Reviewerly/exHarmony huggingface dataset:
Design, Setup, and Evaluation of a Compensation System for the Light Deflection Effect Occurring When Measuring Wrought-Hot Objects Using Optical Triangulation Methods

In this paper, we present a system to compensate for the light deflection effect during the optical geometry measurement of a wrought-hot object. The acquired 3D data can be used to analyze the formed geometry of a component directly after a hot forging process without waiting for the needed cooling time to room temperature. This may be used to parameterize the process and to detect defect components early in the production process, among others. The light deflection as the deviation from the linear path of the light is caused by an inhomogeneous refractive index field surrounding the hot object. We present the design and setup for a nozzle-based forced air flow actuator, which suppresses the light deflection effect. The design process includes a simulation of the developing field, as well as of the interaction of the field with an external forced air flow. The cooling effect of the air flow is evaluated, and conclusions are drawn from the conflicting interests of good measurement conditions against the forced cooling of the hot object. The findings are then implemented in the physical setup of the suppression system. The system is evaluated using a previously established method based on optical triangulation and fringe projection. Other occurring effects and their influence on the evaluation are considered and discussed.
Dynamic primitive granularity control

Dynamic primitive granularity control (DPGC) is a promising avenue for improving the performance of genetic programming (GP). However, it remains almost entirely unexplored. Further, it may pose many unique challenges in its design and implementation that traditional GP implementations do not. This paper presents an implementation of DPGC in order to determine what aspects of conventional GP design and implementation require special consideration. There are some common techniques used in GP that have been found here to negatively impact DPGC's ability to improve performance. Parsimony pressure appears to disproportionately penalize low-level primitives, and a mixed-granularity population suffers from heavy biases towards particular granularity levels, seemingly to the detriment of evolution. This paper provides hypotheses as to why these conventional techniques harm DPGC implementations, as well as several potential alternatives for use in the future that may remedy these detrimental effects.
Stokes–anti-Stokes light-scattering process: A photon-wave-function approach

The Photon wave function Formalism provides an alternative description of some quantum optical phenomena in a more intuitive way. We use this formalism to describe the process of correlated Stokes--anti-Stokes Raman scattering. In this process, two photons from a laser beam are inelastically scattered by a phonon created by the first photon (Stokes processes) and annihilated by the second photon (anti-Stokes process), producing a Stokes--anti-Sokes (SaS) photon pair. We arrive at an expression for the two-photon wave function of the scattered SaS photon pair, which is in agreement with a number of experimental results.
pTrans: A Scalable Algorithm for Reservation Guarantees in Distributed Systems

Providing performance QoS (Quality-of-Service) guarantees in a distributed data center environment poses unique challenges. In a typical setting, a client must be guaranteed a minimum amount of service (its contractual reservation) aggregated over all servers on which it has demand for service. A server may receive service demands from multiple clients, which may in aggregate exceed its service capacity. The system must decide how much service to provide to each client on each server that it has demand, in order to satisfy all clients' reservations. In case there is no feasible allocation of the service, the amount of reservation that is fulfilled must be maximized. In practice, the number of clients is several orders of magnitude larger than the number of servers.
“Nutri-Mental” ─An Android Application For Personal Health And Nutrition Management

These days, people are getting more wellbeing cognizant and will, in general, keep a beware of the dietary addition from the stuffed sustenance things they use. The rising growth of Android in the field of Operating System's has brought many advancements and proficient things such as applications, games and many more but also it leads to many complexities such as Mobile Exploitation done through Kali Software. The rising growth of the Android in the field of Operating System's has brought many advancements and proficient things such as applications, games and many more but also it leads to many complexities such as Mobile Exploitation done through Kali Software. The growth of Applications is increasing day by day. In this paper, the android platform is looked at in much more detail and will understand the future scope of Android and will implement Text To Speech and Vice Versa. This paper proposes an innovative Fitness Tracking app using the Android platform. The app would help the users to maintain a healthier lifestyle and eat more nutritious food. The proposed app gives an insight into nutrition that a person should have by eating a properly balanced diet and will present an outline on further research and development of the application.
Usability Assessment of Augmented Reality-Based Pedestrian Navigation Aid

This study aimed to explore the usability problems of augmented reality (AR)-based pedestrian navigation aid. A field experiment was conducted to investigate the performance and experience of young and older adults when they used two common functions (virtual navigator and auxiliary map) of AR-based navigation aid. Success rate, navigation error and speed variation were measured. A total of 28 younger adults and older adults participated in this experiment. The results showed that almost all participants could complete navigation tasks successfully. Presenting virtual navigator and auxiliary map would result in more navigation errors for participants. In addition, the effect of virtual navigator on the navigation error was related to road condition. Presenting virtual navigator would decrease the navigation error in simple road condition and increase navigation error in complex road condition. Furthermore, older adults preferred AR-based navigation aid than young adults. Finally, 11 usability problems were collected in this study.
A Framework for Digital Forensic Investigation of Big Data

With the development of internet technology, the era of big data with data explosion has come. As a result, the types and the amount of data that need to be processed in cybercrime investigations are increasing. It is a great challenge to find the clues or evidences of crime from the massive data. The emergence of big data technology has brought new directions for digital forensics investigations. It is of practical significance to integrate big data technology and digital forensics to crack down on cybercrime. This paper proposes a big data forensics framework which describes the process and related technologies. In addition, the challenges to realize this framework have been taken into consideration.
Node Classification in Complex Networks using Network Embedding Techniques

Node classification is a technique to assign node labels using the network topology and various node features. Many techniques like random walk-based method, label propagation graph regularizations are proposed in this research. Network embedding is a popular method to capture graph structure in low dimensional vector space and can capture the features of the nodes properly. In this paper, the popular embedding techniques are visualized on various data-sets and introduce the idea of network embedding techniques to perform the node classification task.
Information design for bowel cancer detection

Abstract Colorectal cancer is one of the most commonly occurring cancers in the world, and colonoscopy is the most sensitive procedure to detect it. Colonoscopy success depends on the quality of bowel preparation, yet the way information is designed and communicated to patients does not meet their needs. By considering how information is conveyed through three different outputs (booklet, motion graphics and app), this study investigates the advantages of using visualised information when communicating bowel preparation instructions for colonoscopy screening. A user-centered multiple-methods approach was followed and results show how user performance benefits from the use of information visualisation. A set of guidelines is given to inform the development of bowel preparation instructions and other similar health related communications.
Incremental Clustering With Hard Centers

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FU-PCM</i> is an effective clustering mode by inducing the regularization constraint in C-means to avoid the interference of noises and outliers. However, it is difficult to obtain the satisfactory performance in the context of imbalance category distribution. To address above problem, first, we propose a novel clustering model, which exploits Pearson Correlation Coefficient to auto-balance the optimal equation according to the category distribution. Then, we extend it to the incremental model, which only learns samples at the current frame to update the original model by mapping centers of two adjacent frames to the distinguishable space, and mining hard centers to recognize new categories in online datasets. In this article, offline and online methods are verified in popular datasets, and experimental results demonstrate the effectiveness and efficiency of the proposed models.
A Novel Method for Image and Video Compression Based on Two-Level DCT with Hexadata Coding

In this paper a novel method for 2D image compression is proposed and demonstrated through high quality reconstruction with compression ratios up to 99%. The proposed novel algorithm is based on a two-level discrete cosine transform (DCT) followed by Hexadata coding and arithmetic coding at compression stage. The novel method consists of four main steps: (1) a two-level DCT is applied to an image to reinforce the low frequency coefficients and increase the number of high frequency coefficients to facilitate the compression process; (2) the Hexadata coding algorithm is applied to each high frequency matrix separately through five different keys to reduce each matrix to 1/6 of their original size; (3) build a probability table of original high-frequency data required in the decoding step; and (4) apply arithmetic coding to compress each of the outputs of steps (2) and (3). At decompression stage, arithmetic decoding and a fast matching search algorithm (FMS-Algorithm) decodes the high frequency coefficients of step (2) using the probability table of step (3). Finally, two level inverse DCT is applied to decode the high frequency coefficients to reconstruct the image. The technique is demonstrated on still images including video streaming from YouTube. The results show that the proposed method yields high compression ratios up to 99% with better perceptual quality of reconstructed images as compared with the popular JPEG method.
Finite State Machines Play Extensive-Form Games

Finite state machines are a well-known representation of strategies in (in)finitely repeated or stochastic games. Actions of players correspond to states in the machine and the transition between machine-states are caused by observations in the game. For extensive-form games (EFGs), machines can act as a formal grounding for abstraction methods used for solving large EFGs and as a domain-independent approach for generating sufficiently compact abstractions. We show that using machines of a restricted size in EFGs can both (i) reduce the theoretical complexity of computing some solution concepts, including Strong Stackelberg Equilibrium (SSE), (ii) as well as bring new practical algorithms that compute near-optimal equilibria considering only a fraction of strategy space. Our contributions include (1) formal definition and theoretical characterization of machine strategies in EFGs, (2) formal definitions and complexity analysis for solution concepts and their computation when restricted to small classes of machines, (3) new algorithms for computing SSE in general-sum games and Nash Equilibrium in zero-sum games that both directly use the concept of machines. Experimental results on two different domains show that the algorithms compute near-optimal strategies and achieve significantly better scalability compared to previous state-of-the-art algorithms.
Adaptive Bacterial Foraging Optimization Based on Roulette Strategy

Bacterial foraging optimization has drawn great attention and has been applied widely in various fields. However, BFO performs poorly in convergence when coping with more complex optimization problems, especially multimodal and high dimensional tasks. Aiming to address these issues, we therefore seek to propose a hybrid strategy to improve the BFO algorithm in each stage of the bacteria’s’ foraging behavior. Firstly, a non-linear descending strategy of step size is adopted in the process of flipping, where a larger step size is given to the particle at the very beginning of the iteration, promoting the rapid convergence of the algorithm while later on a smaller step size is given, helping enhance the particles’ global search ability. Secondly, an adaptive adjustment strategy of particle aggregation is introduced when calculating step size of the bacteria’s swimming behavior. In this way, the particles will adjust the step size according to the degree of crowding to achieve efficient swimming. Thirdly, a roulette strategy is applied to enable the excellent particles to enjoy higher replication probability in the replication step. A linear descent elimination strategy is adopted finally in the elimination process. The experimental results demonstrate that the improved algorithm performs well in both single-peak function and multi-peak function, having strong convergence ability and search ability.
Scenario based examination of institutional leaning using fuzzy cognitive maps

Institutionalization refers to systematically carrying out the processes of a company or organization, notwithstanding particular managers or employees. This may be achieved in business operations when an institution’s mission, vision, core values, policies, and strategic aims are converted into action plans with the goal of integrating core values and strategic objectives within institutional structure and culture. When institutions are unable to successfully navigate the institutionalization process, its life cycle may not be sustainable. Thus, to find useful means of evaluating and developing the institutionalization level is a considerable challenge that all organizations must contend with. Though many researchers have examined institutionalization, the institutionalization tendency has generally not been considered on a going-forward basis. In this context, the institutionalization tendency of an organization is a focus of an investigation examining organization using suitable criteria. In this paper, an inclusive model with concepts that affect the institutionalization tendency of organizations is proposed and FCMs method, which is used widely in the analysis of complex systems, is used. First, a model containing concepts that affect institutionalization was created as a result of the literature and expert opinions. Then, the weights of the interactions among the concepts are calculated via expert opinion. Finally, the concepts, which are most important to institutionalization, are revealed with the help of using Fuzzy Cognitive Maps (FCMs). According to the results of this study, organization should attach great importance to Process Management, Human Recourse Management and Nepotism. Moreover, this study also provides that organizations can have insight about their institutionalization situation with scenarios created hypothetically.
How Fast Can You Update Your MST?

Imagine a large graph that is being processed by a cluster of computers, e.g., described by the k-machine model or the Massively Parallel Computation Model. The graph, however, is not static; instead it is receiving a constant stream of updates. How fast can the cluster process the stream of updates? The fundamental question we want to ask in this paper is whether we can update the graph fast enough to keep up with the stream.
An Improved Evaluation Method for Soccer Player Performance Using Affective Computing

Current evaluation methods for soccer player performance either relies on rating from soccer experts or structured statistics of the match, such as shots and tackles. The former needs a lot of manpower and the evaluation is inevitably subjective. The latter can only record the quantity of a player's match events, but cannot reflect the quality (e.g., a wonderful shot or a terrible shot is regarded as a shot). To solve the above problems, an improved evaluation method for soccer player performance using affective computing is proposed. On the basis of statistics, our method also takes advantage of the text information of post-match reports, and employ the affective computing technology to quantify the quality of events. In this way, both the quantity and quality of events are considered. All the players in the Chinese Super League 2019 season are selected as evaluation objects, and the results show that the improved method can evaluate player performance more effectively and reasonably.
Machine Learning Approach for Automatic Categorization of Service Support Requests on University Information Management System

The bug reports and service support requests provided by users to software developers serve as an important source of information for software maintenance. For large software projects, the number of users involved may be high. As a result, large number of service support requests may be generated. In this study, we present a machine learning approach for automatic categorization of service support requests on university information management system of Izmir Katip Celebi University. We have collected a text corpus, which contains 17,831 bug reports and service support requests. On the corpus, preprocessing stages, such as, tokenization, stop word filtering and stemming have been employed. To represent text documents, TF-IDF term weighting scheme has been utilized in conjunction with 1-gram model. In the empirical analysis, five conventional classification algorithms (i.e., Naïve Bayes, k-nearest neighbor algorithm, C4.5 decision tree algorithm, random forest algorithm and support vector machines) have been taken into consideration. The experimental results indicate that the presented machine learning scheme can yield promising results on assigning service support requests to one of the related modules, as student information system, personnel information system, electronic document management system and scientific research projects system. We achieved a classification accuracy of 92.26% with support vector machines.
When Emotions Grow: Cross-Cultural Differences in the Role of Emotions in the Dynamics of Conflictual Discussions on Social Media

Background. The spread of affective content on social media, as well as user grouping based on affect [1], has been a focus of scholarly attention for over a decade. But, despite this, we lack evidence on what roles various particular emotions play in the dynamics of discussions on social media. Emotional contagion theory (Hatfield et al. 2014) adapted for social media suggests that diffusion of emotions happens on individual level, via direct one-time contact with emotionalized content [2]. Other theories, like theories of social influence or social learning [3], thought, suggest multiple, hierarchical, and/or topically-restricted contacts. The idea of affective agenda [4] implies that the dynamics of an emotional discussion needs to be assessed on the aggregate level. The question remains – what role the emotions taken on aggregate level play in the discussion dynamics, being either catalyzers or inhibitors of the discussions. One may suggest that emotions of different stance (positive/negative) may spur/slow down the discussions in various ways. Objectives. We analyze the spread of two polar emotions – anger and compassion – in three Twitter discussions on inter-ethnic conflicts, namely Ferguson protests (the USA, 2014), Charlie Hebdo massacre (France, 2015), and mass harassment in Cologne (Germany, 2015–2016). By analyzing the co-dynamics of the overall discussions and these two emotions we can conclude whether the pattern of the spread of emotions and its link with the discussion dynamics is the same in various language segments of Twitter. Data collection and methods. The data we use were collected by our patented Twitter crawler in the aftermath of the conflicts and include altogether over 2,5 M tweets. We used manual coding by native speakers and machine learning to detect the emotions; then, we visualized the dynamics of growth of the emotional content of the discussions and used Granger test to see whether anger or compassion gave a spur to the discussions. Results. We have received moderate results in terms of the dependence of the number of neutral users upon that of emotional users, but have spotted that the beginnings of the discussions, as well as the discussion outbursts, depend more on compassion, not on angry users, which needs more exploration. We have also shown that the hourly dynamics of emotions replicates that of the larger discussion, and the numbers of angry and compassionate users per hour highly correlate in all the cases.