Reviewerly/exHarmony huggingface dataset:
Rare-aware attention network for image–text matching

Image and text matching bridges visual and textual modality differences and plays a considerable role in cross-modal retrieval. Much progress has been achieved through semantic representation and alignment. However, the distribution of multimedia data is severely unbalanced and contains many low-frequency occurrences, which are often ignored and cause performance degradation, i.e., the long-tail effect. In this work, we propose a novel rare-aware attention network (RAAN), which explores and exploits textual rare content for tackling the long-tail effect of image and text matching. Specifically, we first design a rare-aware mining module, which contains global prior information construction and rare fragment detector for modeling the characteristic of rare content. Then, the rare attention matching utilizes prior information as attention to guide the representation enhancement of rare content and introduces the rareness representation to strengthen the similarity calculation. Finally, we design prior information loss to optimize the model together with the triplet loss. We perform quantitative and qualitative experiments on two large-scale databases and achieve leading performance. In particular, we conduct 0-shot test for rare content and improve rSum by 21.0 and 41.5 on Flickr30K (155,000 image and text pairs) and MSCOCO (616,435 image and text pairs), demonstrating the effectiveness of the proposed method for the long-tail effect.
EBNAS: Efficient binary network design for image classification via neural architecture search

To deploy Convolutional Neural Networks (CNNs) on resource-limited devices, binary CNNs with 1-bit activations and weights prove to be a promising approach. Meanwhile, Neural Architecture Search (NAS), which can design lightweight networks beyond artificial ones, has achieved optimal performance in various tasks. To design high-performance binary networks, we propose an efficient binary neural architecture search algorithm, namely EBNAS. In this paper, we propose corresponding improvement strategies to deal with the information loss due to binarization, the discrete error between search and evaluation, and the imbalanced operation advantage in the search space. Specifically, we adopt a new search space consisting of operations suitable for the binary domain. An L2 path regularization and a variance-based edge regularization are introduced to guide the search process and drive architecture parameters toward discretization. In addition, we present a search space simplification strategy and adjust the channel sampling proportions to balance the advantages of different operations. We perform extensive experiments on CIFAR10, CIFAR100, and ImageNet datasets. The results demonstrate the effectiveness of our proposed methods. For example, with binary weights and activations, EBNAS achieves a Top-1 accuracy of 95.61% on CIFAR10, 78.10% on CIFAR100, and 67.8% on ImageNet. With a similar number of model parameters, our algorithm outperforms other binary NAS methods in terms of accuracy and efficiency. Compared with manually designed binary networks, our algorithm remains competitive. The code is available at https://github.com/sscckk/EBNAS.
Machine learning applications for photovoltaic system optimization in zero green energy buildings

In this paper, the energy supply of a zero-energy building with 220 square meters is considered using optimized nanocomposite solar panels with respect to maximum efficiency. An optimized hybrid machine learning method plays a key role in presenting solar panel modeling with over 0.99% accuracy. Predicting the properties of the nanomaterial solar cell in four different seasons is performed by efficient support vector machines (SVM), and k-nearest neighbors (KNN) machine learning algorithms. In addition, the KNN algorithm is optimized by the Particle Swarm Optimization (PSO) method to improve the capabilities of KNN and reveal the best performance criteria for the photovoltaic modeling characteristics. The parameters of the nanocomposite cells are optimized using the proposed novel Multidisciplinary Optimization Method (MDO) to increase the efficiency of the solar panel by up to 170%. Optimization of solar cell performance with nanocomposite material under energy consumption constraints is carried out to propose the best construction of cells with 3 layers. The presented approach as a solution and indicator for the next generation of commercial and residential buildings can increase the potential values of solar cells to at least 70%.
Designing trust: The formation of employees’ trust in conversational AI in the digital workplace

The COVID-19 pandemic accelerated the adoption and use of AI technologies to support the virtualisation of the workplace. While previous research showed that systems' use critically depends on users' trust, little is known about the development of trust in AI technologies. This research focuses on an AI chatbot as a type of organisational AI system and asks how and why employees' trust towards an AI chatbot is formed and sustained. To answer the research questions, we conducted an interpretive single case study of a global organisation. The study identifies three types of trust experienced by AI chatbot users – emotional, cognitive and organisational – and develops a framework of experiential and sustained trust formation. It contributes to the information systems literature by demonstrating the critical importance of emotional and organisational trust in complementing cognitive trust, as well as the key design features that promote trust in AI chatbot use.
Elite levy spreading differential evolution via ABC shrink-wrap for multi-threshold segmentation of breast cancer images

Differential Evolution (DE) is a commonly used metaheuristic algorithm in different optimization scenarios. However, the original DE suffers from stagnation and premature convergence in solving complex problems. In this study, an Artificial Bee Colony (ABC)-inspired Shrink-wrap (SW) strategy is proposed by combining the idea of a restart operator in ABC. Furthermore, an Elite Levy Spreading (ELS) strategy is proposed, inspired by the ideas of dimensional variation in cuckoo search and linear reduction of the number of flames in the moth-flame optimization. Based on these two strategies, an Enhanced DE (EDE) is proposed, where the ABC-inspired SW strategy is used to enhance the global exploration capability, and the ELS strategy is dedicated to improving the optimal solution quality even further. Based on CEC 2017 test, scalability experiments were conducted on EDE, and performance was compared with state-of-the-art and champion algorithms. The results were analyzed using the Wilcoxon signed rank, Friedman test, and post-hoc statistical tests. Finally, Image Segmentation experiments were performed with some peers on nine breast cancer images to validate the performance of EDE. The above experimental results show that EDE performs satisfactorily in optimization cases and multi-threshold segmentation of breast cancer images.
Aggregated pyramid gating network for human pose estimation without pre-training

In this work, we propose a comprehensive aggregated residual gating structure, the Pyramid GAting Network (PGA-Net) for human pose estimation which can select, distill, and fuse semantic level and natural level information from multiple scales. In comparison, through utilizing multi-scale features, most existing state-of-the-art pose estimation methods are still limited in three aspects. First, multi-scale features contain massively redundant information, which is unfortunately not distilled by most existing approaches. Second, preferring deeper network structures to extract strong semantic features, the conventional methods often ignore original texture information fusion. Third, to attain a good parameter initialization, the current methods heavily rely on pre-training, which is very time-consuming or even unavailable. While better coping with the above problems, our proposed PGA-Net distills high-level semantic features and replenishes low-level original information to reinforce module representation capability. Meanwhile, PGA-Net demonstrates notable training stability and superior performance even without pre-training. Extensive experiments demonstrate that our method consistently outperforms previous approaches even without pre-training, enabling thus an end-to-end model training from scratch. In COCO benchmark, PGA-Net consistently achieves over 3% improvements than the baseline (without pre-training) under various model configurations.1
Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.
An Improved Algorithm for Insulator and Defect Detection Based on YOLOv4

To further improve the accuracy and speed of UAV inspection of transmission line insulator defects, this paper proposes an insulator detection and defect identification algorithm based on YOLOv4, which is called DSMH-YOLOv4. In the feature extraction network of the YOLOv4 model, the improved algorithm improves the residual edges of the residual structure based on feature reuse and designs the backbone network D-CSPDarknet53, which greatly reduces the number of parameters and computation of the model. The SA-Net (Shuffle Attention Neural Networks) attention model is embedded in the feature fusion network to strengthen the attention of target features and improve the weight of the target. Multi-head output is added to the output layer to improve the ability of the model to recognize the small target of insulator damage. The experimental results show that the number of parameters of the improved algorithm model is only 25.98% of that of the original model, and the mAP (mean Average Precision) of the insulator and defect is increased from 92.44% to 96.14%, which provides an effective way for the implementation of edge end algorithm deployment.
Speculative Histories, Just Futures: From Counterfactual Artifacts to Counterfactual Actions

This article engages with history as a speculative space for the purpose of critically engaging with discourses around the politics of technology in HCI. Drawing on approaches within critical design and based on evidence from two different projects, we develop an approach, counterfactual actions , that moves beyond the creation of artifacts and towards more situated, embodied, and performative engagements. In one project, Reimaging Work, we used a participatory game to engage stakeholders from social and economic justice organizations in Chicago. The other project, Future Design Studio, invited audience members at a futurist festival to create artifacts from the future and then invited improvisational actors to build worlds around them. We argue that a focus on counterfactual actions supports a more relational approach to understanding the politics of socio-technical systems and infrastructures, allowing participants to gain a meaningful understanding of the ways in which technology could be designed otherwise in line with ethics, values and social justice concerns.
Models used to characterise blockchain features. A systematic literature review and bibliometric analysis

Blockchain has emerged as an innovative technology with potential to transform business management, through operational efficiency improvements. Nevertheless, several performance and vulnerability issues have been identified for the different typologies supporting the wide range of blockchain-based applications currently implemented in different domains. A variety of analytical and empirical models are being used to evaluate the issues associated with the different blockchain typologies, enabling systematic analyses of the corresponding efficiency impact, and technical or economic threats. A thorough systematic literature review of these models has been performed, followed by a detailed assessment on the way these models have been employed, and the target parameters and applications evaluated (336 research selected and analysed). We propose a co-classification of these models, allowing us to identify which ones are employed to a greater extent to address the different blockchain issues in scientific research. In a second step, a bibliometric analysis on the selected research is conducted, offering a complementary overview of the status of and trends in blockchain modelling, including the most prolific authors and leading contributing countries to the topic. The main outcome and contribution of the paper is the provision of a broad overview on how blockchain issues have been analytically tackled, through the synthesis and meta-analysis of the models used in the scientific literature since the inception of blockchain technology. The results have two main direct applications, firstly supporting novel vulnerability and performance analyses of existing blockchain applications by providing historical information on the models used so far, as well as the key parameters and typology of the blockchain-based applications evaluated. Secondly, in the implementation of new applications, by allowing the recognition of key issues identified that are associated with the different blockchain typologies and to determine the most suitable models to analyse the weaknesses and risks of the alternative designs under evaluation for these new implementations.
VisPhone: Chinese named entity recognition model enhanced by visual and phonetic features

Many Chinese NER models only focus on lexical and radical information, ignoring the fact that there are also certain rules for the pronunciation of Chinese entities. In this paper, we propose VisPhone, which incorporates Chinese characters’ Phonetic features into Transformer Encoder along with the Lattice and Visual features. We present the common rules for the pronunciation of Chinese entities and explore the most appropriate method to encode it. VisPhone uses two identical cross transformer encoders to fuse the visual and phonetic features of the input characters with the text embedding. A selective fusion module is used to get the final features. We conducted experiments on four well-known Chinese NER benchmark datasets: OntoNotes4.0, MSRA, Resume, and Weibo, with F1 scores of 82.63%, 96.07%, 96.26%, 70.79% respectively, improving the performance by 0.79%, 0.32%, 0.39%, and 3.47%. Our ablation experiments have also demonstrated the effectiveness of VisPhone.
Identification and prioritization of the risks in the mass adoption of artificial intelligence-driven stable coins: The quest for optimal resource utilization

Industry 4.0 technologies have been revolutionizing the financial sector over the past few decades through the emergence of disruptive technologies. These disruptive technologies have also given rise to a new monetary taxonomy known as "digital currencies." One form of digital currency that provides a more effective, environmentally friendly, stable, and reasonably priced payment alternative is stablecoin, an artificial intelligence-driven payment rail. However, it poses unique risks to the broader financial system, putting the country's economy at risk if it is adopted as a mainstream means of payment. In this context, the present study identifies and prioritizes several major risk categories and their classifications that prevent stablecoins from becoming mainstream payment instruments. Three sequential stages were followed to complete the entire research. The initial phase identified four risk categories and their classifications through a systematic literature review. Thereafter, Pythagorean fuzzy delphi was used in the study to validate the identified risk categories. To prioritize these risks, the authors employed a Pythagorean fuzzy analytical hierarchy and a combined comprehensive solution approach in the final stage. The results of this study revealed that technical risks, which are the biggest impediment to the widespread adoption of stablecoins as a means of payment, were the most influential criterion, followed by macroeconomic risks and legal and regulatory risks. The least significant criterion has been discovered to be user-centric risks. In sub-criterion ranking, monetary stability risks, relative price stability risks, concentration risks, money laundering (ML)/terrorist financing (TF) and other illicit activities, oracle risks, smart contract failures, operational failures, privacy risks, and consumer protection risks are the leading risks. This study is relevant to individuals, investors, researchers, policymakers, and regulators in the long-term evolution of the stablecoin ecosystem.
Automatic waste detection with few annotated samples: Improving waste management efficiency

Automatic waste detection in natural environments exhibits a great potential to improve the efficiency and reduce the labor cost of waste management. Recent deep learning-based waste detectors rely heavily on substantial annotated samples for training, but annotating sufficient samples for various categories of waste is labor-intensive and time-consuming. To address this issue, this paper simulates the visual system of human beings and develops a few-shot waste detection framework. To enable the proposed framework more suitable for waste detection, a waste proposal module using a comprehensive feature fusion manner is designed to allow the features of support images to fully interact with those of query images, guiding the framework to generate more potential region proposals containing waste. Also, a waste classification module using soft attention mechanism and foreground mask is designed to alleviate the issue of spatial misalignment and achieve the fine-grained classification towards waste-related proposals. The proposed framework is a general detection framework which can flexibly detect various categories of waste with few labeled samples (i.e., less than 30 instances per category). Experimental results show that the proposed framework achieves a mean average precision of 31.16% over 12 waste categories when only few samples (i.e., 30 instances per category) are provided, surpassing a state-of-the-art few-shot detector named AFDNet by 1.68%. This data scale-insensitive nature allows humans to reduce the effort and time required for laborious waste image collection and annotation, significantly increasing the flexibility of automatic waste detection and boosting the efficiency of waste management.
A novel simultaneous permutation–diffusion image encryption scheme based on a discrete space map

The constant increase of devices connected to the internet and the development and spread of technologies such as 5G leads to substantial growth in data-sharing over non-secure channels in real-time applications, augmenting the need for fast cryptography algorithms. Algorithms such as the Advanced Encryption Standard (AES) are not optimized for image encryption, and hence, as an alternative, chaotic encryption strategies have been explored by researchers. However, due to the use of floating-point arithmetic and inefficient memory access, the performance of these chaotic ciphers is insufficient for the encryption of high-resolution images. Thus, this work proposes generating the chaotic sequences based on fixed-point arithmetic to increase computational efficiency, and using a simultaneous permutation–diffusion operation and block encryption to deal with the inefficient memory access. The proposed scheme leads to a throughput 353 times higher than the AES, 75 times higher than the previously higher value in the literature and 11 times higher than the Ascon algorithm. The cipher security evaluation is performed based on statistical attacks, with metrics such as Pearson, histogram variance, Shannon's entropy, correlation coefficients, and NIST tests; differential attacks, with the NPCR, UACI, and MSE metrics; chosen plain-text, with the SSIM metric; brute-force, with the key-space; noise and occlusion attacks, with the PSNR metric.
DSE-Net: Deep simultaneous estimation network for low-light image enhancement

This paper presents a novel approach for low-light image enhancement. We propose a deep simultaneous estimation network (DSE-Net), which simultaneously estimates the reflectance and illumination for low-light image enhancement. The proposed network contains three modules: image decomposition, illumination adjustment, and image refinement module. The DSE-Net uses a novel branched encoder–decoder based image decomposition module for simultaneous estimation. The proposed decomposition module uses a separate decoder to estimate illumination and reflectance. DSE-Net improves the estimated illumination using the illumination adjustment module and feeds it to the proposed refinement module. The image refinement module aims to produce sharp and natural-looking output. Extensive experiments conducted on a range of low-light images demonstrate the efficacy of the proposed model and show its supremacy over various state-of-the-art alternatives.
ADS-detector: An attention-based dual stream adversarial example detection method

Adversarial attacks seriously threaten the security of machine learning models. Thus, detecting adversarial examples has become an important and interesting research topic facing various adversarial attacks. However, the majority of existing adversarial example detection algorithms cannot perform well in detecting adversarial examples with slight perturbations. In this paper, we propose a novel attention-based dual stream detector (ADS-Detector) that can address the detection of adversarial examples with both slight and large perturbations. Specifically, we first design a data process module to generate pixel and prediction confidence stream data from the raw image. Then, we propose an N-layer attention module to extract the channel and spatial feature weights between the pixel and prediction confidence stream data. Eventually, we feed the dual-stream data into the same subdetection model with a convolutional block attention module; then, the output results are combined to determine whether the input image is an adversarial example or not. To validate the performance, we conduct extensive experiments on three public datasets: CIFAR10, Dogs vs. Cats and ImageNet. After sufficient analysis of the simulation results, we find that our proposed method outperforms the others for the detection of adversarial attacks generated by the considered attack methods.