Reviewerly/exHarmony huggingface dataset:
Implementation of Convolution Function Through Fast Fourier Transform in Convolution Neural Networks Computation

This paper investigates the possibility of performing the convolution function in the frequency domain using the fast Fourier transform. The paper presents the proposed structure of the convolution function implementation, comparing the computational complexity of convolution when performed by the classical method and using the FFT. The pros and cons of performing the convolution function using FFT are described. The implementation of the classical convolution function and convolution by FFT in VHDL is done and the results are given: running time, required external memory bandwidth, FPGA resources occupied. Several possible solutions to the increased load on the external memory are proposed.
3Dify: Extruding Common 2D Charts with Timeseries Data

3D charts are not common in financial services. We review chart use in practice. We create 3D financial visualizations starting with 2D charts used extensively in financial services, then extend into the third dimension with timeseries data. We embed the 2D view into the the 3D scene; constrain interaction and add depth cues to facilitate comprehension. Usage and extensions indicate success.
On Discrete Fractional Complex Gaussian Map: Fractal Analysis, Julia Sets Control, and Encryption Application

This work is devoted to present a generalized complex discrete fractional Gaussian map. Analytical and numerical analyses of the proposed map are conducted. The dynamical behaviors and stability of fixed points of the map are explored. The existence of fractal Mandelbrot and Julia sets is examined along with the corresponding fractal characteristics. The influences of the key parameters of the map and fractional order are examined. Moreover, nonlinear controllers are designed in the complex domain to control Julia sets generated by the map or to achieve synchronization between two Julia sets in master/slave configuration. Numerical simulations are provided to attain a deep understanding of nonlinear behaviors of the proposed map. Then, a suggested efficient chaos-based encryption technique is introduced by integrating the complicated dynamical behavior and fractal sets of the proposed map with the pseudo-chaos generated from the modified lemniscate hyperchaotic map.
Farsi Text Detection and Localization in Videos and Images

Automatic text detection and recognition in images and videos have emerged and aroused widespread interest in recent years due to the dramatic growth of visual information. It seems that there is the lack of any effective model for the Farsi text detection in images. In this paper, a new framework is proposed for the Farsi text detection and localization using the up-to-date real-time object detection framework YOLOv5 in videos and images. To evaluate the novel model, a new dataset of news videos is collected. Experimental results show that the proposed model achieves quite promising performance on the new dataset.
Effective Contract As a Means of Monitoring of the Professional Achievements of a Scientific and Pedagogical Worker

Based on the analysis and generalization of research, the experience of implementing effective contracts in the activities of universities, the article provides a rationale for an effective contract as a means of monitoring the professional achievements of a scientific and pedagogical worker in the context of a comprehensive assessment of the effectiveness of the university. The purpose of the article is to reveal the historical roots of the implementation of an effective contract in the practice of higher educational institutions, to describe its functional purpose, main characteristics and implementation experience. The authors reveal the reasons for the transition of universities to a criterion assessment of the effectiveness of the professional activities of scientific and pedagogical workers, the concept and principles of the development and implementation of an effective contract are given, variations in the criteria and indicators of teachers' performance reflected in an effective contract, as well as mechanisms for their provision and accounting are presented. The study allows you to develop an understanding of an effective contract as a method of objective complex assessment of the results of scientific and pedagogical work and an innovative mechanism of educational management.&#x0D; The research materials deepen the understanding of monitoring the professional activity of a scientific and pedagogical worker in the context of globalization, which complements the existing scientific understanding of the determinants of systemic changes in the professional activity of a higher school teacher and theoretical ideas in the field of educational qualimetry.
Matching and analysing conservation–restoration trajectories

The context of this work is an on-going project at the French National Library (BnF), which aims at providing predictions of the documents physical state based on their conservation–restoration histories. A document can be either in a good state and available to the readers, or damaged and unavailable to them. As libraries may contain millions of documents, the manual monitoring and analysis of their physical state is not realistic in practice. We therefore propose to analyse their conservation histories in order to derive reliable predictions of their physical state. To achieve this goal, we introduce in this paper the following contributions. First, we propose a representation of a document conservation history as a conservation–restoration trajectory, and we define its different types of events. We also propose a trajectory matching process that computes a similarity score between two conservation–restoration trajectories considering the terminological heterogeneity of the events, using an ontological model that represents the domain experts knowledge. Second, we provide a trajectory analysis process which identifies the most representative sequences of events of the deteriorated documents. Finally, we propose a prediction model for the physical state of the documents based on the trajectory analysis process. We present some experiments showing the effectiveness of the matching process as well as the prediction model.
Emotional Attention: From Eye Tracking to Computational Modeling

Attending selectively to emotion-eliciting stimuli is intrinsic to human vision. In this research, we investigate how emotion-elicitation features of images relate to human selective attention. We create the EMOtional attention dataset (EMOd). It is a set of diverse emotion-eliciting images, each with (1) eye-tracking data from 16 subjects, (2) image context labels at both object- and scene-level. Based on analyses of human perceptions of EMOd, we report an emotion prioritization effect: emotion-eliciting content draws stronger and earlier human attention than neutral content, but this advantage diminishes dramatically after initial fixation. We find that human attention is more focused on awe eliciting and aesthetic vehicle and animal scenes in EMOd. Aiming to model the above human attention behavior computationally, we design a deep neural network (CASNet II), which includes a channel weighting subnetwork that prioritizes emotion-eliciting objects, and an Atrous Spatial Pyramid Pooling (ASPP) structure that learns the relative importance of image regions at multiple scales. Visualizations and quantitative analyses demonstrate the model's ability to simulate human attention behavior, especially on emotion-eliciting content.
Scenario-based Investigation of Acceptance of Electric Muscle Stimulation

Electrical Muscle Stimulation (EMS) provides multiple interaction possibilities in various fields. It may transmit object affordances, user motions or give rich haptic feedback by adjusting stimulation settings, especially in virtual worlds. Parameters necessary to accept EMS, impacting how users perceive the technology, are yet not thoroughly studied. We investigated users’ acceptance of EMS by conducting an online survey (N=113) and an interview (N=5) study using four scenarios derived from the literature. We used the technology acceptance paradigm (TAM) to assess user acceptance and gathered factors for accepting/rejecting the technology. By connecting survey results and participants’ impressions of the four EMS scenarios to emerging themes, we can show that potential users reject EMS systems if they perceive a high level of risk and a lack of the sense of agency. We drew inferences based on our findings that will guide in designing and developing products that provide acceptable experiences.
Empathic and Empathetic Systematic Review to Standardize the Development of Reliable and Sustainable Empathic Systems

Empathy plays a crucial role in human life, and the evolution of technology is affecting the way humans interact with machines. The area of affective computing is attracting considerable interest within the human–computer interaction community. However, the area of empathic interactions has not been explored in depth. This systematic review explores the latest advances in empathic interactions and behaviour. We provide key insights into the exploration, design, implementation, and evaluation of empathic interactions. Data were collected from the CHI conference between 2011 and 2021 to provide an overview of all studies covering empathic and empathetic interactions. Two authors screened and extracted data from a total of 59 articles relevant to this review. The features extracted cover interaction modalities, context understanding, usage fields, goals, and evaluation. The results reported here can be used as a foundation for the future research and development of empathic systems and interfaces and as a starting point for the gaps found.
Variational cycle-consistent imputation adversarial networks for general missing patterns

Imputation of missing data is an important but challenging issue because we do not know the underlying distribution of the missing data. Previous imputation models have addressed this problem by assuming specific kinds of missing distributions. However, in practice, the mechanism of the missing data is unknown, so the most general case of missing pattern needs to be considered for successful imputation. In this paper, we present cycle-consistent imputation adversarial networks to discover the underlying distribution of missing patterns closely under some relaxations. Using adversarial training, our model successfully learns the most general case of missing patterns. Therefore our method can be applied to a wide variety of imputation problems. We empirically evaluated the proposed method with numerical and image data. The result shows that our method yields the state-of-the-art performance quantitatively and qualitatively on standard datasets.
A User-Centered Approach to the Application of BIM in Smart Working Environments

Considerable research has been performed on smart working environments in the architecture, engineering, and construction industry, with building information modeling considered as a critical element for implementing intelligent working systems. Although much software has been developed, a lack of understanding inhibits a user-centered approach to the application of building information modeling in architectural design offices. This study focuses on usability factors for the development of software and proposes a direction for the adoption of building information modeling in architectural design offices. This study adopts a persona method that focuses on user experience, starting with a survey conducted in two large domestic architectural offices. For developing the persona scenarios, this study provides a conceptual framework of usability, identifies user demands, and characterizes user experience. Four representative personas were developed for the representative types of users in smart working environments. The persona scenarios provide a basis that directly reflects user needs and experiences regarding the use of building information modeling software in architectural design offices. Two implications of the application of building information modeling are proposed based on the scenarios: a user-friendly working environment for smart workflows and a customized training program focusing on user experience for the use of building information modeling software.
Blockchain‐based distributed electricity transaction model

Problems of the current electricity transaction include high maintenance costs, low transaction efficiency, and insufficient security. A possible solution should be proposed tentatively with a blockchain-based transaction model for distributed electricity. This paper attempts to build a decentralized electricity trading scenario within the electricity trading model by introducing blockchain technology, combining the reliability coefficient with both the smart contract and credit value mechanism. Finally, the reliability coefficient and the credit value are introduced into the simulated electricity transaction. The results show that the transaction verification time of the traditional electricity transaction model is 410 seconds at the highest and 330 seconds at the lowest. Meanwhile, the transaction verification time of the blockchain-based distributed power transaction model is 410 seconds at the highest and 300 seconds at the lowest, which means the blockchain-based distributed power transaction has a lower transaction verification time and more efficiency, effectively suppressing the frequency of defaults, reducing the risk of being attacked, and enhancing security.
On Using Explainable Artificial Intelligence for Failure Identification in Microwave Networks

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field networkmanagement application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for automated failure-cause identification in microwave networks. We first show how existing supervised ML algorithms can be used to solve the problem of failure-cause identification, achieving an accuracy around 94 &#x0025;. Then, we explore the application of well-known XAI frameworks (such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME)) to address important practical questions rising during the actual deployment of automated failure-cause identification in microwave networks. These questions, if answered, allow for a deeper understanding of the behavior of the ML algorithm adopted. Precisely, we exploit XAI to understand the main reasons leading to ML algorithm&#x0027;s decisions and to explain why the model makes identification errors over specific instances.
Ontology-based task planning for autonomous unmanned system: framework and principle

Abstract Operational knowledge is an important factor to improve the autonomy of unmanned systems. How to store and represent operational knowledge semantically and normatively, so that unmanned systems can autonomously accomplish task planning according to the high-level order of humans, carry out re-planning, and make decisions has been a new topic in the autonomous unmanned system field. This paper reviews several typical robot ontologies, as well as modeling and representation methods of uncertain information, proposes the concept of Ontology-based Task Planning(OTP) for the unmanned system, and introduces the principles of ontology construction. Finally, the application of ontology in unmanned combat systems are discussed.
An Adaptive Cooperative Coevolutionary Algorithm for Parallel Feature Selection in High-Dimensional Datasets

Nowadays, it is common in many disciplines and application fields to collect large volumes of data characterized by a high number of features. Such datasets are at the basis of modern applications of supervised Machine Learning, where the goal is to create a classifier for newly presented data. However, it is well known that the presence of irrelevant features in the dataset can lead to a harder learning phase and, above all, can produce suboptimal classifiers. For this reason, the ability to select an appropriate subset of the available features is becoming increasingly important. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features since common optimization algorithms often suffer from poor scalability with respect to the size of the search space. In this paper, the problem of feature subset optimization is successfully addressed by a cooperative coevolutionary algorithm based on Differential Evolution. In the proposed algorithm, parallelized for multi-threaded execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adapting the population size during the optimization results in a significant performance. A numerical investigation on some high-dimensional datasets show that, in most cases, the proposed approach can achieve smaller feature subsets and higher classification performance than other state-of-the-art methods.
Unsupervised and optimized thermal image quality enhancement and visual surveillance applications

Thermal images suffer from low-luminance issues under specific conditions, such as heat radiation, distance-to-radiated objects, reflection angles. Low-luminance thermal images degrade the performance of a vision-based system. Present thermal contrast enhancement algorithms usually tolerate the background, noise amplification, and brightness distortion from over-enhancement. This paper introduces; i) an image enhancement algorithm for thermal satellite and aerial images; ii) a novel thermal image quality assessment; iii) a modified nature-inspired algorithm to search optimal parameter for the proposed enhancement algorithm; and iv) the application of the photovoltaic panel inspection by using the proposed algorithms. Experimental results thermal photovoltaic panel images with various luminance scenes from two datasets demonstrate the proposed method's effectiveness compared with state-of-the-art algorithms both qualitatively and quantitatively. Moreover, the proposed algorithm performs favorably for low-luminance thermal images with different illumination levels while enhancing the dark regions' details and preserving mean-brightness.
Gesprochene Lernerkorpora: Methodisch-technische Aspekte der Erhebung, Erschließung und Nutzung

Abstract This article provides an overview of methodological and technical issues that arise in the collection, indexing and use of spoken learner corpora, i. e. corpora containing spoken utterances of learners of a target language. After an introductory discussion of the most important special features of this type of corpus that distinguish it from written language learner corpora and spoken corpora with L1 speakers, we will go into more detail on questions of corpus design. The main part of the paper is then an overview of the methodological and technical procedures of the individual steps of collecting, indexing, providing and using spoken learner corpora. The main aim of this overview is to highlight practices that can be considered best practices according to the current state of research. Finally, we outline the challenges that still exist for this type of corpus.
The Effects of Augmented Reality in the Technical Drawing Course on Engineering Students' Spatial Ability and Academic Achievement

The purpose of this study is to investigate the effects of Augmented Reality (AR) intervention on students' spatial skills and academic achievement. The study was conducted in a freshman technical drawing course at Mechanical Engineering Department. An exploratory quasi-experimental method was used for the study. The study was conducted in two groups (Experiment-Control) as a quasi-experimental pre-post design. The pre-test results were used only to determine the control group and the experimental group. The research process was conducted within a four-week experimental period, including a one-week pilot study. The researchers and lecturers who are recognised experts prepared the instruments. The drawings of the experimental and control groups were evaluated using the evaluation criteria prepared by the two expert lecturers of the course. In the evaluation, two types of scores were given to the students, namely "Academic Achievement Score" and "Spatial Ability Score". Using ANOVA, the effects of AR intervention on students' spatial skills and academic performance were examined over three experimental periods. There was a significant main effect for both groups with a large effect size (η2=.253). However, it was found that there was no significant effect between the control group and the experimental group on spatial skills. However, it was found that there was a significant interaction effect providing the interaction between time and group on spatial abilities. In addition, no statistically significant difference was found between the academic performance of the experimental group and the control group and no significant difference was found in the weekly measurements of the groups. It was suggested that AR applications are very useful for students' spatial skills in technical drawing.
Reconstructing 3D Virtual Face with Eye Gaze from a Single Image

Reconstructing 3D virtual face from a single image has a wide range of applications in virtual reality. Existing approaches synthesize plausible reconstructed virtual faces, however, eye gaze information is usually ignored, which is critical in human-computer interaction. In this paper, we propose to reconstruct 3D virtual face with eye gaze information from a single image. The main challenges lie in two aspects, one is the low reconstruction quality in the eye region, the other one is the lack of an efficient method to obtain precise eye gaze information. To address these problems, we decompose this task into two key steps, i.e., 3D face reconstruction with precise eye region and eye contact guided facial-rotation for eye gaze information. The first step is designed for precise eye region reconstruction through joint optimization on 3D face/eye shapes and textures. The second step consists of two parts: eye contact discriminator and automatic eye contact search algorithm via gradient-based optimization to perform both eye contact and gaze estimation simultaneously. Extensive experiments on different tasks demonstrate the significant gain of the proposed approach, achieving an MSE of (30%), an SSIM of (17.85%), and a PSNR of (8.4%). It also produces lower angular errors (63.01%) in the gaze estimation task compared with human annotations.