Reviewerly/exHarmony huggingface dataset:
Boolean Particle Swarm Optimization with various Evolutionary Population Dynamics approaches for feature selection problems

In the feature selection process, reaching the best subset of features is considered a difficult task. To deal with the complexity associated with this problem, a sophisticated and robust optimization approach is needed. This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The proposed improvement assists the BPSO to avoid local optima obstacles via boosting its exploration ability. In the BPSO-EPD, the worst half of the solutions are discarded by repositioning them around the optimal solutions selected from the best half. Six natural selection mechanisms comprising Best-based, Tournament, Roulette wheel, Stochastic universal sampling, Linear rank, and Random-based are employed to select guiding solutions. To assess the performance of the proposed improvement, 22 well-regarded datasets collected from the UCI repository are employed. The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants. Taking SpecEW dataset as an example, an increment of 6.7% accuracy can be achieved for BSPO-TEPD. Consequently, BPSO-TEPD approach also outperformed other well-known optimizers, including two binary variants of PSO using S-shaped transfer function (SBPSO) and V-shaped transfer function (VBPSO), Binary Grasshopper Optimization Algorithm (BGOA), Binary Gravitational Search Algorithm (BGSA), Binary Ant Lion Optimizer (BALO), Binary Bat algorithm (BBA), Binary Salp Swarm Algorithm (BSSA), Binary Whale Optimization Algorithm (BWOA), and Binary Teaching-Learning Based Optimization (BTLBO). The result emphasizes the excellent behavior of EPD strategies in evolving the ability of BPSO when dealing with feature selection problems.
Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.
Aspect-level sentiment analysis with aspect-specific context position information

In recent years, an increasing number of researchers have focused on the aspect-level sentiment analysis in the field of natural language processing. A coarse-grained sentiment analysis at the document level and a sentiment analysis at the sentence level can only judge an entire text comprehensively, whereas a fine-grained sentiment analysis distinguishes each concrete aspect of the text and makes separate judgments on the sentiment polarity. The word vector representation obtained by a recurrent neural network lacks a description of the distance relationship between the context words and aspect, and traditional models rarely consider the influence of the association between contextual sentences. In this paper, we propose an aspect-level sentiment analysis model with aspect-specific contextual location information. By designing two asymmetrical contextual position weight functions respectively, the model adjusts the weight of contextual words according to the positions of the aspect words in the sentences, and alleviates the interference of the difference in the number of words on both sides of the aspect words on the judgment of sentimental polarity. By utilizing single-sentence-level and multiple-sentence-level bidirectional GRU layers, model will extract the influence of the contextual association of each sentence in the document on the aspect sentiment polarity of individual sentences. In addition, we analyze the distribution properties of hard samples and design a novel loss function for the class imbalance problem in the field of sentiment analysis. For dataset 15Rest, the accuracy of our model is 4.27% higher than that of ASGCN, whereas the f1-score, which is more indicative of the classification performance on an imbalanced dataset, can be seen to be improved by 4.31% in comparison to the ASGCN.
CANet: A Context-Aware Network for Shadow Removal

In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately. At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts. Our source code is available at https://github.com/Zipei-Chen/CANet.
Gabor filter bank with deep autoencoder based face recognition system

These days, face recognition systems are widely being employed in various daily applications such as smart phone unlocking, tracking school attendance, and secure online bank transactions, smarter border control, to name a few. In spite of the remarkable progress, face recognition systems still suffer from occlusions, light variations, camera types and their resolutions. Face recognition is still a dynamic research field. In this paper, we propose an efficient face recognition system based on Gabor filter bank and a deep learning method known as Sparse AutoEncoder (SAE). The main aim of the proposed system is to improve the features extracted by Gabor filter bank using SAE method. Then, these enhanced features are subjected to features reduction using principal component analysis and linear discriminant analysis (PCA + LDA) technique. Finally, the matching stage is accomplished via cosine Mahalanobis distance. Experiments on seven publicly available databases (i.e., JAFFE, AT&T, Yale, Georgia Tech, CASIA, Extended Yale, Essex) show that the proposed system can achieve promising results with the combination of Gabor and SAE, as well as outperform previously proposed methods.
C<sup>3</sup>-SemiSeg: Contrastive Semi-supervised Segmentation via Cross-set Learning and Dynamic Class-balancing

The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data. However, the dominant consistency learning diagram is limited by a) the misalignment between features from labeled and unlabeled data; b) treating each image and region separately without considering crucial semantic dependencies among classes. In this work, we introduce a novel C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -SemiSeg to improve consistency-based semi-supervised learning by exploiting better feature alignment under perturbations and enhancing the capability of discriminative feature cross images. Specifically, we first introduce a cross-set region-level data augmentation strategy to reduce the feature discrepancy between labeled data and unlabeled data. Cross-set pixel-wise contrastive learning is further integrated into the pipeline to facilitate feature representation ability. To stabilize training from the noisy label, we propose a dynamic confidence region selection strategy to focus on the high confidence region for loss calculation. We validate the proposed approach on Cityscapes and BDD100K dataset, which significantly outperforms other state-of-the-art semi-supervised semantic segmentation methods.
Fast Video Moment Retrieval

This paper targets at fast video moment retrieval (fast VMR), aiming to localize the target moment efficiently and accurately as queried by a given natural language sentence. We argue that most existing VMR approaches can be divided into three modules namely video encoder, text encoder, and cross-modal interaction module, where the last module is the test-time computational bottleneck. To tackle this issue, we replace the cross-modal interaction module with a cross-modal common space, in which moment-query alignment is learned and efficient moment search can be performed. For the sake of robustness in the learned space, we propose a fine-grained semantic distillation framework to transfer knowledge from additional semantic structures. Specifically, we build a semantic role tree that decomposes a query sentence into different phrases (subtrees). A hierarchical semantic-guided attention module is designed to perform message propagation across the whole tree and yield discriminative features. Finally, the important and discriminative semantics are transferred to the common space by a matching-score distillation process. Extensive experimental results on three popular VMR benchmarks demonstrate that our proposed method enjoys the merits of high speed and significant performance.
The drivers of cyber risk

Cyber incidents are becoming more sophisticated and their costs difficult to quantify. Using a unique database of cyber events across sectors in the US, we document the characteristics and drivers of cyber incidents. Cyber costs are higher for larger firms and for incidents that impact several organisations simultaneously. Events with malicious intent (i.e. cyber attacks) tend to be less costly, unless they are on the upper tail of the loss distribution. The financial sector is exposed to a larger number of cyber attacks but suffers lower costs, on average. The use of cloud services is associated with lower costs, especially when cyber incidents are relatively small. As cloud providers become systemically important, cloud dependence is likely to increase tail risks. Finally, we document that higher expenditure on IT is associated with future reduced costs from cyber incidents.
LOT: An industrial oriented ontology engineering framework

Ontology Engineering has captured much attention during the last decades leading to the proliferation of numerous works regarding methodologies, guidelines, tools, resources, etc. including topics which are still being investigated. Even though, there are still many open questions when addressing a new ontology development project, regarding how to manage the overall project and articulate transitions between activities or which tasks and tools are recommended for each step. In this work we propose the Linked Open Terms (LOT) methodology, an overall and lightweight methodology for building ontologies based on existing methodologies and oriented to semantic web developments and technologies. The LOT methodology focuses on the alignment with industrial development, in addition to academic and research projects, and software development, that is making ontology development part of the software industry. This methodology includes lessons learnt from more than 20 years in ontological engineering and its application on 18 projects is reported.
Natural attack for pre-trained models of code

Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement. In this paper, we propose ALERT (nAturaLnEss AwaRe ATtack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs. Different from prior works, this paper considers the natural semantic of generated examples at the same time as preserving the operational semantic of original inputs. Our user study demonstrates that human developers consistently consider that adversarial examples generated by ALERT are more natural than those generated by the state-of-the-art work by Zhang et al. that ignores the naturalness requirement. On attacking CodeBERT, our approach can achieve attack success rates of 53.62%, 27.79%, and 35.78% across three downstream tasks: vulnerability prediction, clone detection and code authorship attribution. On GraphCodeBERT, our approach can achieve average success rates of 76.95%, 7.96% and 61.47% on the three tasks. The above outperforms the baseline by 14.07% and 18.56% on the two pre-trained models on average. Finally, we investigated the value of the generated adversarial examples to harden victim models through an adversarial fine-tuning procedure and demonstrated the accuracy of CodeBERT and GraphCodeBERT against ALERT-generated adversarial examples increased by 87.59% and 92.32%, respectively.
Bitcoin and Beyond

We survey extant literature on the economics of blockchain fundamentals, with particular focus on Bitcoin, proof-of-work, and proof-of-stake. We formally clarify Bitcoin's economic significance in solving the double-spending problem without a centralized entity. We then transition to the economics literature, highlighting the key endogenous economic interactions among participants in the Bitcoin ecosystem as well as the economics of proof-of-stake and other potential consensus algorithms. Along the way, we discuss various literature that provides important insights regarding fees, forks, and price volatility. We conclude by reflecting on the next generation of blockchain innovations.
Cervical cancer diagnosis based on modified uniform local ternary patterns and feed forward multilayer network optimized by genetic algorithm

Cervical cancer is one of the most common types of cancer for women. Early and accurate diagnosis can save the patient's life. Pap smear testing is nowadays commonly used to diagnose cervical cancer. The type, structure and size of the cervical cells in pap smears images are major factors which are used by specialist doctors to diagnosis abnormality. Various image processing-based approaches have been proposed to acquire pap smear images and diagnose cervical cancer in pap smears images. Accuracy is usually the primary objective in evaluating the performance of these systems. In this paper, a two-stage method for pap smear image classification is presented. The aim of the first stage is to extract texture information of the cytoplasm and nucleolus jointly. For this purpose, the pap smear image is first segmented using the appropriate threshold. Then, a texture descriptor is proposed titled modified uniform local ternary patterns (MULTP), to describe the local textural features. Secondly, an optimized multi-layer feed-forward neural network is used to classify the pap smear images. The proposed deep neural network is optimized using genetic algorithm in terms of number of hidden layers and hidden nodes. In this respect, an innovative chromosome representation and cross-over process is proposed to handle these parameters. The performance of the proposed method is evaluated on the Herlev database and compared with many other efficient methods in this scope under the same validation conditions. The results show that the detection accuracy of the proposed method is higher than the compared methods. Insensitivity to image rotation is one of the major advantages of the proposed method. Results show that the proposed method has the capability to be used in online problems because of low run time. The proposed texture descriptor, MULTP is a general operator which can be used in many computer vision problems to describe texture properties of image. Also, the proposed optimization algorithm can be used in deep-networks to improve performance.
Multi-view Multi-behavior Contrastive Learning in Recommendation

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior’s performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user. In this work, we propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR) framework, including three new CL tasks to solve the above challenges, respectively. The multi-behavior CL aims to make different user single-behavior representations of the same user in each view to be similar. The multi-view CL attempts to bridge the gap between a user’s sequence-view and graph-view representations. The behavior distinction CL focuses on modeling fine-grained differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on https://github.com/wyqing20/MMCLR .
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-the-art GANs usually suffer from low efficiency and bulky memory usage. To tackle this challenge, firstly, this paper investigates GANs performance from a frequency perspective. The results show that GANs, especially small GANs lack the ability to generate high-quality high frequency information. To address this problem, we propose a novel knowledge distillation method referred to as wavelet knowledge distillation. Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands. As a result, the student GAN can pay more attention to its learning on high frequency bands. Experiments demonstrate that our method leads to 7.08× compression and 6.80× acceleration on CycleGAN with almost no performance drop. Additionally, we have studied the relation between discriminators and generators which shows that the compression of discriminators can promote the performance of compressed generators.
A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain

Industry 4.0 is leading society into a new era characterized by smart communications between consumers and enterprises. While entertainment and fashion brands aim to consolidate their identities, increasing consumers’ participation in new, engaging, and immersive experiences, other industry sectors such as food and drugs are called to adhere to stricter regulations to increase the quality assurance of their processes. The pharmaceutical industry is inherently one of the most regulated sectors because the safety, integrity, and conservation along the distribution network are the main pillars for guaranteeing the efficacy of drugs for the general public. Favoured by Industry 4.0 incentives, pharmaceutical serialization has become a must in the last few years and is now in place worldwide. In this paper, a decentralized solution based on non-fungible tokens (NFTs), which can improve the track and trace capability of the standard serialization process, is presented. Non-fungible tokens are minted in the blockchain and inherit all the advantages provided by this technology. As blockchain technology is becoming more and more popular, adoption of track and trace will increase tremendously. Focusing on the pharmaceutical industry’s use of track and trace, this paper presents the concepts and architectural elements necessary to support the non-fungible token solution, culminating in the presentation of a use case with a prototypical application.
Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.