Reviewerly/exHarmony huggingface dataset:
Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu

Deep neural networks have emerged as a leading approach towards handling many natural language processing (NLP) tasks. Deep networks initially conquered the problems of computer vision. However, dealing with sequential data such as text and sound was a nightmare for such networks as traditional deep networks are not reliable in preserving contextual information. This may not harm the results in the case of image processing where we do not care about the sequence, but when we consider the data collected from text for processing, such networks may trigger disastrous results. Moreover, establishing sentence semantics in a colloquial text such as Roman Urdu is a challenge. Additionally, the sparsity and high dimensionality of data in such informal text have encountered a significant challenge for building sentence semantics. To overcome this problem, we propose a deep recurrent architecture RU-BiLSTM based on bidirectional LSTM (BiLSTM) coupled with word embedding and an attention mechanism for sentiment analysis of Roman Urdu. Our proposed model uses the bidirectional LSTM to preserve the context in both directions and the attention mechanism to concentrate on more important features. Eventually, the last dense softmax output layer is used to acquire the binary and ternary classification results. We empirically evaluated our model on two available datasets of Roman Urdu, i.e., RUECD and RUSA-19. Our proposed model outperformed the baseline models on many grounds, and a significant improvement of 6% to 8% is achieved over baseline models.
Situational Crime Prevention (SCP) techniques to prevent and control cybercrimes: A focused systematic review

Situational Crime Prevention (SCP) is a criminological approach that is shown to reduce crime opportunities drawing from five different strategies comprising 25 techniques. With the global increase in cybercrime, practitioners and researchers are increasingly investigating opportunities for applying SCP strategies and techniques to prevent cyber-focused and cyber-enabled crimes. Recent research proposes ways that SCP can be applied to cybercrime. Yet most of this research utilizes only a few of the SCP techniques and the linkages between the SCP techniques and opportunities for reducing cybercrimes are rarely made explicit. In this paper we evaluate the relevance of the full spectrum of SCP techniques to cybercrime and explicate how computer scientists, cybercrime and cybersecurity researchers and practitioners apply SCP principles to prevent and control cyber-enabled crime. Through a focused systematic review of 352 articles across computer science, criminal justice and criminology literature using the PRISMA method, this paper clarifies terminologies, explores the rise of cybercrimes, and explains the value of SCP for responding to cybercrimes. We provide a review of the current research undertaken to apply SCP to cybercrimes and conclude with a discussion on research gaps and potential future areas of research.
Investigation of the Influence of Artificial Intelligence Markup Language-Based LINE ChatBot in Contextual English Learning

This study is intended to create an innovative contextual English learning environment making use of the widely used communication software, LINE ChatBot, based on the Artificial Intelligence Markup Language (AIML), in order to improve speaking and listening ability among learners. A total of 73 students were invited to participate in learning activities involving a 4-week English conversation exercise including both speaking and listening. Additionally, in order to explore the influence of competition on language acquisition, we added competition characteristics to the learning activities in the experimental group to enhance learning motivation and learning outcomes. The results showed that with the help of the LINE ChatBot contextual learning environment, the performance of both groups of students was slightly enhanced, but no significant differences were found. Meanwhile, extrinsic motivation in both the experimental and control group was improved if they spoke anonymously. That is, the contextual learning environment based on the LINE ChatBot significantly improved the learners' English speaking and listening ability. In addition, the results showed that the addition of a competition element effectively enhanced the learners' intrinsic motivation to learn English on the LINE ChatBot.
Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection

Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-Co V-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis [21] and MixFaceNet [4]) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database [17]. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance.
An implementation framework of blockchain-based hazardous waste transfer management system

The rapid increase in volume and types of hazardous waste (HW) due to China's continuous economic growth makes it significant to conduct HW safety management. However, due to the information asymmetry between the regulators and regulated institutions, problems such as illegal dumping and statistical fraud are common in hazardous waste transfer (HWT) activities. Moreover, there is a severe problem of information isolated island among waste production, transportation, and treatment companies in the HW market. Promoting the information monitoring and tracking of the whole process of HWT is the main challenge for HW management. Blockchain provides an ideal solution to overcome the above challenges. This study developed a framework for the blockchain-based HWT management system, which could support government regulators to obtain relevant information in HWT and improve the efficiency of HWT. The HWT management system was introduced in detail from different perspectives, including infrastructure, blockchain service, functions, and users, emphasizing six information management functions. Finally, the positive impact and various challenges encountered in implementing blockchain in HWT management were discussed. This study applies blockchain to the research field of HWT management, which expands the application of blockchain in the field of solid waste management with great theoretical and practical significance.
A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1&#x0025;, which means 13.9&#x0025; higher than rule-based sentiment dictionary method, and 7.7&#x0025; higher than TF-IDF weighting method.
Disentangled Representation Learning for Recommendation

There exist complex interactions among a large number of latent factors behind the decision making processes of different individuals, which drive the various user behavior patterns in recommender systems. These factors hidden in those diverse behaviors demonstrate highly entangled patterns, covering from high-level user intentions to low-level individual preferences. Uncovering the disentanglement of these latent factors can benefit in enhanced robustness, interpretability, and controllability during representation learning for recommendation. However, the large degree of entanglement within latent factors poses great challenges for learning representations that disentangle them, and remains largely unexplored in literature. In this paper, we present the SEMantic MACRo-mIcro Disentangled Variational Auto-Encoder (SEM-MacridVAE) model for learning disentangled representations from user behaviors, taking item semantic information into account. Our SEM-MacridVAE model achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a pair of shoes or a laptop) through a prototype routing mechanism, as well as capturing the individual preferences with respect to different concepts separately. The micro disentanglement is guaranteed through a micro-disentanglement regularizer stemming from an information-theoretic interpretation of VAEs, which forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). The semantic information including visual and categorical signals extracted from candidate items is utilized to further boost the recommendation performance of the proposed SEM-MacridVAE model. Empirical experiments demonstrate that our proposed approach is able to achieve significant improvement over the state-of-the-art baselines. We also show that the learned representations are interpretable and controllable, capable of potentially leading to a new paradigm for recommendation where users have fine-grained control over some target aspects of the recommendation candidates.
Enhancing Adversarial Training with Second-Order Statistics of Weights

Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the weights can be optimized through gradient descent methods like SGD. In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through Second-Order Statistics Optimization (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> O) with respect to the weights. By relaxing a common (but unrealistic) assumption of previous PAC-Bayesian frameworks that all weights are statistically independent, we derive an improved PAC-Bayesian adversarial generalization bound, which suggests that optimizing second-order statistics of weights can effectively tighten the bound. In addition to this theoretical insight, we conduct an extensive set of experiments, which show that S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> O not only improves the robustness and generalization of the trained neural networks when used in isolation, but also integrates easily in state-of-the-art adversarial training techniques like TRADES, AWP, MART, and AVMixup, leading to a measurable improvement of these techniques. The code is available at https://github.com/Alexkael/S2O.
A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration

In this letter, we address the problem of autonomous exploration of unknown environments with an aerial robot equipped with a sensory set that produces large point clouds, such as LiDARs. The main goal is to gradually explore an area while planning paths and calculating information gain in short computation time, suitable for implementation on an on-board computer. To this end, we present a planner that randomly samples viewpoints in the environment map. It relies on a novel and efficient gain calculation based on the Recursive Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner uses a cuboid-based evaluation method that results in an enviably short computation time. To reduce the overall exploration time, we also use a dead end resolving strategy that allows us to quickly recover from dead ends in a challenging environment. Comparative experiments in simulation have shown that our approach outperforms the current state-of-the-art in terms of computational efficiency and total exploration time. The video of our approach can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/playlist?list =PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl</uri> .
A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images

The spatial correlation among different tissue components is an essential characteristic for diagnosis of breast cancers based on histopathological images. Graph convolutional network (GCN) can effectively capture this spatial feature representation, and has been successfully applied to the histopathological image based computer-aided diagnosis (CAD). However, the current GCN-based approaches need complicated image preprocessing for graph construction. In this work, we propose a novel CAD framework for classification of breast histopathological images, which integrates both convolutional neural network (CNN) and GCN (named CNN-GCN) into a unified framework, where CNN learns high-level features from histopathological images for further adaptive graph construction, and the generated graph is then fed to GCN to learn the spatial features of histopathological images for the classification task. In particular, a novel clique GCN (cGCN) is proposed to learn more effective graph representation, which can arrange both forward and backward connections between any two graph convolution layers. Moreover, a new group graph convolution is further developed to replace the classical graph convolution of each layer in cGCN, so as to reduce redundant information and implicitly select superior fused feature representation. The proposed clique group GCN (cgGCN) is then embedded in the CNN-GCN framework (named CNN-cgGCN) to promote the learned spatial representation for diagnosis of breast cancers. The experimental results on two public breast histopathological image datasets indicate the effectiveness of the proposed CNN-cgGCN with superior performance to all the compared algorithms.
Obstacle analysis of application of blockchain technology in power data trading based on improved DEMATEL–ISM method under fuzzy environment

Power data transaction is significant to the benefit increment of power enterprises and revitalization of social resources. Blockchain has a wide application prospect in data trading, but its concrete implementation faces many difficulties. This article proposes an obstacle analysis framework to understand the barriers of application of blockchain technology in power data trading, aiming to provide a decision-making guidance and management suggestions to relevant personnel. Firstly, an obstacle index system with 11 barriers is determined from three aspects of application, technology and legal policy. Secondly, a quantitative analysis model is constructed with (IT2TrFN), improved hesitant fuzzy linguistic term set (HFLTS), decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM). And the K-means method is employed to optimize the discrimination of influence relations among indicators. Then, the model is applied in a case study to testify its applicability. The result shows that the difficulty in data maintenance is fundamental obstacle, the barriers of smart contract and supervision in transaction are important factors. Finally, a series of optimization measures and suggestions are put forward for different obstacles.
Universal Adversarial Patch Attack for Automatic Checkout Using Perceptual and Attentional Bias

Adversarial examples are inputs with imperceptible perturbations that easily mislead deep neural networks (DNNs). Recently, adversarial patch, with noise confined to a small and localized patch, has emerged for its easy feasibility in real-world scenarios. However, existing strategies failed to generate adversarial patches with strong generalization ability due to the ignorance of the inherent biases of models. In other words, the adversarial patches are always input-specific and fail to attack images from all classes or different models, especially unseen classes and black-box models. To address the problem, this paper proposes a bias-based framework to generate universal adversarial patches with strong generalization ability, which exploits the perceptual bias and attentional bias to improve the attacking ability. Regarding the perceptual bias, since DNNs are strongly biased towards textures, we exploit the hard examples which convey strong model uncertainties and extract a textural patch prior from them by adopting the style similarities. The patch prior is closer to decision boundaries and would promote attacks across classes. As for the attentional bias, motivated by the fact that different models share similar attention patterns towards the same image, we exploit this bias by confusing the model-shared similar attention patterns. Thus, the generated adversarial patches can obtain stronger transferability among different models. Taking Automatic Check-out (ACO) as the typical scenario, extensive experiments including white-box/black-box settings in both digital-world (RPC, the largest ACO related dataset) and physical-world scenario (Taobao and JD, the world's largest online shopping platforms) are conducted. Experimental results demonstrate that our proposed framework outperforms state-of-the-art adversarial patch attack methods.
Attention-Guided Multitask Convolutional Neural Network for Power Line Parts Detection

Power line parts detection refers to the inspection of key parts on transmission lines against the complex background in aerial images and identifying whether exist anomalies that cause transmission failure. Obviously, this process plays a pivotal role in ensuring the safety of power transmission. Most of the existing methods are based on deep convolutional neural networks. However, the complexity and variability of the aerial image background and the problem of unmanned aerial vehicles (UAVs) shooting perspective and distance pose a challenge for previous works. This study aims to improve the detection accuracy of the model and propose an attention-guided multitask convolutional neural network (AGMNet). First, to enhance the feature representation of objects in aerial images, we construct spatial region attention blocks that are suitable for object detection. It can be inserted into any existing convolutional backbone network. Due to its efficient feature tensor computation method, the network can obtain competitive results with less computational memory. Second, we introduce a multitask framework that creatively considers the identification of rust levels and abnormal conditions of power line components, which has not been considered in previous works. Finally, we incorporate the refinable region proposal network (RPN) structure and multiscale training strategy to improve the robustness of the network. The experimental results on the testing datasets show that the proposed AGMNet can recognize the power parts (dampers and suspension clamps) with a mean average precision (mAP) of 95.3% and simultaneously identify their rust levels with an mAP of 75.4% and abnormal conditions with an mAP of 92.7%.
An Efficient Approach to Sharing Edge Knowledge in 5G-Enabled Industrial Internet of Things

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications.
A no-Reference Stereoscopic Image Quality Assessment Network Based on Binocular Interaction and Fusion Mechanisms

In contemporary society full of stereoscopic images, how to assess visual quality of 3D images has attracted an increasing attention in field of Stereoscopic Image Quality Assessment (SIQA). Compared with 2D-IQA, SIQA is more challenging because some complicated features of Human Visual System (HVS), such as binocular interaction and binocular fusion, must be considered. In this paper, considering both binocular interaction and fusion mechanisms of the HVS, a hierarchical no-reference stereoscopic image quality assessment network (StereoIF-Net) is proposed to simulate the whole quality perception of 3D visual signals in human cortex, including two key modules: BIM and BFM. In particular, Binocular Interaction Modules (BIMs) are constructed to simulate binocular interaction in V2-V5 visual cortex regions, in which a novel cross convolution is designed to explore the interaction details in each region. In the BIMs, different output channel numbers are designed to imitate various receptive fields in V2-V5. Furthermore, a Binocular Fusion Module (BFM) with automatic learned weights is proposed to model binocular fusion of the HVS in higher cortex layers. The verification experiments are conducted on the LIVE 3D, IVC and Waterloo-IVC SIQA databases and three indices including PLCC, SROCC and RMSE are employed to evaluate the assessment consistency between StereoIF-Net and the HVS. The proposed StereoIF-Net achieves almost the best results compared with advanced SIQA methods. Specifically, the metric values on LIVE 3D, IVC and WIVC-I are the best, and are the second-best on the WIVC-II.