Reviewerly/exHarmony huggingface dataset:
Blockchain-based E-commerce for the COVID-19 economic crisis

The beginning of 2020 is associated with the emergence and spread of the COVID-19 disease. The characteristics of this virus, such as high transmission power and lack of definitive treatment have caused problems in all aspects of organizational economics. Restrictions that were imposed to deal with the virus affected the global economy. Fear of being exposed to the virus, quarantine and lockdown led to a massive increase in online shopping. However, people's concern about the health and authenticity of the products offered online and their incompatibility with consumer standards raised concerns about the reliability of the existing e-commerce models. Fraud, counterfeit products, ethical sourcing and product safety are some of the concerns that affected online business acceptance. To address these challenges, we examine the use of a blockchain-based e-commerce approach to guarantee authenticity through blockchains trace and trace capabilities. In this approach, we evaluate the profit gains achieved through addressing consumer concerns on safety and authenticity. To examine these benefits, we use a game theory leader-follower approach. We evaluate the profitability of e-commerce in three scenarios, including when non of seller and e-commerce website use blockchain, only when the seller uses the blockchain and when both the seller and the e-commerce websites use blockchain. The evaluation results show that the situation in which both the seller and the website use blockchain has the highest profitability for the seller due to the maximum reduction of customer concerns.
PIMIP: An Open Source Platform for Pathology Information Management and Integration

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform, PIMIP (Pathology Information Management & Integration Platform). PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible.
RCC: A Paradigm for Training a Robust Chinese Text Classification Model

Adversarial attacks against language models have gained more and more attention in recent years, and various adversarial text generation models have been proposed. Chinese language models are more vulnerable to character-level tampering attacks due to the language nature. In this paper, we implement a set of white-box attack algorithms against Chinese text classification models, which significantly reduce the accuracy of multiple baseline models on multiple classification tasks while ensuring that one can recover the original utterance. We utilize follow strategies, and propose a paradigm to enhance the robustness of Chinese classification models: 1) generating adversarial text during training as a dynamic data augmentation, 2) introducing extra glyph and phonology information into Chinses language models.
Consensus variational Bayesian moving horizon estimation for distributed sensor networks with unknown noise covariances

This paper deals with state estimation over distributed sensor networks with unknown process noise covariance matrix (PNCM) and measurement noise covariance matrices (MNCMs). The unknown PNCM and MNCMs are modeled with constrained inverse Wishart distributions and estimated together with the state trajectory within a moving horizon window. Each local node computes its local estimates by combining variational Bayes (VB) inference with Moving Horizon Estimation (MHE). Then, in order to diffuse the information through the network, at each time instant three consensus tasks are performed in parallel: the first one is a consensus on the parameters of the local approximated PNCM posterior; the second one is a consensus on the prior information on the state trajectory over the moving window; the third one is a consensus on the measurements over the moving window. The resulting consensus-based distributed VBMHE (D-VBMHE) algorithm generalizes existing consensus filters to the problem of jointly estimating the state trajectory over a window as well as to unknown PNCM and MNCMs. Simulation and experimental results on target tracking case-studies demonstrate the outperformance of the proposed D-VBMHE compared to state-of-the-art conventional and adaptive consensus filters.
A Weakly Supervised Framework for 2D/3D Vascular Registration Oriented to Incomplete 2D Blood Vessels

Registering preoperative 3D computed tomography (CT) to intraoperative 2D digital subtraction angiography (DSA) can complete the missing part of 2D vessels by 3D-2D projection, which is useful for the guidance of vascular interventional surgery. However, vessel excalation in DSAs remains hard for registration in clinical circumstances. To overcome this challenge, we proposed a weakly supervised 2D/3D vascular registration framework to improve the performance on DSAs with incomplete vessels. We combined a CNN regressor with a novel 3D-2D mask projection module to build the full network. Then a two-step training procedure is employed, including supervised pre-training on simulated images and weakly supervised finetuning on DSAs. In addition, a patch-based content loss is introduced in the finetuning step to give an appropriate similarity measure for images with incomplete vessels. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to improve the backbone regressor’s registration performance on incomplete vessels and has the ability to complete vessels in DSAs with high recall.
Attitude Towards Intention to Use Mobile-Based Teaching Assessment Based on TAM and a Novel Extended TAM Model

In recent years the combination of a rapid development in mobile technology and the pandemic scenario in which currently the world is still in, have caused an increasing demand for online systems that can replace the traditional methods in a variety of areas. For instance, the rising usage of e-learning and e-health are evidence of this reality. Consequently, Mobile Based Teaching Assessment (MBTA) has become the ideal candidate to replace traditional classroom evaluation. The intention of end users to use MBTA as an alternative to traditional evaluation is the main indicator of its adoption in a regular basis. This study has two main goals, the first one is to use the Technology Acceptance Model (TAM) to evaluate how people’s feelings towards the usage of MBTA, the second goal is to propose an extended TAM that incorporates intrinsic motivation to the traditionally extrinsic regular TAM. Partially least squares (PLS) were used in this study, and data was gathered from 75 students and academic support personnel using a simple sampling technique, as well as structural model to evaluate the proposed model. Based on the findings of this research, the attitude of individuals directly influences their desire to use MBTA. Along with that, individual’s attitude is influenced by a variety of factors, this is more evident in the extended TAM proposed. Despite the indications achieved with TAM regarding the implementation of mobile-based teaching assessment in an academic context, the claimed user attitude explains 75% of the variance in user intention towards MBTA. The importance of the suggested paradigm, as well as the accuracy of the extended TAM proposed, is shown in the results of this work.
StreaMRAK a streaming multi-resolution adaptive kernel algorithm

Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data size far exceeds the memory size. Such applications are increasingly common in data mining, bioinformatics, and control. A powerful paradigm for computing on data sets that are too large for memory is the streaming model of computation, where we process one data sample at a time, discarding each sample before moving on to the next one. In this paper, we propose StreaMRAK - a streaming version of KRR. StreaMRAK improves on existing KRR schemes by dividing the problem into several levels of resolution, which allows continual refinement to the predictions. The algorithm reduces the memory requirement by continuously and efficiently integrating new samples into the training model. With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum. The results show that the proposed algorithm is fast and accurate.
An Ontology-based Approach for Automatic Specification, Verification, and Validation of Software Security Requirements: Preliminary Results

Critical software vulnerabilities are often caused by incorrect, vague, or missing security requirements. Hence, there is a strong need in the software engineering community for tools that facilitate software engineers in eliciting and evaluating security requirements. Although several methods have been proposed for specifying, verifying, and validating security requirements, they require a lot of manual effort by requirement engineers, which hinders their practicality. To this end, we introduce a software security requirements specification mechanism, able to automatically identify the main concepts of a given set of security requirements expressed in natural language. Our mechanism applies syntactic and semantic analysis in order to transform requirements into appropriately structured ontology objects. We also propose a software security requirements verification and validation mechanism, which compares a given security requirement to a curated list of well-defined security requirements based on similarity checks, identifies inconsistencies, and proposes refinements. Both of the proposed mechanisms comprise standalone tools, implemented in the form of web services. The capabilities of the proposed mechanisms are demonstrated through a set of test cases.
Novel Method for the Segmentation of Brain Images Using the Fcm Clustering Approach as Well as Rough Set

The aim of this article is to provide a new technique for image segmentation that makes use of both the FCM clustering method & rough set theory. The image is segmented into many tiny areas based on observable relationships among features as indicated by segmentation results produced using FCM at various clustering coefficients. The discrepancy between areas is then calculated using value reductions to get weight values with each feature, as well as the similarity of each region is determined using the discrepancy degree-created equivalence link. Eventually, the segmentation of the image is performed by merging areas depending on the total resemblance equivalency relation. Multiple brain images were segmented to verify this technique. When compared to the FCM technique, the experimental findings indicate that the suggested method may result in much reduced error rates and much more effective segmentation of the fuzzy border area.
KG-Visual: A Tool for Visualizing RDF Knowledge Graphs

RDF Knowledge graphs are semantic networks to infer information from data based on RDF triples using SPARQL queries. Querying RDF Knowledge graphs using visualization allows different views of the graph and provides more insights. A new visualization tool, KG-Visual is developed in this paper using Python dashboard, JavaScript and RDF libraries. This tool tackles the limitations of existing tools by providing a customizable and reliable visualization for RDF knowledge graphs. In addition to the essential functionalities such as search, drag and drop nodes, zoom-in and zoom-out over a knowledge graph, the tool holds the capability to run SPARQL queries and visualize the results on the knowledge graph, which makes this open-source tool a potential candidate amongst most RDF visualization tools.
Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning

Cloud computing’s automation, scalability, and availability were vital features in the early days of digital transformation. Meanwhile, substantial concerns were expressed about cloud security and privacy. Due to the COVID-19 outbreak, several businesses have had serious issues speeding up their cloud migration efforts. This work intends to improve steganography in ad-hoc cloud systems using deep learning. This study is implemented in two phases. Phase 1: The ‘Ad-hoc Cloud System’ concept and deployment method were created using V-BOINC, a tool that allows developers to bypass application-level security checks, the implemented ad-hoc cloud system was compared Amazon AC2 and showed high evaluation rate in some matrices. Phase 2: We evaluate the data transmission security in ad-hoc cloud systems using a modified steganography with deep learning usage to replace or enhance an image-hiding system. In this study, the proposed model inputs data/images into the ad-hoc cloud system to guarantee high rate of data/image concealing. Statistically, a systematic steganography model hides lower message detection rates, the proposed deep steganography approach outperformed several attacks in the ad-hoc cloud environment.
Multi-Input CNN-LSTM for End-to-End Indian Sign Language Recognition

Sign language predominantly involves the use of various hand postures and hand motions to enable visual communication. However, signing is mostly unfamiliar and not understood by normal hearing people due to which signers often rely on sign language interpreters. In this chapter, a novel multi-input deep learning model is proposed for end-to-end recognition of 50 common signs from Indian Sign Language (ISL). The ISL dataset is developed using multiple wearable sensors on the dominant hand that can record surface electromyogram, tri-axial accelerometer, and tri-axial gyroscope data. Multi-channel data from these three modalities is processed in a multi-input deep neural network with stacked convolutional neural network (CNN) and long short-term memory (LSTM) layers. The performance of the proposed multi-input CNN-LSTM model is compared with the traditional single-input approach in terms of quantitative performance measures. The multi-input approach yields around 5% improvement in classification accuracy over the traditional single-input approach.
Methodological Support of the Updated Federal State Educational Standard of Primary General Education

The article discusses scientific and methodological approaches and characterizes the updated version of the Federal State Educational Standard for Primary General Education (FSES PGE) adopted by the Ministry of Education of the Russian Federation in May 2021. The structure of the Approximate educational program and its most important part - exemplary work programs of primary school subjects are considered. On the example of the work program on the subject "World around", a description of the goals, content, planned learning outcomes is given, accompanied by comments for the teacher.
A Nature-Inspired DNA Encoding Technique for Quantum Session Key Exchange Protocol

Security is today’s foremost concern over the transmission channel. To lessen this security threat, DNA-based cryptography has emerged as an alternative to encryption of the data and exchanging of message between the sender and the receiver over a secured communication channel. Quantum superposition of states guarantees security against threats on any quantum communication channel. Again, DNA code set provides a way of encoding a binary text to its equivalent nucleotide bases. The idea of using quantum computing and DNA code set to perform the quantum session key exchange is that each pattern has its own unique properties and so, using a scheme that applies both is more secured than using any one alone. This is a nature-inspired technique, in which a plain text in binary coded form is transformed to its DNA equivalent sequence that is given a Watson-Crick’s transformation to obtain a first level of encoded message. This message is further encrypted using a key that is obtained by a quantum session key exchange protocol to prevent from the common threats in the transmission channel. Moreover, using our protocol, sender and receiver can easily detect the intervention of any attacker over the transmission network by calculating the error in the channel.
The Impact of Internet of Things Self-Security on Daily Business and Business Continuity

The number of devices operating on IoTs has exceeded billions globally. This chapter aims to examine the cyber security risks of such systems with widespread use and investigate some IoT vulnerabilities. It examines the effects of these vulnerabilities on business life and personal life, and the precautions to be taken to eliminate them. In addition, the regulations and measures to be applied at the state level is discussed. The safe use of IoT systems cannot be achieved solely by individual awareness. An awareness and sense of responsibility in the manufacturing layer is also a must. This chapter investigates the reasons behind the lack of security precautions taken in the manufacturing phase of IoT devices and suggests solutions. It also discusses the details of malwares such as Mirai, whose targets are mainly IoT vulnerabilities.
Examining Usability on Atreya Bot: A Chatbot Designed for Chemical Scientists

Conversational agents are a contemporary topic in human-computer interaction, implemented in multi-disciplinary industrial applications to support consumers. Information seeking task can be competently incorporated into dialogue-based agents, so that searchers can easily fulfil their information needs. In this study, we proposed "Atreya", an interactive chatbot for chemistry scholars to examine and access the ChEMBL database. Atreya chatbot accesses the ChEMBL database and extracts drug-related information such as relevant drugs for a particular disease and associated targets. Telegram, a cloudbased messaging application is used to host Atreya. In this study, respondents were given a complex information seeking task to complete in order to conduct usability tests on Atreya Bot. The usability questionnaire based on different metrics was filled out by the searchers during information seeking activity on the Atreya Bot. The results were then calculated, and analysis was performed by 3 standard usability metrics – SUS (System Usability Scale), CUQ (Chatbot Usability Questionnaire) and UEQ (User Experience Questionnaire). These questionnaires help us measure attributes such as usability, efficiency, etc., provided by the bot. We found positive feedback results for all three metrics, which mainly emphasize that the Atreya bot is simple to use as the numerous functions in this system are well integrated and can support students in information seeking.
Optimal Actor-Critic Policy With Optimized Training Datasets

Actor-critic(AC) algorithms are known for their efficacy and high performance in solving reinforcement learning problems, but they also suffer from low sampling efficiency. An AC based policy optimization process is iterative and needs to access the agent-environment to evaluate and update the policy by rolling out the policy, collecting rewards and states (i.e. samples), and learning from them. It ultimately requires a huge number of samples to learn an optimal policy. To improve sampling efficiency, we propose a strategy to optimize the training dataset that contains significantly less samples collected from the AC process. The dataset optimization is made of a best episode only operation, a policy parameter-fitness model, and a genetic algorithm module. The optimal policy network trained by the optimized training dataset exhibits superior performance compared to many contemporary AC algorithms in controlling autonomous dynamical systems. Evaluation on standard benchmarks shows that the method improves sampling efficiency, ensures faster convergence to optima, and is more data-efficient than its counterparts.
Influence of Augmented Reality on Consumer Behaviour in Online Retailing

This study examines the influence of augmented reality on consumer behaviour in online retailing based on the stimulus-organism-response model. In this context, the affective and cognitive response, and the effect on purchasing behaviour are investigated in more detail. For this purpose, a quantitative study was carried out and analysed using structural equation modelling. The results show a positive influence of the perceived augmentation both on emotions during the use of AR and on the perceived amount of information. The attitude towards the use of AR has the greatest impact on purchasing behaviour, followed by the perceived amount of information. In addition, emotions indirectly effect the purchasing behaviour through its attitude as a mediator.
Euclidean and Rapid Jacobian-based Saliency Maps Attacks

Machine learning models is widely used in a variety of situations, such as autonomous driving, speech recognition and malware detection. Recent research shows that “adversarial examples” can fool Deep learning classifiers. Research on the security of machine learning models has also attracted extensive attention. The study of adversarial examples is of great significance to the improvement of model robustness. In particular, the Jacobian-based Saliency Map Attack(JSMA) is an attack based on L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> distance, only change two pixels per iteration. In this paper, we mainly introduce Euclidean Jacobian-based Saliency Maps Attack(EJSMA) and Rapid Jacobian-based Saliency Maps Attacks(RJSMA). These attacks are improved on the basis of the JSMA, faster and more efficient version of JSMA. EJSMA uses the Grad-CAM method to construct a new saliency map, RJSMA aims to reduce the size of the Jacobian matrix and increase the speed of attack. To demonstrate the efficacy of these attacks, our experiment is carried out around two Deep Neural Network(DNN). EJSMA and RJSMA applications on 1) LeNet-5, a gradient-based backpropagation algorithm is used for supervised training of the network, on the MNIST [2] database, and on 2) a more complex NNC on the CIFAR-10 [3]. For instance, on LeNet-5, EJSMA exceed 98.93% in success rate for a maximum authorized distortion of 14.5%,compared with JSMA, 11 pp have been added. RJSMA greatly improves upon the speed of TJSMA, RJSMA are 3 times faster than TJSMA and 3.42 times faster than JSMA on MNIST. The new attacks can also be used to defend models and Improve the robustness of the model.