Reviewerly/exHarmony huggingface dataset:
Flexible Threshold Ring Signature in Chronological Order for Privacy Protection in Edge Computing

Edge computing can process data at the network edge rather than in the remote cloud. To protect the security of the collected data, blockchain can be used in edge computing. Any request sent from an edge server requesting data storage is a transaction added into the blockchain, which should include the data owners’ signature. For protecting the privacy of data owners, a threshold ring signature can be utilized for the blockchain. In this article, we propose a flexible threshold ring signature scheme in chronological order that has two advantages for solving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">update problem</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">chronological problem</i> in practice. Our scheme is a nontrivial extension of Yuen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s signature scheme and has three security properties: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unforgeability</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anonymity,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">chronological sorting</i> . We prove our scheme without random oracles and test our scheme over the Intel Edison development platform simulating practical edge servers.
Smart City Services Monitoring Framework using Fuzzy Logic Based Sentiment Analysis and Apache Spark

Smart city technologies are reshaping our everyday life. New application and systems architectures appears to enhance information and communications technologies, allowing cities to harness their resources more intelligently, also to include citizens in the development of their environments by analyzing the tremendous amount of data generated by people, which cover wide diversity of domains, thus, providing valuable contribution to tackle environmental and socio-economic problems efficiently. The problem encountered under these circumstances is that continuously analyzing users content, especially text content to extract expressed sentiment in real-time is a challenging task that needs scalable and high performance distributed systems, also the opinion of a person concerning a particular city service, politic decision or even a street reputation may not be binary, e.g., positive or negative, due to the fuzzy character of our language. To this end we propose an architecture that will take advantage of distributed and streaming computing framework combined with applying fuzzy logic to classify expressed sentiments in real time, thus to offer insight to city governance and to expand citizen participation in the evolution of their cities.
Combining Linked Open Data Similarity and Relatedness for Cross OSN Recommendation

The emergence of online social networks (OSNs) and linked open data (LOD) bring up opportunities to experiment on a new generation of cross-domain recommender systems in which the true benefit of LOD can be exploited, particularly to address the new user problems. In this article, the authors explore the feasibility of combining the two axes of comparison, similarity and relatedness, in LOD space, and introduce a new LOD-based similarity measure. The reason is to take benefit more from LOD to compare general resources, which can be useful in the context of cross-OSN recommendation. Experimental evaluation demonstrates the effectiveness of the proposed approach.
An Improved Denoising of Medical Images Based on Hybrid Filter Approach and Assess Quality Metrics

Degradation of images and segmentation are the two most demanding fields for medical image processing, particularly when explicitly applied. The involvement of noise not only deteriorates the visual quality but also the precision of the segmentation which is vital to the medical diagnosis process of development. The complicated and monotonous main task is to manually denoise medical images such as CT, ultrasound and large numbers of clinical routine MRI images. The medical image must be denoised automatically. The proposed approach is associated with less complexity, this follows from the fact that, the design of system and time for optimization. Results show their efficacy for noise removal in medical ultrasound and MRI images .The final results of the proposed scheme in terms of noise reduction and structural preservation are excellent. However the proposed scheme is compared with existing methods and the performance of the proposed method in terms of visual quality, image quality index, peak SNR and PSNR is shown to be superior to existing methods.
Agent-Based Model Characterization Using Natural Language Processing

This paper reports on Natural Language Processing (NLP) as a technique to analyze phenomena towards specifying agent-based models (ABM). The objective of the ABM NLP Analyzer is to facilitate non-simulationists to actively engage in the learning and collaborative designing of ABMs. The NLP model identifies candidate agents, candidate agent attributes, and candidate rules all of which non-simulationists can later evaluate for feasibility. IBM's Watson Natural Language Understanding (NLU) and Knowledge Studio were used in order to annotate, evaluate, extract agents, agent attributes, and agent rules from unstructured descriptions of phenomena. The software, and related agent-attribute-rule characterization, provides insight into a simple but useful means of conceptualizing and specifying baseline ABMs. Further, it emphasizes on how to approach the design of ABMs without the use of NLP by focusing on the identification of agent, attributes and rules.
Rapid Cigarette Detection Based on Faster R-CNN

Since the target detection algorithm based on deep learning is easy to be affected by light and image quality in cigarette detection applications, resulting the high false detection rate and high hardware occupancy rate, a rapid cigarette detection method based on Faster Regions with Convolutional Neural Networks (Faster R-CNN) model is proposed. This model based on the Faster R-CNN algorithm. Firstly, the algorithm of face detection is used to extract the range of face as the area of cigarette detection, therefore, the area of cigarette detection is reduced and many cigarette-like targets are filtered. At the same time, cigarette initial detection strategy based on color segmentation is introduced to judge the presence of a cigarette in face images preliminarily, after this step, the CPU occupancy and network complexity are reduced. The experimental results shows the proposed method reducing false detection rate of cigarette detection effectively and releasing the CPU occupancy rate greatly, and on the premise of ensuring the detection accuracy, the time of cigarette detection is reduced to some extent.
Visual Arts Search on Mobile Devices

Visual arts, especially paintings, appear everywhere in our daily lives. They are not only liked by art lovers but also by ordinary people, both of whom are curious about the stories behind these artworks and also interested in exploring related artworks. Among various methods, the mobile visual search has its merits in providing an alternative solution to text and voice searches, which are not always applicable. Mobile visual search for visual arts is far more challenging than the general image visual search. Conventionally, visual search, such as searching products and plant, focuses on locating images containing similar objects. Hence, approaches are designed to locate objects and extract scale-invariant features from distorted photos that are captured by the mobile camera. However, the objects are only part of the visual art piece; the background and the painting style are both important factors that are not considered in the conventional approaches. In this article, an empirical investigation is conducted to study issues in photos taken by mobile cameras, such as orientation variance and motion blur, and how they influence the results of the mobile visual arts search. Based on the empirical investigation results, a photo-rectification pipeline is designed to rectify the photos into perfect images for feature extraction. A new method is proposed to learn high discriminative features for visual arts, which considers both the content information and style information in visual arts. Apart from conducting solid experiments, a real-world system is built to prove the effectiveness of the proposed methods. To the best of our knowledge, this is the first article to solve problems for visual arts search on mobile devices.
Recognizing MNIST Handwritten Data Set Using PCA and LDA

In data analyzing and machine learning, PCA and LDA are widely used tools, these are linear transformation methods to reduce the dimension observation, and these are used in many practical applications like compression and data visualization in machine learning. Here, I have applied these two methods that are principal component analysis (PCA) and linear discriminant analysis (LDA) in handwritten digit recognition. The main motto of this paper is to present a simple and clear understanding of these methods. Here, this paper depicts that LDA can outperform PCA when training data set is huge, and PCA can outperform LDA when training data set is small.
Cooperative Co-evolutionary Metaheuristics for Solving Large-Scale TSP Art Project

As the amount and scale of cities in Traveling Salesman Problem (TSP) rise, the algorithmic complexity is exponentially increasing. The difficulty is how to design a suitable algorithm to solve large-scale TSPs. A Cooperative Co-evolutionary Ant Colony Optimization algorithm (CC-ACO) is proposed in this paper based on the concept of divide and conquer. The Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm tackles the problem by dividing it into a set of smaller and simpler sub-components and the ACO algorithms are designed for optimizing them separately. Numerical tests are then conducted to investigate algorithms, analyze results, and compare performances. The simulation findings show a significant efficiency of the suggested algorithm on the TSPLIB data set. Finally, we extend the large-scale TSP problem to the field of art and use the presented algorithm to optimize the path of discrete pixels in the picture, showing the artistic painting of the TSP art project.
The Internet of Responsibilities - Connecting Human Responsibilities using Big Data and Blockchain

Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations.
Lucy With Agents in the Sky: Trustworthiness of Cloud Storage for Industrial Internet of Things

The industrial Internet of Things (IIoT) has the potential to transform several industries. However, reliable data collection, processing, and analytics cannot be performed if the underlying cloud storage is not trustworthy. The current IIoT architectures tend to adopt cloud computing as a backbone for their implementation and further deployment. Therefore, the trustworthiness of the cloud storage is of paramount importance for the design of efficient and reliable IIoT. This becomes very challenging if the data storage is outsourced to a third party, which may raise many problems such as noncompliance to service-level agreement, data theft, and privacy issues. Mobile agents have a number of characteristics including mobility, lightweight, autonomous, reactive, and intelligent making them ideal for deployment in many distributed applications. Therefore, in this article, we propose a multiagent-based approach to address the geolocation assurance problem of the outsourced data in the context of IIoT. As a result, we achieve efficient geolocation assurance with manageable costs without the need of a trusted third party.
Artificial intelligence using hyper-algebraic networks

This work presents a novel paradigmatic revision of traditional neural networks, using network theoretic methods and Conformal Geometric Algebra. A unique theoretical framework called the ‘hyperfield cognition framework’ expands upon the mathematical foundations of neural networks in five-dimensional Conformal Geometric Algebraic space. This framework allows one to construct a novel theoretical computational engine, which is similar to a standard artificial neural network, but admits numerous added benefits: permits multiple training programs simultaneously, affords computational multiplicity in a single engine, reduces sensitivity to adversarial perturbations in training sets, affords broader capabilities and plasticity in the training of networks and produces robust and streamlined ‘neural networks’. We call this novel five-dimensional neural network a ‘hyperfield cognition network’ (HCN). This paper demonstrates the utility and merit of the proposed Hyperfield Cognition Network by presenting two case studies, the first of which investigates the fuel efficiency of various automobiles and the second of which models the residuary resistance per unit weight of displacement of ships.
Exploring mixed reality based on self-efficacy and motivation of users

This study addresses the question of how to facilitate instruction and practice with virtual reality to mitigate the detrimental impact of cognitive load associated with use in simple procedural tasks. The study collected data from 63 college students aged 18 years and above from a university in the southern part of the USA. Each study participant completed a questionnaire that consisted of 22 questions using a seven-point Likert scale. The results show that there are no significant differences between motivation and self-efficacy as it relates to three selected activities: Roboraid, Tutorial and Freeplay. The opportunity for meaningful learning through the use of the mixed reality is enticing; there is value in exploring facilitation of these learning opportunities through redistribution of cognitive load. This article is part of the special collection Mobile Mixed Reality Enhanced Learning edited by Thom Cochrane, James Birt, Helen Farley, Vickel Narayan and Fiona Smart. More articles from this collection can be found here.
Obstacle Detection Using Sensor Based System For An Four Wheeled Autonomous Electric Robot

Recently, many industries are using autonomous robots due to their high performance and reliability and which remains as a great help for human beings. Autonomous cars are considered as one of the most trending topics all over the web a car with no steering wheels, pedal, and with the most advanced technology autopilot. An autonomous vehicle is able to recognize there surrounding and finding the path there itself. Autonomous robot senses their surrounding with the help of different sensors such as Lidar, radar, ultrasonic sensor, infrared sensor; GPS etc. the control system used in autonomous vehicle read the information from the surrounding and find the appropriate path as well as the various obstacles are present on the path. Most of the autonomous vehicles are used for the obstacle detection purpose. In this project, the obstacle detection system is developed and the IR sensor usually used for the distance measurement. The sensor-based four-wheeled autonomous electric robot used to move the vehicle in the predefined path and avoid the obstacle during motion using the data logged from the IR sensor. Suppose during motion the robot doesn't have enough space to pass through the obstacle it must be stopped there itself or otherwise it moves by avoiding the obstacle. The system can be designed in Simulink environment.
Digital Literacy of Lawyers as a Condition of Legal Support for Business in the Digitization Era

Goal. In the context of formation of information society, digital literacy is pivotal in the solution of problems facing the education. This paper has been prepared in order to provide a scientific substantiation of an integrated approach to solving the problems of improving digital literacy of students and teachers in preparing lawyers for the business sector, as well as increased use of tailored education with the formation of an individual educational path. Methods. In order to achieve the goal set, the authors of research use a number of methods: general scientific (analysis, synthesis, generalization, induction and deduction, classification, interpretation), special scientific (comparative, statistical, sociological). Results. The research has shown that the formation of individual educational paths, designed to be instrumental in training lawyers for the modern economy, implies the introduction of alternative forms of provision of educational services based on digital technologies. This area of development of legal education requires a fundamental change in the level of digital literacy of teachers, as well as acquisition of tutor competencies by them. A wide variety of doctrinal interpretations of the concept of digital literacy prevents from choosing an efficient toolkit for solving the national task of assisting citizens in eliminating digital illiteracy. The digital literacy of teachers should include knowledge and skills in the field of educational policy and ethics of the application of information and communication technologies, the ability to make the best use of them in the teaching process, professional growth and management of educational activities. Comparison of tools that are practically applied in domestic and foreign universities within the framework of the general and specific model of the formation of an individual educational path revealed their advantages and disadvantages, as well as a range of problems of the submodel for individuals with disabilities. Conclusions. The authors offer a suggestion to enshrine the legal definition of the concept of digital literacy in the Russian legislation, to more actively involve major companies in the IT retraining of teachers, particularly teachers of the humanities, within the scope of relevant professional development programs. The mechanism for the implementation of the submodel of the formation of an individual educational path for individuals with disabilities calls for improvement in terms of development of alternative certification procedure, as well as security of the right to extend the time needed for learning the master’s programs.
On the Lightweight McEliece Cryptosystem for Low-Power Devices

In this paper we consider a problem of secured data transmission for low-power devices such as RFID (Radio Frequency IDentification) tags or some other devices for Internet of Things (IoT) for which low power consumption plays significant role. In fact, the privacy aspect involved with technology of RFID and IoT could become a major issue in the perspective of a global adoption. We considered well-known McEliece cryptosystems both in classical case (based on Goppa Codes) and based on Quasi-Cyclic Moderate-Density Parity-Check Codes (QC-MDPC) as a major security element of small and low-power devices. We also estimate a trade-off between complexity and security level of suggested system.
Diverse receptive field network with context aggregation for fast object detection

Current context-utilizing detectors are all based on two-stage approaches. However, their computational efficiency and context quality extremely depend on the accuracy of proposal-generating methods, which limits their performance and makes them hardly perform real-time detection. In this work, we present a context-exploited method that integrates features in different receptive fields to obtain contextual representation. Based on this idea, we put forward the multi-branch diverse receptive field modules (DRF modules) and their design principles to encode context. To further utilize contextual information for fast object detection, we propose a one-stage diverse receptive field network (DRFNet). In DRFNet, the DRF modules are first applied to capture rich context as the basis, then a parallel structure is constructed to exploit the context at different scales along with DRF modules. Comprehensive experiments indicate that the context introduced by our methods improves the detection performance and DRFNet achieves a good trade-off between speed and accuracy.
Feasibility and advantage of reservoir computing on single-electron devices

In this paper, applying reservoir computing (RC) to a single-electron (SE) device/circuit is discussed. RC, as one of the recurrent neural networks (NNs), consists of the input, reservoir, and output layers. It is known that NNs have redundancy for fluctuation. In addition, in RC, the weight functions of some of the connections are not necessarily updated, although it is needed for conventional NNs. Moreover, the number of connections in the reservoir layer can be reduced. Generally, nano-electronic devices, including SE devices, suffer from fluctuation and the large number of connecting paths between elements. By applying RC to the devices, these problems are expected to be overcome. In this study, the SE-RC circuit was designed and its operation was simulated by Monte Carlo simulation. The results indicate that the approach of applying RC to the SE circuits is very effective and can be a candidate for circuit architecture.