Reviewerly/exHarmony huggingface dataset:
P‐23: A Subjective Method for Evaluating Foveated Image Quality in HMDs

Analyzing the perceptual image quality rendered on a head‐mounted display (HMD) is of primary importance to provide a flawless visual quality to VR headset users. In this paper, we propose a new subjective testing method to evaluate the quality of foveated images in HMDs. The images we use are coded by progressively increasing the compression ratio from the fixation point to the periphery. Our method is based on a standardized flickering paradigm which temporally interleaves the original and reconstructed images to make the differences more prominent. Results indicate that observers can identify more visual impairments near the fixation point than in periphery. This new subjective testing approach is, therefore, effective in assessing barely visible image artifacts of foveated imaging in HMDs.
Model of a Centralized System for Quality Assurance in Higher Education

Quality assurance in higher education is a task that is invariably the focus of the authorities. Quality standards place increasingly higher demands on educational institutions. Quality assurance procedures are cumbersome, they require a large amount of information to be collected by the higher education staff, information which is subject to verification by experts from the evaluating institution. The use of a centralized software system for external quality evaluation will unify the accreditation procedures and will attribute to their optimization. This article proposes a formal generalized net model of a centralized system for quality assurance in higher education. It aims to outline principles, guidelines, models and processes that guide the creation of specific software applications. The model described is based on our experience in creating and using the COMPASS stand-alone application for (self)assessment and accreditation in higher education.
Trajectory Planning of Fixed-wing UAV Using Kinodynamic RRT Algorithm

Trajectory planning is one of the key issues in autonomous flight of fixed wing unmanned aerial vehicles (UAV). This paper proposes a method for fixed wing UAV trajectory planning based on kinodynamic RRT* algorithm and verified through Simulink flight test. This method hinges on a state space model for fixed wing UAV flight dynamics using feedback linearization. Kinodynamic RRT* is used to plan the trajectory of the UAV. Simulink/MATLAB flight simulation test is used as a fast and low-cost way to verify the numerical results of the algorithm. The results show that the method proposed can quickly generate a feasible flight trajectory, and the error with the simulated flight data is within a reasonable range.
Performance Analysis of Various Entropy Measures in Categorical Data Clustering

Categorical data clustering is gaining importance in recent years. Shannon entropy based similarity and dissimilarity measures are popular for clustering categorical data. However, there are several other generalized entropy measures like Renyi and Tsallis. The paper presents a thorough comparative study of performance of various entropy measures in clustering categorical data. Exhaustive experimental analysis reveals that Renyi entropy measure outperforms other measures in clustering categorical data.
Distributed computational framework in TOTEM architecture enabled by blockchain

TOTEM: Token for controlled computation is a newly proposed framework that integrates blockchain with big data systems. This framework allows users to send the computational code towards the data and analyse instead of the conventional method of data analysis which required data to be sent across the network. The framework provides a TOTEM defined SDK for users to code according to the standard format and rules defined. TOTEM execute the user code within the data owner’s environment. Proper mechanisms are there to manage and prevent the malicious functions that may occur in the user code by defining a proper set of rules and putting constraints on the user computational code by introducing totem value. Authorised users are assigned with a predefined totem value and when the user submits the code the smart contract will estimate totem value for performing the user-submitted code. If the totem value is sufficient for the execution it will pass the code to the computational part of the framework. The customised computational part of the framework consists of a big data system such as Apache Hadoop and a new system that have a totem manager and updates to coordinate the controlled computation of the user code. In this paper, we discuss and demonstrate the proof of concept for the customised computational part of the framework.
Adaptation of Block Chain Technology in manufacturing sector

Block chain technology, hailed as the new internet is a revolutionary concept that can ensure transparent transactions through data authenticity and integrity with wide coverage area ranging from business to healthcare to real estate and designed to operate in the framework of an interlinked network or cluster of computing systems. Taking the example of the manufacturing sector, there are several transactions including sales, purchase and other expenditures that are to be accounted and there is a need for several ledgers maintained such as cash books, sales and purchase accounts which can be replaced by a single distributed ledger in public domain in chronological order. Also, in project activities there are capital costs that include infrastructure as well as technology costs, operational expenses and several direct and indirect expenses. Block chain technology can play a vital role in these transactions and can have wide scope in the area of logistics particularly in supply order processing including smart contracting, supply chain management, energy management, assets transfer, part processing, design, patent handling, electronic commerce, digital strategies and in ERP methodologies. While the idea of such a system is proposed to be the most preferred mode of transactions with data security and traceability and decentralization there are several challenges that need to be addressed before its implementation in totality. This paper intends to bring out the requirements analysis through expectations of the manufacturing sector by which it can possibly benefit from the integration of blockchain technology into the system.
Towards a code of ethics for autonomous and self-adaptive systems

Software systems are playing an increasingly important role in many domains of our society. To ensure that software will support the public good, software engineers, who create and maintain the software, shall adhere to ethical principles. A joint task force of IEEE and ACM has brought such a set of principles together in a Code of Ethics. These principles describe responsibilities for software engineers and guidelines to assist them when making decisions in the benefit of public good. With the emergence of computing systems that take autonomous decisions, there is growing consensus that new ethical principles will be required. Since self-adaptive systems are characterized by autonomy, the need for new principles applies to these systems. Based on the Code of Ethics and leveraging on ongoing initiatives, we suggest an initial set of new ethical principles for autonomous and self-adaptive systems as an inspiration for an extended Code of Ethics for this important class of systems.
Towards A Dependency-Driven Taxonomy of Software Types

Context: The evidence on software health and ecosystems could be improved if there was a systematic way to identify the types of software for which empirical evidence applies. Results and guidelines on software health are unlikely to be globally applicable: the context and the domain where the evidence has been tested are more likely to influence the results on software maintenance and health.
Explanation of Photon Navigation in the Mach-Zehnder Interferometer

Photons in interferometers manifest the functional ability to simultaneously navigate both paths through the device, but eventually appear at only one outlet. How this relates to the physical behaviour of the particle is still ambiguous, even though mathematical representation of the problem is adequate. This paper applies a non-local hidden-variable (NLHV) solution, in the form of the Cordus theory, to explain photon path dilemmas in the Mach–Zehnder (MZ) interferometer. The findings suggest that the partial mirrors direct the two reactive ends of the Cordus photon structures to different legs of the apparatus, depending on the energisation state of the photon. Explanations are provided for a single photon in the interferometer in the default, open-path, and sample modes. The apparent intelligence in the system is not because the photon knows which path to take, but rather because the MZ interferometer is a finely-tuned photon-sorting device that auto-corrects for randomness in the frequency phase to direct the photon to a specific detector. The principles also explain other tunnelling phenomena involving barriers. Thus, navigation dilemmas in the MZ interferometer may be explained in terms of physical realism after all.
Development of a method for automatic generation and optimization of fuzzy controller parameters using genetic algorithm

The method of automatic synthesis of a fuzzy controller and optimization of its parameters based on a genetic algorithm is developed. A distinctive feature of the method is an algorithm for processing statistical data about the operation of a real industrial facility, which makes it possible to form the initial knowledge base of a fuzzy controller (the number and type of membership functions used, the base of control rules). The use of a genetic algorithm allows optimizing the parameters of a fuzzy controller in such a way as to ensure the best quality indicators of its operation: the duration and oscillation of the transient process, the value of the steady-state error. The proposed method is automated due to the development of a special software application in the Matlab modeling environment, and requires minimal human participation in its work. Simulation modeling is carried out and results are presented that confirm the correctness of the proposed method and the possibility of its practical use. The method operation can be represented as a sequence of the following stages: forming the initial parameters of a fuzzy controller; searching for optimal lengths of term-sets of input-output linguistic variables; searching for optimal parameters of term-sets of input–output linguistic variables.
Secure Key Generation and Distribution Using Polarization Dynamics in Fiber

Dynamic properties of optical signals in fiber channel provide a unique, random and reciprocal source for physical-layer secure key generation and distribution (SKGD). In this paper, an inherent physical-layer SKGD scheme is proposed and demonstrated, where the random source is originated from the dynamic fluctuation of the instant state of polarization (SOP) of optical signals in fiber. Due to the channel reciprocity, highly-correlated fluctuation of Stokes parameter of SOP is shared between the legal partners, where an error-free key generation rate (KGR) of 196-bit/s is successfully demonstrated over 25-km standard single-mode fiber (SSMF). In addition, an active polarization scrambler is deployed in fiber to increase the KGR, where an error-free KGR of 200-kbit/s is achieved.
The Parallelization and Optimization of K-means Algorithm Based on Spark

Aiming at the deficiency of K-means clustering algorithm, Both the random selection of initial clustering center and the empirical determination of K value have a certain impact on k-means clustering results. A k-means clustering algorithm based on canopy algorithm and maximum and minimum distance is proposed. K-value is generated by canopy algorithm to avoid setting k-value artificially, The clustering center set was selected by using the weighted density method to reduce the impact of outliers on clustering results. Then the center point is selected by the maximum and minimum distance to avoid the clustering result falling into local optimum. The algorithm is parallelized on spark, Finally, the experimental results of UCI dataset show that the improved k-means algorithm not only improves the clustering quality, but also reduces the average iteration times of the algorithm. Experimental results show that the improved algorithm can effectively improve the efficiency and parallel computing ability of the algorithm.
Applications of Graph Theory and Probability in the Board Game Ticket to Ride

In the board game Ticket to Ride, players race to claim routes and connect cities on a map of the U.S. In this work, we identify winning strategies for and potential improvements to Ticket to Ride by applying probabilistic and graph-theoretic concepts. We find that longer routes are overvalued, presenting a simple winning strategy for opportunistic players. The scoring scheme we propose—based on indicator random variables—prevents exploitation from this strategy and improves the competitive nature of the game. Using a variety of game data visualizations, we also investigate why players who connect particular pairs of cities perform better than others. In addition, we build a statistical model from the effective resistance of the game’s underlying graph structure to suggest how to choose the best pairs of cities.
An improved semi-global matching method with optimized matching aggregation constraint

Abstract Stereo dense image matching (DIM) is a key technique in generating dense 3D point clouds at low cost, among which semi-global matching (SGM) is one of the best compromise between the matching accuracy and the time cost. Most commercial or open-source DIM software packages therefore adopt SGM as the core algorithm for the 3D point generation, which computes matching results in 2D image space by simply aggregating the matching results of multi-directional 1D paths. However, such aggregations of SGM did not consider the disparity consistency between adjacent pixels in 2D image space, which will finally decrease the matching accuracy. To achieve higher-accuracy while keep the high time efficiency of SGM, this paper proposes an improved SGM with a novel matching aggregation optimization constraint. The core algorithm formulates the matching aggregation as the optimization of a global energy function, and a local solution of the energy function is utilized to impose the disparity consistency between adjacent pixels, which is capable of removing noises in the matching aggregation results and increasing the final matching accuracy at low time cost. Experiments on aerial image dataset show that the proposed method outperformed the traditional SGM method and another improved SGM method. Compared with the traditional SGM, our proposed method can increase the average matching accuracy by at most 11%. Therefore, our proposed method can applied in some smart 3D applications, e.g. 3D change detection, city-scale reconstruction, and global survey mapping.
Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking

In this letter, we show that the challenging model adaptation task in visual object tracking can be handled by simply manipulating pixels of the template image in Siamese networks. For a target that is not included in the offline training set, a slight modification of the template image pixels will improve the prediction result of the offline trained Siamese network. The popular adversarial example generation methods can be used to perform template pixel manipulation for model adaptation. Different from current template update methods, which aim to combine the target features from previous frames, we focus on the initial adaptation using target ground-truth in the first frame. Our model adaptation method is pluggable, in the sense that it does not alter the overall architecture of its base tracker. To our knowledge, this work is the first attempt to directly manipulating template pixels for model adaptation in Siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than some other state-of-the-art trackers. Our code is available at https://github.com/lizhenbang56/MTP.
Obfs4 Traffic Identification Based on Multiple-feature Fusion

Tor is currently the most used anonymous browser. Users can communicate anonymously on the Internet through Tor and some criminals can use Tor for illegal and criminal activities. In order to deal with congestion, Tor introduced a bridge mechanism to replace the previous ingress node. Obfs4 is one of the most important bridges used by Tor. It uses an improved elliptic curve encryption algorithm and random padding to hide message information, the anti-detection ability is extremely strong. In order to effectively identify Obfs4 traffic, this paper proposes a Obfs4 identification method based on Multiple-feature fusion. Through research on Obfs4 protocol, data packet structure, node publishing strategy, and node distribution, this paper proposes many ways to obtain multiple features, including randomness characteristics, sequential characteristics, handshake packet length characteristics, and communication packet statistics characteristics. In addition, this paper proposes a machine learning algorithm based on a weighted Gaussian kernel function, which can modify the weight of different features to change the degree of influence of different features on the final classification result. Finally, the weight of each feature and the parameters used by the algorithm are determined through experiments. The accuracy of the algorithm is 93.82%, the recall is 99.00%, and the accuracy is 94.34%, which is much better than other algorithms mentioned in this paper. At the same time, this paper proves that there are some loopholes in Obfs4's anonymity mechanism, and its effective fingerprint can be obtained from the information it leaks to carry out attacks.
Privacy-Preserving Dispute Resolution in the Improved Bingo Voting

Dispute resolution mechanisms are important components of voting schemes, deterring a voting authority to change the election outcome as any alteration can be proved by such mechanisms. However, these mechanisms are useless if not triggered by voters, who should not have to choose to either raise a dispute or keep their vote private. Hence, voting schemes should include privacy-preserving dispute resolution.In this work, we advance the formal analysis in the symbolic model of an improved version of the Bingo Voting scheme, whose enhancements include privacy-preserving dispute resolution mechanisms. Most of our analysis of several verification, dispute resolution, and privacy properties is done automatically using ProVerif, which we complement with manual induction proofs as necessary. We find that the scheme meets some properties only if one makes additional trust assumptions to those stated in []. For example, we find that dispute resolution is met assuming an honest voting authority. Moreover, our work provides an understanding of privacy-preserving dispute resolution in general, which can be beneficial to similar analyses of other voting schemes.
A Robust Unscented Kalman Filter applied to Ultra-wideband Positioning

Ultra-wideband (UWB) is well suited for indoor positioning due to its high resolution and good penetration through objects. As one of nonlinear filter algorithms, unscented Kalman filter (UKF) is widely used to estimate the position. However, UKF cannot resist the effect of outliers. The performance of the filter algorithm will be inevitably influenced. In this study, a robust UKF (RUKF) method accompanied by hypothesis test and robust estimation is proposed. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed RUKF. Simulation experiment results are given to demonstrate that the RUKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the RUKF does not work well. The measured experiment results show that the outliers will be generated in the non-line-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimised residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed RUKF is effective to resist the effects of outliers and improves the positioning accuracy.
Proposing method for Public record maintenance using Block chain

There are a lot of documents that are maintained by intermediaries just to ensure the authenticity and integrity of the documents. There is a lot of resources put in for maintenance purpose but still most of the times the system fails to deliver what it promised. With the advent of Blockchain technology, we can help the authorities to maintain it. The solution which we propose will have immutable ledger entries of every change ever made to the document. So we can always trace back the record. To provide a workable solution for the existing process we propose the following implementation using the Merkel tree algorithm for data storing.
Automatic Question Tagging using k-Nearest Neighbors and Random Forest

Stack Overflow is one of the most widely used platforms for asking questions and queries on topics related to computer science, software development and general computer programming. Tagging of the questions is particularly useful for indexing information based on the tags. Currently, a user enters the tag manually for a question asked by him/her. The question should contain at least one tag manually typed by the user. It can be seen that most of the questions asked should either have more tags associated with it or aren't tagged accurately and appropriately. Since there are a huge number of tags, the process of searching through all the tags manually and find relevant ones can be cumbersome and is therefore overlooked by most of the users asking the questions. This research is focused on exploring methods for developing an autonomous tagging system using Machine learning methods like k-Nearest Neighbors and Random Forest along with some crucial data preprocessing steps like Stemming, Tokenization and removing Stop words. The dataset for the above research is taken from kaggle.com which has a 10% Stackoverflow question dataset open for all. The results of the following proposed system for automatic tagging were satisfactory. Random Forest gave an average percentage accuracy of 70% across all the tags while k-Nearest Neighbors performed slightly better giving an accuracy of 75%.
Detection of Sybil Attack on Tor Resource Distribution

Tor anonymous communication system's resource publishing is vulnerable to enumeration attacks. Zhao determines users who requested resources are unavailable as suspicious malicious users, and gradually reduce the scope of suspicious users through several stages to reduce the false positive rate. However, it takes several stages to distinguish users. Although this method successfully detects the malicious user, the malicious user has acquired many resources in the previous stages, which reduce the availability of the anonymous communication system. This paper proposes a detection method based on Integer Linear Program to detect malicious users who perform enumeration attacks on resources in the process of resource distribution. First, we need construct a bipartite graph between the unavailable resources and the users who requested for these resources in the anonymous communication system; next we use Integer Linear Program to find the minimum malicious user set. We simulate the resource distribution process through computer program, we perform an experimental analysis of the method in this paper is carried out. Experimental results show that the accuracy of the method in this paper is above 80%, when the unavailable resources in the system account for no more than 50%. It is about 10% higher than Zhao's method.