Reviewerly/exHarmony huggingface dataset:
Blockchain in International Trade Documents Management Using NAHP Technique

This study aims to explore the potential of blockchain technology in digitalization of documents used in foreign trade processes and to embody the advantages it can bring to customs clearance processes. The work is in two stages. In the first stage, the authors examined the document flow between the parties in customs clearance processes through three different digitalization scenarios using blockchain technology. They have come to the conclusion that rapid adaptation to innovations based on blockchain technology in the field of document management in international trade customs clearance processes is advantageous. In the second stage, they analyzed the priorities of the rational decision-making process with the NAHP method. The main challenges facing the transition to new technology are the need to train staff for new systems, the need to develop and improve new internal processes, the time spent reaching agreements with trading partners in the transition period, and the alignment of technological developments with legislation.
Entropy fluctuation and correlation transfer in tunable discrete-time quantum walk with fractional Gaussian noise

We study the time correlation in the von Neumann entropy fluctuation of the tunable discrete-time quantum walk in one dimension, induced by the coin disorder arising from the temporal fractional Gaussian noise (fGn). The fGn is characterized by the Hurst exponent H, which provides three different correlation scenarios, namely antipersistent (0<H<0.5), memoryless (H=0.5), and persistent (0.5<H<1). We show the correlation of fGn is transferred to the coin's degree of entanglement and eventually transpires in the time correlation of the von Neumann entropy fluctuation. This study hints at the potential of using noise correlation as a resource to sustain information backflow via the interaction of quantum system with the noisy environment.
The Impact of Covid-19 Pandemic on Tourism and Hospitality Industry: Jordan as a Case Study

Tourism is one of the sectors most affected by the Covid-19 pandemic since tourism was linked to the outbreak of the virus through the travel of tourists from the affected countries. Furthermore, tourism and related sectors were among the sectors that nearly closed completely through the full and partial lockdown, which represented the most prominent government procedures to limit the spread of the virus. Therefore, this study is the first of its kind that attempted to explore the impact of the pandemic on the tourism sector and assess the government’s role in supporting the sector and preventing its collapse. Both qualitative and quantitative approaches were used to identify the main issues that must be covered to explore the impact of the pandemic on the tourism sector, and then develop a questionnaire that was answered by 59 tourism organizations. Based on the data analyses and the hypotheses testing the study concluded that despite the great procedures and efforts undertaken by the Jordanian government to support the tourism sector, they did not meet the magnitude of the harm and did not fulfill the sector hopes in terms of injecting liquidity, supporting small and medium enterprises, and empowering their workers to continue to work and meet the new tourism requirements for the post-Covid-19 era. The study suggested an agenda to improve the government's support of the tourism sector and bring it to safety.
Efficient High-Radix GF(p) Montgomery Modular Multiplication Via Deep Use Of Multipliers

High-radix Montgomery Modular Multiplication (MMM) is flexible and consumes fewer cycles, but turns inefficient especially when it comes to low-bit calculation. In this brief, we propose a high-radix MMM algorithm, called Separated Iterative Digit-Digit Modular Multiplication (S-IDDMM), which accelerates the MMM by taking full advantage of multipliers and adders. The relationship among clock cycles, pipeline of multiplications and number of multipliers for the proposed algorithm is derived and utilized to optimize the overall efficiency jointly. Xilinx Virtex-7 FPGA implementations of S-IDDMM in 256 bits and 512 bits are constructed with radix-32 and radix-64, respectively. Compared with IDDMM design reported elsewhere (Amiet et al., 2016), our calculation time is reduced by more than 2 times with a similar area cost, due to deep use of multipliers and higher frequency.
Learning Relative Feature Displacement for Few-Shot Open-Set Recognition

Few-shot learning (FSL) usually assumes that the query is drawn from the same label space as the support set, while queries from unknown classes may emerge unexpectedly in many open-world application scenarios. Such an open-set issue will limit the practical deployment of FSL systems, which remains largely unexplored. In this paper, we investigate the problem of few-shot open-set recognition (FSOR) and propose a novel solution, called Relative Feature Displacement Network (RFDNet), which empowers FSL systems to reject queries from unknown classes while accurately classifying those from known classes. First, we suggest a different relative feature displacement learning (RFDL) paradigm for FSOR, i.e., meta-learning a feature displacement relative to a pretrained reference feature embedding, based on our insightful observations on the randomness drift issue of previous meta-learning based for FSOR methods, as well as the generalization ability of the feature embedding pretrained for general classification. Second, we design the RFDNet framework to implement the RFDL paradigm, which is mainly featured by a task-aware RFD generator and a marginal open-set loss. Comprehensive experiments on three public datasets, i.e., miniImageNet, CIFAR-FS and tieredImageNet, demonstrate that RFDNet can consistently outperform the state-of-the-art methods, achieving improvement of 5.2%, 2.0% and 1.7% respectively, in terms of AUROC for unknown-class rejection under the 5-way 5-shot setting.
Category Relevance Redirection Network for Few-Shot Classification

The metric learning method has been successfully developed in few-shot learning by learning the deeply embedded metric space. However, many methods ignore the relationship between the support set and the query set. In this paper, we propose the category relevance redirection network (CRRNet), which is a novel metric learning-based few-shot learning classification network. The learning of CRRNet includes two stages. In the category relevance pre-training stage, the convolutional block attention module (CBAM) and the classifier are jointly trained using the center loss and cross entropy loss. In the category redirection training stage, the meta fusion block and the weight generator are used to represent the weighted query set features, which are then input into the spatial attention generator to obtain the discriminative features for classification. CRRNet can not only learn the relevant category characteristics of the support set samples, but emphasizes the relevant category characteristics of the current task’s query samples. Experiments on three datasets show that CRRNet can greatly improve the embedding learning performance and classification accuracy of metric-based few-shot classification. For the 5-way 1-shot and 5-way 5-shot scenarios, CRRNet may produce 9.68 percent and 12.42 percent increases over the traditional methods on the miniImageNet dataset, respectively.
Evolutionary Multilabel Adversarial Examples: An Effective Black-Box Attack

Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attack. Minor malicious modifications of examples will lead to the DNN misclassification. Such maliciously modified examples are called adversarial examples. So far, the work on adversarial examples is mainly focused on multiclass classification tasks; there is less work in the field of multilabel classification. In this article, for the first time, a differential evolution (DE) algorithm that can effectively generate multilabel adversarial examples is proposed, which is called MLAE-DE. Different from traditional DE, we designed a complementary mutation operator for MLAE-DE, which can improve attack performance and reduce the number of fitness evaluations. As a black-box attack, MLAE-DE does not need to access model parameters and only uses model outputs to generate adversarial examples. Experiments on two typical multilabel classification models and three typical datasets under the black-box settings are conducted in this article. Experimental results demonstrate that, comparing with the existing black-box attack algorithms for multilabel classification models, the attack success rate of our proposed algorithm is much better.
Towards a Power-Balanced Participatory Design Process

Participatory designers have taken inspiration from other practices like the social sciences to develop socially just and horizontal processes to collaborate with communities. In the current work, we take the premise that designers do not have enough means to address concepts of power and politics in design practice. Therefore, we elaborate upon how designers could develop horizontal relationships within participatory design practices. Informed by the legacy of Paulo Freire, a research-through-design study exploring new ways of engaging and interacting with the community has been conducted. The study setup allowed for reflection upon the changing role of the designer in a community context. We conclude with a series of propositions and discuss their contribution to power-balanced relationships in participatory design processes.
Implementing ChaCha based crypto primitives on programmable SmartNICs

Control and management plane applications such as serverless function orchestration and 4G/5G control plane functions are offloaded to smartNICs to reduce communication and processing latency. Such applications involve multiple inter-host interactions that were traditionally secured using SSL/TLS gRPC-based communication channels. Offloading the applications to smartNIC implies that we must also offload the security algorithms. Otherwise, we need to send the application messages to the host VM/container for crypto operations, negating offload benefits.
Spirituality based codesign

Different views and critical practices have aimed to build a Participatory Design that is truly democratic in its process and results. Among different approaches, the studies of Akama, Hagen and Whaanga-Schollum, and of Light and Ibarra urge designers to build their processes from perspectives that are both personal and alternative to those developed in the Global North, with the goal of establishing a respectful, reciprocal, and relational, or sentipensante1 , Participatory Design. This paper connects such theoretical background with a codesign process, which was carried out with a group of parents, caregivers and health professionals involved with Prader-Willi Syndrome. The initial conclusions pointed out that spiritual disciplines rooted in alternative ontologies can inform processes, attitude, feelings, and behavior in design, but it is essential that designers have intimacy and proficiency with their chosen spiritual discipline and make room for self-reflection as well as for self-criticism.
On cultivating the installable base

This paper argues that technically-inclined PD practices that wish ”to matter” are well predisposed to significantly contribute to public repositories of computational alternatives. It makes that argument by providing an account from an ongoing collaboration with a group of artist collectives that seek to build its own alternative social and publishing platform. That process explicitly relies on and contributes to existing Free-Libre and Open Source software (F/LOSS) applications in the process. Using this collaboration as an example, it then argues, based on Leigh-Starr’s notion of ”installed base”, that contributions to the installable base of computational alternatives can be a key contribution of PD practices. With this notion, this paper adds further substance to discussions of how to rethink PD’s relation with socio-technical work by highlighting concrete points of intervention where PD can meaningfully contribute to technology design.
Self-Supervised Image Aesthetic Assessment Inspired by Aesthetic Domain Knowledge

Photographic rules describe how to create high-quality images by imposing restrictions on some aspects like lighting and color. Such rules referred to as domain knowledge, turn out to be crucial in enhancing the performance of image aesthetic assessment. Although many research efforts have been made, aesthetic domain knowledge is underutilized, and a large amount of labeled data are typically required. To remedy these issues, we propose an improved multi-task self-supervised method under the guidance of aesthetic domain knowledge. In the pre-training phase, we design multiple pretext tasks for a na&#x00EF;ve network to predict the levels of the properties related to photographic rules. That enables the network to learn visual features sensitive to image aesthetic information. After that, the well-trained network is applied to evaluate the aesthetic quality of images. Experiments on two benchmark datasets elucidate that the features learned by the network can discern aesthetic images according to their difference in photographic properties. The promising results demonstrate that the proposed method can successfully leverage aesthetic domain knowledge to learn effective features from large-scale unlabeled data for image aesthetic assessment.
A Configurable Hardware Accelerator Based on Hybrid Dataflow for Depthwise Separable Convolution

As one of the most commonly used neural network models, the lightweight convolutional neural network has been widely adopted in the field of object detection, pattern recognition and so on. In order to achieve the real-time calculation in embedded system, the acceleration of depthwise separable convolution has become the focus of research in recent years. In this paper, a configurable hardware accelerator for depthwise separable convolution is proposed. The accelerator contains a high-speed dedicated processing engine array which can be configured to carry out depthwise convolution and pointwise convolution with high hardware utilization. The storage is fully utilized by applying hybrid scheduling strategies. Moreover, the design of MobileNet-V2 is validated on the platform of Zynq 7020, giving a high rate up to 186 frames/s.
Re-thinking Pitfalls of Premodel Building for Adaptive CNNs Model Selection on ImageNet

Although recent approaches to combine machine learning (ML) enhanced models and adaptive model selections using the built models became popular in many areas, however, there could be crucial pitfalls of built models in terms of 1) feature extraction and importance, 2) unbalanced datasets and 3) different types of ML estimators. In this case study using the ImageNet dataset, we thoroughly investigate the effects and possible pitfalls of features, datasets, and ML estimators for adaptive model selection of CNN inference network models targeting embedded systems. Based on the comprehensive results and analysis, we summarize opportunistic alternatives to minimize the pitfalls in the model building phase using more accurate feature extraction algorithms, various datasets in balance and size, and different types of interpretable (explaining important features) and black-box models.
Distributed Storage and Traceability System for Aviation Equipment Biographies Based on Consortium Blockchain

Aiming at the safety and efficiency problems in the whole life cycle management of aviation equipment, propose an efficient solution based on federated blockchain. Firstly, the data collected by the terminal sensing device is hash transformed through the blockchain system, and the initial data security is achieved by using the irreversibility principle of the hash algorithm while reducing the data volume of a single transaction in the block; secondly, the block is generated by the representative nodes elected through the Delegated-Proof of Stake (DPoS), and the consensus algorithm of the blockchain data verification adopts the delegated Byzantine Fault-Tolerance (DBFT) consensus algorithm to improve the consensus efficiency; finally, the transaction time curves are fitted and plotted according to the number of participating nodes, block size and network bandwidth in the simulation experiments to calculate the transaction efficiency under different situations. The experimental results show that the federated chain system is proved to achieve secure and efficient aviation equipment history storage and traceability.
Automatic Recognition of Parallel Sentence Based on Sentences-Interaction CNN and Its Application

Automated essay evaluation is to make the computer simulating human evaluate students&#x2019; essay. The final grade of one essay is highly related to its literacy. The use of rhetorical devices can increase the literacy of one essay, and parallelism is one of the common rhetoric. Therefore, the identification of parallelism can contribute to automated essay evaluation. However, the research of automatic recognition of parallel sentence is so rare so far. In this paper, we design a deep learning method based on sentences interactive-convolutional neural network to recognize parallelism in the light of word co-occurrence, similar syntactic structure and tone of expression. Firstly, one or more interactive matrices whose elements represent the similarities in some ways between every token in two sentences are constructed. Then the convolutional neural network is employed to capture local features. The experimental results show that our feature selection is effective and the recognition effect is comparable with similar studies.
PBFT Algorithm for Internet of Things

Due to the limited computing and storage capabilities of IoT devices, it is difficult to meet the computing needs of the Practical Byzantine Fault Tolerance (PBFT) algorithm. And the traditional PBFT algorithm is difficult to meet the throughput required by this system due to the prolonged consensus time. In order to solve the above problems, this paper proposes an improved PBFT algorithm, First, the node roles are divided, and only the consensus node role participates in the consensus, which reduces the communication overhead in the consensus process and makes enterprises of various strengths suitable for joining the system. Secondly, a scoring mechanism is added to improve the node selection and replacement method according to the points obtained by the nodes, and effectively eliminate Byzantine nodes, select a reliable node as the master node to the greatest extent, reduce the frequency of view change, and improve the speed of node change. Finally, the execution process of the consensus protocol is optimized, the communication complexity is reduced from O(N^2) to O(N), and the overall efficiency of the system is improved.
Comparative Analysis of Cloud Computing Based Face Recognition Services

With growing crime rate around the world, it has become essential to have security surveillance systems that uses evolved mix of advanced technologies to provide a robust and a holistic approach to mitigating security risks. On the other hand, there have been rapid advancement in technologies which have led to the progress of security awareness and embedded system to cope up with the higher demand of the security surveillance. With immense variety of systems available nowadays, this paper aims to compare the various existing facial recognition cloud services used for real time security and surveillance with main focus on cloud-based architecture.
A Hybrid approach on Tracking Criminal Investigation and Suspect Prediction

Criminal investigation is a machinery that involves examining of evidence in order to discover, detect and authenticate occurrence of crime. Application of number of sophisticated scientific systems, together known as forensic science, is a necessity in a modern investigation. Criminal Investigation Tracker with Suspect Prediction is a revolutionary step towards digital world and controlling the growing rate of crime. It can be used during crime investigation by an agency to assist in their work. The research is designed with UI/UX elements similar to a CRM, that help manage cases and evidence along with client-server architecture to ensure all the necessary documentation as well information is centrally available in place with deployed Access Control System, to promote remote working. The research packages certain Free and Open-Source tools for analysis of evidence to best of abilities of the development team to further aid the investigation officers in their work by sharing the workload between dedicated forensics teams with much better tools at their disposal as well as the investigating officers if they want to quickly analyze some piece of evidence. The research paper focuses on how modern technology can be used to reduce the hectic work and making investigation easier for the investigating officers. This also brings a doorway for getting a faster rate of justice inside the system and quick actions to any crime. Furthermore, improvising on existing investigation assistant software in use. This is a leap in investigation methods used by various agencies.
Twitter Sentiment Analysis of India vs Pakistan T20 World Cup Match Using SVM Classifier

Feeling investigation or assessment mining is the computational investigation of individuals &#x2019; viewpoints, perspectives, evaluations, and feelings toward people, elements, occasions, issues, points and their traits. With the huge prominence of online entertainment, individuals have been utilizing this stage to communicate their thoughts, subsequently making it simpler for analysists to concentrate and work on datasets recovered from SNS. It includes extricating tweets from twitter, an illustration of SNS, information purifying and use of an appropriate calculation to get the satisfactory opinion examination. In the ebb and flow research, tweets have been gathered for opinion investigation, for an occasion that happened as of late (India versus Pakistan T20 World Cup). For this a contextual analysis has been done, utilizing the straight help vector classifier. The accuracy result has been determined while dealing with texts and emojis. The outcomes plainly show the dependence of SVM execution upon input dataset.
Feature Ensemble based method for verification of Offline Signature images

Signature verification is the most researched area in computer vision as it has less intra-class and less inter-class similarity. Our objective is to reduce the intra-class variation and increase the inter-class similarity. This paper has proposed an ensemble based approach where method of ensemble of feature created by using geometrical and Mobile Net features along with the ensemble of classifier has utilized. The proposed method has been tested over the publicly available dataset BHSig260. This paper has achieved 99.4% and 99.3% accuracy over Bengali and Hindi dataset respectively.
A Statistical Comparative Study For Detecting Outliers In Electrical Data

The rapid development of artificial intelligence models in electrical engineering drew attention to the outlier detection methods because they represent one of the main causes of model overfitting. In this paper, three popular outlier detection algorithms, boxplots, z-score and standard deviation, are analyzed in the context of electrical data composed of voltages, currents and harmonics. The study aims to establish an algorithm that can accurately detect outliers so the data can be used in others more advanced models.
Comparison of Data Hiding Techniques for Video Watermarking Applications

The basic idea for video watermarking technique is concealing information in the video host. There are three popular techniques that can be used for data hiding those are steganography, watermarking and cryptography. These methods are compared and this paper intends to overview video watermarking techniques of tamper detection and authentication.