Reviewerly/exHarmony huggingface dataset:
Fidelity of noisy multiple-control reversible gates

The effect of frequency noise on correct operation of the multiple-control Toffoli, Fredkin, and Peres gates has been discussed. In the framework of the Ising model, the energy spectrum of a chain of atoms with nuclear spins one-half in a spinless semiconductor matrix has been obtained, and allowed transitions corresponding to the operation algorithm of these gates have been determined. The fidelities of the obtained transitions were studied depending on the number of control qubits and parameters of the radio-frequency control pulses. It has been shown that correct operation of the Toffoli and Fredkin gates does not depend on the number of control qubits, while the Peres gate fidelity decreases significantly with the increasing number of control signals. The calculated ratios of the Larmor frequency to the exchange interaction constant correspond with the results of other studies.
The ‘Northumbria Temporal Image Forensics’ Database: Description and Analysis

This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.
Integrating CCM4DSR into ADR to Improve Problem Formulation

Action Design Research (ADR) is a commonly used methodology for conducting Design Science Research (DSR). The first activity in ADR is Problem Formulation, which defines the scope of the research and establishes the working relationships between the researcher(s) and the client(s). However, while tasks and principles are defined for Problem Formulation in ADR, specific ways of performing those tasks to achieve ADR’s principle of “theory-ingrained artifact” are lacking in the methodology description. This paper proposes combining Coloured Cognitive Mapping for Design Science Research (CCM4DSR) with ADR to support problem formulation. The combination is demonstrated and evaluated using the case study of an ADR project aiming to develop a purposeful artefact to support melanoma survivors in diligently performing effective skin check behaviours. The paper presents example coloured cognitive maps (CCMs) drawn from the case study and discusses how combining CCM4DSR with ADR contributes to ADR’s problem formulation activities, as well as subsequent ADR activities.
A Super-pixel based Method for Instance Segmentation Post-processing

We present a simple post-processing method for object instance segmentation. Instances segment images into parts with rich semantics while less texture consistency. Superpixels segment images into parts with great texture consistency while less semantics. We design a method, joining super-pixel to the instance segmentation workflow, in order to enhance the instance segmentation results. The workflow is, firstly calling a certain instance segmentation method (for example, Mask Region Convolutional Neural Network (R-CNN), Mask R-CNN) on the image to get prediction masks preliminary; then utilizing super-pixels as the assistant information to modify the prediction masks; and finally obtaining the better segmentation results. Our method is train-free, while it can refine the instance segmentation masks. Our experiments performed on multiple neural networks and the Microsoft Common Objects in Contexts (MS-COCO) dataset demonstrate the effectiveness of our method.
Similarity measure of agents’ ontologies in cohesive hybrid intelligent multi-agent system

Abstract The work is devoted to the issues of building cohesive hybrid intelligent multi-agent systems that implement the following mechanisms for ensuring the joint work of heterogeneous agents: coordination of goals and agent ontologies, development of the problem solving protocol. These mechanisms are similar to the processes that occur in long-standing expert teams. In them, experts, interacting in the course of solving problems, exchange views, knowledge, take into account each other’s goals, establish formal and informal norms of communication. In the paper, in particular, a method for estimating the similarity of ontologies is considered, which is the part of the method for estimating the cohesion of agents of the hybrid intelligent multi-agent system. The estimation of cohesion is used by system’s agents as the interaction effectiveness criterion when making decisions about the need to converge goals, ontologies or correction of the interaction protocol.
BlockVoke – Fast, Blockchain-Based Certificate Revocation for PKIs and the Web of Trust

A reliable certificate revocation mechanism is crucial, as illustrated by the recent revocation of 1.7 million certificates issued by the Let's Encrypt certificate authority. It is just as essential to get revocation information to users in an efficient and timely manner without impacting their privacy. Existing approaches such as Certificate Revocation Lists (CRLs) or the Online Certificate Status Protocol (OCSP) fail with respect to either of those metrics, while approaches that try to mitigate both, such as OCSP-Staple and Must-Staple suffer from soft-failure modes and meager adoption rates. To address these issues, we propose the BlockVoke scheme, which decentralizes revocations, allowing certificate owners as well as CAs to revoke certificates, and distribute revocation information rapidly. Our approach furthermore allows the revocation of CA root certificates, which is not possible with traditional approaches. The use of a blockchain as an underlying layer ensures the continued availability and immutability of revocation information. BlockVoke interacts favorably with approaches such as CRLite and Certificate Revocation Vectors (CRV), allowing organizations to update revocation filters with as little delay as required by their security policies. We also demonstrate the cost-efficiency of our approach in comparison to other approaches such as CRLs, showing its high feasibility.
Methodology for Distributed-ROM-Based Implementation of Finite State Machines

This brief explores the optimization of distributed-ROM-based finite state machine (FSM) implementations as an alternative to conventional implementations based on look-up tables (LUTs). In distributed-ROM implementations, LUTs with constant output value (called constant LUTs) and LUTs with the same content (called equivalent LUTs) can be saved. We propose a methodology to implement FSMs using distributed ROM that includes: 1) a greedy state encoding algorithm; 2) an algorithm to find the way of interconnecting the address signals to the ROM that maximize the number of constant or equivalent LUTs; and 3) a set of architectures to implement the columns of the ROM. The results obtained have been compared with conventional LUT-based implementations using standard benchmarks. The proposed technique reduces the number of LUTs in a 91% of cases and increases the speed in all cases.
Implementation Of Naive Bayes Algorithm On IoT-based Smart Laundry Mobile Application System

Laundry is one of the services to facilitate the homework in washing clothes. Customers are given convenience, just by delivering the package and waiting for the finished clothes until the time specified by the laundry. As the time progresses, the processing of laundry becomes less practical because customers must visit the laundry place in advance when they want to wash their clothes. The benefit of this laundry application aims to help customers who have high mobility, especially in urban areas and not have much time to come to the laundry. This IoT-based laundry (Internet of Things) Android application is very helpful because customers receive notifications and don't need to process manually, clothes will be carried over automatically by the laundry officers, by applying the Naïve Bayes algorithm which Measures the weight, distance, and moisture levels of the clothing ready to be taken by the laundry personnel, to determine which clothes will be processed first. If you want to see the progress of your clothes and payment transactions everything is enough on your smartphone, and for laundry parties is facilitated to take customer clothing, because there is already a list of customer names ready to be processed first.
Distributed Nonlinear Fusion UKF

This paper is concerned with the distributed fusion estimation problem for nonlinear multi-sensor systems. As is well known, the weighted fusion methods minimize the trace of fusion estimate error covariance, and it can be divided into three categories: scalar weighting, diagonal matrix weighting and matrix weighting, in which the matrix weighting has the highest accuracy. However, the matrix weighting needs to know the cross covariances between the local estimates, which cannot be easily and precisely calculated in the nonlinear systems. To overcome this shortage, this paper proposes a novel method to calculate the cross covariances between the local unscented Kalman filters based on statistical linear regression, and the corresponding distributed nonlinear matrix weighting fusion criterion is developed. Finally, a target tracking system is employed to show the advantages and the effectiveness of the proposed method.
Design Space and Users’ Preferences for Smartglasses Graphical Menus: A Vignette Study

We address in this work visual menus for smartglasses by proposing a wide range of design options, such as shape, location, orientation, and presentation modalities, which we compile in a design space with eight dimensions. From this design space, we select a subset of fourteen 2-D menus, for which we collect users’ preferences during a vignette experiment with N=251 participants. We report numerical measures of absolute, relative, and aggregate preference for smartglasses menus, and employ a particular Thurstone’s pairwise comparison technique, the Bradley-Terry model, to evaluate menu designs. Our results highlight key variables influencing users’ preferences regarding the visual appearance of smartglasses menus, which we use to discuss opportunities for future work.
Deep invariant texture features for water image classification

Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.
AmazonRep: A Reputation System to Support Amazon’s Customers Purchase Decision Making Process based on Mining Product Reviews

In this paper, we develop AmazonRep, a reputation system to support Amazon's customers purchase decision making process based on mining product reviews and their attributes. Different from existing reputation systems that incorporate review sentiment orientation, review helpfulness votes and review time for the purpose of generating and visualizing reputation toward various e-commerce items (movies, products, hotels, services...), disregarding important factors such as user credibility, our system mainly focus on considering all these factors (review sentiment orientation, review helpfulness votes, review time and user credibility) during reputation generation and visualization. Experimental results coming from an analysis of 13 Amazon's products (books, movies, laptops, smartphones, video games, washing machines and refrigerators) demonstrate the usefulness of the proposed system in generating and visualizing reputation.
A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset

An automated approach to text readability assessment is essential to a language and can be a powerful tool for improving the understandability of texts written and published in that language. However, the Persian language, which is spoken by over 110 million speakers, lacks such a system. Unlike other languages such as English, French, and Chinese, minimal research studies have been conducted to develop an accurate and reliable text readability assessment system for the Persian language. In the present research, the first Persian dataset for text readability assessment was gathered, and the first model for Persian text readability assessment using machine learning was introduced. The experiments revealed that this model was accurate and could assess the readability of Persian texts with a high degree of confidence. The results of this study can be used in several applications such as medical and educational text readability evaluation and have the potential to be the cornerstone of future studies in Persian text readability assessment.
Towards an Ontology for Teamwork Enabled Services

Teamwork ability is a crucial aspect of humans, agents and other intelligent systems. This study is dedicated to the conceptual formal modeling of service teamworking by considering concepts defined within a vast range of models and theories of human and agent team development. We make the conceptual based modeling by using ontologies to define teams, roles, services and their modeling domains abstracting the most important concepts of team development whilst providing a decent level of formality and unambiguity. An ontology named TrEWSOnto is proposed as a model to assist the development process of teamwork enabled services in tandem with the representation of their composition process activity. This includes (i) the definition of concepts that are used to improve the teamwork ability of peer services, (ii) the description of the main concepts are used for the role modeling composition, and (iii) the description of situations that teamwork roles should monitor to catch potential "unhealthy" behavior happen during the service activity in order to run proactively and avoid obstacles or collisions. The result is a structure of five ontology sections together forming a representation for TeamwoRk Enabled Web (TrEW) Services and the environment they live.
Aircraft Detection of Remote Sensing Images Based on Faster R-CNN and Yolov3

With the development of computer vision and satellite remote sensing technology, the intelligent detection of remote sensing image targets has gradually become a hot research topic. In this paper, we investigate two popular deep learning detection methods into aircraft target detection in remote sensing images. The UCAS_AOD-Dataset was expanded and labeled after selection of proper images. Then the two deep learning models, Faster R-CNN and YOLOv3 were trained and validated. The experimental results show that the mAP of Faster R-CNN and YOLOv3 reached 90.06% and 85.98%. Both models can effectively detect aircraft targets.
An Empirical Study of Importance of Different Sections in Research Articles Towards Ascertaining Their Appropriateness to a Journal

Deciding the appropriateness of a manuscript to the aims and scope of a journal is very important in the first stage of peer review. Editors should be confident about the article’s suitability to the intended journal to further channel its progress through the steps in the review process. However, not all sections in a research article are equally contributory or essential to determine its aptness to the journal under consideration. Here in this work, we investigate which sections in a manuscript are more significant to decide on its belongingness to the intended journal’s scope. Our empirical studies on two Computer Science journals suggest that the meta information from bibliography and author profiles can reach a competitive benchmark to full-text performance. The features we develop in this study display the potential to evolve as a decision support system for the journal editors to identify out-of-scope submissions.
Vehicle video surveillance system based on image fusion and parallel computing

Summary Autonomous driving has gradually moved towards practical applications in recent years. It is particularly critical to provide reliable real‐time environmental information for autonomous driving systems. At present, vehicle video surveillance systems based on multi‐source video and target detection algorithms can effectively solve these problems. However, the previous vehicle video surveillance systems are often unable to balance the surveillance effect and the surveillance frame rate. Therefore, we will introduce a vehicle video surveillance system based on parallel computing and computer vision in this article. First, multiple fisheye cameras are used to collect surround‐view environmental information. Second, we will use a low‐light camera, infrared thermal imager, and millimeter‐wave radar to provide forward‐view environmental information at night. Correspondingly, we designed the surround‐view image fusion algorithm and the forward‐view image fusion algorithm based on parallel computing. At last, a monocular camera and detection algorithms are used to provide forward‐view detection results. In a word, this vehicle video surveillance system will benefit the practical application of autonomous driving.
Information technology for career guidance of creatively gifted university students

The article presents a structural description of the functioning of the regional information and communication system “Student and Labor” (SAL). This system is aimed at identifying and developing creatively gifted youth in the field of science, techniques and technology and is focused on the gradual involvement of university students in real work and interaction with leading enterprises in the region. The first cycle of the SAL system functioning is described, which has a complete character and has already led to a concrete result. The bank of information about the leading enterprises of the region that took part at the initial stage of the functioning of the SAL system were formed as well as the bank of 150 themes of scientific research for student projects, which were proposed by the enterprises themselves. All projects are focused on the practical significance of research for the enterprises themselves, as well as for students, their supervisors from universities and scientific consultants from enterprises that have embarked on joint research activities, which at the first stages are mostly of informational and educational nature. The Union of Employers of the Samara Region and the Council of Rectors of Universities in the Samara region are already taking the necessary measures to implement the described cycle of the functioning of the system SAL in the current academic year.
Synthesis of sorting algorithms using multisets in Theorema

Using multisets, we develop novel techniques for mechanizing the proofs of the synthesis conjectures for list–sorting algorithms, and we demonstrate them in the Theorema system. We use the classical principle of extracting the algorithm as a set of rewrite rules based on the witnesses found in the proof of the synthesis conjecture produced from the specification of the desired function (input and output conditions). The proofs are in natural style, using standard rules, but most importantly domain specific inference rules and strategies. In particular the use of multisets allows us to develop powerful strategies for the synthesis of arbitrarily structured recursive algorithms by general Noetherian induction, as well as for the automatic generation of the specifications of all necessary auxiliary functions (insert, merge, split), whose synthesis is performed using the same method. The proof techniques are implemented in the Theorema system and generate 8 sorting algorithms and 19 auxiliary functions.
Optimizing the Neural Architecture of Reinforcement Learning Agents

Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architectures of these neural networks are quite simple and typically are constructed manually. In this work, we study recently proposed neural architecture search (NAS) methods for optimizing the architecture of RL agents. We create two search spaces for the neural architectures and test two NAS methods: Efficient Neural Architecture Search (ENAS) and Single-Path One-Shot (SPOS). Next, we carry out experiments on the Atari benchmark and conclude that modern NAS methods find architectures of RL agents outperforming a manually selected one.
Big Data Analytics in Healthcare Applications: Privacy Implications for Individuals and Groups and Mitigation Strategies

Big data analytics in healthcare present a potentially powerful means for addressing public health emergencies such as the COVID-19 pandemic. A challenging issue for health data to be used, however, is the protection of privacy. Research on big data privacy, especially in relation to healthcare, is still at an early stage and there is a lack of guidelines or best practice strategies for big data privacy protection. Moreover, while academic discourse focuses on individual privacy, research evidence shows that there are cases such as mass surveillance through sensing and other IoT technologies where the privacy of groups needs also to be considered. This paper explores these challenges, focusing on health data analytics; we identify and analyse privacy threats and implications for individuals and groups and we evaluate recent privacy preserving techniques for contact tracing.