Reviewerly/exHarmony huggingface dataset:
Investigating Perceptual Biases in Icon Arrays

Icon arrays are graphical displays in which a subset of identical shapes are filled to convey probabilities. They are widely used for communicating probabilities to the general public. A primary design decision concerning icon arrays is how to fill and arrange these shapes. For example, a designer could fill the shapes from top to bottom or in a random fashion. We investigated the effect of different arrangements in icon arrays on probability perception. We showed participants icon arrays depicting probabilities between 0% and 100% in six different arrangements. Participants were more accurate in estimating probabilities when viewing the top, row, and diagonal arrangements, but they overestimated the proportions with the central arrangement and underestimated the proportions with the edge arrangement. They were biased to either overestimate or underestimate when viewing the random arrangement depending on the objective proportions, following a cyclical pattern consistent with existing findings in the psychophysics literature.
A New Ontology-Based Method for Arabic Sentiment Analysis

Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.
Post-Quantum Security for Ultra-Reliable Low-Latency Heterogeneous Networks

We consider the problem of post-quantum secure and ultra-reliable communication through a heterogeneous network consisting of multiple connections. Three performance metrics are considered: security, throughput, and in-order delivery delay. In this setting, previous work has looked, individually, at the trade-offs between in-order delivery delay and throughput, and between security and throughput. This is the first work considering the trade-off between all three for heterogeneous communication networks, while taking the computational complexity into account. We present LL-HUNCC, a low latency hybrid universal network coding cryptosystem. LL-HUNCC is an efficient coding scheme which allows for secure communications over a noisy untrusted heterogeneous network by encrypting only a small part of the information being sent. This scheme provides post-quantum security with high throughput and low in-order delivery delay guarantees. We evaluate LL-HUNCC via simulations on a setting inspired by a practical scenario for heterogeneous communications involving a satellite communication link and a 5G communication network. Under this scenario, we compare LL-HUNCC to the state-of-the-art where all communication paths are encrypted via a post-quantum public-key cryptosystem.
Instance-based transfer learning method via modified domain-adversarial neural network with influence function: Applications to design metamodeling and fault diagnosis

The availability of a large amount of high-quality data is critical to the performance of machine-learning models. It is challenging to obtain a training dataset because data collection is costly and time-consuming. However, data scarcity can be overcome and an accurate model can be obtained if data from similar models are reused. In this paper, we propose an instance-based transfer learning method to obtain a more accurate model for situations with data scarcity. The proposed method uses a modified domain-adaptation technique to generate auxiliary target-domain data from source-domain data. Subsequently, useful data are selected from the auxiliary target-domain data to preclude the negative transfer that may leverage source-domain data to reduce the learning performance in the target domain. A modified domain-adversarial neural network was used to generate auxiliary target-domain data in the context of instance-based transfer learning. Particularly, the feature extractor and domain discriminator were trained to extract the domain-invariant features from the source and target domains, whereas the target generator was trained to generate auxiliary target-domain data using the domain-invariant features. Additionally, an influence function that can measure the influence of individual training samples on the learning performance was applied to identify useful data. Three case studies were conducted to validate the proposed method: a mathematical function example, drone blade metamodeling, and bearing fault diagnosis. The results of these case studies indicate a significant improvement in neural network prediction despite data scarcity.
Gradient-Weighted Class Activation Mapping for Spatio Temporal Graph Convolutional Network

Spatio-temporal graph convolutional networks (STGCN) have become popular recently because they can handle structured data with dynamic temporal variations. However, the lack of interpretability limits the potential application of STGCNs. Gradient-based class activation maps (Grad-CAM) are a popular technique to interpret convolutional neural networks for grid structured data such as images. In this paper, we design an extension of Grad-CAMs for spatio temporal graph convolution (STG-Grad-CAM) to improve the interpretability of STGCNs. As a proof of concept we provide results for a skeleton-based activity recognition task. We show which body joints are responsible for a particular task and how their temporal dynamics contribute to the classification output. We present a brief study of the interpretability of a recognition task by changing the model depth and the training and testing protocol. To find the efficacy of STG-Grad-CAM, we compute faithfulness of STG-Grad-CAM to the model measured by the impact of occlusions to the graph nodes. For explainability of STGCN, we compute contrastivity of the model for different classes based on the outcome of STG-Grad-CAM. In the cross-person setting, we observe better contrastivity than the cross-view setting.
Tts4pretrain 2.0: Advancing the use of Text and Speech in ASR Pretraining with Consistency and Contrastive Losses

An effective way to learn representations from untranscribed speech and unspoken text with linguistic/lexical representations derived from synthesized speech was introduced in tts4pretrain [1]. However, the representations learned from synthesized and real speech are likely to be different, potentially limiting the improvements from incorporating unspoken text. In this paper, we introduce learning from supervised speech earlier on in the training process with consistency-based regularization between real and synthesized speech. This allows for better learning of shared speech and text representations. Thus, we introduce a new objective, with encoder and decoder consistency and contrastive regularization between real and synthesized speech derived from the labeled corpora during the pretraining stage. We show that the new objective leads to more similar representations derived from speech and text that help downstream ASR. The proposed pretraining method yields Word Error Rate (WER) reductions of 7-21% relative on six public corpora, Librispeech, AMI, TEDLIUM, Common Voice, Switchboard, CHiME-6, over a state-of-the-art baseline pretrained with wav2vec2.0 and 2-17% over the previously proposed tts4pretrain. The proposed method outperforms the supervised SpeechStew by up to 17%. Moreover, we show that the proposed method also yields WER reductions on larger data sets by evaluating on a large resource, in-house Voice Search task and streaming ASR.
Handcrafted localized phase features for human action recognition

Human action recognition is one of the most important topics in computer vision. Monitoring elderly people and children, smart surveillance systems and human-computer interaction are a few examples of its applications. The aim of this study is to recognize human activities by utilizing the phase information extracted from the frequency domain of the video data as handcrafted features. Rather than estimating optical flow or computing motion vectors, we aim to utilize the localized phase information as descriptors of the motion dynamics of the scene. Phase correlation information extracted from each two co-sited blocks from each two consecutive frames of video clips were used to train a model using KNN classifier to model the action. To evaluate the performance of our method, an extensive work has been done on three large and complex datasets: UCF101, Kinetics-400 and Kinetics-700. The results show that our approach succeeds on recognizing human actions across all these datasets with high accuracy.
Look, Listen and Pay More Attention: Fusing Multi-Modal Information for Video Violence Detection

Violence detection is an essential and challenging problem in the computer vision community. Most existing works focus on single modal data analysis, which is not effective when multi-modality is available. Therefore, we propose a two-stage multi-modal information fusion method for violence detection: 1) the first stage adopts multiple instance learning strategies to refine video-level hard labels into clip-level soft labels, and 2) the next stage uses multi-modal information fused attention module to achieve fusion, and supervised learning is carried out using the soft labels generated at the first stage. Extensive empirical evidence on the XD-Violence dataset shows that our method outperforms the state-of-the-art methods.
Scalable Data Association and Multi-Target Tracking Under a Poisson Mixture Measurement Process

Measurement rates for both targets and clutter have been assumed to be known a priori in most existing tracking systems, whereas practically the rates may be unknown to users or time-varying. This paper therefore fills this gap by developing a Poisson mixture process tracker (PMPT) to capture the temporal characteristics of the rate parameters under a Poisson mixture measurement process. Specifically, the Generalized inverse Gaussian (GIG) distribution is proposed as a prior for Poisson rates, and two novel priors, the time independent GIG prior and the GIG Markov chain prior, are designed. In addition, a scalable inference framework is introduced to enable efficient data association and parallel updating of target states under a sequential Markov chain Monte Carlo (MCMC) scheme with linear complexity in the number of measurements and targets. Results show that our proposed method can provide a robust solution in highly dynamic detection probability environments.
Robust double-weighted guided image filtering

Guided image filter (GIF) is an edge-preserving smoothing operator with low time complexity; However, it does not preserve sharp edges, and therefore exhibits halo artifacts caused by the edge blurring. To address this problem, weighted versions of the GIF have been proposed in the literature. Along the same line of research, we present a robust double-weighted guided image filter (RDWGIF) by incorporating the robust edge-aware weighting (REAW) and the mollifier from Sobolev space theory into the cost function of the GIF. The REAW is proposed based on a new measure called Maximum Neighbor Difference (MND) and the Tukey’s biweight from robust statistics. To illustrate its effectiveness in preserving sharp edges and reducing halo artifacts, the proposed filter was applied to edge aware smoothing, image denoising, single image detail enhancement, tone mapping of high dynamic range images, and texture removal smoothing. The experimental results show that the RDWGIF outperforms previous weighted versions of the GIF by qualitative comparison and quantitative evaluation.
A Blockchain-Based Privacy Information Security Sharing Scheme in Industrial Internet of Things

Due to the competitive relationship among different smart factories, equipment manufacturers cannot integrate the private information of all smart factories to train the intelligent manufacturing equipment fault prediction model and improve the accuracy of intelligent manufacturing equipment fault detection. The use of a low fault recognition rate model for smart factories will cause additional losses for them. In this work, we propose a blockchain-based privacy information security sharing scheme in Industrial Internet of Things (IIoT) to solve the sharing problem of private information in smart factories. Firstly, we abstract smart factories as edge nodes and build decentralized, distributed trusted blockchain networks based on Ethereum clients on simulated edge devices and propose an Intelligent Elliptic Curve Digital Signature Algorithm (IECDSA) to guarantee the ownership of shared information by edge nodes. Secondly, we propose the Reputation-based Delegated Proof of Stake (RDPoS) consensus algorithm to improve the security and reliability of the Delegated Proof of Stake (DPoS) consensus algorithm. Furthermore, we design and implement an incentive mechanism based on information attributes to increase the motivation of edge nodes to share information. Finally, the proposed solution is simulated. Through theoretical and simulation experiments, it is proved that the blockchain-based privacy information security sharing scheme in IIoT can improve the enthusiasm of edge nodes to share information on the premise of ensuring the security of information sharing.
AR Technologies in Engineering Education: Applications, Potential, and Limitations

Over the past decade, the use of AR has significantly increased over a wide range of applications. Although there are many good examples of AR technology being used in engineering, retail, and for entertainment, the technology has not been widely adopted for teaching in university engineering departments. It is generally accepted that the use of AR can complement the students’ learning experience by improving engagement and by helping to visualise complex engineering physics; however, several key challenges still have to be addressed to fully integrate the use of AR into a broader engineering curriculum. The presented paper reviews the uses of AR in engineering education, highlights the benefits of AR integration in engineering curriculums, as well as the barriers that are preventing its wider adoption.
A Critical Review of Cloud Computing Environment for Big Data Analytics

Cloud computing offers a secure, scalable, and widely accessible platform for enormous data distributed management systems. There are a variety of appealing characteristics in cloud computing for its consumer market as many cloud services and deployment strategies supplied by Cloud Service Providers (CSPs) are based on customer demands. Furthermore, cloud computing can store and handle a massive volume of information and do real-time big data analytics, presenting customers with helpful information and patterns. Furthermore, many CSPs, such as AWS, Microsoft Azure, and Google Cloud Platform, offers benefits that help customers be more productive by executing activities according to their demands. This paper provides a critical review of the cloud computing environment provided by contemporary CSPs and learns about the present issues of cloud computing for big data analytics.
Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers

We present a novel resource-efficient end-to-end architecture for lipreading that achieves state-of-the-art results on a popular and challenging benchmark. In particular, we make the following contributions: First, inspired by the recent success of the EfficientNet architecture in image classification and our earlier work on resource-efficient lipreading models (MobiLipNet), we introduce Efficient-Nets to the lipreading task. Second, we show that the currently most popular in the literature 3D front-end contains a max-pool layer that prohibits networks from reaching superior performance and propose its removal. Finally, we improve our system’s back-end robustness by including a Transformer encoder. We evaluate our proposed system on the “Lipreading In-The-Wild” (LRW) corpus, a database containing short video segments from BBC TV broadcasts. The proposed network (T-variant) attains 88.53% word accuracy, a 0.17% absolute improvement over the current state-of-the-art, while being five times less computationally intensive. Further, an up-scaled version of our model (L-variant) achieves 89.52%, a new state-of-the-art result on the LRW corpus.
Integrating Text Inputs for Training and Adapting RNN Transducer ASR Models

Compared to hybrid automatic speech recognition (ASR) systems that use a modular architecture in which each component can be in-dependently adapted to a new domain, recent end-to-end (E2E) ASR system are harder to customize due to their all-neural monolithic construction. In this paper, we propose a novel text representation and training framework for E2E ASR models. With this approach, we show that a trained RNN Transducer (RNN-T) model’s internal LM component can be effectively adapted with text-only data. An RNN-T model trained using both speech and text inputs improves over a baseline model trained on just speech with close to 13% word error rate (WER) reduction on the Switchboard and CallHome test sets of the NIST Hub5 2000 evaluation. The usefulness of the proposed approach is further demonstrated by customizing this general purpose RNN-T model to three separate datasets. We observe 20-45% relative word error rate (WER) reduction in these settings with this novel LM style customization technique using only unpaired text data from the new domains.
News Recommendation Via Multi-Interest News Sequence Modelling

A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user’s interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models. Our source code is publicly available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Target-Distractor Aware Deep Tracking With Discriminative Enhancement Learning Loss

Numerous tracking approaches attempt to improve target representation through target-aware or distractor-aware. However, the unbalanced considerations of target or distractor information make it diffcult for these methods to benefit from the two aspects at the same time. In this paper, we propose a target-distractor aware model with discriminative enhancement learning loss to learn target representation, which can better distinguish the target in complex scenes. Firstly, to enlarge the gap between the target and distractor, we design a discriminative enhancement learning loss. By highlighting the hard negatives that are similar to the target and shrinking the easy negatives that are pure background, the features sensitive to the target or distractor representation can be more conveniently mined. On this basis, we further propose a target-distractor aware model. Unlike existing methods of preference target or distractor, we construct the target-specific feature space by activating the target-sensitive and the distractor-silence feature. Therefore, the appearance model can not only represent the target well but also suppress the background distractor. Finally, the target-distractor aware target representation model is integrated with a Siamese matching network for visual tracking for achieving robust and realtime visual tracking. Extensive experiments are performed on eight tracking benchmarks show that the proposed algorithm achieves favorable performance.
Phishing Detection on Ethereum via Attributed Ego-Graph Embedding

In recent years, the losses caused by phishing scams on Ethereum have reached a level that cannot be ignored. In such a phishing detection scenario, network embedding is seen as an effective solution. In this brief, we propose an attributed ego-graph embedding framework to distinguish phishing accounts. We first obtain the account labels from an authority site and the transaction records from Ethereum on-chain blocks. Then we extract ego-graphs for each labeled account to represent it. To learn representations for ego-graphs, we utilize non-linear substructures sampled from ego-graphs and use a skip-gram model. Finally, a classifier is applied to graph embeddings to predict phishing accounts. To overcome the limit that transaction attributes are not encoded into ego-graph embeddings, we give nodes and subgraphs with richer attribute-based semantics. Specifically, we propose a novel node relabeling strategy based on Ethereum transaction attributes including transaction amount, number, and direction, and differentiating nodes and subgraphs by new labels. Through this, structural and attributed features of the Ethereum transaction networks can be learned at the same time. Experimental results show that our framework achieves effective performance on class imbalanced phishing detection on Ethereum.
Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Purpose This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security. Design/methodology/approach The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review. Findings It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions. Originality/value The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.