Reviewerly/exHarmony huggingface dataset:
Barriers to blockchain adoption for supply chain finance: the case of Indian SMEs

Abstract Small and medium enterprises (SMEs) in India are suffering from the long-standing challenges related to asymmetric information, high transaction costs, SMEs’ opacity and limited access to credit. Blockchain technology, which is still in its infancy in terms of adoption in India, can facilitate SMEs to counter these challenges. Fuelled by this motivation, the study aims to investigate the significant barriers to blockchain adoption in supply chain finance practices by Indian SMEs. Using fuzzy-analytic hierarchy process, sensitivity analysis, and fuzzy-decision-making trial and evaluation laboratory this paper identifies the blockchain barriers, prioritises them and examine their cause and effect relationships. The results of the study indicate that technology barriers are the most influential barriers that impede blockchain adoption. The findings will help the policymakers and practitioners to take suitable measures to overcome these barriers and fuel the adoption of blockchain in Indian SMEs.
LAP-IoHT: A Lightweight Authentication Protocol for the Internet of Health Things

The Internet of Health Things (IoHT), which is an extension of the Internet of Things (IoT) in healthcare, has provided a new type of telemedicine approach. In IoHT, wearable sensors are used to collect patient health data, and information is transmitted remotely to doctors who can develop accurate treatment plans and provide timely telemedicine services to patients. However, patient health data are transmitted over a public channel, which means that the privacy and medical data of patients are at significant risk of leakage and can be confronted by serious security problems. We proposed a lightweight authentication protocol known as LAP-IoHT for IoHT environments to overcome the various threats that are currently faced by IoHT. We verified the security of LAP-IoHT using a Real-or-Random model and demonstrated its significant performance advantage by conducting a comparative analysis with other similar protocols for a better adaptation to the IoHT environment.
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

While researches on model-based blind single image super-resolution (SISR) have achieved tremendous successes recently, most of them do not consider the image degradation sufficiently. Firstly, they always assume image noise obeys an independent and identically distributed (i.i.d.) Gaussian or Laplacian distribution, which largely underestimates the complexity of real noise. Secondly, previous commonly-used kernel priors (e.g., normalization, sparsity) are not effective enough to guarantee a rational kernel solution, and thus degenerates the performance of subsequent SISR task. To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel. Specifically, instead of the traditional i.i.d. noise assumption, a patch-based non-i.i.d. noise model is proposed to tackle the complicated real noise, expecting to increase the degrees of freedom of the model for noise representation. As for the blur kernel, we novelly construct a concise yet effective kernel generator, and plug it into the proposed blind SISR method as an explicit kernel prior (EKP). To solve the proposed model, a theoretically grounded Monte Carlo EM algorithm is specifically designed. Comprehensive experiments demonstrate the superiority of our method over current state-of-the-arts on synthetic and real datasets. The source code is available at https://github.com/zsyOAOA/BSRDM.
Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering

Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we propose <u>s</u>elf-supervised discriminative feature learning for <u>d</u>eep <u>m</u>ulti-<u>v</u>iew <u>c</u>lustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views&#x2019; embedded features to form the global features, which can overcome the negative impact of some views&#x2019; unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features&#x2019; diversity. Experiments on various types of multi-view datasets show that SDMVC outperforms 14 competitors including classic and state-of-the-art methods. The code is available at <uri>https://github.com/SubmissionsIn/SDMVC</uri>.
Deep Ensemble Learning for Human Activity Recognition Using Wearable Sensors via Filter Activation

During the past decade, human activity recognition ( HAR ) using wearable sensors has become a new research hot spot due to its extensive use in various application domains such as healthcare, fitness, smart homes, and eldercare. Deep neural networks, especially convolutional neural networks ( CNNs ), have gained a lot of attention in HAR scenario. Despite exceptional performance, CNNs with heavy overhead is not the best option for HAR task due to the limitation of computing resource on embedded devices. As far as we know, there are many invalid filters in CNN that contribute very little to output. Simply pruning these invalid filters could effectively accelerate CNNs , but it inevitably hurts performance. In this article, we first propose a novel CNN for HAR that uses filter activation. In comparison with filter pruning that is motivated for efficient consideration, filter activation aims to activate these invalid filters from an accuracy boosting perspective. We perform extensive experiments on several public HAR datasets, namely, UCI-HAR ( UCI ), OPPORTUNITY ( OPPO ), UniMiB-SHAR ( Uni ), PAMAP2 ( PAM2 ), WISDM ( WIS ), and USC-HAD ( USC ), which show the superiority of the proposed method against existing state-of-the-art ( SOTA ) approaches. Ablation studies are conducted to analyze its internal mechanism. Finally, the inference speed and power consumption are evaluated on an embedded Raspberry Pi Model 3 B plus platform.
ReYOLO: A traffic sign detector based on network reparameterization and features adaptive weighting

Traffic sign detection is a challenging task. Although existing deep learning techniques have made great progress in detecting traffic signs, there are still many unsolved challenges. We propose a novel traffic sign detection network named ReYOLO that learns rich contextual information and senses scale variations to efficiently detect small and ambiguous traffic signs in the wild. Specifically, we first replace the conventional convolutional block with modules that are built by structural reparameterization methods and are embedded into bigger structures, thus decoupling the training structures and the inference structures using parameter transformation, and allowing the model to learn more effective features. We then design a novel weighting mechanism which can be embedded into a feature pyramid to exploit foreground features at different scales to narrow the semantic gap between multiple scales. To fully evaluate the proposed method, we conduct experiments on a traditional traffic sign dataset GTSDB as well as two new traffic sign datasets TT100K and CCTSDB2021, achieving 97.2%, 68.3% and 83.9% mAP (Mean Average Precision) for the three-class detection challenge in these three datasets.
SPIRAL: Fast, High-Rate Single-Server PIR via FHE Composition

We introduce the SPIRAL family of single-server private information retrieval (PIR) protocols. SPIRAL relies on a composition of two lattice-based homomorphic encryption schemes: the Regev encryption scheme and the GentrySahai-Waters encryption scheme. We introduce new ciphertext translation techniques to convert between these two schemes and in doing so, enable new trade-offs in communication and computation. Across a broad range of database configurations, the basic version of SPIRAL simultaneously achieves at least a 4.5 × reduction in query size, 1.5 × reduction in response size, and 2 × increase in server throughput compared to previous systems. A variant of our scheme, SPIRALSTREAMPACK, is optimized for the streaming setting and achieves a server throughput of 1.9 GB/s for databases with over a million records (compared to 200 MB/s for previous protocols) and a rate of 0.81 (compared to 0.24 for previous protocols). For streaming large records (e.g., a private video stream), we estimate the monetary cost of SPIRALSTREAMPACK to be only 1.9 × greater than that of the no-privacy baseline where the client directly downloads the desired record.
Self-Sovereign Identity: A Systematic Review, Mapping and Taxonomy

Self-Sovereign Identity (SSI) is an identity model centered on the user. The user maintains and controls their data in this model. When a service provider requests data from the user, the user sends it directly to the service provider, bypassing third-party intermediaries. Thus, SSI reduces identity providers' involvement in the identification, authentication, and authorization, thereby increasing user privacy. Additionally, users can share portions of their personal information with service providers, significantly improving user privacy. This identity model has drawn the attention of researchers and organizations worldwide, resulting in an increase in both scientific and non-scientific literature on the subject. This study conducts a comprehensive and rigorous systematic review of the literature and a systematic mapping of theoretical and practical advances in SSI. We identified and analyzed evidence from reviewed materials to address four research questions, resulting in a novel SSI taxonomy used to categorize and review publications. Additionally, open challenges are discussed along with recommendations for future work.
Multi-scale semantic deep fusion models for phishing website detection

In view of semantic counterfeiting characteristics of phishing websites and their multi-scale composition, this paper fully considers the semantic information of different scales, and proposes three semantic-based phishing detection models at different depths using various deep learning methods. The proposed three models are Multi-scale Data-layer Fusion (MDF) model, Multi-scale Feature-layer Fusion (MFF) model and Multi-scale In-depth Fusion(MIF) model. Experimental results on a constructed complex dataset show that the three models all have good recognition capabilities and the MIF model achieves the best performance on a complex dataset, with an F1-Measure of 0.9830, AUC value of 0.9993 and a false positive rate of 0.0047. Then with further comparison with both visual and text methods and an active discovery experiment lasting for 6 months with 3016 phishing websites detected in the real network environment, it is found that the proposed model is both competitive and practical for real detection scenarios.
Distance Perception in Virtual Reality: A Meta-Analysis of the Effect of Head-Mounted Display Characteristics

Distances are commonly underperceived in virtual reality (VR), and this finding has been documented repeatedly over more than two decades of research. Yet, there is evidence that perceived distance is more accurate in modern compared to older head-mounted displays (HMDs). This meta-analysis, based on 137 samples from 61 publications, describes egocentric distance perception across 20 HMDs and examines the relationship between perceived distance and technical HMD characteristics. Judged distance was positively associated with HMD field of view (FOV), positively associated with HMD resolution, and negatively associated with HMD weight. The effects of FOV and resolution were more pronounced among heavier HMDs. These findings suggest that future improvements in these technical characteristics may be central to resolving the problem of distance underperception in VR.
VCA

For online analysis of the video content complexity in live streaming applications, selecting low-complexity features is critical to ensure low-latency video streaming without disruptions. To this light, for each video (segment), two features, i.e., the average texture energy and the average gradient of the texture energy, are determined. A DCT-based energy function is introduced to determine the block-wise texture of each frame. The spatial and temporal features of the video (segment) are derived from this DCT-based energy function. The Video Complexity Analyzer (VCA) project aims to provide an efficient spatial and temporal complexity analysis of each video (segment) which can be used in various applications to find the optimal encoding decisions. VCA leverages some of the x86 Single Instruction Multiple Data (SIMD) optimizations for Intel CPUs and multi-threading optimizations to achieve increased performance. VCA is an open-source library published under the GNU GPLv3 license.
Bangla-BERT: Transformer-Based Efficient Model for Transfer Learning and Language Understanding

The advent of pre-trained language models has directed a new era of Natural Language Processing (NLP), enabling us to create powerful language models. Among these models, Transformer-based models like BERT have grown in popularity due to their cutting-edge effectiveness. However, these models heavily rely on resource-intensive languages, forcing other languages into multilingual models(mBERT). The two fundamental challenges with mBERT become significantly more challenging in a resource-constrained language like Bangla. It was trained on a limited and organized dataset and contained weights for all other languages. Besides, current research on other languages suggests that a language-specific BERT model will exceed multilingual ones. This paper introduces Bangla-BERT<sup>a</sup>, a monolingual BERT model for the Bangla language. Despite the limited data available for NLP tasks in Bangla, we perform pre-training on the largest Bangla language model dataset, BanglaLM, which we constructed using 40 GB of text data. Bangla-BERT achieves the highest results in all datasets and vastly improves the state-of-the-art performance in binary linguistic classification, multilabel extraction, and named entity recognition, outperforming multilingual BERT and other previous research. The pre-trained model is assessed against several non-contextual models such as Bangla fasttext and word2vec the downstream tasks. Finally, this model is evaluated by transfer learning based on hybrid deep learning models such as LSTM, CNN, and CRF in NER, and it is observed that Bangla-BERT outperforms state-of-the-art methods. The proposed Bangla-BERT model is assessed by using benchmark datasets, including Banfakenews, Sentiment Analysis on Bengali News Comments, and Cross-lingual Sentiment Analysis in Bengali. Finally, it is concluded that Bangla-BERT surpasses all prior state-of-the-art results by 3.52%, 2.2%, and 5.3%.
An edge computing based anomaly detection method in IoT industrial sustainability

In recent years, the evolving Internet of Things (IoT) technology has been widely used in various industrial scenarios, whereby massive sensor data involving both normal data and anomalous data are generated. However, the anomalies involved might have a great impact on the industrial sustainability. Actually, in most scenarios, it is of vital importance to detect the anomalies and processed accordingly in real time. In previous work, some methods, which analyze the collected industrial sensor data on cloud computing center to detect anomalies, casually increase both the transmission pressure of bandwidth and the computing pressure on cloud center. By contrast, some other methods based on edge computing detect anomalies on edge nodes, but they only take the anomalies of sensor data from single-source time series into account and ignore the correlation anomalies between the data from multi-source time series. In this paper, we first present an anomaly detection model in distributed edge computing. Then, the Edge Computing based Anomaly Detection Algorithm (ECADA), which can detect the anomalies from both single-source time series or multi-source time series is proposed. Finally, we conduct a series comparison experiments in order to demonstrate the effectiveness of the algorithm proposed in the paper. And the experimental results demonstrate that both the anomalies from single-source time series and the anomalies from multi-source time series can be accurately detected by the proposed algorithm. Specifically, it performs more efficient and effective when there exist the anomalies of new type.
Improving performance and efficiency of Graph Neural Networks by injective aggregation

Aggregation functions are regarded as the multiplication between an aggregation matrix and node embeddings, based on which a full rank matrix can enhance representation capacity of Graph Neural Networks (GNNs). In this work, we fill this research gap based on the full rank aggregation matrix and its functional form, i.e., the injective aggregation function, and state that injectivity is necessary to guarantee the rich representation capacity to GNNs. To this end, we conduct theoretical injectivity analysis for the typical feature aggregation methods and provide inspiring solutions on turning the non-injective aggregation functions into injective versions. Based on our injective aggregation functions, we create various GNN structures by combining the aggregation functions with the other ingredient of GNNs, node feature encoding, in different ways. Following these structures, we highlight that by using our injective aggregation function entirely as a pre-processing step before applying independent node feature learning, we can simultaneously achieve satisfactory performance and computational efficiency on the large-scale graph-based traffic data for traffic state prediction tasks. Through comprehensive experiments on standard node classification benchmarks and practical traffic state data (for Chengdu and Xi’an cities), we show that the representation capacity of GNNs can be improved by using our injective aggregation functions just by changing the model in simple operations.
Spiral Gaussian mutation sine cosine algorithm: Framework and comprehensive performance optimization

Sine Cosine Algorithm (SCA), as a recently viral population-based meta-heuristic, which is in the extensive application for a variety of optimization cases. Regardless of the concerns on its novelty, SCA updates the population based on a simple updating rule with a basic structure and few parameters. However, it is considered that SCA remains the weakness of low diversity, slow convergence speed, stagnation in local optimum, and low accuracy of solutions. In that case, an attempt is made to design a new SCA version to overcome these shortcomings, namely FGSCA, combined with two strategies, including spiral motion and Gaussian mutation. Spiral motion is inspired by Moth-flame Optimization (MFO), which is employed to strengthen the capacity of exploitation based on the original SCA. The Gaussian mutation is adopted to increase the population's diversity generated by SCA and strengthen local exploration capability. The principle is to update the population generated by SCA, respectively, and then choose the best from these two new populations by greedy selection. Combining the two strategies effectively strengthens the original SCA's performance and maintains a proper exploitation and exploration balance. To examine the performance of FGSCA, it is used for making comparisons with eight well-known meta-heuristic algorithms (MAs), six reported SCA variants, and ten improved MAs on 23 well-known benchmark test problems and 30 standard IEEE CEC2014 benchmark test problems. Results indicate that the overall performance of FGSCA is superior to twenty-four comparative MAs on 53 benchmark test problems. Besides, FGSCA is employed to resolve three practical engineering problems. Results exhibit that FGSCA harvests the best results among twenty-four comparative MAs. Thus, FGSCA is expected to be a mighty and efficient tool for dealing with various complex optimization problems.
Adversarial Training Methods for Deep Learning: A Systematic Review

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training schema that utilizes an alternative objective function to provide model generalization for both adversarial data and clean data. In this systematic review, we focus particularly on adversarial training as a method of improving the defensive capacities and robustness of machine learning models. Specifically, we focus on adversarial sample accessibility through adversarial sample generation methods. The purpose of this systematic review is to survey state-of-the-art adversarial training and robust optimization methods to identify the research gaps within this field of applications. The literature search was conducted using Engineering Village (Engineering Village is an engineering literature search tool, which provides access to 14 engineering literature and patent databases), where we collected 238 related papers. The papers were filtered according to defined inclusion and exclusion criteria, and information was extracted from these papers according to a defined strategy. A total of 78 papers published between 2016 and 2021 were selected. Data were extracted and categorized using a defined strategy, and bar plots and comparison tables were used to show the data distribution. The findings of this review indicate that there are limitations to adversarial training methods and robust optimization. The most common problems are related to data generalization and overfitting.