explorative power of existing SSA and SHO is insufficient for tackling complex day-to-day life challenges. This research presents five enhanced hybrid versions of SSA and SHO that use space transformation search (STS), oppositional-based learning (OBL), mutation operator, and quadratic approximation operator to increase the performance of the existing approach (QAO). STS-SSA, OBL-MO-SSA, STS-SHO, OBL-MO-SHO and QASHO are the suggested algorithms. Five suggested hybrid algorithms are evaluated using benchmark problems from IEEE-CEC-2017 to prove their efficiency. Efficacy metrics, complexity study, convergence ratio and statistical connotation are used to characterize the competence and strength of produced models, and the results are equated to modern algorithms. The statistical significance was established using the Friedman and Holms test. The suggested hybrid algorithms have also been castoff to train MLPs, Pi-Sigma neural networks (PSNN), functional link artificial neural networks (FLANN) and wavelet neural network (WNN) using target datasets from the UCI warehouse. The findings show that the five hybrid algorithms developed are effective and reliable, with the potential to tackle real-world optimization problems.
LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions

Over the last two decades, face recognition (FR) has become one of the most prevailing biometric applications for effective people identification as it offers practical advantages over other biometric modalities. However, current state-of-the-art findings suggest that FR under adverse and challenging conditions still needs improvements. This is because face images can contain many variations like face expression, pose, and illumination. To overcome the effect of these challenges, it is necessary to use representative face features using feature extraction methods. In this paper, we present a new feature extraction method for robust FR called Local Binary Pattern and Wavelet Kernel PCA (LWKPCA). The proposed method aims to extract the discriminant and robust information to minimize recognition errors. This is obtained first by the best use of nonlinear projection algorithm called RKPCA. Then, we adapted the algorithm to reduce the dimensionality of features extracted using the proposed Color Local Binary Pattern and Wavelets transformation called Color LBP and Wavelet Descriptor. The general idea of our descriptor is to find the best representation of face image in a discriminant vector structure by a novel feature grouping strategy generated by the Three-Level decomposition of Discrete Wavelet Transform (2D-DWT) and Local Binary Pattern (LBP). Extensive experiments on four well-known face datasets namely ORL, GT, LFW, and YouTube Celebrities show that the proposed method has a recognition accuracy of 100% for ORL, 96.84% for GT, 99.34% for LFW, and 95.63% for YouTube Celebrities.
Ultra-Low Latency, Stable, and Scalable Video Transmission for Free-Viewpoint Video Services

With IT and video technologies evolving rapidly, video services with user interaction emerge, and bring about attracting experiences. The growing bandwidth and latency demands call for advanced transmission techniques which utilize wired, wireless, and 5G cellular network, and cooperate unicast, multicast, and broadcast. Free-viewpoint video, as one of the interactive video applications, can provide users with immersion of real world scenes, and shows potential to be an essential application in future digital life. However, such an application is different from other video services in aspects of computation and communication features, and existing researches are not up to the low frame latency, smooth playback, and high service scalability challenges. In this paper, we target on the above problems, and achieve ultra-low latency and stable video transmission for free-viewpoint video services by improving the performance of multiple system components accordingly. Firstly, the key performance indicator is formulated for such services, and an edge computing based video transmission schema is proposed to achieve ultra-low frame latency. Secondly, a pair of stabilization algorithms at both server and client sides based on application features are introduced to ensure stable encoded video streams before transmission, and smooth playback after that. Thirdly, a cloud-edge-client hybrid free-viewpoint service architecture is proposed, which can provide hierarchical experiences and enlarge the service scale with restricted resources. Experimental results prove the effectiveness of the proposed transmission schema and scheduling algorithms, and feasibility of the proposed service architecture is revealed by qualitative analyses.
Construction of Expurgated and Extended Goppa Codes With Dihedral Automorphism Groups

In this paper, we determine all dihedral subgroups in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGL_{2}(\mathbb {F}_{q})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q=2^{l}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> is a positive integer; and construct binary expurgated and extended Goppa codes with dihedral automorphism groups. Moreover, it is easy to obtain binary quasi-cyclic expurgated or extended Goppa codes.
Unsupervised Person Re-identification Based on Skeleton Joints Using Graph Convolutional Networks

With the remarkable progress of deep learning methods, person re-identification has received a lot of attention from researchers. However, the majority of previous work mainly focus on supervised learning setting, which requires expensive data annotations. In this paper, we address this problem by proposing a purely unsupervised learning model. Inspired by the effectiveness of modeling the spatio-temporal information of pedestrian video, we mine the relationships between human body joints. Specifically, we propose a novel framework by learning inter-frame and intra-frame relationships for discriminative feature learning via two Graph Convolutional Networks (GCN) modules: spatial and temporal. The spatial module captures the structural information of the human body and the temporal module propagates information across adjacent frames. At the end, we perform hierarchical clustering by selecting P identities and K instances (PK sampling) to generate pseudo-labels for the unlabeled data. By iteratively optimizing these modules, our model extracts robust spatial-temporal information that can alleviate the occlusion problem. We conduct experiments on two benchmarks: MARS and DukeMTMC-VideoReID datasets, where we demonstrate the effectiveness of our proposed method.
GraNDe: Near-Data Processing Architecture With Adaptive Matrix Mapping for Graph Convolutional Networks

Graph Convolutional Network (GCN) models have attracted attention given their high accuracy in interpreting graph data. One of the primary building blocks of a GCN model is aggregation, which gathers and averages the feature vectors corresponding to the vertices adjacent to each individual vertex. Aggregation works by multiplying the adjacency and feature matrices. The size of both matrices exceeds the on-chip cache capacity, and the adjacency matrix is highly sparse. These lead to little data reuse and cause numerous main-memory accesses during the aggregation process. Thus, aggregation exhibits memory-intensive characteristics. We propose GraNDe, an NDP architecture that accelerates memory-intensive aggregation operations by locating processing elements near the DRAM datapath to exploit rank-level parallelism. By exploring the data mapping of the operand matrices to DRAM ranks, we discover that the optimal mapping differs depending on the configuration of a specific GCN layer. With our optimal layer-by-layer mapping scheme, GraNDe shows a speedup up to 4.3× compared to the baseline system on open-graph benchmark datasets.
Deep Stacked Ensemble for Breast Cancer Diagnosis

Breast cancer is considered one of the major public health issues and a leading cause of death among women in the world. Its early diagnosis can significantly help to increase the chances of survival rate. Therefore, this study proposes a deep stacking ensemble technique for binary classification of breast histopathological images over the BreakHis dataset. Initially, to form the base learners of the deep stacking ensemble, we trained seven deep learning (DL) techniques based on pre-trained VGG16, VGG19, ResNet50, Inception_V3, Inception_ResNet_V2, Xception, and MobileNet with a 5-fold cross-validation method. Then, a meta-model was built, a logistic regression algorithm that learns how to best combine the predictions of the base learners. Furthermore, to evaluate and compare the performance of the proposed technique, we used: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), and (2) Scott Knott (SK) statistical test to cluster and identify the outperforming models. Results showed the potential of the stacked deep learning techniques to classify breast cancer images into malignant or benign tumor. The proposed deep stacking ensemble reports an overall accuracy of 93.8%, 93.0%, 93.3%, and 91.8% over the four magnification factors (MF) values of the BreakHis dataset: 40X, 100X, 200X and 400X, respectively.
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?

Abstract While automated driving is often advertised with better-than-human driving performance, this chapter reviews that it is nearly impossible to provide direct statistical evidence on the system level that this is actually the case. The amount of labeled data needed would exceed dimensions of present-day technical and economical capabilities. A commonly used strategy therefore is the use of redundancy along with the proof of sufficient subsystems’ performances. As it is known, this strategy is efficient especially for the case of subsystems operating independently, i.e., the occurrence of errors is independent in a statistical sense. Here, we give some first considerations and experimental evidence that this strategy is not a free ride as the errors of neural networks fulfilling the same computer vision task, at least in some cases, show correlated occurrences of errors. This remains true, if training data, architecture, and training are kept separate or independence is trained using special loss functions. Using data from different sensors (realized by up to five 2D projections of the 3D MNIST dataset) in our experiments is more efficiently reducing correlations, however not to an extent that is realizing the potential of reduction of testing data that can be obtained for redundant and statistically independent subsystems.
Inference With Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach

We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods that either exhibit prohibitive complexity as the population grows or do not guarantee convergence. Our method is based on optimal transport, or more specifically, multimarginal optimal transport theory. In particular, we consider an inference problem with aggregate observations, that can be seen as a structured multimarginal optimal transport problem where the cost function decomposes according to the underlying graph. Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP). We further specialize the SBP algorithm to cases associated with hidden Markov models due to their significance in control and estimation. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations. We also show that in the special case where the aggregate observations are in Dirac form, our algorithm naturally reduces to the standard belief propagation algorithm.
RSTC: A New Residual Swin Transformer for Offline Word-Level Writer Identification

Writer identification has steadily progressed in recent decades owing to its widespread application. Scenarios with extensive handwriting data such as page-level or sentence-level have achieved satisfactory accuracy, however, word-level offline writer identification is still challenging owing to the difficulty of learning good feature representations with scant handwriting data. This paper proposes a new Residual Swin Transformer Classifier (RSTC), comprehensively aggregating local and global handwriting styles and yielding robust feature representations with single-word images. Local information is modeled by the Transformer Block through interacting strokes. Global information is featurized by holistic encoding using the Identity Branch and Global Block. Moreover, the pre-training technique is exploited to transfer reusable knowledge learned from a task similar to writer identification, strengthening the model&#x2019;s representation of handwriting features. The proposed method is tested on the IAM and CVL benchmark datasets and achieves state-of-the-art performance, which demonstrates RSTC&#x2019;s superior modeling capability on word-level writer identification.
On the Effectiveness of Pre-Trained Language Models for Legal Natural Language Processing: An Empirical Study

We present the first comprehensive empirical evaluation of pre-trained language models (PLMs) for legal natural language processing (NLP) in order to examine their effectiveness in this domain.Our study covers eight representative and challenging legal datasets, ranging from 900 to 57K samples, across five NLP tasks: binary classification, multi-label classification, multiple choice question answering, summarization and information retrieval.We first run unsupervised, classical machine learning and/or non-PLM based deep learning methods on these datasets, and show that baseline systems' performance can be 4%∼35% lower than that of PLM-based methods.Next, we compare general-domain PLMs and those specifically pre-trained for the legal domain, and find that domain-specific PLMs demonstrate 1%∼5% higher performance than general-domain models, but only when the datasets are extremely close to the pretraining corpora.Finally, we evaluate six general-domain state-of-the-art systems, and show that they have limited generalizability to legal data, with performance gains from 0.1% to 1.2% over other PLM-based methods.Our experiments suggest that both general-domain and domain-specific PLM-based methods generally achieve better results than simpler methods on most tasks, with the exception of the retrieval task, where the best-performing baseline outperformed all PLM-based methods by at least 5%.Our findings can help legal NLP practitioners choose the appropriate methods for different tasks, and also shed light on potential future directions for legal NLP research.
A Robust Authentication and Authorization System Powered by Deep Learning and Incorporating Hand Signals

Hand gesture recognition signals have several uses. Communication for visually challenged people, such as the elderly or the handicapped, health care, automobile user interfaces, security, and surveillance are just a few of the possible applications. A deep learning-based edge computing system is designed and implemented in this article, and it is capable of authenticating users without the need of a physical token or physical contact. To authenticate, the sign language digits are represented by hand gestures. Deep learning is used to categorize digit hand motions in a language based on signs. The suggested deep learning model’s characteristics and bottleneck module are based on deep residual networks. The collection of sign language digits accessible online shows that the model achieves a classification accuracy of 97.20%, which is excellent. Model B+ of the Raspberry Pi 3 is used as an edge computing device, and the model is loaded on it. Edge computing is implemented in two phases. First, the gadget collects and stores initial camera data in a buffer. The model then calculates the digit using the first photograph in the buffer as input and an inference rate of 280 ms.
Multitask Fine-Tuning for Passage Re-Ranking Using BM25 and Pseudo Relevance Feedback

Passage re-ranking is a machine learning task that estimates relevance scores between a given query and candidate passages. Keyword features based on the lexical similarities between queries and passages have been traditionally used for the passage re-ranking models. However, such approaches have a limitation; it is difficult to find semantic and contextual features beyond word-matching information. Recently, several studies based on neural pre-trained language models such as BERT overcome the limitations of traditional keyword-based models and they show significant performance improvements. Such ranking models have the advantage of finding the contextual features of queries and documents better than traditional keyword-based methods. However, these deep learning-based models require large amounts of data for training. Such training data is usually manually labeled with high cost, and how to utilize the data efficiently is an important issue. This paper proposes a fine-tuning method for efficient training of the neural re-ranking model. The proposed model utilizes data augmentation by simultaneously learning the ranking and MLM tasks during the fine-tuning stages. For the MLM task, different parts of a passage are masked at each training epoch. Even if only one pair of query and passage is given, the model is exposed to diverse cases with passages dynamically masked from the one. Also, the probability distribution of term importance is trained on the model.We calculate term importance weight by two novel methods using BM25 and pseudo relevance feedback. Terms are sampled and masked according to the importance weight. The ranking model learns representation based on the term weight distribution by executing the MLM task. A novel method with pseudo relevance feedback is applied for calculating term importance. It enables the neural ranking models to form representation according to feedbacks from an initial search stage. The proposed model is trained with data from the MS MARCO leaderboard for the re-ranking task. Our model achieves the state-of-the-art MRR@10 score in the leaderboard except for the ensemble-based method. In addition, our model demonstrates significant performance in three different evaluation metrics: MRR@10, Mean Rank, and Hit@(5,10,20,50).