Reviewerly/exHarmony huggingface dataset:
Symmetric Image Contents Analysis and Retrieval Using Decimation, Pattern Analysis, Orientation, and Features Fusion

Image retrieval procedures locate interest points and consider the features as the visual property of an image in computer vision. These primitive features define visual attributes as local or global features for content based image retrieval. The visual attributes of an image, include spatial information, shape, texture, object and color, describe the image category. The present research contributes feature detector by performing non-max suppression after detecting edges and corners based on corner score and pixel derivation-based shapes on intensity-based interest points. Thereafter, interest point description applied on interest point features set by applying symmetric sampling to cascade matching produced by validating dense distributed receptive fields after estimating perifoveal receptive fields. Spatial color-based features vector are fused with retinal and color-based feature vector extracted after applying L2 normalization on spatially arranged color image. Dimensions are reduced by applying PCA on massive feature vectors produced after symmetric sampling and transmitted to bag-of-word in fused form for indexing and retrieval of images. Extensive experiments are performed on well-known benchmarks corel-1000, core-10000, caltech-101, image net, alot, coil, ftvl, 102-flowers and 17-flowers. In order to measure the competitiveness, we designed a comparison of proposed method with seven descriptors and detectors RGBLBP, LBP, surf, sift, DoG, HoG and MSER. The proposed method reports remarkable AP, AR, ARP, ARR, P&R, mAP and mAR rates in many categories of image datasets.
Local quantum uncertainty in a two-qubit Heisenberg spin chain with intrinsic decoherence

Abstract Quantum correlation plays an important role in quantum information processing, for which various quantifiers have been proposed so far. In this paper, we address the dynamics of local quantum uncertainty (LQU) as a reliable quantifier of quantum correlation in a two-qubit Heisenberg spin chain in the presence of nonuniform external magnetic field and Dzyaloshinski-Moriya interaction with intrinsic decoherence. The influences of the initial states, external magnetic field strength, Dzyaloshinski-Moriya interaction strength and intrinsic decoherence rate on the dynamics of LQU have been in detail investigated. Our analytical results show that the dynamics of LQU is strongly depended on the form of initial states. For an initial correlated state, the dynamical behaviors of LQU exhibit either monotonic decay or damped oscillations with respect to time. While for an initial separable state, quantum correlation quantified by LQU can be created due to the Dzyaloshinski-Moriya interaction and Heisenberg anisotropic interaction. Besides, the relationship between LQU and l -norm coherence or concurrence is also demonstrated in the present model.
Internet of Things (IOT): Implications in Society

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A Deep‐Learning Approach to the Dynamics of Landau–Zenner Transitions

Abstract Traditional approaches to the dynamics of the open quantum systems with high precision are often resource intensive. How to improve computation accuracy and efficiency for target systems is an extremely difficult challenge. In this work, combining unsupervised and supervised learning algorithms, a deep‐learning approach is introduced to simulate and predict Landau–Zenner dynamics. Data obtained from multiple Davydov Ansatz with a low multiplicity of four are used for training, while the data from the trial state with a high multiplicity of ten are adopted as target data to assess the accuracy of prediction. After proper training, our method can successfully predict and simulate Landau–Zenner dynamics using only random noise and two adjustable model parameters. Compared to the high‐precision dynamics data from multiple Davydov Ansatz with a multiplicity of ten, the error rate falls below 0.6%.
Haptic feedback is more important than VR experience for the user experience assessment of in-car human machine interfaces

The user experience (UX) of a product is important for a customer’s decision, especially in the automotive sector. In recent years, a frontloading of UX assessments from physical prototypes to virtual prototypes has occurred. Nevertheless, there are clear indications that established UX assessments methods cannot simply be transferred 1-to-1 from real to virtual evaluation setups as results get biased. For the UX assessment of an in-car human machine interface (HMI) the haptic properties of the knobs and buttons are just as important as their visual features. Therefore, it is mandatory to also simulate the haptic behavior of the in-car HMI in virtual reality (VR) assessment scenarios. However, accurately simulating this haptic behavior adds considerable costs and effort, diminishing the beneficial effects of VR UX assessments. Due to confidentiality reasons the volunteering participants of VR UX studies in the automotive sectors are typically employees with a varying degrees of VR experience. It is unknown if this gap in VR experience biases the results of UX assessments in VR. This paper presents a UX assessment study of an in-car HMI in VR in comparison to reality. From a practitioners viewpoint two research question are investigated: (1) How much is the UX assessment impaired by missing haptic simulation? (2) Does VR experience influence UX assessment ratings in VR? The results show that missing haptic feedback severely diminishes UX ratings, leaving no doubt that meaningful UX assessments in VR can only be performed when haptic is included. This means that more effort has to be put into earlier VR assessments severely reducing its benefits compared to UX assessments with physical prototypes. However, the VR experience of the participants only has a slight effect on the UX assessment results.
Motivating Antecedents and Consequences of Blockchain Technology in the Insurance Industry

Quantum leaps in technology affect all phases of business models over numerous industries and are the fundamental characteristic of any technological revolution. Emerging technologies provide new avenues for industries to increase their competitive advantage and enhance the economic progression. The aim of this study is to advance a theoretical model on motivating antecedents and consequences of blockchain technology in insurance industry based on the evidence from past literature. This chapter is approached from the theoretical viewpoint, so it meticulously assesses and examines the prior literature to debate on the role of blockchain technology in the insurance industry. The outcome of the present study is dispassionate and corresponding to the conclusions of the prior literature. Blockchain helps in achieving innovation, augmenting transparency, refining data standards, and advancing an integrated approach for quality service. Thus, the espousal of blockchain in insurance is developing very quickly, and it has become the default platform for the complete insurance industry.
Unsupervised skill transfer learning for autonomous robots using distributed Growing Self Organizing Maps

A persistent challenge in the cognitive development of autonomous robotics is the unsupervised and unstructured nature of skill transfer learning where the Self Organizing Map (SOM) has been used as the enabling technology. The Growing Self-Organizing Map (GSOM) algorithm is an unsupervised, structure-adapting machine learning algorithm conventionally used for data exploration, clustering, visualization, outlier detection and dimensionality reduction. In this paper, we present the design and development of a new distributed algorithm based on the GSOM for unsupervised skill transfer learning in autonomous robotics settings which overcomes the key limitations of the SOM in real-life scenarios. We posit this new algorithm will be directly applicable to skill transfer learning scenarios that require unsupervised, incremental and on-going self-learning of multi-tasks and knowledge transfer. The distributed and scalable properties of the proposed algorithm handle large volumes of data required for unsupervised skill transfer learning, based on data parallelization. It generates multiple maps representing diverse skill knowledge, which are then projected together to a single embedding. The new algorithm is positioned within an autonomous developmental robotics framework for knowledge acquisition and skill transfer learning. This framework was further adapted to three contemporary distributed computing platforms, Hadoop, Spark and Hama. Empirical evaluation of these three adaptations using several benchmark and real-life datasets demonstrates its practical value and computational efficiency for unsupervised skill transfer learning in autonomous robots.
Prevention of XSS attack using Cryptography &amp;amp; API integration with Web Security

The vulnerability of web-based applications has increased manifold primarily due to the ever-increasing time spent on the web. Cross site scripting (XSS) attack are one of the most prominent attacks that are executed when the attacker get success over API key authentication and successfully execute their malicious script code at server end. Of the two types of XSS that are popular in the hacking world i.e. server side XSS and client side XSS, server-side attacks are more frequent and vulnerable. Various methodologies have been developed in the recent past to handle XSS attacks API authentication based techniques, handling access at server end etc. However, these methodologies suffer from one or more limitation of handling the attack from one perspective. In this paper, we demonstrate a multi-layer prevention technique wherein we defend the attacker hit at API key authentication level using an encrypted that prevents the attacker easy access to API directly. In the next layer we convert the script code execution request into plain text which is coming from any untrusted party, so that plain text will not execute over browser. Thus, our proposed framework provides an effective solution to protect our environment from XSS attacks.
High dynamic defocus response method for binary defocusing fringe projection profilometry

The measurement accuracy of high-speed binary defocusing fringe projection profilometry depends on the fringe pitch and defocus degree. During the measurement process, the degree of defocus changes with the measurement depth of the scenes. This makes it difficult to obtain a suitable defocus degree and achieve high-precision measurement owing to dynamic changes in the measurement object or environment. To address this problem, we propose a highly dynamic defocus response method to adaptively adjust fringe pitches for binary defocusing fringe projection profilometry. As the defocus degree changes significantly, the proposed method can respond quickly and adjust the fringe pitches adaptively to the scenes. Therefore, a high-precision dynamic measurement can be achieved for the current measuring scene. In this study, considering the effect of random error and nonlinear error, we established a complete phase-error model and used it as an optimization function. Based on this function, we obtained the optimal fringe pitch expression with the defocus degree and harmonic response parameters as variables. With the proposed method, we can obtain the defocus degree and harmonic response parameters during the measurement process and calculate the optimal fringe pitches for the current scenes. Thus, the proposed method can dynamically adapt to the measuring depth change and achieve an accurate measurement without modifying any hardware parameters.
Image Caption Generation Using Multi-Level Semantic Context Information

Object detection, visual relationship detection, and image captioning, which are the three main visual tasks in scene understanding, are highly correlated and correspond to different semantic levels of scene image. However, the existing captioning methods convert the extracted image features into description text, and the obtained results are not satisfactory. In this work, we propose a Multi-level Semantic Context Information (MSCI) network with an overall symmetrical structure to leverage the mutual connections across the three different semantic layers and extract the context information between them, to solve jointly the three vision tasks for achieving the accurate and comprehensive description of the scene image. The model uses a feature refining structure to mutual connections and iteratively updates the different semantic features of the image. Then a context information extraction network is used to extract the context information between the three different semantic layers, and an attention mechanism is introduced to improve the accuracy of image captioning while using the context information between the different semantic layers to improve the accuracy of object detection and relationship detection. Experiments on the VRD and COCO datasets demonstrate that our proposed model can leverage the context information between semantic layers to improve the accuracy of those visual tasks generation.
An Academic Conference in Virtual Reality?-Evaluation of a SocialVR Conference

One of the first academic conferences in head-mounted display (HMD)-based Social Virtual Reality (SociaIVR) was realized. The conference aims to support knowledge acquisition and informal exchange regarding the technology SocialVR itself and the use of Virtual and Augmented Reality technologies (VR/AR) in vocational education. The paper presents results of an explorative study of 75 conference participants. Results indicate that SocialVR is generally suited to host an academic conference. In some areas, it seems inferior or equivalent to other digital formats or face-to-face events. In other areas, it offers added value. Further research is needed to take advantage of these positive effects.
Is There an AI Cold War?

Regulation is a means societies use to create the stability, public goods, and infrastructure they need to thrive securely. This policy brief is intended to both document and to address claims of a new AI cold war: a binary competition between the United States and China that is too important for other powers to either ignore or truly participate in directly, beyond taking sides. We argue that while some of the claims of this narrative are based at least in part on genuine security concerns and important unknowns, evidence for its extreme binary nature is lacking. This absence of factual evidence is concerning, because related geopolitical tensions may be used to interfere with regulation of AI and agencies associated with its development. Here we first document and then analyze the extremely bipolar picture prominent policymakers and political commentators have been recently painting of the AI technological situation, portraying China and the United States as the only two global powers. We then examine the plausibility of these claims using two measures: internationally registered AI patents and the market capitalization of the companies that hold them. These two measures, while each somewhat arbitrary and imperfect, are often deployed in the context of the binary narrative and can therefore be seen as conservative choices in that they should favor exactly the “champions” of that narrative. In fact, these measures do not produce bipolar results: Chinese capacity has been exaggerated and that of other global regions deprecated. These findings call into question the motivation behind the documented claims, though they also further illuminate the uncertainty concerning digital technology security. We recommend that all parties engage in contributing to a safe, secure, and transparent regulatory landscape.
Robot navigation based on improved A* algorithm in dynamic environment

Purpose The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time. Design/methodology/approach To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms. Findings The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment. Originality/value This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.
The impact of working from home on the success of Scrum projects: A multi-method study

With the COVID-19 pandemic, Scrum teams had to switch abruptly from a traditional working setting into an enforced working from home one. This abrupt switch had an impact on software projects. Thus, it is necessary to understand how potential future disruptive events will impact Agile software teams’ ability to deliver successful projects while working from home. To investigate this problem, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares-Structural Equation Modeling (PLS-SEM) surveying 138 software engineers who worked from home within Scrum projects. We concluded that all the latent variables identified in our model are reliable, and all the hypotheses are significant. This paper emphasizes the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from home and the use of Scrum both contribute to project success, with Scrum acting as a mediator.
A Methodological Approach to Evaluate Security Requirements Engineering Methodologies: Application to the IREHDO2 Project Context

An effective network security requirement engineering is needed to help organizations in capturing cost-effective security solutions that protect networks against malicious attacks while meeting the business requirements. The diversity of currently available security requirement engineering methodologies leads security requirements engineers to an open question: How to choose one? We present a global evaluation methodology that we applied during the IREHDO2 project to find a requirement engineering method that could improve network security. Our evaluation methodology includes a process to determine pertinent evaluation criteria and a process to evaluate the requirement engineering methodologies. Our main contribution is to involve stakeholders (i.e., security requirements engineers) in the evaluation process by following a requirement engineering approach. We describe our experiments conducted during the project with security experts and the feedback we obtained. Although we applied it to evaluate three requirements engineering methods (KAOS, STS and SEPP) in the context of network security, our evaluation methodology can be instantiated in other contexts and other methods.
Easy Pose-Error Calibration for Articulated Serial Robot Based on Three-Closed-Loop Transformations

Most of the existing robot calibration methods have high technical requirements for the installation of end tools and detection sensors as well as other calibration equipments involved, which makes the calibration cumbersome and expensive. This article proposes a novel and easy pose-error calibration method for articulated serial robots based on three-closed-loop transformations. First, the principle of the three-closed-loop transformations of the coordinate systems is introduced, where the loops can be mutually verified to improve the accuracy of the calibration. Then, a hand–eye calibration method that can directly obtain the coordinate conversion between the camera and the tool without need to consider the installation or transformation error of the tool at the robot end is proposed. Furthermore, the entire calibration process is economical without using any expensive special instruments, and the use conditions of the calibration equipments are loose. Finally, experimental results show that the pose error of the robot can be precisely detected and verified by different closed loops.
Digital Museum Transformation Strategy Against the Covid-19 Pandemic Crisis

During the last decade, the art industry and especially the museums, the art galleries and the auctions has stagnated due to the global financial crisis (2007–2017) and latest lockdowns brought by the Covid-19 pandemic (2020-). Many art institutions introduced digital technologies into their operations and transitioned to the online art market. Visits to art exhibitions include interaction with other visitors of similar interests. Hence, digital art venues must create the same environment vibes and offer similar opportunities as the physical ones. The paper indicates the application of the avatar technology as an interaction method in digital museum spaces. It also provides the functional requirements, a navigation process, and the implementation scenarios for the adaptation of such technologies. The application of this digital strategy can transform the art market, contribute to the museum’s sustainability, and offer the public different ways of learning and entertainment.
Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

Class imbalance and concept drift are two primary principles that exist concurrently in data stream classification. Although the two issues have drawn enough attention separately, the joint treatment largely remains unexplored. Moreover, the class imbalance issue is further complicated if data streams with concept drift. A novel Cost-Sensitive based Data Stream (CSDS) classification is introduced to overcome the two issues simultaneously. The CSDS considers cost information during the procedures of data preprocessing and classification. During the data preprocessing, a cost-sensitive learning strategy is introduced into the ReliefF algorithm for alleviating the class imbalance at the data level. In the classification process, a cost-sensitive weighting schema is devised to enhance the overall performance of the ensemble. Besides, a change detection mechanism is embedded in our algorithm, which guarantees that an ensemble can capture and react to drift promptly. Experimental results validate that our method can obtain better classification results under different imbalanced concept drifting data stream scenarios.