Reviewerly/exHarmony huggingface dataset:
Impulse noise removal by using a nonconvex TGV regularizer and nonconvex fidelity

With the aim of acquiring high-quality restored images, this paper derives a novel nonconvex variational model for the removal of impulse noise. The investigated solver integrates the superiorities of nonconvex second-order total generalized variation (TGV) regularization and nonconvex data fidelity. More precisely, the usage of a nonconvex TGV regularizer helps to eliminate the staircase artifacts and simultaneously preserve edge details. Nonconvex fidelity, which enhances sparsity, is adopted to effectively detect impulse noise. Computationally, incorporating the popular iteratively reweighted ℓ1 algorithm and variable splitting method, we propose to adopt an efficient alternating direction method of multipliers for the purpose of rapidly resolving the designed optimization problem. Additionally, simulation examples have been executed to compare our method with several state-of-the-art techniques. Experimental results and quantitative comparisons confirm that the developed strategy outperforms other competitors for suppressing impulse noise (even high density) in both visual outcomes and recovery accuracy.
On counting propositional logic and Wagner's hierarchy

We introduce an extension of classical propositional logic with counting quantifiers. These forms of quantification make it possible to express that a formula is true in a certain portion of the set of all its interpretations. Beyond providing a sound and complete proof system for this logic, we show that validity problems for counting propositional logic can be used to capture counting complexity classes. More precisely, we show that the complexity of the decision problems for validity of prenex counting formulas perfectly matches the appropriate levels of Wagner's counting hierarchy.
Moving Target Detection and Tracking Technology Based on Genetic Algorithm

Detecting and tracking moving targets is a hot topic in current computer vision research. It has integrated cutting-edge science and technology in many fields such as image processing, pattern recognition, artificial intelligence, morphology, and automatic control technology. The use of fields such as visual navigation is also becoming more common. This paper aims to study the moving target detection and tracking technology based on genetic algorithm. On the basis of analyzing the image preprocessing technology, the classical moving target detection method and the operation steps of the genetic algorithm, the genetic algorithm is used as the best search strategy to obtain the target. In order to verify the tracking accuracy of the proposed algorithm, this paper selects 5 data sets and conducts experimental tests on them. The results show that all targets in test set 4 and test set 5 are moving, and the targets always move in the same direction, and the field of view Wide and unobstructed, the tracking accuracy is high.
User Portrait Based on Artificial Intelligence

With the development of Internet technology, various applications of smart devices and mobile devices have appeared one after another, and people have been inseparable from the use of smart devices. The information generated in device applications has also grown exponentially, making it difficult for businesses to accurately deliver product information to users. Therefore, this paper uses artificial intelligence technology to build a user portrait recommendation system, analyzes user characteristics by intelligently mining user data, recommends information that suits users’ needs, and applies the system to online shopping to recommend products to users according to their purchasing preferences, and introduce prediction algorithms to predict user needs, improve the accuracy of product recommendations, and help merchants achieve refined recommendations.
A New Filter Feature Selection Method Based on a Game Theoretic Decision Tree

A game theoretic decision tree is used for feature selection. During the tree induction phase the splitting attribute is chosen based on a game between instances with the same class. The assumption of the approach is that the game theoretic component will indicate the most important features. A measure for the feature importance is computed based on the number and depth of occurrences in the tree. Results are comparable and better in some cases than those reported by a standard random forest approach based also on trees.
MobileNet-Based Model for Histopathological Breast Cancer Image Classification

Nowadays, Breast cancer is a massive health problem worldwide. To fight against this disease, we propose a high-performance Computer-Aided Diagnosis system using deep learning. Specifically, we focus on the classification of histopathological images of breast cancer into two classes (benign and malignant). For that, we present a Mobilenet-based breast cancer classification model. This model is trained with a new extended Breakhis dataset, which is created by applying some data augmentation techniques. According to the experiments, our proposed model gives a very competitive result and the accuracy reaches 0.9.
SR-Net: A Super-Resolution Image Based on DWT and DCNN

Recently, a surge of several research interests in deep learning has been sparked for image super-resolution. Basically, a deep convolutional neural network is trained to identify the correlation between low and high-resolution image patches. In other side, profiting from the power of wavelet transform to extract and predict the “missing de-tails” of the low-resolution images, we propose a new deep learning strategy to predict missing details of wavelet sub-bands in order to generate the high-resolution image which we called a super-resolution image based on discrete wavelet transform and deep convolutional neural network (SR-DWT-DCNN). By training various images such as Set5, Set14 and Urban100 datasets, good results are obtained proving the effectiveness and efficiency of our proposed method. The reconstructed image achieves high resolution value in less run time than existing methods based on based on the evaluation with PSNR and SSIM metrics.
I-DLMI: Web Image Recommendation Using Deep Learning and Machine Intelligence

Web Image Recommendation is the need of the hour because of the increasing exponential contents especially the multimedia content in the World Wide Web. The IDLMI framework has been proposed which is a query centric knowledge driven approach for Web Image recommendation. This model hybridizes Wiki Data and YAGO for entity enrichment and generation of Meta Data takes place which is further subjected to semantic similarity computation and mapped reduce algorithm for mapping and reducing the complexity within the Pearson’s correlation coefficient. LSTM (Deep Learning Intrinsic Classifier) is used for automatic classification of data set. The model also classifies the data for upper ontology which enhances auxiliary knowledge. Performance of the proposed I-DLMI framework approach is calculated by utilizing F-measure, Precision, Recall, Accuracy percentages and False Discovery Rate (FDR) as the potential metrics. The proposed model furnishes the largest average precision of 96.02%, largest average recall percentage of 98.19%, largest average accuracy percentage of 97.10% and the largest F-measure percentage of 97.09%, while the lowest FDR is 0.17.
Human Interaction and Classification Via K-ary Tree Hashing Over Body Pose Attributes Using Sports Data

Human interaction has always been a critical aspect of social communication. Human action tracking and human behavior recognition are all indicators that assist in investigating human interaction and classification. Several features are considered to analyze human interaction classification in images and videos, including shape, the position of the human body parts, and their environmental effects. This paper approximated different human body key points to track their occurrence under challenging situations. Such tracking of critical body parts requires numerous features. Therefore, we first estimated human pose using key points and 2D human skeleton features to get full human body features. The extracted features are then served to t-DSNE in order to eliminate the redundant features. Finally, the optimized features are infused into the recognizer engine as a k-ary tree hashing algorithm. The experimental results have shown significant results on two benchmark datasets, including the UCF Sports Action dataset with an accuracy of 88.50% and an 89.45% mean recognition rate on the YouTube Action database. The results revealed that the proposed system had achieved better human body part tracking and classification when compared with other state-of-the-art techniques.
Uncertain Configurable IoT Composition With QoT Properties

Concerns about Quality-of-Service (QoS) have arisen in an Internet-of-Things (IoT) environment due to the presence of a large number of heterogeneous devices that may be resource-constrained or dynamic. As a result, composing IoT services has become a challenging task. At different layers of the IoT architecture, quality approaches have been proposed that take a variety of QoS factors into account. Things are not implemented with QoS or exposed as services in the IoT context. Actually, the Quality-of-Thing (QoT) model of a thing is composed of duties that are each associated with a set of non-functional properties. It is difficult to evaluate the QoT as a non-functional parameter for heterogeneous thing composition. Uncertainty emerges as a consequence of the plethora of things as well as the variety of the composition paths. In this paper, we establish a standard method for aggregating Things with uncertainty awareness while taking QoT parameters into account.
Navigating cyber resilience in seaports: challenges of preparing for cyberattacks at the Port of Rotterdam

Purpose Cyber resilience has emerged as an approach for seaports to deal with cyberattacks; it emphasizes ports’ ability to prepare for an attack and to keep operating and recover quickly. However, little research has been undertaken on the challenges of governing cyber risks in seaports. This study aims to address this gap. Design/methodology/approach Governing cyber resilience is shaped by distributed responsibilities, uncertainties and ambiguities. The authors use this conceptualization to explore the governance of cyber risks in seaports, taking the Port of Rotterdam as a case study and analyzing semistructured interviews with stakeholders, participatory observation and policy documents and legislation. Findings The authors found that many strategies for governing cyber risks remain dedicated to protecting computer systems against cyberattacks. Nevertheless, port stakeholders have also developed strategies in anticipation of disruptions. However, these strategies appear informal and uncoordinated due to a lack of information exchange, insufficient knowledge regarding cyber risks and disagreement about how to make the Port of Rotterdam cyber resilient. What mainly hampers the cyber resilience of the port is the lack of a comprehensive regulatory framework and economic incentives. The authors conclude that resilience is merely an ideal at the Port of Rotterdam, meaning related governance strategies remain incremental and await institutionalization. Originality/value This paper offers insights into the cyber resilience of critical socio-technical systems, which have been underexposed in cyber resilience debates, but, when exploited, can manifest in large-scale disruptions.
A Dataset of Underrepresented Languages in Eye Tracking Research

A number of factors come together to limit the diversity of eye-tracking research, where the majority of papers are conducted with stimuli in the English language. Studying eye movement over other languages is important considering that each language provides unique insights into human cognition. Recently, there have been valued efforts to present datasets from other languages, yet these efforts focused mostly on European languages. In this paper we highlight issues that limit diversity in eye tracking research on reading, and we present our work in collecting an open-access multilingual reading dataset of underrepresented languages. Utilizing a high-frequency research eye tracker (EyeLink 1000 Plus), we record eye tracking data of native and second language readers of English, Spanish, Chinese, Hindi, Russian, Arabic, Japanese, Kazakh, Urdu, and Vietnamese. The dataset includes demographics, language proficiency self-reporting, and answers to comprehension questions. The current version of the dataset, which we make publicly available, consists of 97 trials by 40 participants. With the goal of increasing the number of participants and included languages, we aim to make studying underrepresented languages more accessible to researchers and tool makers.
Gaze Pattern Recognition in Dyadic Communication

Analyzing gaze behaviors is crucial to interpret the nature of communication. Current studies on gaze have focused primarily on the detection of a single pattern, such as the Looking-At-Each-Other pattern or the shared attention pattern. In this work, we re-define five static gaze patterns that cover all the status during a dyadic communication and propose a network to recognize these mutual exclusive gaze patterns given an image. We annotate a benchmark, called GP-Static, for the gaze pattern recognition task, on which our method experimentally outperforms other alternate solutions. Our method also achieves the state-of-art performance on other two single gaze pattern recognition tasks. The analysis of gaze patterns on preschool children demonstrates that the statistic of the proposed static gaze patterns conforms with the findings in psychology.
Multimodal machine learning for cognitive load based on eye tracking and biosensors

Eye tracking and virtual reality are set to drive the coming decade's most innovative developments in healthcare. Two key application areas stand at the forefront: cost-effective clinical and paraclinical training, and interactive virtual settings for patients in therapy and rehabilitation. As such, our main research focus will be to develop multimodal solutions based on eye tracking and bio-signals for cognitive load assessment within the broader spectrum of applied computer science and digital health. One of the objectives is to respond to the need for healthcare to become ever more individualized and multimodal, as exemplified by personalized medicine and digital therapeutics. The use of eye-tracking in conjunction with digital biomarkers intends to be a quantitative basis for care providers to adapt their therapies to user and patient needs.
On The Visibility Of Fiducial Markers For Mobile Eye Tracking

Invisible fiducial markers are introduced for localization of Areas Of Interest (AOIs) in mobile eye tracking applications. Fiducial markers are made invisible through the use of film passing Infra-Red (IR) light while blocking the visible spectrum. An IR light source is used to illuminate the markers which are then detected by an IR-sensitive camera, but which are imperceptible by the human eye. We provide the first empirical study that demonstrates such invisible markers are not distracting to a given task, as demonstrated in a flight simulator where distraction of visible and invisible markers are compared between experienced and novice pilots. Fixation frequency and subjective distraction scores showed that visible markers disrupted natural gaze behaviour, particularly in novice pilots. Our findings show that invisible markers should be used when there is a need for them to remain inconspicuous.
Detecting colour vision deficiencies via Webcam-based Eye-tracking: A case study

Webcam-based eye-tracking platforms have recently re-emerged due to improvements in machine learning-supported calibration processes and offer a scalable option for conducting eye movement studies. Although not yet comparable to the infrared-based ones regarding accuracy and frequency, some compelling performances have been observed, especially in those scenarios with medium-sized AOI (Areas of Interest) in images. In this study, we test the reliability of webcam-based eye-tracking on a specific task: Eye movement distribution analysis for CVD (Colour Vision Deficiency) detection. We introduce a new publicly available eye movement dataset based on a pilot study (n=12) on images with dominant red colour (previously shown to be difficult with dichromatic AOI to investigate CVD by comparing attention patterns obtained in webcam eye-tracking sessions). We hypothesized that webcam eye tracking without infrared support could detect differing attention patterns between CVD and non-CVD participants and observed statistically significant differences, allowing the retention of our hypothesis.
GE-Simulator: An Open-Source Tool for Simulating Real-Time Errors for HMD-based Eye Trackers

As eye tracking in augmented and virtual reality (AR/VR) becomes established, it will be used by broader demographics, increasing the likelihood of tracking errors. Therefore, it is important when designing eye tracking applications or interaction techniques to test them at different signal quality levels to ensure they function for as many people as possible. We present GE-Simulator, a novel open-source Unity toolkit that allows the simulation of accuracy, precision, and data loss errors during real-time usage by adding gaze vector errors into the gaze vector from the head-mounted AR/VR eye tracker. The tool is customisable without having to change the source code and changes in eye tracking errors during and in-between usage. Our toolkit allows designers to prototype new applications at different levels of eye tracking in the early phases of design and can be used to evaluate techniques with users at varying signal quality levels.
Pupillometry for Measuring User Response to Movement of an Industrial Robot

Interactive systems can adapt to individual users to increase productivity, safety, or acceptance. Previous research focused on different factors, such as cognitive workload (CWL), to better understand and improve the human-computer or human-robot interaction (HRI). We present results of an HRI experiment that uses pupillometry to measure users’ responses to robot movements. Our results demonstrate a significant change in pupil dilation, indicating higher CWL, as a result of increased movement speed of an articulated robot arm. This might permit improved interaction ergonomics by adapting the behavior of robots or other devices to individual users at run time.
SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks

Neural networks that process the raw eye-tracking signal can outperform traditional methods that operate on scanpaths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We, therefore, present SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human micro- and macro-movements. We demonstrate how the generated synthetic data can be used to pre-train a model using contrastive learning. This model is fine-tuned on labeled human data for the task of interest. We show that for the task of predicting reading comprehension from eye movements, this approach outperforms the previous state-of-the-art.
Watch out for those bananas! Gaze Based Mario Kart Performance Classification

This paper is about a small eye tracking study for scan path classification. Seven participants played Mario Kart while wearing a head mounted eye tracker. In total, we had 64 recordings, but one had to be removed (Only 79 gaze samples were recorded). We compared different scan path classification features to estimate the performance of the participants based on the ranking they achieved. The best performing feature was ENCODJI which incooperates saccades and the heatmap in one feature. HOV, which uses saccade angles, performed well for all tasks but was outperformed by the heatmap (HEAT) for the last two groups.
Design and implementation of an energy management system based on distributed IoT

A Distributed IoT-based energy management system is planned and executed in this article. We tested the suggested strategy at Duhok and Shekhan, two institutes far apart. Utilizing ESP32 microcontrollers, the suggested system features two main phases (Monitoring and Controlling). There has been carefully monitoring and regulation of all power usage indicators, including Voltage, current, and Frequency. Three primary approaches are taken with each institution: monitoring all metrics for the whole institution, monitoring all metrics for the uncontrolled Lab, and monitoring and regulating all metrics for the controlled Lab. This technology gathers data incessantly, resulting in a large database for energy management. Up to this point, we have collected over two million data rows, and by the end of 2023, we anticipate collecting over ten million. Server-side protocols Message Query Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) were used to collect the implementation results. According to the implemented findings, one can see that the energy consumption reduction ratios for the Duhok-controlled laboratory were (Weekly = 1.94%, Monthly = 21.65%, and Year-to-Date = 26.38%). There are several critical values recorded during the system's implementation at Shekhan Institute: maximum Voltage (281.2 V), Maximum Frequency (86.3 Hz), and minimum Power-Factor (0.07).
Perbandingan Algoritma Naive Bayes dan Decision Tree(C4.5) dalam Klasifikasi Dosen Berprestasi

Abstract – Enhancing the execution of Tri Dharma for lecturers is one of the factors in obtaining and sustaining the level of universities with good institution achievement. The Rectorate should exercise consideration while making a decision to reward lecturers who do very well. The information was gathered through speaking with members of the rectorate staff to classify lectures at Sebelas Maret University. In this study, accuracy results in the classification based on lecturers' accomplishments will be compared. International and national publications, education level, the length of doctoral studies, becoming an associate professor, and the length of certification as a lecturer are the features considered in the classification. To categorize lecturers according to their accomplishment, the algorithms Naive Bayes and Support Vector Machine were applied. 350 records of training data and 130 records of testing data total 500 records in this study. From 2018 to 2021, the study was carried out at Sebelas Maret University. The accuracy value obtained from 10-fold cross validation the testing using the Naive Bayes method is 94,80%, while the accuracy value obtained from the testing using the Decision Tree is 95,80%.
Klasifikasi Jenis Buah Jambu Biji Menggunakan Algoritma Principal Component Analysis dan K-Nearest Neighbor

The maturity level of guava fruit can be determined by looking at various factors. Shape is one of the factors that play a role in identifying certain objects. The classification of guava fruit can be seen from the shape, texture and color. The shape of the guava fruit is quite diverse ranging from round (Round shape) to oval (Pear shape). So a Matlab application was built to determine the type of guava based on its color, shape and texture. K-Nearest Neighbor can classify objects based on learning data that is closest to the object so that the results can be more accurate. Principal Component Analysis (PCA) is a statistical technique for simplifying many-dimensional data sets into lower dimensions (extration features). The combination of K-Nearest Neighbor with Principal Component Analysis produces a fairly high accuracy for determining the type of guava using a total of 45 images and divided into two data including training data with a total of 36 guava data and test data with a total of 9 guava data.