Reviewerly/exHarmony huggingface dataset:
An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

Abstract A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices.
Development for Multisensor and Virtual Simulator–Based Automatic Broadcast Shooting System

To solve the limitations of complexity and repeatability of existing broadcast filming systems, a new broadcast filming system was developed. In particular, for Korean music broadcasts, the shooting sequence is stage and lighting installation, rehearsal, lighting effect production, and main shooting; however, this sequence is complex and involves multiple people. We developed an automatic shooting system that can produce the same effect as the sequence with a minimum number of people as the era of un-tact has emerged because of COVID-19. The developed system comprises a simulator. After developing a stage using the simulator, during rehearsal, dancers’ movements are acquired using UWB and two-dimensional (2D) LiDAR sensors. By inserting acquired movement data in the developed stage, a camera effect is produced using a virtual camera installed in the developed simulator. The camera effect comprises pan, tilt, and zoom, and a camera director creates lightning effects while evaluating the movements of virtual dancers on the virtual stage. In this study, four cameras were used, three of which were used for camera pan, tilt, and zoom control, and the fourth was used as a fixed camera for a full shot. Video shooting is performed according to the pan, tilt, and zoom values ​​of the three cameras and switcher data. Only the video of dancers recorded during rehearsal and that produced by the lighting director via the existing broadcast filming process is overlapped in the developed simulator to assess lighting effects. The lighting director assesses the overlapping video and then corrects parts that require to be corrected or emphasized. The abovementioned method produced better lighting effects optimized for music and choreography compared to existing lighting effect production methods. Finally, the performance and lighting effects of the developed simulator and system were confirmed by shooting using K-pop using the pan, tilt, and zoom control plan, switcher sequence, and lighting effects of the selected camera.
Distinguishing Unique Earthquakes with Overlapping Signals in Oklahoma

Abstract During routine operations monitoring Oklahoma earthquakes, we found that certain earthquakes occurred closely both in space and time and had overlapping phases at the recording stations. Through further scrutiny and analysis, we determined that rather than being distinctly different earthquakes, some of the earthquakes exhibited multiphase arrivals and longer than expected coda due to unique ray paths that encounter impedance contrasts such as at the sedimentary rock-basement. Of course, some of these events truly were distinct events, which we term overlapping earthquakes, for which perceived coda duration overlaps and obscures the phase arrivals of the second event due to the source proximity in both time and space. We detail our classification scheme to separate the local earthquakes in Oklahoma as single, overlapping earthquakes, or those associated with multiphase arrivals. We forward model seismic wave propagation in a 2D crustal model and develop a methodology that utilizes waveform correlation to distinguish phases from overlapping earthquakes to those from crustal reverberations. Duration analysis shows a more elongated duration, qualitatively similar to the duration produced by overlapping earthquakes, at the sites where multiphase arrivals are observed.
Enhancing the user authentication process with colour memory cues

The authentication process is the first line of defence against potential impostors, and therefore is an important concern when protecting personal and organisational data. Although there are many options to authenticate digital users, passwords remain the most common authentication mechanism. However, with password numbers increasing, many users struggle with remembering multiple passwords, which affects their security behaviour. Previous researchers and practitioners have attempted to suggest ways to improve password memorability and security simultaneously. We introduce novel approach that utilises colour as a memory cue to increase password memorability and security. A longitudinal study examined in total over 3000 passwords that were created, learnt and recalled (password process) over a period of five-weeks. By adding colour to the password process, our results suggest that password memorability and security can be increased simultaneously. Through giving the user the option of choosing the colours (compared with colours being preselected), encourages users to create more personal and meaningful memory cues when creating their passwords. Additionally, colour also provided another security parameter by increasing password entropy. These unique results have practical implications for researchers and practitioners that could positively impact password security, and the financial losses suffered due to password security breaches.
Online Intelligent Course Education Based on Grid Model Simplification

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective.
Coded Parallel Transmission for Half-Duplex Distributed Computing

This work studies a general distributed coded computing system based on the MapReduce-type framework, where distributed computing nodes within a half-duplex network wish to compute multiple output functions. We first introduce a definition of communication delay to characterize the time cost during the date shuffle phase, and then propose a novel coding strategy that enables parallel transmission among the computation nodes by delicately designing the data placement, message symbols encoding, data shuffling, and decoding. Compared to the coded distributed computing (CDC) scheme proposed by Li et al., the proposed scheme significantly reduces the communication delay, in particular when the computation load is relatively smaller than the number of computing nodes K. Moreover, the communication delay of CDC is a monotonically increasing function of K, while the communication delay of our scheme decreases as K increases, indicating that the proposed scheme can make better use of the computing resources.
Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition

INTRODUCTION: A solution to subject-independent HAR prediction through machine learning classification algorithms using statistical equivalency for comparative analysis between independent groups with non-subject training dependencies.OBJECTIVES: To indicate that the multinomial predictive classification model that was trained and optimized on the one-subject control group is at least partially extensible to multiple independent experiment groups for at least one activity class.METHODS: Gradient boosted machine multinomial classification algorithm is trained on a single individual with the classifier trained on all activity classes as a multinomial classification problem.RESULTS: Levene-Wellek-Welch (LWW) Statistic calculated as 0.021, with a Critical Value for LWW of 0.026, using an alpha of 0.05.CONCLUSION: Confirmed falsifiability that incorporates reproducible methods into the quasi-experiment design applied to the field of machine learning for human activity recognition.
Target Recognition, Localization, and Motion Control of Soccer Robot

The realization of target tracking includes many technologies, among which the most critical and core element is the target tracking algorithm. Based on this, this paper first uses an omnidirectional vision image enhancement algorithm to identify the target and then designs an adaptive target recognition algorithm based on SVM to identify the actual target. The results show that, in the case of different illuminance, the fluctuation range of the four evaluation indexes after image enhancement is smaller than that of the original image, indicating that the brightness distribution of the image after enhancement is more uniform, which eliminates interference factors for the subsequent image recognition and tracking. The difference of recognition rate between the two algorithms is small when the illumination value is medium. When the illumination value is high or low, the recognition rate of the adaptive algorithm is much higher than that of the threshold method.
The Hmong Medical Corpus: a biomedical corpus for a minority language

Abstract Biomedical communication is an area that increasingly benefits from natural language processing (NLP) work. Biomedical named entity recognition (NER) in particular provides a foundation for advanced NLP applications, such as automated medical question-answering and translation services. However, while a large body of biomedical documents are available in an array of languages, most work in biomedical NER remains in English, with the remainder in official national or regional languages. Minority languages so far remain an underexplored area. The Hmong language, a minority language with sizable populations in several countries and without official status anywhere, represents an exceptional challenge for effective communication in medical contexts. Taking advantage of the large number of government-produced medical information documents in Hmong, we have developed the first named entity-annotated biomedical corpus for a resource-poor minority language. The Hmong Medical Corpus contains 100,535 tokens with 4554 named entities (NEs) of three UMLS semantic types: diseases/syndromes, signs/symptoms, and body parts/organs/organ components. Furthermore, a subset of the corpus is annotated for word position and parts of speech, representing the first such gold-standard dataset publicly available for Hmong. The methodology presented provides a readily reproducible approach for the creation of biomedical NE-annotated corpora for other resource-poor languages.
Turnover Intention Among Insurance Employees: Examining the Role of HRM Practices and Supervisor Behavior

This study investigated the antecedents of employee turnover intentions among insurance employees of Hyderabad, Sindh, Pakistan. A structured questionnaire was used to collect the data based on a seven-point Likert scale from insurance employees of Hyderabad. A structured questionnaire consisting of 18 question items. Data collected by convenience sampling. 100Structured questionnaires were distributed among insurance employees of Hyderabad and based on that data analysis and interpretations were done with the help of SPSS version 22.0 and SEM smart PLS3. SPSS was used to determine descriptive statistics and SEM smart PLS was used to test the hypotheses and model verification.HRM practices were found to be the significant predictors of employee turnover intentions among insurance employees of Hyderabad, Sindh, Pakistan. Whereas supervisor behavior was tested negative with employee turnover intentions. Future Researchers were guided by the suggestions and insights revealed by the current study that HRM practices contribute to decreasing employee turnover intentions. This study also explained the inter-relationship of independent factors(HRM practices and supervisor behavior), which were assumed to be the major predictors of employee turnover intentions. This study particularly focused on employee turnover intentions of insurance employees of Hyderabad and generally insurance employees of the whole province. This study also provided recommendations to insurance companies to reduce employee turnover intentions.
On the collapse revival of entanglement in a two-dimensional noncommutative harmonic oscillator: An algebraic approach

In this paper, we employ an algebraic approach to investigate the dynamics of entanglement in a two-dimensional noncommutative harmonic oscillator. We start by using the so-called the Bopp shift to convert the Hamiltonian describing the system into an equivalent commutative one. This allows us to take advantage of the Schwinger oscillator construction of [Formula: see text] Lie algebra to reveal through the time evolution operator that there is a connection between the noncommutativity and entanglement. The degree of entanglement between the states is evaluated by using the purity function. The remarkable emerging feature is that, as long as the noncommutativity parameter is nonvanishing, the system exhibits the phenomenon of collapse and revival of entanglement.
Semantic reasons

Abstract An analysis of a predicate normally takes the form of a condition that is both necessary and sufficient for the predicate's application. Here I consider the idea, due originally to Friedrich Waismann, that semantic analyses might include conditions that are defeasible, and so allow for exceptions. Analyses of this sort can be expressed in nonmonotonic logic, a post‐Waismann development. I'll argue that defeasibility makes analysis tractable, without making it trivial. I'll also show that a defeasible account of vague predicates can predict our intuitions about particular cases (for instance, that a seven‐foot person counts as tall) without drawing artificial boundaries.
Amplifying victim vulnerability: Unanticipated harm and consequence in data breach notification policy

Loss of control over one’s identity through identity usurpation, or identity theft, results in victimization characterized by multiple species of harm: material harms such as financial loss; medical harms such as psychological distress and consequential physiological illness; and moral harms such as infringement of autonomy. Digital data breaches are a common means by which identity can be usurped and laws have been enacted requiring data-holders to notify data subjects when their personal information held on digital databases has been compromised. The intention is that victims should then be able to undertake their own mitigation measures. This paper explores the efficacy of this approach as a solution and argues that this policy – particularly in the light of new digital criminal methodologies – creates a conflict of victims’ interests. It is an unintended outcome of policy that exacerbates, rather than resolves, identity usurpation and associated victimization in the digital environment.
Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness

The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm's shortcomings of low optimization accuracy and instability. It also creates high-accuracy solutions in recognized domains quickly. To retain student individuality, students can be divided into two groups: Human psychological variables are incorporated in the algorithm's improvement: in the "teaching" step of the original BBO algorithm; the "expectation effect" theory of social psychology is combined: "field-independent" and "field-dependent" cognitive styles. As a consequence, low-weight deep neural networks have been designed in such a manner that they require fewer resources for optimal design while also improving quality. A responsive student update component is also introduced to duplicate the effect of the environment on students' learning efficiency, increase the method's global search capabilities, and avoid the problem of falling into a local optimum in the first repetition.
Application of Deep Reinforcement Learning to NS-SHAFT Game Signal Control

Reinforcement learning (RL) with both exploration and exploit abilities is applied to games to demonstrate that it can surpass human performance. This paper mainly applies Deep Q-Network (DQN), which combines reinforcement learning and deep learning to the real-time action response of NS-SHAFT game with Cheat Engine as the API of game information autonomously. Based on a personal computer, we build an experimental learning environment that automatically captures the NS-SHAFT's frame, which is provided to DQN to decide the action of moving left, moving right, or stay in same location, survey different parameters: such as the sample frequency, different reward function, and batch size, etc. The experiment found that the relevant parameter settings have a certain degree of influence on the DQN learning effect. Moreover, we use Cheat Engine as the API of NS-SHAFT game information to locate the relevant values in the NS-SHAFT game, and then read the relevant values to achieve the operation of the overall experimental platform and the calculation of Reward. Accordingly, we successfully establish an instant learning environment and instant game training for the NS-SHAFT game.
Pemodelan Banjir Rob Wilayah Jakarta Utara Menggunakan Sistem Informasi Geografis

Rising sea levels originating from high tides and inundating land are known as tidal flooding. The cases of tidal floods are increasing day by day, especially in areas directly adjacent to the high seas. One of the areas that are prone to tidal flooding is North Jakarta. This study aims to determine the areas affected by the tidal flood and the extent of the area affected by the tidal flood in the Jakarta City area. This research can be used to take preventive action for residents to face the tidal flood disaster. The tidal flood height modeling was obtained based on the value of Mean Sea Level (MSL), Higher High Water Level (HHWL), and the trend of global rising tides. The result of this study is a map of areas affected by tidal flooding, where the lowest is at an altitude of 0.4 m in Penjaringan and Cilincing. At the height of this tidal flood, the impact was approximately 54 hectares of residential areas, 59 hectares of buildings, and 146 hectares of agricultural activities. The highest area affected by the tidal flood is at an altitude of 1.96 m which inundates the coastal District of Penjaringan, Cilincing, Pademangan, and Tanjung Priok Port. The height of the highest tidal flood impacts 983 ha of residential areas, 493 ha of buildings, and 830 ha of the agricultural sector were inundated. At the height of the tidal flood, which is almost 2 m high, it is necessary to carry out careful disaster mitigation efforts for the local government.
Abstraction for Non-Ground Answer Set Programs (Extended Abstract)

Abstraction is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and over-approximates the set of answer sets, as well as an abstraction-&amp;-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.
A local multi-block with various constraints approach for stereo vision in StereoPi

The conventional local block matching algorithms are fast but have an accuracy issue, making them unsuitable to be implemented on real-time applications. This paper studies a local block matching algorithm with different cost computations, combining a cost aggregation with various blocks' shape and sizes, using the computer and StereoPi. Compared to other local block matching algorithms, the Ranking-Gradient-Similarity Multi-Block (RGSMB) algorithm can extract information with a higher cost uniqueness within the predefined local window. Using KITTI 2015 as input data, RGSMB has better accuracy than other local block matching approaches. It is at least 5% better than Census Transform (CT), 23% better than Ranking Transform (RT), and 62% better than Sum of Absolute Differences (SAD) in terms of accuracy. Furthermore, it can achieve lower computational costs on the StereoPi, a low-cost computer vision embedded system. The RGSMB can achieve 2.5 fps with the right image resolution and parameter settings.
Ensemble Multi-Relational Graph Neural Networks

It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able to flexibly remedy the weakness of GNNs. However, this optimization objective is only proved for GNNs with single-relational graph. Can we infer a new type of GNNs for multi-relational graphs by extending this optimization objective, so as to simultaneously solve the issues in previous multi-relational GNNs, e.g., over-parameterization? In this paper, we propose a novel ensemble multi-relational GNNs by designing an ensemble multi-relational (EMR) optimization objective. This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations. We further analyze the nice properties of EnMP layer, e.g., the relationship with multi-relational personalized PageRank. Finally, a new multi-relational GNNs which well alleviate the over-smoothing and over-parameterization issues are proposed. Extensive experiments conducted on four benchmark datasets well demonstrate the effectiveness of the proposed model.