Reviewerly/exHarmony huggingface dataset:
Tangible Stickers: A Sensor Based Tangible User Interface

In this paper we present the Tangible Stickers, a tangible interface framework which is based on small devices that include Inertial Measurement Units (IMU) sensors, such as gyroscopes and accelerometers. These Tangible Input Devices (TID) are attached to physical objects turning them into input devices, which transmit the sensed data wirelessly to a paired server. The server maintains the states of its paired devices in a stateful manner and expose these devices with their state to interactive applications connected to the server. These applications interact with the paired devices and augment their attached physical objects creating a tangible user interface. Our framework enables an application developer to easily incorporate a tangible interface into their applications, which communicate with the server to receive the state of these devices, and update the state of their digital counterparts. We have implemented the proposed framework, tested our implementation on various scenarios and conducted a user study, whose results were encouraging.
Critical Aspects of the Software Industry in Chile: An Ethnographic Study

Detecting problems in the software industry is of vital importance to develop improvements and understand their origins and consequences. With detailed analysis of the national industry, we can be aware of its most significant weaknesses. This paper studies the challenges affecting the software industry in Chile through an ethnographic study performed through embedded interactions with software development companies. We identified that budget availability is the most relevant and limiting factor for the industry to execute the development of new software efficiently. This research offers a baseline on which to recommend improvements to the problems afflicting Chile's software industry.
Multiple Learners Combination: Stacking

Advanced computational techniques have found ever increasing applications in bioinformatics. In this field, machine learning techniques applied to prediction problems are of particular relevance. Of course, the more accurate the models are, the better the prediction will be. However, for free domain problems, theoretical evidence shows that there is no predictor always having better performance than another. The ensemble methods arise from the idea that instead of looking for the learning algorithm inducing the “optimal” learner for that particular problem, accuracy can be improved by combining more “weak” learner to obtain a “strong” one. This article presents stacking, a general method for combining multiple learner that can be approached to cross-fold validation.
USING THE GO-LAB SOFTWARE FOR THE ELECTRONIC COLLABORATION OF TEACHERS AND STUDENTS IN THE RESEARCH ENVIRONMENT

The article outlines the possibilities of using the Go-Lab ecosystem to organize collaborative learning in Inquiry Based Learning, provides guidance for teachers to organize the learning process. The digital tools, applications of the Go-Lab ecosystem for use by teachers of virtual online labs (Phet.Colorado), applications, research spaces of the Graasp environment are considered. To achieve the goal, we use theoretical methods of systematic and comparative analysis of educational and scientific-methodical literature. Examples and useful links are provided for teachers who want to develop their own online environments for organizing and conducting research training in a collaborative environment. The possibilities of organizing electronic collaboration for teachers and students with the help of digital services and applications Go-Lab are presented. Students' electronic collaboration is carried out through communication to exchange ideas, share responsibility for the result, joint research and reporting, and facilitate their preparation for professional selection and professional team’s work. The possibilities of using not only the Go-Lab a and other external services Web 2.0 for collaborative research (Padlet, Wiki, Speak Up, etc.) are explored. Some research study scenarios in Go-Lab (a basic, open-cut saw, six hats) and organization of cooperation in them. To illustrate the range of Go-Lab application features, examples of their use are described at various stages of the study. These tools are analyzed by describing their functional capabilities, as well as the impact on student learning. Further research requires the use of Go-Lab applications to organize collaboration at various stages of research in high school and in higher education.
35.3: <i>Invited Paper:</i> Effects of Luminance, Contrast and Saturation of HDR QLED Display on Visual System Based on Eye Movement

This paper investigated the effects of luminance, contrast and saturation of HDR video presented on QLED display on visual system. Results showed that mainly, contrast and saturation influenced visual neural systems controlling eye movements, as well as subjective evaluation of display quality and viewing preference, while luminance generally had no significant influence.
Ion Implantation and Electrical Annealing for Trimming Silicon MZIs and Facilitating One-Time Programmable Photonic Circuits

We use Ge ion implantation and electrical annealing, via TiN micro-heater, to trim silicon MZIs. The results show the possibility for switching the output power from the drop port to through port permanently, locally and rapidly.
Low-Resolution Person Re-identification by a Discriminative Resolution-Invariant Network

Person Re-identification (re-id) needs to tackle with the problem of changing resolutions because the pedestrians from surveillance systems or public datasets have low-resolution problem (LR-REID) including low quality, blurry textures and so on, which results in a difficult challenge to extract the identity information under various resolutions. However, most existing re-id models are trained by high-resolution (HR) images, which will achieve poor performance when conducted directly on low-resolution images. In this paper, we propose a novel Discriminative Resolution-invariant Network (DRINet) to explore the subspace where LR and HR features are highly correlated and we can extract discriminant features in the commonly shared feature space. Firstly, we adopt ResNet as the backbone and impose the softmax loss together with the triplet loss to learn distinguishing features. Secondly, we impose the KL divergence loss on the backbone features to minimize the discrepancies between LR and HR features. Finally, we integrate the sparse auto-encoder (SAE) structure to find a subspace which is robust to the resolution variations. Experimental results verify the effectiveness of the DRINet in improving the LR-REID performance and the superiority of the DRINet against the state-of-the-art methods.
Face manifold: manifold learning for synthetic face generation

The face is a crucial aspect of human communication and identity. Accurately estimating face structure is a fundamental task in computer vision, with significant applications in various fields, including facial recognition and medical surgeries. Deep learning techniques have made notable progress in 3D face reconstruction from 2D images. However, this approach demands large 3D face datasets, often tackled by synthetic face generation. Unfortunately, synthetic datasets can contain non-possible faces, which pose significant challenges. This paper presents a novel approach to synthetic diverse face dataset generation by leveraging face manifold learning. We divide the face structure into shape and expression groups and use a fully convolutional autoencoder network to handle non-possible faces while preserving dataset diversity. The proposed method is used to train deep 3D reconstruction networks and results indicate that our proposed method demonstrates its effectiveness in denoising highly corrupted faces. Additionally, we assess the diversity of the generated dataset qualitatively and quantitatively, comparing it to existing methods, and find that our manifold learning method outperforms state-of-the-art methods significantly. The reliability results show that more than 99% of the generated faces are acceptable as real faces.
Students' Perceptions in Software Modelling Using UML in Undergraduate Software Engineering Projects

The article establishes student perceptions in software modelling using UML in their final undergraduate projects. The research was done at a University in Botswana. The research used a mixed methods approach, an open-ended questionnaire was administered to the participants. A sample size of 140 participants that met the inclusion criteria was used in the research. The key findings from the research were that students find modelling using UML to be challenging. Some of the project supervisors were assigned to assist students with their projects were not knowledgeable with the student's project topic.
Research on Weapon Equipment Acquisition Benefit Evaluation Based on Artificial Neural Network

The evaluation of weapon equipment acquisition benefit is an important basis and traction to promote Weapon equipment acquisition. This paper defined the concept of weapon equipment acquisition benefit evaluation, construct the evaluation index of weapon equipment acquisition benefit, established the evaluation model of weapon equipment acquisition benefit based on BP artificial neural network, and gave a case to study. The conclusion can provide a reference for promoting the practice of weapon equipment acquisition benefit evaluation.
The concept of "information model" in the informatics course

The article deals with the concept of "information model", which is widely used in informatics courses. Various points of view on this concept in educational, scientific and methodical literature are considered. The publication reflects the personal experience of the author of teaching the course "Computer modeling" in South Ural State University of Humanities and Education.
Usefulness Examination of a Learning Task Analysis Diagram in Instructional Design Made by University Students Majoring in Technology Education

The purpose of this study is to verify the usefulness of a learning task analysis diagram in instructional design made by university students who major in technology education. 8 university students who experienced teaching practice made learning task analysis diagrams and then those were analyzed. As a result, the characteristics of the learning task analysis diagrams were clarified, were clarified, including the type of diagram, the number of learning tasks, the transition of extraction, and the flow of learning. In addition, as a results of questionnaire survey, it was suggested that the structure and sequence of learning tasks are visually represented, which is useful in instructional design.
OCSVM-based Evaluation Method for Generative Neural Networks

Humanity has desired to create machines that can comprehend fine arts for a long while. Recently, several articles report neural network models that managed to create something which can be considered as art, such as paintings or music. Naturally, these proposals are also leveraged in the field of robotics and autonomous vehicles for tasks such as training environment generation and efficient exploration in reinforcement learning. The common feature of all these approaches is the utilization of generative neural networks. These generative models are a trendy and actively researched area of machine learning methods. However, it is hard to find a general and objective metric for the evaluation of such models. In this paper, a One-Class Support Vector Machine-based solution is proposed. The basic concept is demonstrated via experiments on a set of GANs. The presented method can be extended and refined in further, more exhaustive studies to serve as an alternative for the current state-of-the-art procedures.
Coherence Proprieties of Entangled Bi-Modal Field and Its Application in Holography and Communication

This chapter examines the coherence properties of two modes of entangled photons and its application quantum communication and holography.It is proposed novel two-photon entangled sources which take into account the coherence and collective phenomena between the photon belonging to two different modes obtained in two-photon cooperative emission or Raman or lasing.The generation of the correlated bimodal entangled field in two-photon emission or Raman Pump, Stokes and anti-Stokes modes is proposed in the free space and cavity-induced emission.The application two-photon and Raman bimodal coherent field in communication and holography are given in accordance with the definition of amplitude and phase of such entangled states of light.At first, this method does not appear to be essentially different in comparison with the classical coherent state of information processing, but if we send this information in dispersive media, which separates the anti-Stokes and Stokes photons from coherent entanglement fields, the information is drastically destroyed, due to the quantum distribution of photons in the big number modes may be realized in the situation in which the mean value of strength of bimodal field tends to zero.The possibility of restoration of the signal after the propagation of the bimodal field through different fibers, we may restore the common square amplitude and phase.
A Formative Study for Record-time Manual Annotation of First-person Videos

To efficiently edit first-person videos, manually highlighting important scenes while recording is helpful. However, little study has been performed on how such annotation contributes to video editing and affects user behavior during recording. To elicit fundamental requirements for designing useful record-time annotation techniques, we conducted a study using a set of prototype wearable camera system and a video editing interface that enables users to annotate scenes during recording. We asked participants to perform video recording and editing tasks with two different interface settings. We observed that the participants edited videos more efficiently with detailed annotation techniques, whereas focussing on annotating scenes affected their record-time behavior. We conclude the paper with the design guidelines developed from the findings.
Measurement of Transverse Chromatic Aberrations in Virtual Reality Headsets

We demonstrate a digital test pattern and a methodology for measuring the transverse chromatic aberrations in head-mounted displays. The test pattern was also used for psychophysics TCA measurements, which agreed with the laboratory results.
Test Case Analysis and Design

Test case analysis and design are concerned with analysing the requirements to determine the test conditions and designing the test cases (using various techniques). The requirements and test conditions are used to specify the test cases, where each test case includes input, a procedure for carrying out the test, and the expected results. The quality of the testing is influenced by the quality of the test cases, and they need to be designed to cover the requirements. Traceability of the test cases to the requirements ensures that the test cases are sufficient to verifythat all of the requirements have been implemented and tested.
Extending an Anomaly Detection Benchmark with Auto-encoders, Isolation Forests, and RBMs

In this paper, the recently published benchmark of Goldstein and Uchida [3] for unsupervised anomaly detection is extended with three anomaly detection techniques: Sparse Auto-Encoders, Isolation Forests, and Restricted Boltzmann Machines. The underlying mechanisms of these algorithms differ substantially from the more traditional anomaly detection algorithms, currently present in the benchmark. Results show that in three of the ten data sets, the new algorithms surpass the present collection of 19 algorithms. Moreover, a relation is noted between the nature of the outliers in a data set and the performance of specific (clusters of) anomaly detection algorithms.
Feature Enhancement for Joint Human and Head Detection

Human and head detection have been rapidly improved with the development of deep convolutional neural networks. However, these two detection tasks are often studied separately, without taking advantage of the relationship between human and head. In this paper, we present a new two-stage detection framework, namely Joint Enhancement Detection (JED), to simultaneously detect human and head based on enhanced features. Specifically, the proposed JED contains two newly added modules, i.e., the Body Enhancement Module (BEM) and the Head Enhancement Module (HEM). The former is designed to enhance the features used for human detection, while the latter aims to enhance the features used for head detection. With these enhanced features in a joint framework, the proposed method is able to detect human and head simultaneously and efficiently. We verify the effectiveness of the proposed method on the CrowdHuman dataset and achieve better performance than baseline method for both human and head detection.
25.1: <i>Invited Paper:</i> Prototyping of e‐Tile for an Enlargement of e‐Paper Applications to Architecture

We have introduced a novel concept for large area displays, “e‐Tile”. A typical e‐Tile configuration, in which 100 pixels are mounted on a 100 mm square board, is designed and prototyped. One promising application is an unobtrusive information board, which is far less annoying than the conventional vivid LED/LCD in public spaces.
Using Statistical Modeling for Collection Development at the Library of the College of Engineering of Nanjing Agricultural University

This paper selected the library of the College of Engineering, Nanjing Agricultural University as a subject and then surveyed four groups of samples: new-book replica rate, book-use rate, book-borrowed number, and number of books in the collection. Results were obtained by inspecting these data via the statistical methods of correlation and regression analyses, and two relationships were found: 1) the quadratic curve between the new-book replica rate and book-use rate; and 2) the power equation between the book-borrowed number and the number of books in the collection. Finally, after the new-book replica rate and the number of books in the collection were compared and predicted, respectively, this paper formulates a reasonable strategy for the library’s hard-copy book purchase procedure. It would be helpful for the librarians who are interested in document resource developing or purchase of hard-copy books; however, this paper provides a method specific to engineering colleges. There are certain limitations in this paper, such as, the timespan of the four-group sample data is only one year.
Factual Extraction with Choicest Retrieval in CBIR Using Angular Robust Techniques

Content-Based Image Retrieval (CBIR) plays a vital role in various digital image processing fields such as image retrieval, classification, feature extraction, clustering, and indexing. In some cases, the high computational time as well the poor performance of similarity score makes the process of image retrieval an unsuitable one for CBIR having larger datasets. To overcome this trauma, the article presents a combined scheme to perform image retrieval and feature extraction using an eminent utilization of angular patterned wavelet Fourier descriptor with Randomized Robust Learning. With this prominent technique, both the local and the global feature descriptors are extracted to attain a maximum retrieval accuracy. Furthermore, the learning process is made prominent, owing to the unique extracted features. It makes the process of indexing similar images faster and the retrieval of relevant images with less computational time in an organized manner. Our proposed methodology is executed in MATLAB and is compared with the existing methodologies regarding retrieval accuracy and surfing time by fast indexed output.
The new biological revolution: the creation of synthetic human entities with embryo-like features

The need to prevent and cure genetic diseases has never been greater in recent times.However, as the quest for heathier offspring and qualitative reproduction grows, buoyed by successes through daunting researches, there are increasingly nascent ramifications of this quest; which is the increasing quest for modern couples to choose babies with some subtle genetic characteristics that goes against the aim of the initial quest to prevent and cure genetic diseases.Modern privileged couples especially in the Western societies are increasing shaping and modelling the type or characteristics of the baby they want to have.This was made possible by the scientific evidence that had emerged.There are increasing concerns that as the society evolves and new research reveals more of human genomics, the ethical boundaries would increasingly become more eroded and less clear cut, and therefore lead to increases in the misuse or abuse of this initial aim of our scientific quest.This could subsequently plunge humanity into self-made destruction.In this article, the author reviews the emerging evidence and speculates the trajectory of the debacle of misuse and abuse of human genomics. Conclusion:In view of the ethical issues raised in this article, the author wonders what would happen to humanity as its science advances.As humanity exploits its own genomics to its advantage, it could well be exploiting its genomics at its own peril.
Restricted Boltzmann Machines: an Eigencentrality-based Approach

Restricted Boltzmann Machines (RBMs) are usually trained using contrastive divergence learning, which requires setting suitable values for a number of hyper-parameters, and requires a degree of practical know-how. This paper presents a radically different approach to RBMs, which we refer to as the EigenCentrality-based Restricted Boltzmann Machine (ECRBM). Rather than actually `training' the RBM, the dataset is first represented as a bipartite graph consisting of a layer of attribute-value nodes and a layer of object nodes, with the representation chosen such that the components of an eigenvector calculated from the graph can be used to estimate the conditional probabilities of the attribute values. Matrix factorization is then used to replace this graph with a smaller graph, having the important property that eigenvectors calculated on this smaller graph preserve the interpretation of the eigenvector components as conditional probabilities. The model can be used to estimate and sample from any conditional or marginal distribution, and can therefore be applied to a diverse range of machine learning tasks including classification, regression, missing value imputation, outlier detection, and random vector generation. It can be applied to mixed-attribute datasets as easily as it can be applied to datasets containing variables of the same modality. We demonstrate the method by applying it to classification and random vector generation on a number of mixed-attribute datasets.