Reviewerly/exHarmony huggingface dataset:
ID-Service: A Blockchain-Based Platform to Support Digital-Identity-Aware Service Accountability

Accountability refers to the need of individuals or organizations to account for their activities, accept responsibility, and disclose results in a transparent manner. Nowadays, the pervasivity of digital systems is making increasingly critical security, reliability, and trustworthiness of such services. When a service is delivered by involving different (eventually conflicting) parties, accountability could be achieved by including in digital transactions a trusted third party (TTP). Blockchain decentralizes trust, thus avoiding to rely on a single TTP. However, to deal with accountability in concrete solutions, the issue of securely integrating digital identity and Blockchain should be solved. The paper describes the results of a three-year research project merging academic and industrial expertise, to design and implement a Blockchain-based platform for service accountability integrating eIDAS-compliant Public Digital Identity. The platform has been used in several real-life contexts made available by industrial project partners, which demonstrated the effectiveness and novelty of the solution.
Blockchain-based Secure and Reliable Manufacturing System

The manufacturing paradigm with the incorporation of modern emerging technologies such as cloud computing, IoT, cyber-physical systems (CPS), industrial automation as well as the service-oriented manufacturing process has paved the way to build a world where the smart industry or Industry 4.0 is a reality. However, these technological revolutions aren't ready for existing manufacturing platforms that suffer from security and reliability issues mainly because of its dependency on the trust of the central controller. Therefore the main purpose of this study is to orchestrate the development of a distributed decentralized peer to peer connected system architecture that will improve the reliability of the manufacturing process by providing a secure and immutable control information exchange platform. In that regard, we adopt blockchain, the core technology behind the development of cryptocurrencies because of its salient properties which include security, immutability, auditability, anonymity, and decentralization. In this paper, we present a blockchain-based system architecture with the help of clustering and authentication mechanisms to facilitate the development of a decentralized and highly secured system for advanced manufacturing. To evaluate the performance of the proposed system, we conducted a numerical analysis that shows that our proposed methodologies can bring more advantages to advanced manufacturing beyond security and reliability.
OpenVINO Deep Learning Workbench: A Platform for Model Optimization, Analysis and Deployment

Dramatic advances in the field of deep learning have led to superhuman results of neural models on various specialized tasks. There is an urgent need in platforms that provide performance tuning, analysis, and deployment capabilities of these models. Several pioneering platforms that have been emerging for the last five years are thoroughly analyzed and compared across numerous criteria. The OpenVINO Deep Learning Workbench (DL Workbench) is a tool designed to improve the usability and workflows for neural network performance, optimization and deployment. DL Workbench tends to play one of leading roles in terms of feature completeness across other existing platforms in the field. It provides unique capabilities of model optimization and deployment to the target hardware. By applying developer experience (DX) insights to the design of innovative deep learning solutions such as the DL Workbench, a high degree of usability can be achieved to support the complex tasks of developing highly optimized deep learning solutions for target hardware.
Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering

This article is concerned with Gaussian filtering in nonlinear continuous-discrete state-space models. We propose a novel Taylor moment expansion (TME) Gaussian filter, which approximates the moments of the stochastic differential equation with a temporal Taylor expansion. Differently from classical linearization or Itô-Taylor approaches, the Taylor expansion is formed for the moment functions directly and in time variable, not by using a Taylor expansion on the nonlinear functions in the model. We analyze the theoretical properties, including the positive definiteness of the covariance estimate and stability of the TME filter. By numerical experiments, we demonstrate that the proposed TME Gaussian filter significantly outperforms the state-of-the-art methods in terms of estimation accuracy and numerical stability.
A Distributed Framework For EA-Based NAS

Evolutionary Algorithms (EA) are widely applied in Neural Architecture Search (NAS) and have achieved appealing results. Different EA-based NAS algorithms may utilize different encoding schemes for network representation, while they have the same workflow. Specifically, the first step is the initialization of the population with different encoding schemes, and the second step is the evaluation of the individuals by the fitness function. Then, the EA-based NAS algorithm executes evolution operations, e.g., selection, mutation, and crossover, to eliminate weak individuals and generate more competitive ones. Lastly, evolution continues until the max generation and the best neural architectures will be chosen. Because each individual needs complete training and validation on the target dataset, the EA-based NAS always consumes significant computation and time inevitably, which results in the bottleneck of this approach. To ameliorate this issue, this article proposes a distributed framework to boost the computation of the EA-based NAS algorithm. This framework is a server/worker model where the server distributes individuals requested by the computing nodes and collects the validated individuals and hosts the evolution operations. Meanwhile, the most time-consuming phase (i.e., individual evaluation) of the EA-based NAS is allocated to the computing nodes, which send requests asynchronously to the server and evaluate the fitness values of the individuals. Additionally, a new packet structure of the message delivered in the cluster is designed to encapsulate various network representations and support different EA-based NAS algorithms. We design an EA-based NAS algorithm as a case to investigate the efficiency of the proposed framework. Extensive experiments are performed on an illustrative cluster with different scales, and the results reveal that the framework can achieve a nearly linear reduction of the search time with the increase of the computational nodes. Furthermore, the length of the exchanged messages among the cluster is tiny, which benefits the framework expansion.
Generating Efficient Behaviour with Predictive Visibility Risk for Scenarios with Occlusions

Safety is one of the most significant challenges for autonomous driving. However, autonomous vehicles need also to be as efficient as possible while ensuring safety. In this paper we present an approach that helps the vehicle to drive efficiently in scenarios with occlusions, while being safe and comfortable. We quantity the visibility risk (VR) representing the collision risk with possible hidden obstacles in occlusions and anticipate the predictive VR by forecasting the scene in the short-term. The predictive VR can be integrated into the cost functional of arbitrary cost-based planning approach. By doing this, the vehicle is motivated to maintain a better view on the Region of Interest (ROI) on the map, which further opens up the scope of available behaviours and thus pursuing more efficient behaviour becomes possible. The proposed method is evaluated with a proof-of-concept planner decomposed into lateral (e.g. paths) and longitudinal directions (e.g. accelerations) in one overtaking scenario on a curved two-way street with dynamic occlusions and one intersection scenario with static occlusion, which shows promising results in vehicle driving efficiency.
A detailed review of Cloud Security: Issues, Threats &amp; Attacks

Cloud computing is considered as an on-demand delivery of services in which applications and infrastructure are allocated to users as metered services over networks. Cloud computing services are much cheaper as the user does not have to setup any computing hardware support. It is an emerging technology that deliver computing services such as online business applications and data storage over the Internet. Implementing cloud enables a distributed working environment, where it reduces expenditure of the organization, provides data, information security and so on. As many organizations are adopting cloud computing, attackers exploit the cloud to obtain unauthorized control on the valuable data stored in it. Evolution of traditional computing to cloud has led to many security challenges for both customers and service providers. Different types of services are provided by trusted cloud providers over the Internet by using many technologies, which arises different security threats. This paper discusses about the cloud security issues, threats and related attacks.
SLOAN: Scale-Adaptive Orientation Attention Network for Scene Text Recognition

Scene text recognition, the final step of the scene text reading system, has made impressive progress based on deep neural networks. However, existing recognition methods devote to dealing with the geometrically regular or irregular scene text. They are limited to the semantically arbitrary-orientation scene text. Meanwhile, previous scene text recognizers usually learn the single-scale feature representations for various-scale characters, which cannot model effective contexts for different characters. In this paper, we propose a novel scale-adaptive orientation attention network for arbitrary-orientation scene text recognition, which consists of a dynamic log-polar transformer and a sequence recognition network. Specifically, the dynamic log-polar transformer learns the log-polar origin to adaptively convert the arbitrary rotations and scales of scene texts into the shifts in the log-polar space, which is helpful to generate the rotation-aware and scale-aware visual representation. Next, the sequence recognition network is an encoder-decoder model, which incorporates a novel character-level receptive field attention module to encode more valid contexts for various-scale characters. The whole architecture can be trained in an end-to-end manner, only requiring the word image and its corresponding ground-truth text. Extensive experiments on several public datasets have demonstrated the effectiveness and superiority of our proposed method.
Security Evaluation of Smart Contract-Based On-chain Ethereum Wallets

Ethereum is a leading blockchain platform that supports decentralised applications (Dapps) using smart contract programs. It executes cryptocurrency transactions between user accounts or smart contract accounts. Wallets are utilised to integrate with Dapps to manage and hold users' transactions and private keys securely and effectively. Ethereum wallets are available in different forms, and we especially examine on-chain smart contract wallets to measure their safeness property. We have conducted an exploratory study on 86 distinct bytecode versions of Ethereum smart contract wallets and analysed them using four popular security scanning tools. We have identified that, on average, 10.2% of on-chain wallets on the Ethereum platform are vulnerable to different problems. We propose a novel analysis framework to classify the security problems in smart contract wallets using the experimental data. Most of the vulnerabilities detected from smart contract wallets are related to security issues in programming code and interaction with external sources. Our experimental results and analysis data are available at https://github.com/ppraithe/on-chain-wallet-contracts .
Analysis of Deep Feature Extraction for Colorectal Cancer Detection

Colorectal cancer is the most common cancer worldwide in the third. Detection of colon cancer is an essential task for the histopathologist as they have to analyze the morphology of the images at different magnifications. In this study, we classified benign and adenocarcinoma using 10000 images of benign colon tissue. We proposed a feature extraction method by the deep convolutional neural network. First, we learn the features of data from ResNet-50 and DenseNet-121. Then, we conduct colon cancer classification by popular classifiers such as SVM, Random Forest, K-Nearest Neighbor, and XGBoost. We evaluated our models on two kinds of testing data (25% and 15% of the whole dataset). In this research, the data was conducted on the Kaggle colon tissue dataset. The experimental results indicate that the extraction of features in DenseNet-121 based architecture leads to higher accuracy, sensitivity, and specificity of ResNet-50 architecture for all classifiers. DenseNet-121 gets about 98.53% and 98.63% with KNN classifier for accuracy and sensitivity, respectively.
Performing multi-target regression via gene expression programming-based ensemble models

Multi-Target Regression problem comprises the prediction of multiple continuous variables given a common set of input features, unlike traditional regression tasks, where just one output target is available. There are two major challenges when addressing this problem, namely the exploration of the inter-target dependencies and the modeling of complex input–output relationships. This work proposes a Symbolic Regression method following the basis of Gene Expression Programming paradigm to solve the multi-target regression problem, and called GEPMTR. It evolves a population of individuals, where each one represents a complete solution to the problem by using a multi-genic chromosome, and encodes a mathematical function for each target variable involving the input attributes. The proposed model can estimate the inter-target dependencies by applying some genetic operators. Furthermore, three ensemble-based methods are developed to better exploit the inter-target and input–output relationships. The effectiveness of the proposals is analyzed through an extensive experimental study on 18 datasets. The codification schema and the process followed to ensure a diverse population in GEPMTR lead to obtain an effective proposal to solve the MTR problem. Furthermore, the EGEPMTR-B ensemble method obtained the best performance across all proposed models, being the best in 8 out of 11 cases, demonstrating that more sophisticated mechanisms were not needed for ensuring that GEPMTR method would properly model the existing inter-target dependencies. Finally, the experimental results also showed that the proposed approach attains competitive results compared to state-of-the-art, showing the possibilities that can bring this research line for effectively solving the MTR problem.
Architecture Correlation Analysis (ACA): Identifying the Source of Side-channel Leakage at Gate-level

Power-based side-channel leakage is a known problem in the design of security-centric electronic systems. As the complexity of modern systems rapidly increases through the use of System-on-Chip (SoC) integration, it becomes difficult to determine the precise source of the side-channel leakage. Designers of secure SoC must therefore proactively apply expensive countermeasures to protect entire subsystems such as encryption modules, and this increases the design cost of the chip. We propose a methodology to determine, at design time, the source of side-channel leakage with much greater accuracy, at the granularity of a single cell. Our methodology, Architecture Correlation Analysis, uses a leakage model, well known from differential side-channel analysis techniques, to rank the cells within a netlist according to their contribution to the sidechannel leakage. With this analysis result, the designer can selectively apply countermeasures where they are most effective. We demonstrate Architecture Correlation Analysis (ACA) on an AES coprocessor in an SoC design, and we determine the sources of side-channel leakage at the gate-level within the AES module as well as within the overall SoC. We validate ACA by demonstrating its use in an optimized hiding countermeasure.
Toward Autonomous Adaptive Intelligence: Building Upon Neural Models of How Brains Make Minds

This article surveys the development of mathematical laws, circuits, and architectures that model how our brains make our minds, and shows how these contributions provide a blueprint for developing general-purpose autonomous adaptive algorithms and robots for intelligent applications in engineering and technology. The mathematical laws for short-term memory, medium-term memory, and long-term memory that provide the contemporary foundation for all subsequent biological neural network models were published in 1968, followed by a steady stream of model discovery and development to the present time. The Cohen-Grossberg model and theorem that was published in this journal in 1983 was one step in this series of developments. It proved global limit theorems for a general class of neural networks using a Lyapunov function as one tool. These theorems provide a guarantee that learning in these networks generates stable memories. This article surveys additional mathematical foundations for neural network design and applications and describes a modeling method for the incremental discovery of models with increasingly powerful and general functional capabilities. This method is illustrated by the adaptive resonance theory, or ART, architectures which explain how our brains autonomously learn to attend, recognize, and predict objects and events in a changing world, along the way explaining how our brains become conscious, as well as the computational constraints on learning that forced evolution to discover conscious states of mind. Multiple types of resonances support different kinds of conscious awareness and enable the explanation and prediction of large psychological and neurobiological databases about perception, cognition, emotion, and action. Because ART can be derived from a thought experiment about a universal problem of error correction in a changing world, its expanding applications to the development of autonomously intelligent systems should transform future technologies.
Reliable advanced encryption standard hardware implementation: 32- bit and 64-bit data-paths

Cryptographic primitives are extensively used in today's applications to provide the desired security. Malicious or accidental faults that occur in the hardware implementations of cryptographic primitives, specifically in this paper the Advanced Encryption Standard (AES), can result in an erroneous output of encryption/decryption process and reduce the reliability of the cryptographic hardware. The use of a suitable fault-tolerant scheme for AES, to recover it from failures or attacks and bring it back to an operational state, is crucial for reliability, and consequently for security purposes. In this paper, two novel online fault-tolerant schemes are proposed for AES. In the proposed fault-tolerant architecture, the round path is modified and divided it into two pipeline stages. The proposed fault-tolerant schemes are based on a combination of hardware and time redundancies, where a new hardware redundancy is proposed for the AES round function and a time redundancy for the hardware of the AES key expansion unit. The presented fault-tolerant schemes are valid for all versions of AES and are independent of its S-box implementation manner. Both ASIC and FPGA implementations of the original and the proposed fault-tolerant AES along with Full TMR (Triple Modular Redundancy) and Full TTR (Triple Time Redundancy) structures are reported as traditional fault-tolerant schemes. It is shown that the first proposed fault-tolerant architecture, named TMRrp&TTRke32, outperforms these approaches and the previous report in the literature in terms of area overhead and therefore power consumption. Also, the other approach, named TMRrp&TTRke64, is better than the other approaches in achieving a trade-off between area overhead and throughput overhead.
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic

Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal policy always overfits to the training environment and may not be extended to situations never encountered during training. For practical applications, the randomness of environment usually leads to some devastating events, which should be the focus of safety-critical systems such as autonomous driving. In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm. Minimax formulation aims to seek optimal policy considering the most severe variations from environment, in which the protagonist policy maximizes action-value function while the adversary policy tries to minimize it. Distributional framework aims to learn a state-action return distribution, from which we can model the risk of different returns explicitly, thereby formulating a risk-averse protagonist policy and a risk-seeking adversarial policy. We implement our method on the decision-making tasks of autonomous vehicles at intersections and test the trained policy in distinct environments. Results demonstrate that our method can greatly improve the generalization ability of the protagonist agent to different environmental variations.
Modeling the relationship between students' prior knowledge, causal reasoning processes, and quality of causal maps

Despite the 30 years of research on the effects of using computer-based mapping tools to construct concept maps, the research has provided little insight into the cognitive processes and strategies students use to create more accurate maps and the types of behaviors that can be mined in computer-based mapping tools to reveal and support processes that improve student performance. This study used video analysis to identify, operationally define, and code processes of reasoning students used while using a computer-based tool to construct causal maps. The process data was then used to conduct a path analysis to determine which and how specific processes impact the quality of students' causal maps and to determine how students’ prior knowledge on the given problems influence which processes students use. The findings reveal that students exhibiting the tendency to use more backward than forward processing and use more breadth than depth-first processing produced higher quality causal maps, and that the influence of prior content knowledge on what processes students use are not conclusive. The findings and the methods developed in this study present new ideas on ways to integrate learning analytics into computer-based mapping tools to model and manage the cognitive processes students use to construct better cognitive maps.