Reviewerly/exHarmony huggingface dataset:
On Security of an Identity-Based Dynamic Data Auditing Protocol for Big Data Storage

In this article, we point out the security weakness of Shang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s identity-based dynamic data auditing protocol for big data storage. Specifically, we identify that their protocol is vulnerable to a secret key reveal attack, i.e., the service provider (SP) can reveal the secret key of the data owner (DO) from the stored data. Further, SP can also generate a proof to pass the challenge of TPA (third party auditor) even if all block and tag pairs have been deleted. We hope that by identifying these design flaws, similar weaknesses can be avoided in future designs.
An online optimization algorithm for the real-time quantum state tomography

Considering the presence of measurement noise in the continuous weak measurement process, the optimization problem of online quantum state tomography (QST) with corresponding constraints is formulated. Based on the online alternating direction multiplier method (OADM) and the continuous weak measurement (CWM), an online QST algorithm (QST-OADM) is designed and derived. Specifically, the online QST problem is divided into two subproblems about the quantum state and the measurement noise. The proposed algorithm adopts adaptive learning rate and reduces the computational complexity to $${\mathscr {O}}(d^3)$$ , which provides a more efficient mechanism for real-time quantum state tomography. Compared with most existing algorithms of online QST based on CWM which require time-consuming iterations in each estimation, the proposed QST-OADM can exactly solve two subproblems at each sampling. The merits of the proposed algorithm are demonstrated in the numerical experiments of online QST for 1-, 2-, 3-, and 4-qubit systems.
COVID-19 and Media datasets: Period- and location-specific textual data mining

The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.
Towards trustworthiness and transparency in social human-robot interaction

Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot's cognitive capabilities (i.e. the ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user's profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot's behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-agent systems and integrates different multi-agent programming dimensions.
Variational Autoencoder Based Unsupervised Domain Adaptation For Semantic Segmentation

Unsupervised domain adaptation, which transfers supervised knowledge from a labeled domain to an unlabeled domain, remains a tough problem in the field of computer vision, especially for semantic segmentation. Some methods inspired by adversarial learning and semi-supervised learning have been developed for unsupervised domain adaptation in semantic segmentation and achieved outstanding performances. In this paper, we propose a novel method for this task. Like adversarial learning-based methods using a discriminator to align the feature distributions from different domains, we employ a variational autoencoder to get to the same destination but in a non-adversarial manner. Since the two approaches are compatible, we also integrate an adversarial loss into our method. By further introducing pseudo labels, our method can achieve state-of-the-art performances on two benchmark adaptation scenarios, GTA5-to-CITYSCAPES and SYNTHIA-to-CITYSCAPES.
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise.

Greedy-GQ is an off-policy two timescale algorithm for optimal control in reinforcement learning. This paper develops the first finite-sample analysis for the Greedy-GQ algorithm with linear function approximation under Markovian noise. Our finite-sample analysis provides theoretical justification for choosing stepsizes for this two timescale algorithm for faster convergence in practice, and suggests a trade-off between the convergence rate and the quality of the obtained policy. Our paper extends the finite-sample analyses of two timescale reinforcement learning algorithms from policy evaluation to optimal control, which is of more practical interest. Specifically, in contrast to existing finite-sample analyses for two timescale methods, e.g., GTD, GTD2 and TDC, where their objective functions are convex, the objective function of the Greedy-GQ algorithm is non-convex. Moreover, the Greedy-GQ algorithm is also not a linear two-timescale stochastic approximation algorithm. Our techniques in this paper provide a general framework for finite-sample analysis of non-convex value-based reinforcement learning algorithms for optimal control.
An Evaluation Of Design Choices For Pedestrian Attribute Recognition In Video

Person attribute recognition in surveillance data is a challenging task. Attributes are often visible in very localized regions and recognition thus suffers from poor image quality, changing lighting conditions, viewing angles, and occlusions. Previous research has focused predominantly on recognition in single images. In this work, we investigate the applicability of several recent strategies to include temporal information into the recognition process. We identify the most promising building blocks and create a strong baseline model, which achieves state-of-the-art attribute recognition accuracy in videos and provides a good basis for future research. Finally, we show that the resulting attributes can serve as a basis for description-based person retrieval.
Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

In image forensics, to determine whether the image is impurely transformed, it extracts and examines the features included in the suspicious image. In general, the features extracted for the detection of forgery images are based on numerical values, so it is somewhat unreasonable to use in the CNN structure for image classification. In this paper, the extraction method of a feature vector is using a least-squares solution. Treat a suspicious image like a matrix and its solution to be coefficients as the feature vector. Get two solutions from two images of the original and its median filter residual (MFR). Subsequently, the two features were formed into a visualized pattern and then fed into CNN deep learning to classify the various transformed images. A new structure of the CNN net layer was also designed by hybrid with the inception module and the residual block to classify visualized feature vector patterns. The performance of the proposed image forensics detection (IFD) scheme was measured with the seven transformed types of image: average filtered (window size: 3 × 3), gaussian filtered (window size: 3 × 3), JPEG compressed (quality factor: {90, 70}), median filtered (window size: {3 × 3, 5 × 5}), and unaltered. The visualized patterns are fed into the image input layer of the designed CNN hybrid model. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, the area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed IFD scheme approached to `1' on the designed CNN hybrid model. Experimental results show high efficiency and performance to classify the various transformed images. Therefore, the grade evaluation of the proposed scheme is ``Excellent (A)”.
An LDA based model for semantic annotation of Web English educational resources

With the development and application of Web Semantic, users are no longer satisfied with basic metadata descriptions such as titles and link texts and string-matching search results. They hope that the resource description can provide the theme ideas, topics, and topics involved in the resource. Potential semantic information contains documents such as teaching methods and knowledge-concept relationships. This research starts from the demand for semantic annotation of resources in the process of resource library construction and sharing, and uses the LDA model to semantically model the document resources in the resource library to mine potential topics in the document. From “document-topic-keyword” scheme, the semantic description of teaching resources at different levels enriches the metadata attributes and content of resources, and adds more related topics and corresponding keyword descriptions related to disciplines, teaching content, teaching methods, etc., providing resource retrieval and sharing. The experimental results show that the LDA model can catalogues teaching resources from a macro perspective, and model LDA on teaching subject resources of the same teaching content. It can mine the inherent semantic topic features and detailed differences of resources. The final performance analysis verifies LDA’s advantages of the model in parallel computing in the big data environment.
Effective and Efficient: Toward Open-world Instance Re-identification

Instance Re-identification (ReID) system facilitates various applications that require painful and boring video watching. Its efficiency and effectiveness accelerate the process of video analysis. In this tutorial, we summarize ReID technologies and provide an overview. We'll introduce fundamental technologies, existing challenges, trends, etc. This tutorial would be useful for multimedia content analysis and system-level multimedia retrieval, especially for an effective and efficient open-world ReID system for the practical, large-scale, and open-set domain.
Semantic Node-RED for rapid development of interoperable industrial IoT applications

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated. We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.
Modelling human active search in optimizing black-box functions

Abstract Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper, we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian optimization. As surrogate models of the unknown function, both Gaussian processes and random forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper, we analyse experimentally how Bayesian optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian optimization provides a general model to represent individual patterns of active learning in humans.
Segmentation of Breast Using Ultrasound Image for Detection Breast Cancer

Breast cancer condition when cancer cells form in breast tissue. Cancer can also form in fat or connective tissue in the breast. Is a type of cancer that is deadly and difficult to detect at an early age but along with the development of technology in the health sector many methods are used for early detection of cancer, one of which is by using USG (Ultrasonography). Ultrasound is an imaging procedure using high-frequency sound wave technology to produce images of the inner body, such as organs and soft tissues. The ultrasound test results have very low quality, giving rise to many different perceptions in diagnosing the disease. From these problems sparked an idea to detect breast cancer automatically. The watershed transform algorithm is used in the segmentation process to produce the location of the cancer and can distinguish objects based on background. The result of segmentation using watershed is the second segmentation process using thresholding binaries to separate the image of the cancer as the object being observed. The final step is the process of calculating the area of cancer. The result of this paper is a comparison of the calculation of the area of cancer between Hospital data and trial results, from the results of these trials the system accuracy reached 88.65% of all data tested and an error of 11.35%. From these tests it can be concluded that the method used is the watershed transform algorithm capable of segmenting the image of the breast ultrasound image.
Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems

Traceability links between software artifacts serve as an invaluable resource for reasoning about software products and their development process. Most conventional methods for capturing traceability are based on pair-wise artifact relations such as trace matrices or navigable links between two directly related artifacts. However, this limited view of trace links ignores the propagating effect of artifact connections as well as the trace link properties at a project level. In this work, we propose the use of network structures to provide another perspective from which reasoning on a collective of trace events is possible. We explore various network analysis techniques in the issue tracking system of sixty-six open source projects. Our observation reveals two salient properties of the traceability network, i.e. scale free and triadic closure. These properties provide a strong indication of the applicability of network analysis tools and can be used to identify and examine important "hub" issues. As a stepping stone, these properties can further support project status analysis and link type prediction. As a proof-of-concept, we demonstrate the effectiveness of applying the triadic closure property to link type prediction.
Quantum Codes Obtained From Constacyclic Codes Over a Family of Finite Rings F<sub>p</sub>[u₁, u₂, …, u<sub>s</sub>]

In this article, we construct some MDS quantum error-correcting codes (QECCs) from classes of constacyclic codes over R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> + u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> + ··· + u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> , u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> = u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = 0, for odd prime p and i, j = 1, 2, <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋯</sub> , s, i ≠ j. Many QECCs with improved parameters than the existing ones in some of the earlier papers are provided. We present a set of idempotent generators of the ring Rs, and using that we define linear codes, determine all units, and study constacyclic codes over this ring. Among others, we study dual containing constacyclic codes over Rs and construct (non-binary) QECCs. An algorithm to construct QECCs from dual containing constacyclic codes over Rs is obtained that can provide many quantum codes.
An Evaluation of Gas Consumption Prediction on Ethereum based on Transaction History Summarization

The author uses data about transactions on Ethereum as sources for studying the relationship between the historic of transactions for a given address and the amount of gas consumed for a transaction. The author combines data about transactions, and blocks to predict the gas usage for a transaction. Specifically, how much gas will be consumed for the next transaction, given the initiator’s transaction history. The results demonstrate the value of considering the transaction history for gas usage predictions.
TrustVote: On Elections We Trust with Distributed Ledgers and Smart Contracts

Electronic voting (e-voting) plays a crucial role in democratic countries. Unfortunately, implementing a trusted e-voting system is always a challenge. Scholars have been trying to propose solutions to improve e-voting systems. However, existing e-voting approaches still need to adopt new technologies to maintain their integrity and modernity. Unlike other technologies that were used in e-voting, blockchain has the potential for perfecting e-voting requirements and fostering trust in e-voting systems. The first part of this paper presents a state-of-the-art review of blockchain-based e-voting implementations. The second part proposes a blockchain-based e-voting system (TrustVote). TrustVote was realised using public and permissioned blockchain. This work results show that the permissioned blockchain-based implementation of TrustVote outperforms the identical version of the same system that uses public blockchain. The results also show that while several challenges and requirements still need to be addressed, using permissioned blockchain for implementing an e-voting system can help to satisfy many of the requirements for e-voting and pave the road for a new generation of e-voting systems that people can trust.
Private Outsourced Bayesian Optimization

This paper presents the private-outsourced-Gaussian process-upper confidence bound (PO-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee. We consider the outsourced setting where the entity holding the dataset and the entity performing BO are represented by different parties, and the dataset cannot be released non-privately. For example, a hospital holds a dataset of sensitive medical records and outsources the BO task on this dataset to an industrial AI company. The key idea of our approach is to make the BO performance of our algorithm similar to that of non-private GP-UCB run using the original dataset, which is achieved by using a random projection-based transformation that preserves both privacy and the pairwise distances between inputs. Our main theoretical contribution is to show that a regret bound similar to that of the standard GP-UCB algorithm can be established for our PO-GP-UCB algorithm. We empirically evaluate the performance of our PO-GP-UCB algorithm with synthetic and real-world datasets.
Discrete Genetic Algorithm for Solving Task Allocation of Multi-robot Systems

In this paper, a Discrete Genetic Algorithm (DGA) is proposed for solving the task allocation problem in a multi-robot scenario. In the proposed DGA, a discrete population generation is proposed to adjust the genetic algorithm for solving the task allocation problem of a multi-robot system which aims to maintain the balance of exploration and exploitation. In the proposed scenario, the defined problem consists of assigning the robots to the known targets in two-dimensional search space. The scalability of the proposed DGA algorithm is tested in terms of the number of robots. The comparison confirms the superiority of the proposed method compared to the existing methods.