Reviewerly/exHarmony huggingface dataset:
EMICS'20: Eye Movements as an Interface to Cognitive State

Eye movement recording has been extensively used in HCI and offers the possibility to understand how information is perceived and processed by users. Hardware developments provide the ubiquitous accessibility of eye recording, allowing eye movements to enter common usage as a control modality. Recent A.I. developments provide powerful computational means to make predictions about the user. However, the connection between eye movements and cognitive state has been largely under-exploited in HCI. Despite the rich literature in psychology, a deeper understanding of its usability in practice is still required. This EMICS SIG will provide an opportunity to discuss possible application scenarios and HCI interfaces to infer users' mental state from eye movements. It will bring together researchers across disciplines with the goal of expanding shared knowledge, discussing innovative research directions and methods, fostering future collaborations around the use of eye movements as an interface to cognitive state, and providing a solid foundation for an EMICS workshop at CHI 2021.
Challenges for a Computational Cognitive Psychology for the New Digital Ecosystem

Advances in computational cognitive psychology have played an important role in understanding and engineering human–information interaction systems. These computational models include several addressing the cognition involved in the human sensemaking process, user models that capture the knowledge that humans acquire from interaction, and how people judge the credibility of online Twitter users who influence decision-making. The models presented in this chapter build on earlier information foraging models in which it is important to model individual-level knowledge and experience because these clearly influence human–information interaction processes. This chapter concludes with a discussion of challenges to computational cognitive models as digital information interaction becomes increasingly pervasive and complex.
Supporting Complex Decision Making by Semantic Technologies

Complex decisions require stakeholders to identify potential decision options and collaboratively select the optimal option. Identifying potential decision options and communicating them to stakeholders is a challenging task as it requires the translation of the decision option’s technical dimension to a stakeholder-compliant language which describes the impact of the decision (e.g., financial, political). Existing knowledge-driven decision support methods generate decision options by automatically processing available data and knowledge. Ontology-based methods emerged as a sub-field in the medical domain and provide concrete instructions for given medical problems. However, the research field lacks an evaluated practical approach to support the full cycle from data and knowledge assessment to the actual decision making. This work advances the field by: (i) a problem-driven ontology engineering method which (a) supports creating the necessary ontology model for the given problem domain and (b) harmonizes relevant data and knowledge sources for automatically identifying decision options by reasoners, and (ii) an approach which translates technical decision options into a language that is understood by relevant stakeholders. Expert evaluations and real-world deployments in three different domains demonstrate the added value of this method.
Meta-level support for facilitating participation in website (re-)design activities

Complex websites need to evolve continuously as they contribute to shaping the relationships between involved stakeholders. The paper presents conceptual ideas and a prototypical tool to facilitate participation in web redesign activities. The proposed meta-level support allows to augment dedicated parts of web pages by sets of design options. End users are provided with means to explore and assess the alternative design ideas in a parallel prototyping manner and to create and share annotations. A real-world example is used to motivate and illustrate the suggested approach. The contribution to end-user design and cultures of participation is discussed.
Exploration of GPU sharing policies under GEMM workloads

Lately, cloud computing has seen explosive growth, due to the flexibility and scalability it offers. The ever-increasing computational demands, especially from the machine learning domain, have forced cloud operators to enhance their infrastructure with acceleration devices, such as General-Purpose (GP)GPUs or FPGAs. Even though multi-tenancy has been widely examined for conventional CPUs, this is not the case for accelerators. Current solutions support "one accelerator per user" schemes, which can lead to both under-utilization and starvation of available resources.
EyeCrowdata: Towards a Web-based Crowdsourcing Platform for Web-related Eye-Tracking Data

Eye-tracking studies are commonly used for identifying the usability problems of Web pages and gaining insights into how the design of Web pages can be improved for better user experience. Similar to other user studies, eye-tracking studies should be carefully designed and conducted by considering ethical issues and confounding factors, and therefore these studies typically require a considerable amount of time. Recruiting a large number of participants is also an important issue as eye-tracking sessions may not be conducted in parallel in case of limited resources such as equipment and researchers. Previous work highlighted the need for a Web-based platform to crowdsource Web-related eye-tracking data and facilitate data sharing, thus allowing the replication of existing analysis. Previous work also presented a preliminary structured literature review on what kinds of metrics are required for such a platform. In this paper, we also focus on Web-related eye-tracking studies, and we present an overview of the extended version of the structured literature review along with a prototype for a Web-based platform for crowdsourcing Web-related eye-tracking data called EyeCrowdata.
A Modified Parallel Heapsort Algorithm

Sorting is a fundamental and essential problem required in the wide range of application fields, and so many sorting algorithms have been developed. Among those algorithms, heapsort is one of the most elegant and efficient sorting algorithms. But no parallel heapsort algorithm had been presented until the authors developed a restricted parallel algorithm a few years ago. This parallel algorithm had a restriction which makes it difficult to be used universally for general data sets. So, in this article, the authors present a modified parallel algorithm which is free from such restriction and can be used for any data set. This new algorithm can achieve almost the same performance as the restricted algorithm the authors developed before.
Multi-channel face reconstruction system based on sketch features using Conditional Adversarial Networks

Face Reconstruction is an important research area in field of Computer Vision and Artificial Intelligence, and has a wide range of related applications in real life. Previous work primarily focuses on face feature extraction through images of real faces, in contrast, our work reconsiders generating real faces with discernible features based on sketch and outlines, in order to be used for face super-resolution reconstruction and face generation, and ultimately for feature face generation using CAN, such as generating real faces according to the descriptions of suspect's facial features by the police. The research content in this paper includes an algorithm for Asian people's face generation without gender bias, a method for generating feature face data sample through digital image processing, and an innovative CAN architecture. The experiments are based on over 14, 000 face samples, which are manually collected from volunteers. Our results show that our face reconstruction system based on sketch features using CAN has higher levels of authenticity, accuracy, and applicability.
Uncertain String Matching Based on Bitmap Indexing

String matching was a basic issue in computer science. It was used extensively in various applications such as biological sequence data, keyword search, pattern recognition. However, in the presence of uncertainty, string matching becomes a probabilistic searching problem. This paper proposed a new indexing over uncertain strings to support efficient string matching. Considering specialty of the sequence data in bioinformatics, we design an indexing structure based on adjacency list. Greedy and range maximum query techniques were combined to realize Greedy Uncertain String Matching (GUSM) algorithm. To achieve further speed-up matching and reduce index space, adjacency list structure was mapped into bitmap indexing. Based on it, Bitmap Uncertain String Matching(BUSM) was proposed to improve matching effectively. Experiment based on multiply datasets were conducted to evaluate the performance of our indexes and algorithms.
Potential Well Analysis of Multi Scale Quantum Harmonic Oscillator Algorithms

The multiscale quantum harmonic-oscillator algorithm is an intelligent optimization algorithm based on quantum harmonic wave functions. Although it is effective for many optimization problems, an analysis for its performance is still lacking. This paper discusses the harmonic-oscillator potential well, delta-function potential well, and infinite-square potential well in terms of their application in evolutionary algorithms. Of the three, the harmonic-oscillator potential well is considered to give the most precise approximation for complex objective functions. When combined with the harmonic-oscillator potential well, the multiscale quantum harmonic-oscillator algorithm exhibits good adaptability in terms of the convergence of the wave function. To verify its global optimization performance, experiments are conducted using a double-well function to analyze the convergence of the multiscale quantum harmonic-oscillator algorithm and a suite of benchmark functions to compare the performance of different potential wells. The experimental results indicate that the multiscale quantum harmonic-oscillator algorithm with the harmonic-oscillator potential well is a better practical choice than the other two potential well models, and show that the multiscale quantum harmonic-oscillator algorithm is a potential quantum heuristic algorithm for optimization.
Interacting with Linked Data: A Survey from the SIGCHI Perspective

The Semantic Web can be defined as an extension of the current Web, in which data is given well-defined meaning, better-enabling computers and people to work together. Linked Data (LD) has been envisioned as an essential element for the Semantic Web, listing a set of best practices for publishing and connecting structured data on the Web. Enabling humans to interact with this data is a crucial and challenging step to bring the Semantic Web forward. In order to better understand how the Human-Computer Interaction community has contributed to this effort, this late-breaking work presents a review focusing on the ACM Special Interest Group on Computer-Human Interaction (SIGCHI) venues. Our findings show that despite LD being a topic of interest to a variety of stakeholders, there are missing possibilities for end-users to query, browse and visualize LD, underlying the need for further investigations.
A neural network model for short-term PV - energy forecasting

Abstract Nowadays, energy consumption in the world is growing becomes relevant to solve the problem of replacing traditional sources with alternative ones. The solution to this problem is impossible without preliminary forecasting of energy production by alternative sources. In this paper, we consider the problem of solving the problem forecasting electric energy by solar power plants, considering the influence of external factors (weather conditions) using a model implemented on the basis a neural network.
Quantifying Sign Avatar Perception: How Imperfect is Insufficient?

The aim of this study was to identify relevant aspects of sign avatar animation and to quantify their effect on human perception, to better address user expectation in the future. For this, 25 users assessed two types of avatar animations, one upper baseline generated utilizing motion capture data, and a reference with absolute total positional differences in the mm range generated utilizing machine learned synthetic data. As expected, user evaluation of the synthesized references showed a considerable loss in rating scores. We therefore computed a variety of signal-specific differences between both data types and investigated their correlations to the collected user ratings. Results reveal general statistically significant inter-dependencies of avatar movement and perception helpful for the generation of any type of virtual avatar in the future. However, results also suggest that it is difficult to determine concrete avatar features with a high influence on the user perception in the current study design.
Smart Mirror with Virtual Twin

Smart Mirror with a virtual twin who helps the user as a close companion. The virtual twin monitors the user's physical appearance and tracks the data gathered from given inputs. Since this is an intelligent virtual twin it uses machine learning techniques. It helps to improve the user's mental and physical health by detecting medical conditions and providing suitable suggestions in a more personalized way. This virtual twin not only focuses on physical or mental health conditions but also gives friendly suggestions about suitable styles which helps to improve the person's life quality.
Epidemics Fuzzy Decision-Making Applications and Fuzzy Genetic Algorithms Efficiency Enhancement

Fuzzy logic is an innovative scientific field with several successful applications. Genetic algorithms and fuzzy logic systems fusion provide real-world problems modeling through the development of intelligent and adaptive systems. Moreover, the statistical analysis of the epidemiology of infectious diseases, which combines fuzzy logic aspects, is vital for perceiving their evolution and control potential. Author's objective is initially to provide a review of the efficiency of fuzzy logic applications. The advanced implementation of fuzzy logic theory in epidemiology and the application of fuzzy logic for controlling genetic algorithms within strategies based on the human experience and knowledge known as fuzzy logic controllers (FLCs) are analyzed. Outcomes of this review study show that not only can fuzzy sets be efficiently implemented in epidemiology but also prove the effectiveness of fuzzy genetic algorithms applications, thus suggesting that fuzzy logic applications are a really promising field of research.
DTCo: An Ensemble SSL Algorithm for X-ray Classification

In the last decades, the classification of images was established as a typical method for diagnosing many abnormalities and diseases. The purpose of an efficient classification method is considered essential in modern diagnostic medicine in order to increase the number of diagnosed patients and decrease the analysis time. The significant storage capabilities of electronic media have enabled research centers to accumulate repositories of classified (labeled) images and mostly of a large number of unclassified (unlabeled) images. Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, seeing as they exploit the explicit classification information of labeled data with the knowledge hidden in the unlabeled data resulting in the creation of powerful and effective classifiers. In this work, we propose a new ensemble self-labeled algorithm, called DTCo, for X-ray classification. The efficacy of the presented algorithm is illustrated by a series of experiments against other state-of-the-art self-labeled methods.
Efficient architecture design for the AES-128 algorithm on embedded systems

Many applications make use of the edge devices in wireless sensor networks (WSNs), including video surveillance, traffic monitoring and enforcement, personal and health care, gaming, habitat monitoring, and industrial process control. However, these edge devices are resource-limited embedded systems that require a low-cost, low-power, and high-performance encryption/decryption solution to prevent attacks such as eavesdropping, message modification, and impersonation. This paper proposes a field-programmable gate array (FPGA) based design and implementation of the Advanced Encryption Standard (AES) algorithm for encryption and decryption using a parallel-pipeline architecture with a data forwarding mechanism that efficiently utilizes on-chip memory modules and massive parallel processing units to support a high throughput rate. Hardware designs that optimize the implementation of the AES algorithm are proposed to minimize resource allocation and maximize throughput. These designs are shown to outperform existing solutions in the literature. Additionally, a rapid prototype of a complete system-on-chip (SoC) solution that employs the proposed design on a configurable platform has been developed and proven to be suitable for real-time applications.
Study conclusion mechanisms in hybridization of neural and logic intelligent systems

Abstract The paper investigates the integration problems neural networks sections allocated in accordance with the functions performed. As an initial example, we study two trained neural matrices, one of which performs the function of recognizing the position of the manipulator and its speed, and the second generates responses using the corresponding robot drives. In addition to training mechanisms based on the adjustment of transfer weights, the mechanisms based on the supply of excitation / inhibition waves from the edges of the neural network are also affected. At the same time, training is reduced to multiple forced guiding of the robot manipulator along the target path, during which the desired paths of beatings are automatically found using comparators. The matrices are trained separately and then joined using a standard signal generated by a special unit. The division of the neural network into two sections allows building the necessary logic between them. This hybridization significantly expands the range of tasks. As a test problem, the task of searching for a given part from a jointly located set of parts in general position was selected. At the same time, the control system finds a given part, rotates its predetermined model until a matching image is obtained, and thereby understanding how the part is located and how best to capture it occurs.
Automated Classification of Malaria Parasite Stages Using Convolutional Neural Network-Classification of Life-cycle Stages of Malaria Parasites

Malarial is a mosquito born deadly disease that quickly grows from person to person because of the infectious mosquito bite. Knowing accurately the life-cycle stages of malaria parasite is critical for accurate drag selection for early recovery. When the infected mosquito bites the host, cell morphology and appearance greatly change through four major developmental stages namely ring, trophozoite, schizont, and gametocytes in the host's liver and later in the red blood cells (RBCs). Microscopy images carry the signatures of the above changes. However, widely used image analysis based computational techniques require expertise in analyzing morphological, texture, and color variations in the images. In this study, we investigate the strength of convolutional neural network (CNN) towards effective classification of malaria parasite stages. We design a customized CNN model to discriminate five classes including the control and four malaria parasite stages as mentioned above. With an imbalanced dataset having 46,973 single-cell thin blood smear images, the proposed method achieves 97.7% average accuracy, which is about 8~10% higher when compared with a pre-trained CNN model and a widely used hand crafted feature based model using support vector machine (SVM) classifier.
Asymptotic secrecy of the information protection by the usage of simple integer splitting method

Abstract The integer splitting method is a special method that is used to encrypt the text. In this method, each text character is represented as an integer in accordance with the selected code table after that this integer is replaced on the base of another number with a sequence of k integers ( k -splitting level) and this is performed by the usage of modular arithmetic. This article studies the property of asymptotic secrecy of the simple integer splitting method when it is used in the field of cryptography; the research is conducted from the point view of the hacker. The purpose of using integer splitting method is to increase the level of security and provide the ability to control the degree of protection of information. So a lemma was proven in this article and as a conclusion we can notice that the more usage of the splitting level leads to the more level of safety and protection of information.
The Construction of Teaching Resource System of Fine Arts Based on Big Data

In recent years, the number of built in fine arts teaching resource and click-through rates increased rapidly, but many resources database based on the traditional online learning system, teaching paths of a single, one-sided emphasis on “hits”, reduced to “material center” and other issues, and students before that the huge resource repository is difficult to find suitable for their own learning resources and learning route, this study by collecting large amounts of log data, based on the technology of data mining in the fine arts professional learning in the process of learning behavior data, through the study of artificial intelligence algorithm analysis to construct characteristic model, to recommend learning path, the object, Effectively use the established online learning system to improve the learning efficiency of learners.
Pemodelan Matematika Penyebaran COVID-19 Di Provinsi Kalimantan Selatan

Mathematical modeling in epidemiology has a very important role in the study of the dynamics of an epidemic. The outbreak of Covid-19, which is currently being spread widely in the world requires in-depth study, starting from the search for sources, prediction of spread patterns, to strategies for handling this virus outbreak. Mathematical modeling can be applied to support various fields of the study. In this paper, we discuss mathematical modeling of the spread of Covid-19 by providing analysis and predictions based on data from the case of Covid-19 in South Kalimantan Province. This study was conducted by estimating parameters of the SIR Model, which is accommodates the death cases in the data, supported by several methods, namely Runge Kutta Method and Nonlinear Least Squares Method. Our analysis to the data and the model yields a Basic Reproduction Number , which means that one individual infected by Covid-19 can produce three new infected individuals. Whereas our prediction shows that infected cases can reach to 37.82% and cases of death can reach to 0.49% of the population who remained in normal activities during the PSBB. The peaks of this case are estimated to occur in the 2nd week of August to the 1st week of October 2020. The fewer people who have normal activities, then the spread of Covid-19 is predicted to pass faster with smaller cases of infection and death. Conversely, the more people who have normal activities, then the spread of Covid-19 in South Kalimantan can take longer and take a higher number of victims.
Partial Univalence in n-truncated Type Theory

It is well known that univalence is incompatible with uniqueness of identity proofs (UIP), the axiom that all types are h-sets. This is due to finite h-sets having non-trivial automorphisms as soon as they are not h-propositions.