Reviewerly/exHarmony huggingface dataset:
Lightweight Digital Certificate Management and Efficacious Symmetric Cryptographic Mechanism over Industrial Internet of Things

The certificate authority, a trusted entity, issues digital certificates which contain identity credentials to help Industrial Internet of Things (IIoT) devices to represent their authenticity in a secure means. The crucial challenge of a digital certificate is to how design a secure certification authority management system that can counteract cyberattacks on the IIoT network. Moreover, current IIoT systems are not capable of implementing complex mathematical operations due to their constrained power capacity and processing capability. This paper proposes an effective, secure symmetric cryptographic mechanism (ESSC) based on the certificate authority management and Elliptic Curve Diffie Hellman (ECDH) to share a digital certificate among IIoT devices. The proposed certificate authority is used to securely exchange the shared secret key and to resolve the problem of spoofing attacks that may be used to impersonate the identity of the certificate authority. Also, ESSC uses the shared secret key to encrypt the sensitive data during transmission through the insecure communication channel. This research studies the adversary model for ESSC on IIoT and analyzes the cybersecurity of ESSC in the random oracle model. The findings that result from the experiments show that ESSC outperforms the baseline in terms of communication, computation, and storage costs. ESSC thus provides an adequate lightweight digital certificate management and cryptographic scheme which can help in the detection and prevention of several cyberattacks that can harm IIoT networks.
Research on Visual Art Design Method Based on Virtual Reality

In today's society, computer technology has been deeply rooted in the hearts of people. Computers are wonderful tools for creative thinking. It is an extension of our visual function and the function of the visual cortex of the brain. Through this extension, we can see more scenes that we could not see before. As a computer simulation system that creates and feels virtual worlds, 3D digital virtual reality technology uses a computer as a media simulation or a real or imaginary scene. It is a system simulation of interactive 3D dynamic vision and entity behavior based on diversified information fusion. As the creator of visual arts, we must try to observe the world at a deeper level and establish a model that resonates with the viewer. At every level, our technology will convey the way we view the world more deeply. We will be more amazed at the richness of the real world. This chapter explores a visual art design method based on virtual reality.
Blockchain-based approach to achieve credible traceability of agricultural product transactions

Abstract The traditional traceability system for agricultural product transactions is susceptible to information alteration and damage, which may lead to issues regarding product quality and food safety. And blockchain is a technology that boasts tamper-proofing, complete traceability and time-stamped storage. Considering the above, this study proposes a new blockchain-based approach to the quality management of agricultural products and introduces Solidity-based prototype smart contracts for agricultural product transactions. Test results show that the new traceability system can offer good performance in terms of data upload and block response time. The method proposed in this paper can be used as a solution for quality management of agricultural product that boasts whole process transparency, full-link reliability and joint supervision by all nodes in the system.
Detection of Geothermal Anomaly Areas With Spatio-Temporal Analysis Using Multitemporal Remote Sensing Data

Thermal infrared (TIR) remote sensing is an important technology for detecting geothermal anomalies. However, detection results have been found to have a certain dependency on the distribution of ground objects and imaging conditions at different times, and pseudo-anomalous areas are easily extracted. To solve this problem, a new geothermal anomaly detection method is proposed in this article and implemented in the Jiaonan uplift in the Yishu fault zone. A temperature inversion experiment is carried out on TIR remote sensing images based on the radiation transfer equation. Then, a gradient operator is used to extract high-temperature regions in various periods, and geothermal anomaly areas are selected through the spatio-temporal analysis method proposed in this article after excluding the influence of impervious surfaces, water bodies, and vegetation. The temperature anomaly points, which are all high-temperature points in each inversion result and geothermal anomaly areas extracted by the proposed method, are compared with the five geothermal anomaly points, which are determined based on 150 geothermal wells and the geological structure in the study area. The spatial locations of the temperature anomaly points and geothermal anomaly areas are close to those of the geothermal anomaly points. Compared with the mean grad method, the proposed method is found to effectively delete some pseudo-anomaly areas under the premise of ensuring the extraction accuracy of geothermal anomaly areas.
Co-word analysis and academic performance from the Australasian Journal of Educational Technology in Web of Science

Since its inception in 1985, the Australasian Journal of Educational Technology (AJET) has been dedicated to the diffusion of studies on the integration of technology in higher education. Its track record in this field has placed it in the first quartile of the Scimago Journal &amp; Country Rank. The objective of the study was to reveal to the scientific community the journey and evolution that this journal has had throughout its existence in Web of Science. A bibliometric methodology was used, supported by a scientific mapping from a unit of analysis of 798 documents. For this reason, a co-word analysis can be a fundamental tool for understanding the characteristics of their production and their impact on the scientific community. There is an evident progressive evolution of the studies published in the Australasian Journal of Educational Technology, with a first phase focused on the design and implementation of educational technology in learning environments, a second phase focused on the enrichment of technology and its acceptance within the processes of teaching and learning, and finally a stage focused on student and teacher perceptions of the implementation of technology in the educational context.&#x0D; Implications for practice or policy:&#x0D; &#x0D; We have established a contextual framework to show researchers current trends in educational technology and delineate future lines of research.&#x0D; Those interested in this subject will have a set of bibliometric indicators to delve into the field of educational technology.&#x0D; Higher education teachers and students will discover the evolution of the use of technology in learning spaces.&#x0D;
Connectivity matrix model of quantum circuits and its application to distributed quantum circuit optimization

Abstract As quantum computation grows, the number of qubits involved in a given quantum computer increases. But due to the physical limitations in the number of qubits of a single quantum device, the computation should be performed in a distributed system. In this paper, a new model of quantum computation based on the matrix representation of quantum circuits is proposed. Then, using this model, we propose a novel approach for reducing the number of teleportations in a distributed quantum circuit. The proposed method consists of two phases: the pre-processing phase and the optimization phase. In the pre-processing phase, it considers the bi-partitioning of quantum circuits by Non-Dominated Sorting Genetic Algorithm (NSGA-III) to minimize the number of global gates and to distribute the quantum circuit into two balanced parts with equal number of qubits and minimum number of global gates. In the optimization phase, two heuristics named Heuristic I and Heuristic II are proposed to optimize the number of teleportations according to the partitioning obtained from the pre-processing phase. Finally, the proposed approach is evaluated on many benchmark quantum circuits. The results of these evaluations show an average of 22.16% improvement in the teleportation cost of the proposed approach compared to the existing works in the literature.
The Semi-Hierarchical Dirichlet Process and Its Application to Clustering Homogeneous Distributions

Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose the semi-hierarchical Dirichlet process, a novel hierarchical prior that extends the hierarchical Dirichlet process of Teh et al. (2006) and that avoids the degeneracy issues of nested processes recently described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer to the homogeneity question and embed the proposed prior in a random partition model; this procedure allows us to give a more comprehensive response to the above question and in fact find groups of populations that are internally homogeneous when I≥2 such populations are considered. We study theoretical properties of the semi-hierarchical Dirichlet process and of the Bayes factor for the homogeneity test when I=2. Extensive simulation studies and applications to educational data are also discussed.
Interpretable video tag recommendation with multimedia deep learning framework

Purpose Tags help promote customer engagement on video-sharing platforms. Video tag recommender systems are artificial intelligence-enabled frameworks that strive for recommending precise tags for videos. Extant video tag recommender systems are uninterpretable, which leads to distrust of the recommendation outcome, hesitation in tag adoption and difficulty in the system debugging process. This study aims at constructing an interpretable and novel video tag recommender system to assist video-sharing platform users in tagging their newly uploaded videos. Design/methodology/approach The proposed interpretable video tag recommender system is a multimedia deep learning framework composed of convolutional neural networks (CNNs), which receives texts and images as inputs. The interpretability of the proposed system is realized through layer-wise relevance propagation. Findings The case study and user study demonstrate that the proposed interpretable multimedia CNN model could effectively explain its recommended tag to users by highlighting keywords and key patches that contribute the most to the recommended tag. Moreover, the proposed model achieves an improved recommendation performance by outperforming state-of-the-art models. Practical implications The interpretability of the proposed recommender system makes its decision process more transparent, builds users’ trust in the recommender systems and prompts users to adopt the recommended tags. Through labeling videos with human-understandable and accurate tags, the exposure of videos to their target audiences would increase, which enhances information technology (IT) adoption, customer engagement, value co-creation and precision marketing on the video-sharing platform. Originality/value The proposed model is not only the first explainable video tag recommender system but also the first explainable multimedia tag recommender system to the best of our knowledge.
Constant Function Market Makers: Multi-asset Trades via Convex Optimization

The rise of Ethereum and other blockchains that support smart contracts has led to the creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable, and SushiSwap, which enable agents to trade cryptocurrencies without trusting a centralized authority. While traditional exchanges use order books to match and execute trades, DEXs are typically organized as constant function market makers (CFMMs). CFMMs accept and reject proposed trades based on the evaluation of a function that depends on the proposed trade and the current reserves of the DEX. For trades that involve only two assets, CFMMs are easy to understand, via two functions that give the quantity of one asset that must be tendered to receive a given quantity of the other, and vice versa. When more than two assets are being exchanged, it is harder to understand the landscape of possible trades. We observe that various problems of choosing a multi-asset trade can be formulated as convex optimization problems and can therefore be reliably and efficiently solved.
THE MAIN PERSPECTIVES OF THE QUALITY OF LIFE OF STUDENTS IN THE SECONDARY CYCLE: AN OVERVIEW OF OPPORTUNITIES, CHALLENGES AND ELEMENTS OF GREATEST IMPACT

The level of Quality of Life (QoL) influences and reflects some elements of student health and impacts on academic performance.The objectives of this study were to analyze content to identify the main research opportunities and challenges on the qualityoflifeof students aged 15 to 16 years in public and private schools, and to verify if there is a correlation between student QoL, social, economic, and educational indicators.The methods adopted in this research were bibliographic research, content analysis and multivariate statistical analysis.The academic contribution of this work was the articulation of a theory on student quality of life, and the applied contribution was subsidy for the government and educational institutions to elaborate strategies, considering the correlations between the economic, social and educational variables.
Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.
Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.
Fast Depth Intra Coding Based on Depth Edge Classification Network in 3D-HEVC

As the extension of high efficiency video coding (HEVC) standard, three dimensional-HEVC (3D-HEVC) is the latest 3D video coding standard.3D-HEVC adopts many complicated coding algorithms to generate additional intermediate views for 3D video representation, which result in extremely high coding complexity.Therefore, this paper proposed a fast depth intra coding approach to reduce the 3D-HEVC complexity, which is based on convolutional neural network (CNN).First, we established a database based on the independent view of the depth map, which includes coding unit (CU) partition data of the depth map.Second, we constructed a depth edge classification CNN (DEC-CNN) framework to classify the edges for the depth map and embedded the network into a 3D-HEVC test platform.Finally, we utilized the pixel value of the binarized depth image to correct the above classification results.The experimental results demonstrated that our approach can reduce the intra coding time by 72.5% on average under negligible degradation of coding performance.This result outperforms the other state-of-the-art methods to reduce the coding complexity of 3D-HEVC.
Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations.These models are regression-based, and they just return rating predictions.This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities.The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences.Additionally, recommendation reliabilities can be gracefully provided to users: "probably you will like this film", "almost certainly you will like this song", etc.This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines.Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines.Experiments have been performed making use of four popular public datasets, showing generalizable quality results.Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields.
A Cryptographic Test of Quantumness and Certifiable Randomness from a Single Quantum Device

We consider a new model for the testing of untrusted quantum devices, consisting of a single polynomial time bounded quantum device interacting with a classical polynomial time verifier. In this model, we propose solutions to two tasks—a protocol for efficient classical verification that the untrusted device is “truly quantum” and a protocol for producing certifiable randomness from a single untrusted quantum device. Our solution relies on the existence of a new cryptographic primitive for constraining the power of an untrusted quantum device: post-quantum secure trapdoor claw-free functions that must satisfy an adaptive hardcore bit property. We show how to construct this primitive based on the hardness of the learning with errors (LWE) problem.
Explainable outlier detection: What, for Whom and Why?

Outlier algorithms are becoming increasingly complex. Thereby, they become much less interpretable to the data scientists applying the algorithms in real-life settings and to end-users using their predictions. We argue that outliers are context-dependent and, therefore, can only be detected via domain knowledge, algorithm insight, and interaction with end-users. As outlier detection is equivalent to unsupervised semantic binary classification, at the core of interpreting an outlier algorithm we find the semantics of the classes, i.e., the algorithm’s conceptual outlier definition. We investigate current interpretable and explainable outlier algorithms: what they are, for whom they are, and what their value proposition is. We then discuss how interpretation and explanation and user involvement have the potential to provide the missing link to bring modern complex outlier algorithms from computer science labs into real-life applications and the challenges they induce.
CertainNet: Sampling-Free Uncertainty Estimation for Object Detection

Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In perception for autonomous driving, measuring the uncertainty means providing additional calibrated information to downstream tasks, such as path planning, that can use it towards safe navigation. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size. To achieve this, we propose an uncertainty-aware heatmap, and exploit the neighboring bounding boxes provided by the detector at inference time. We evaluate the detection performance and the quality of the different uncertainty estimates separately, also with challenging out-of-domain samples: BDD100K and nuImages with models trained on KITTI. Additionally, we propose a new metric to evaluate location and size uncertainties. When transferring to unseen datasets, CertainNet generalizes substantially better than previous methods and an ensemble, while being real-time and providing high quality and comprehensive uncertainty estimates.