Reviewerly/exHarmony huggingface dataset:
Blockchain Interoperability

Blockchain technology is becoming a promising technological solution for enterprise applications with the rise of interoperable solutions. A cross-chain architecture facilitates interoperability, thus improves its chain efficiency, reduces fragmentation, and allows users and features to flow more freely across multiple blockchains. However, enabling interoperability in silo networks will make a significant functional trade-off on the security and performance of the system. This paper review trade-offs in blockchain technologies related to interoperability.
BlastNet

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, heterogeneous CPU and GPU resources must be efficiently utilized for the inference of multiple DNN models. Such a cross-processor real-time DNN inference paradigm poses major challenges due to the inherent performance imbalance among different processors and the lack of real-time support for cross-processor inference from existing deep learning frameworks. In this work, we propose a new system named BlastNet that exploits duo-block - a new model inference abstraction to support highly efficient cross-processor real-time DNN inference. Each duo-block has a dual model structure, enabling efficient fine-grained inference alternatively across different processors. BlastNet employs a novel block-level Neural Architecture Search (NAS) technique to generate duo-blocks, which accounts for computing characteristics and communication overhead. The duo-blocks are optimized at design time and then dynamically scheduled to achieve high resource utilization of heterogeneous CPU and GPU at runtime. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive results show that BlastNet achieves 35.07 % less deadline missing rate with a mere 1.63% of model accuracy loss.
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client’s impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.
Aperiodic Local SGD: Beyond Local SGD

Variations of stochastic gradient decedent (SGD) methods are at the core of training deep neural network models. However, in distributed deep learning, where multiple computing devices and data segments are employed in the training process, the performance of SGD can be significantly limited by the overhead of gradient communication. Local SGD methods are designed to overcome this bottleneck by averaging individual gradients trained over parallel workers after multiple local iterations. Currently, both for theoretical analyses and for practical applications, most studies employ periodic synchronization scheme by default, while few of them focus on the aperiodic schemes to obtain better performance models with limited computation and communication overhead. In this paper, we investigate local SGD with an arbitrary synchronization scheme to answer two questions: (1) Is the periodic synchronization scheme best? (2) If not, what is the optimal one? First, for any synchronization scheme, we derive the performance boundary with fixed overhead, and formulate the performance optimization under given computation and communication constraints. Then we find a succinct property of the optimal scheme that the local iteration number decreases as training continues, which indicates the periodic one is suboptimal. Furthermore, with some reasonable approximations, we obtain an explicit form of the optimal scheme and propose Aperiodic Local SGD (ALSGD) as an improved substitute for local SGD without any overhead increment. Our experiments also confirm that with the same computation and communication overhead, ALSGD outperforms local SGD in performance, especially for heterogeneous data.
Adaptive HEVC video steganography based on distortion compensation optimization

With the development of digital video communication, the steganography based on H.265/HEVC has attracted more and more attention. Because of higher security and better compatibility, the traditional video steganography often used quantized discrete cosine transform (QDCT) or quantized discrete sine transform (QDST) coefficients to embed secret messages. However, the QDCT/QDST-based steganographic methods will cause distortion drift seriously. Although the distortion compensation optimization method can eliminate the intra-frame distortion drift, it ignored the influence of inter-frame distortion. This limitation prompts authors to propose an adaptive HEVC steganography, which eliminates the intra-frame distortion drift while reducing inter-frame distortion. First, the distortion of intra-block and inter-block is analyzed with the prediction and transform process. Then, the corresponding embedding influence is defined, which includes intra-block distortion, inter-frame distortion accumulation, the block texture complexity and the cost of coefficient groups. Finally, the proposed adaptive cost function is designed based on the above definition and the syndrome trellis codes (STC) is used for embedding. The experimental results show that the proposed method has better performance compared with the state-of-the-art coefficient-based steganography in visual quality, bit rate increment and anti-steganalysis.
Group Action Key Encapsulation and Non-Interactive Key Exchange in the QROM

In the context of quantum-resistant cryptography, cryptographic group actions offer an abstraction of isogeny-based cryptography in the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) setting. In this work, we revisit the security of two previously proposed natural protocols: the Group Action Hashed ElGamal key encapsulation mechanism (GA-HEG KEM) and the Group Action Hashed Diffie-Hellman non-interactive key-exchange (GA-HDH NIKE) protocol. The latter protocol has already been considered to be used in practical protocols such as Post-Quantum WireGuard (S &P ’21) and OPTLS (CCS ’20). We prove that active security of the two protocols in the Quantum Random Oracle Model (QROM) inherently relies on very strong variants of the Group Action Strong CDH problem, where the adversary is given arbitrary quantum access to a DDH oracle. That is, quantum accessible Strong CDH assumptions are not only sufficient but also necessary to prove active security of the GA-HEG KEM and the GA-HDH NIKE protocols. Furthermore, we propose variants of the protocols with QROM security from the classical Strong CDH assumption, i.e., CDH with classical access to the DDH oracle. Our first variant uses key confirmation and can therefore only be applied in the KEM setting. Our second but considerably less efficient variant is based on the twinning technique by Cash et al. (EUROCRYPT ’08) and in particular yields the first actively secure isogeny-based NIKE with QROM security from the standard CDH assumption.
Hey chatbot, why do you treat me like other people? The role of uniqueness neglect in human-chatbot interactions

Resistance to chatbots is a real challenge that companies must overcome, although they (i.e., chatbots) have several advantages. Based on the stereotype content model, this research seeks to understand customer reactions in the context of human-chatbot interactions by integrating the concept of uniqueness neglect as a moderator of customer reactions to the competence of bank chatbots. A sample of 378 respondents was collected in France using the snowball sampling technique, and hypotheses were tested using SmartPLS. We find that chatbot competence does influence customer satisfaction, the latter of which in turn affects both recommendation intention and continuance intention. Further, we find that uniqueness neglect moderates the effect of chatbot competence on satisfaction such that the effect is stronger (weaker) when uniqueness neglect is low (high). We find that warmth does not have a significant moderating effect. This study is among the first attempts to understand customer reactions to interactions with bank chatbots and offers insightful theoretical and managerial implications of use to both academics and practitioners alike.
Sparse possibilistic c-means clustering with Lasso

Krishnapuram and Keller first proposed possibilistic c-means (PCM) clustering in 1993. Afterward, PCM was widely studied with various extensions. The PCM algorithm and its extensions always treat feature components under equal importance, but, in real applications, different features may better have different weights. Recently, Yang and Benjamin in 2021 proposed a feature-weighted PCM clustering with feature reduction. Although Yang and Benjamin (2021) can reduce feature dimensions, it still encounters the curse of dimensionality for high dimensional data. One possible way to address this problem is to conduct a sparse clustering technique. In this paper, we further study the PCM clustering by incorporating the idea of sparsity with different feature weights. We propose two approaches that use the PCM clustering with the least absolute shrinkage and selection operator (Lasso). The first one is the sparse PCM subject to a Lasso constraint of feature weights, called S-PCM1. The second is the sparse PCM by adding a Lasso penalty term of feature weights in the objective function, called S-PCM2. We show that S-PCM1 and S-PCM2 are theoretically the same, and both can induce sparsity in features, but they use different procedures in algorithms. Synthetic and real data sets are used to compare S-PCM1 and S-PCM2 with some existing sparsity clustering algorithms. Experimental results and comparisons demonstrate the effectiveness and usefulness of the proposed S-PCM1 and S-PCM2 clustering algorithms.
3D scanning method for robotized inspection of industrial sealed parts

This work presents a novel approach for 3D scanning inspection of industrial sealed parts based on data fusion from a 2D-laser beam sensor and the motion pattern of a robotic arm. The method provides as output the 3D geometrical shape and volume of the inspected part in order to allow for automatic compliance check according to process requirements. The solution is implemented and tested in sealed riveted fasteners, which are common in the automotive and aerospace industry. The effectiveness and robustness of the method is evaluated through the comparison of the obtained results with those from a 3D laser scanner system. The evaluation campaign was performed in a noisy environment (i.e., without illumination and temperature control), representative of an industrial shop floor. Statistical analyses show the system can perform geometry prediction with an overall error of 0.340 mm and is able to reject non-compliant sealed structures with a reliability of 96.6%, confirming that the proposed method is suitable to modern collaborative robotized aerospace and automotive assembly cells.
Comparative study of orthogonal moments for human postures recognition

Human posture recognition has recently attracted a significant attention from the computer vision community. However, as in any pattern recognition problem, the features extracted from human posture images must be relevant; otherwise, the overall performance of the recognition system may be affected. Among the large number of existing features, the orthogonal moments have been successfully used in many image analysis and pattern recognition applications. However, to the best of our knowledge, their performances in human posture recognition have not been yet examined. Thus, the objective in this paper is to evaluate and compare the performances of various types of orthogonal moments, namely, Zernike, pseudo-Zernike, orthogonal Fourier–Mellin, Gegenbauer, exact Legendre, Chebyshev, Krawtchouk and Hahn moments for human postures recognition problem. The performance evaluations of these moments, as well as a comparison between them, are performed on three public datasets, namely Zhao & Chen dataset, URFD dataset and SDUFall dataset. The obtained results showed that, using moments up to order 8 on Zhao & Chen dataset, and up to order 6 on URFD and SDUFall datasets, Krawtchouk moments and Hahn moments outperform all the other moments, reaching an accuracy of about 98.5% to 99%. The robustness of the different orthogonal moments against noise and segmentation errors was also evaluated. The obtained results showed again the outperformance of Krawtchouk and Hahn moments compared to the other moments, with a performance drop of less than 1.46% in the presence of high noise level, and less than 6.2% in the presence of severe segmentation errors.
Prioritizing suitable locations for national-scale solar PV installations: Israel's site suitability analysis as a case study

The integration of renewables is one of the main challenges and driving forces in electricity markets in recent years. Accordingly, the study of selecting suitable locations for installing renewable facilities has gained momentum. Although studies have examined various siting criteria, electric grid interconnection feasibility issues are not often included due to the lack of complete national mapping information and data. We couple Geographical Information System (GIS) data with an Analytic Hierarchy Process (AHP) method to create a model for identifying and prioritizing locations that are better suited than others for PV installations based on techno-economic, environmental and electrical criteria. We test our model using Israel's entire national distribution power grid configuration and land use characteristics as a case study. Our results indicate that while a total area of 323 km2 is considered appropriate for PV installations in Israel, only 42.6% of that area (137.6 km2) can be classified into high or very high suitability levels. Prioritizing PV locations is of great importance because they determine the effectiveness of the PV integration in terms of how much it costs to generate electricity and optimal land use management. Hence, our methodology provides decision makers with a practical tool for expanding solar PV efficiently.
STI-Net: Spatiotemporal integration network for video saliency detection

Image saliency detection, to which much effort has been devoted in recent years, has advanced significantly. In contrast, the community has paid little attention to video saliency detection. Especially, existing video saliency models are very likely to fail in videos with difficult scenarios such as fast motion, dynamic background, and nonrigid deformation. Furthermore, performing video saliency detection directly using image saliency models that ignore video temporal information is inappropriate. To alleviate this issue, this study proposes a novel end-to-end spatiotemporal integration network (STI-Net) for detecting salient objects in videos. Specifically, our method is made up of three key steps: feature aggregation, saliency prediction, and saliency fusion, which are used sequentially to generate spatiotemporal deep feature maps, coarse saliency predictions, and the final saliency map. The key advantage of our model lies in the comprehensive exploration of spatial and temporal information across the entire network, where the two features interact with each other in the feature aggregation step, are used to construct boundary cue in the saliency prediction step, and also serve as the original information in the saliency fusion step. As a result, the generated spatiotemporal deep feature maps can precisely and completely characterize the salient objects, and the coarse saliency predictions have well-defined boundaries, effectively improving the final saliency map's quality. Furthermore, “shortcut connections” are introduced into our model to make the proposed network easy to train and obtain accurate results when the network is deep. Extensive experimental results on two publicly available challenging video datasets demonstrate the effectiveness of the proposed model, which achieves comparable performance to state-of-the-art saliency models.
An enhanced vortex search algorithm based on fluid particle density transfer for global and engineering optimization

The vortex search algorithm is a new meta-heuristic algorithm for solving optimization problems. However, it has some disadvantages such as weak stability, tendency to trap into local minima, and high iterations. To overcome these disadvantages, this paper proposes a density transfer vortex search algorithm based on Cauchy distribution and Archimedean spiral surrounding for global and engineering optimization. The algorithm is divided into two populations. In the first population, the mutual action of the Cauchy random step and Gaussian step is introduced to improve the searchability. To improve the exploitation ability of the algorithm in the current optimal solution, an Archimedean spiral surrounding mechanism is introduced in the second population. Moreover, we introduce the vortex flow mechanism, which mimics the physical characteristics of the vortex flow. With the number of iterations increasing, candidate solutions of the first population are gradually affected by the vortex, and their density continuously shifts to the second population. The relationship between the exploration and exploitation in different iteration periods is reasonably adjusted through the density transfer of two populations. In the experiment, the performance of the proposed algorithm and six competitive algorithms are tested on the IEEE CEC2021 global optimization benchmark suite, eight real-world engineering optimizations and a scheduling problem for automated storage and retrieval systems (AS/RS). The experimental results show that the proposed algorithm has significant improvements in convergence precision, speed and stability. The proposed algorithm not only solves complex global optimizations and real-world engineering optimizations with good convergence performance, but also improves efficiency and reduces energy consumption when solving the scheduling problem of AS/RS.
Fine-tuning transformers: Vocabulary transfer

Transformers are responsible for the vast majority of recent advances in natural language processing. The majority of practical natural language processing applications of these models are typically enabled through transfer learning. This paper studies if corpus-specific tokenization used for fine-tuning improves the resulting performance of the model. Through a series of experiments, we demonstrate that such tokenization combined with the initialization and fine-tuning strategy for the vocabulary tokens speeds up the transfer and boosts the performance of the fine-tuned model. We call this aspect of transfer facilitation vocabulary transfer.
A security model to protect the isolation of medical data in the cloud using hybrid cryptography

A cloud computing platform delivers a cost-efficient path for cloud users to store and access data privately over remote storage via an Internet connection. Medical data is stored in the cloud since it is scalable, secure, reliable, provides ubiquitous access, and is highly available. It is required to be isolated in terms of physical, network, and operational for protecting data from internal threats or external cyber-attacks. It can be achievable through hybrid cryptography in which symmetric, as well as asymmetric cryptosystem, is being utilized. To assure the confidentiality and integrity of medical data, an improved Robust S-box-based Advanced Encryption Standard (IRS-AES) is proposed with Runge-Kutta Optimization (RKO) algorithm. RKO generates an enhanced and secured RS-box through the computation of the Mackey-Glass equation. Initially, the medical data are compressed using Improved Huffman Coding (IHC). The Deoxyribonucleic Acid (DNA)-based Modified Elliptic Curve Cryptography (MECC) algorithm is introduced for key generation, and the best key is selected with Bald Eagle Search optimization (BES) algorithm. Finally, the medical data are encrypted IRS-AES algorithm and stored in the cloud. The proposed RS-box security strength is evaluated using non-linearity, Strict Avalanche Criterion (SAC), Differential Probability (DP), Bit Independence Criterion (BIC), and Linear Probability (LP) parameters. The efficiency of the proposed algorithm is evaluated with image, audio, and video dataset. The evaluation metrics such as communication overhead, file upload time, computational cost, Mean Square Error (MSE), encryption and decryption time, Peak Signal-to-Noise Ratio (PSNR), key generation time, and Signal-to-Noise Ratio (SNR) are used for validating the proposed approach. The performance of the proposed approach is enhanced with the communication overhead of 11.51 for the image dataset. MSE and PSNR obtained are 16.2 and 8.465 for an audio dataset, 75.21 and 3.5 for the video dataset.
LDA-2IoT: A level dependent authentication using two factor for IoT paradigm

The widespread expansion of IoT-based services is transforming people’s living habits. With the vast data generation and intelligent decision making system, IoT supports many industries to improve their quality of services. The major challenge for IoT developers is to design a secure data transmission system and a trustworthy inter-device and user-device communication system. The data starts its journey from the sensing devices and reaches the user dashboard via a different medium. An authentication among IoT devices provides a reliable and lightweight key generation system. This paper puts forward a novel authentication approach for the IoT paradigm and postulates an ECC-based two-factor Level Dependent Authentication for Generic IoT (LDA-2IoT) in which users at a particular level in the hierarchy can access the sensors deployed below or at the equal level of the hierarchy. Security analysis is given based on the Dolev–Yao channel and widely accepted random oracle-based ROR model. A real-time implementation of the proposed scheme with MQTT protocol over a lab IoT environment shows that it is highly efficient in terms of network throughput, computation cost, and communication cost compared to existing schemes.