Reviewerly/exHarmony huggingface dataset:
Error Analysis of Photometric Stereo with Near Quasi‐Point Lights

Abstract The shape recovery quality of photometric stereo is sensitive to complicated error factors under close range lighting of quasi‐point lights. However, the error performance of photometric stereo under this practical scenario is still obscure. This paper presents a comprehensive error analysis of photometric stereo with near quasi‐point lights (NQPL‐PS). Five main error factors are identified under this scenario and their corresponding analytical formulations are introduced. Statistic computation and experiments are used to validate the theoretical formulations and inspect the relationships between normal inaccuracies and each type of discrepancies. In addition, the impacts of multiple system parameters of an NQPL‐PS configuration on the normal estimation error are also studied. In order to evaluate the relative importance of various error factors, a probability‐based evaluation criterion is proposed, which focuses on the error performance over the state space and the error space, rather than the simple comparison of the values of normal inaccuracy. The assessment results show that the non‐uniformity of illuminants, and the calibration error in the position of light sources hold the dominant places among those five error factors. This paper provides insights for the accuracy improvement and system design of NQPL‐PS.
Thai Tokenizer Invariant Classification Based on Bi-LSTM and DistilBERT Encoders

This research aims to solve Thai word boundaries problem by grouping vectors of words obtained from various tokenizers in the same sentence using Bi-LSTM and DistilBERT encoders. Generally, one sentence can achieve different sets of words from multiple tokenizers, resulting in uncontrollable accuracies in various tasks. The proposed method based on Bi-LSTM and DistilBERT is to train and transform data with triplet hard loss to a particular domain where the embedding vectors of each same sentence are much closer to each other. In this study, sentiment classification is conducted to show that our method can be used as pre-trained process for other tasks.
Exploring Graph Embeddings for Making Recommendations using Graph Neural Networks

Graphs are influential and organized data types with abilities and shortcomings that are very different from standard data types such as text and images. This information is connected and deployed in the knowledge structure. Graphs permit us to model this structure to explore unseen associations. Graph Neural Networks (GNNs) have been rising in approval in the past years. One of the dominant tasks tackled with this promising technology is a recommendation. The proposed approach has outlined both conventional and GNN-based Recommender system (RS) approaches to predict the movie rating. Inversely from the conventional approach, GNN-based are made on graphs where several things are connected either absolutely or completely. With the advanced developments of graph learning, discovering edge associations in a graph is a promising domain for building efficient RS. First, this research study signifies a general notation of a graph and one common example. Second, the required operations aggregator, and the updator for feature aggregation are described. Finally, a detailed description for fine-tuning the model is described. Lastly, the research study is concluded by comparing the proposed approach with the conventional approach, where the results are encouraging.
Effect of Human Resource Management Functions on the Balanced Scorecard – Case Study Jordanian Public Joint-stock Companies

The acceleration of contemporary and influencing changes in the future of administrative organizations has imposed on these organizations new, added challenges, which are evident in their relentless pursuit of realizing, absorbing, and introducing advanced systems adapting to modern ideas and trends, which give organizations the modernity and development character. The paper's study sought to determine the impact of human resource management functions on the balanced scorecard in Jordanian public joint-stock companies. Appropriate statistical methods were applied to the research process. In light of the results obtained, the study recommends the need to work on creating an organizational culture that promotes the optimal and effective use of human resource management functions in the organizational environment. Upgrading these functions to the desired higher levels, achieving compatibility between design and analysis of work with organizational structures, can provide incentives for employees and develop business procedures to improve the balanced scorecard of the organization.
An RF I/Q Sample Data Set Acquisition Method Based on Environmental Characteristic Parameters

In the field of electromagnetic spectrum, research on radio monitoring methods can help to improve the management and control of electromagnetic spectrum resources. At present, the commonly used methods based on energy detection can scan the target frequency band in a wide frequency range to quickly monitor the utilization and distribution of electromagnetic spectrum resources. The traditional radio monitoring method is detecting the energy data of the target frequency band, which can only represent the final result of the antenna segment and has a little effect on the distribution characteristics of the electromagnetic spectrum. With the rapid development of wireless communication technology and the increasing number and type of radio frequency equipment, the difficulty of managing electromagnetic spectrum resources is also increasing. The electromagnetic spectrum detection methods have gradually been received attention, which are based on radio frequency I/Q sample data. In this paper, a new electromagnetic spectrum detection method named as an RF I/Q sample data set acquisition method based on environmental characteristic parameters which collect the original RF I/Q sample data to improve the detection probability is described. The RF I/Q sample data is the original data of the current electromagnetic spectrum after the wireless signal has undergone environmental propagation loss which contains all the information of the wireless signal in the target frequency band and can be used for subsequent signal analysis and research. Data sets based on RF I/Q samples can improve the ability to manage and control electromagnetic spectrum resources by using radio monitoring methods. The theoretical Results prove that the performance of An RF I/Q sample data set acquisition method based on environmental characteristic parameters is better compared to energy detection method alone.
Your tapstroke tells who you are

In this paper, we present TapAuth, a novel smartphone user authentication system that leverages vibrations generated from a user's tap inputs. TapAuth is based on the observation that the vibrations have their unique characteristic depending on the user due to differences in finger structure. Specifically, TapAuth collects audio and motion data using only built-in sensors, while users are entering PIN code. Then, it authenticates the user by verifying the validity of the code and comparing the collected data with a pre-built training dataset. Our experimental results with 20 real-world users show that TapAuth can achieve high accuracy.
Automated Surveillance System using Raspberry pi

Due to the increasing number of crimes and the increasing number of people living in cities, the requirement for a reliable and effective surveillance security system is spreading worldwide. Due to the continuous recording and the manpower required to monitor the activities of unauthorized individuals, the security systems consume a lot of memory and are not able to provide instant notification. In comparison to current monitoring systems, the Raspberry Pi has a number of advantages. Its mobility is one among them. This research work has introduced an Internet-based monitoring system that can be accessed remotely via the internet. This paper will look at how a PIR sensor and image processing may be used to monitor and track objects. Live video streams will be used to demonstrate how this can be accomplished.
Realization of Virtual Reality Technology in College Students' Social Anxiety Intervention Platform based on SQL Information Sequence Data Mining

In this paper, under the framework of SQL information sequence mining, combined with well-known social anxiety scales at home and abroad, a social anxiety question bank and its computerized adaptive test (CAT-SA) are constructed. The IRT analysis includes: unidimensionality test, item model-data fitting test, local independence test, DIF test, and selecting items that meet the IRT requirements to construct a social anxiety question bank and its CAT. According to the two-stage adaptive assessment with similar significance in the evaluation scene design, the ITC_SoPI scale was used to evaluate the individual's degree of adaptation and subjective feeling to the virtual environment. VR-based social anxiety assessment can provide more detailed and comprehensive assessment information and data, and the assessment results are doser to the self-assessment scale and the individual's daily life and state.
A New Merging Numerous Small Files Approach for Hadoop Distributed File System

In the current era of big data, enormous data is being recorded every second from multiple streams and multiple environments of different types. This hugely generated data is processed with the support of specialized tools such as Hadoop which ensures the processing of data by considering the memory, process allocation, size, and storage. Hadoop framework is known to be efficient with few files of large size rather than many files of small size which caused lots of issues for the framework to work efficiently and the time required for the processing is hugely increased. To eliminate this issue, this work proposes a new algorithm for merging many files of small size into a single large file based on certain match criteria (type and size). This process will be executed before the files are passed to the Hadoop framework. The proposed algorithm ensures that it will generate the least number of large files that reduces the I/O memory load and correlates with the efficiency of the Hadoop framework. The results prove that the proposed algorithm increases the efficiency and the time required by the Hadoop framework for processing by approximately 40% over all the possible factors that hinder the performance.
Helping reviewers assess statistical analysis: A case study from analytic methods

Abstract Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers.
Free Resolutions and Generalized Hamming Weights of Binary Linear Codes

In this work, we explore the relationship between the graded free resolution of some monomial ideals and the Generalized Hamming Weights (GHWs) of binary codes. More precisely, we look for a structure that is smaller than the set of codewords of minimal support that provides us with some information about the GHWs. We prove that the first and second generalized Hamming weights of a binary linear code can be computed (by means of a graded free resolution) from a set of monomials associated with a binomial ideal related with the code. Moreover, the remaining weights are bounded above by the degrees of the syzygies in the resolution.
Feasibility of the Development of New PVC/Fine Sand Imitation Stone Composite Materials to Promote Rural Urbanization

At present, with the acceleration of rural urbanization, people’s market demand for new building materials is also increasing, and the research and development of new PVC/fine sand imitation stone composite materials are also in continuous progress and development. The purpose of this article is to analyze the feasibility of developing new PVC/fine sand imitation stone composite materials to promote rural urbanization and propose the application of more new PVC/fine sand imitation stone composite materials to our urbanization construction. In, we first conducted research on the new PVC/fine sand imitation stone composite material and rural urbanization, and then, we conducted research on the various properties of the new PVC/fine sand imitation stone composite material and the development of rural urbanization. The research results show that the new PVC/fine sand imitation stone composite material has at least 400% improvement in compressive performance compared with the traditional PVC material, and it is also better than the traditional PVC material in terms of the material expansion and contraction performance, which is more conducive to the application our rural urbanization is under construction.
Engineering Annotations: A Generic Framework for Gluing Design Artefacts of Interactive Systems

Along the design process of interactive system multiple intermediate artefacts (such as user interface prototypes, task models, dialog models?) are created, tested, revised, and improved until the development team produces a full-fledged system. However, relevant information for describing the design solution and/or supporting design decisions (such as rational about the design, decisions made, recommendations, etc.) are not explicitly captured in the models/artefacts, hence the need for annotations. Many approaches argue against information duplication to increase maintainability of the artefacts. Nonetheless, annotations created on one artefact are usually relevant to other artefacts/models. So that, there is a need for tools and techniques to coordinate annotations across artefacts/models which is the contribution of the present work. In this paper, we propose a model-based approach that was conceived to handle annotations in a systematic way along the development process of interactive systems. As part of the solution, we propose an annotation model built upon the W3C's Web Annotation Data Model. The feasibility of the approach is demonstrated by means of a tool suite featuring a plugin, which has been deployed and tested over the multi-artefacts. The overall approach is illustrated on the design of an interactive cockpit application performing two design iterations. The contribution brings two main benefits for interactive systems engineering: i) it presents a generic pattern for integrating information in multiple usually heterogenous artefacts throughout the design process of interactive systems; and ii) it highlights the need for tools helping to rationalize and to document the various artefacts and the related decisions made during interactive systems design.
Self-Supervised Implicit Attention: Guided Attention by The Model Itself

We propose Self-Supervised Implicit Attention (SSIA), a new approach that adaptively guides deep neural network models to gain attention by exploiting the properties of the models themselves. SSIA is a novel attention mechanism that does not require any extra parameters, computation, or memory access costs during inference, which is in contrast to existing attention mechanism. In short, by considering attention weights as higher-level semantic information, we reconsidered the implementation of existing attention mechanisms and further propose generating supervisory signals from higher network layers to guide lower network layers for parameter updates. We achieved this by building a self-supervised learning task using the hierarchical features of the network itself, which only works at the training stage. To verify the effectiveness of SSIA, we performed a particular implementation (called an SSIA block) in convolutional neural network models and validated it on several image classification datasets. The experimental results show that an SSIA block can significantly improve the model performance, even outperforms many popular attention methods that require additional parameters and computation costs, such as Squeeze-and-Excitation and Convolutional Block Attention Module. Our implementation will be available on GitHub.
Interactive Exploration of Large Dendrograms with Prototypes

Hierarchical clustering is one of the standard methods taught for identifying and exploring the underlying structures that may be present within a dataset. Students are shown examples in which the dendrogram, a visual representation of the hierarchical clustering, reveals a clear clustering structure. However, in practice, data analysts today frequently encounter datasets whose large scale undermines the usefulness of the dendrogram as a visualization tool. Densely packed branches obscure structure, and overlapping labels are impossible to read. In this article we present a new workflow for performing hierarchical clustering via the R package called protoshiny that aims to restore hierarchical clustering to its former role of being an effective and versatile visualization tool. Our proposal leverages interactivity combined with the ability to label internal nodes in a dendrogram with a representative data point (called a prototype). After presenting the workflow, we provide three case studies to demonstrate its utility.
Natural Language Processing based Bug Categorization

Bug emergencies, such as deciding how to handle an impending bug report, are consuming an increasing percentage of designer assets in large open-source projects. This research study proposes the implementation of AI techniques to assist the bug emergencies by applying the text arrangement to foresee the designer who must concentrate on the issue based on the bug's portrayal. The proposed approach is demonstrated by using a variety of bug reports from a large open-source project. The evaluation process reveals the effectiveness of utilizing the administered Bayesian learning to predict and report different tasks to engineer.
Museum and exhibit design: How forms and places of knowledge exchange influence community participation and empowerment

Museums have been criticized for sharing a singular narrative that does not reflect the diverse beliefs and values of their communities.Participatory design has been recognized as an approach capable of processes that allow communities to contribute to designing museums and exhibits.However, as the participatory design processes change according to the specific situation, they can lead to a wide range of outcomes, and degrees of community participation.This paper explores how a specific element of the participatory design approach, knowledge exchange, influences the degree of community participation.Three examples of participatory design processes in museum and exhibit design are analyzed and compared to understand how the degree of participation varies through Arnstein's Ladder of Citizen Participation.Results reveal that processes that are community-driven and embrace frequent knowledge exchange between designers and community members achieve higher levels of community participation.
Predictive Scaling of Elastic Pod Instances for Modern Applications on Public Cloud through Long Short-Term Memory

In Cloud Computing (CC) environment, container virtualization through Docker engine is bringing the new digital transformation in the enterprise multi-tier application architecture in this modern era. Elastic pod containers attracts and helps the application developers in developing and executing the cloud-native modern applications with key benefits such as light weight, agility to launch, easy deployment through images, consuming less power, minimum cost, less carbon footprints with increased resource utilization and provisioning on public cloud data centers. In existing, reactive auto-scaling mechanism of pod resources is used to add or remove resources manually or rule based for handling static workloads from users and most of the times it may lead to over provision or under provisioning of instances that violates QOS and SLA. In this paper, predictive horizontal scaling of pods utilizing custom metrics with orchestration through Kuberenetes is proposed with LSTM (Long Short-Term Memory) based on Deep Learning (DL) technique. DL is data hungry for right prediction of replicas of a cluster that is needed in advance to run cyclic workloads and to handle the sudden spike of demand by using the cloud large dataset real time traces. Moreover, LSTM model is compared with GRU (Gated Recurrent Unit) and experimental results shows that the results of LSTM prediction has less absolute error rate on comparing with GRU to keep the resource provisioning accuracy better for running the modern workloads seamlessly on public cloud.
Frontal Face Landmark Generation using GAN

In the area of video surveillance, there are many challenges identified due to illumination variation, pose variation, angular variation, occlusion etc for face identification. It is very crucial to identify face images from CCTV footages, uncontrolled environment because of low resolution, poor quality of camera and distance between camera and face. This leads to poor quality image generation and difficult to identify face. This paper proposes a novel system which handles pose variation of face. In this paper, GAN (Generative Adversarial Network) is used to generate landmark of frontal face from side view of the face image. 2D face can be generated based on the frontal face landmark we have generated. Experimental results show that frontal face landmark can be generated easily which is further helpful to generate front face.
Identification of Extragalactic Globular Clusters Using Machine Learning Techniques

Classification of extragalactic globular clusters (GC) is a process that requires a considerable amount of time from experts. To facilitate this laborious process, in this study, we studied a machine learning-based classification pipeline and demonstrated its application on the classification of GC and other celestial object classes in the galaxy M81. Due to the lack of annotated data, we also need to infer pseudo labels of celestial objects in the images based on data clusters. The proposed pipeline starts with a feature extraction step using the autoencoder method. Then, after obtaining the feature vector, the objects are grouped into 12 clusters by a clustering algorithm. After that, the cluster labels are used as pseudo labels for training a classification model. The experimental results based on 10-fold cross-validation showed that the combination of subspace clustering and deep neural network reached the best average accuracy of 78.68% among the three clustering algorithms and the five classification models under comparison.