Reviewerly/exHarmony huggingface dataset:
Cumulative Impact of Testing Factors in Usability Tests for Human-Centered Web Design

The study examines the cumulative impact of factors that affect usability testing for user-centered web design, namely the so-called ‘contextual fidelity model’ factors that include product properties, task features, user traits, and environment/context factors. Today, the design, user experience and usability (DUXU) research experiences a lack of studies that would assess combinatorial, rather than individual, effects of these factors upon user performance. We address this gap by seeing both independent factors and the resulting user states as complex and dynamic, and testing the combined impact of aesthetic quality of websites, user traits, and individual/group experiment settings upon formation of two dysfunctional user states that critically affect user performance, namely monotony and anxiety. We develop a research design that allows for assessing the combinatorial effects in formation of user dysfunctionality. For that, we conduct a study with 80 assessors of Russian/European and Chinese origin in individual/group setting, employing two types of tasks and websites of high/low aesthetic quality. As the results of our experiment show, group task solving enhances the synchronous impact of website aesthetics and task features upon user states. Interaction of high-quality design, group environment, and monotonous tasks provides for an antagonistic effect when aesthetic layout in a group environment significantly reduces the fatigue rate. Low aesthetic quality in a group environment leads to cumulative enhancing of dysfunctionality for both monotony and anxiety. We conclude by setting questions and prospects for further research.
Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.
Evaluating the Predictive Performance of Positive- Unlabelled Classifiers

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.
Can counterfactual explanations of AI systems’ predictions skew lay users’ causal intuitions about the world? If so, can we correct for that?

skew lay users' causal intuitions about the world?If so, can we correct for that?
Underwater 3D reconstruction using a photometric stereo with illuminance estimation

Underwater image sensing is directly affected by the change in illuminance towards a camera caused by the refraction of light in different media. In this study, the convergence of a near-field point light source in water is analyzed using a light propagation model. The photometric stereo (PS) formula is determined based on an accurate estimation of the illuminance entering the camera. An underwater PS system is designed to verify the proposed method's feasibility. The experimental results demonstrate improved accuracy in normal calculation. This helps achieve accurate underwater 3D reconstruction of objects that is suitable for underwater surface microdefect detection.
A Cybersecurity Model for a Roblox-based Metaverse Architecture Framework

The adoption of virtual reality (VR) and augmented reality (AR) headsets in futuristic and science fiction has made it possible for the Metaverse to exist as a single, universal, immersive virtual universe. By extending technology outside of our physical reality, the Metaverse alters the human experience. The four categories we use to categorize metaverse definitions are environment, interface, interaction, and social value. Currently, it is unclear what the metaverse's structure and elements are. A cybersecurity framework for these devices is necessary as the world grows more interconnected and immersive technologies are increasingly widely used in business, government, and consumer markets. Used was a literature review. The goal of the study is to create a cybersecurity model for a Roblox-based Metaverse architecture framework that can be applied to internationalization, the value chain of education, and the delivery of online and e-learning education. The research is conducted using the Interpretivist Paradigm, which is characterized by a subjectivist epistemology, a relativist ontology, a naturalist methodology, and a balanced axiology. Both the qualitative methodology and the quantitative methodology with an experimental research design were applied. A systematic literature review was conducted on the metaverse, AR and VR. By describing each aspect of the metaverse, we categorized definitions of the metaverse into four categories: environment, interface, interaction, and social value. The interface is based on the level of immersion and there are physical, immersive, and 3D interface options available. There are realistic, unreal, and blended environments in the metaverse. Social networking, teamwork, and persona discourse are the three categories used to describe interaction in the metaverse. A major advantage of 6G is that it facilitates instant communication in phones, computers, wearable devices, robotics, and more. The Cybersecurity model for a Roblox-based Metaverse architecture framework developed is a Bayesian Network, which is a directed acyclic graph that has an associated probability distribution function that can be used for multivariate analysis.
Antecedents of mobile banking app adoption during COVID19: A perspective of Jordanian consumer

The effect of contextual factors namely information quality, service quality, system quality, Technological readiness, trust in applications (app) and COVID-19 health anxiety, on the intention and consequently the actual use of Mobile Payment (MP) app was examined in this study. Trust, as mediator to the relationship between technological readiness and intention to use MB-app was examined also. Data were obtained from 740 Jordanian Mobile Banking (MB) app users through an online survey. The relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety, Technological readiness and the intentions to use MB-app and the actual use of MB-app was empirically examined. The results showed a positive relationship between service quality, system quality, information quality, trust in the app and COVID-19 health anxiety, and the intentions to use MB-app, and in turn he actual use of MB-app, and a positive mediation of trust on the relationship between COVID-19 health anxiety and the intentions to use MB-app.
Deep Learning for Cognitive Computing Systems

Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.
A power-efficient optimizing framework FPGA accelerator for YOLO

Due to the high parallelism and efficiency, Convolution acceleration and buffer pipeline architecture are common solutions for convolutional neural network (CNN) acceleration, however, existing accelerate solutions exhibit limited performance to accelerate CNN models. This paper presents a novel architecture as YOLO accelerate solution, which based on convolution loop unrolling and data flow pipeline optimization to reduce power consumption and maintain computational accuracy. The convolution loop unrolling algorithm is used to convert the numerous multiplication operations into addition operations to reduce the amount of computation. In the meanwhile, a buffer pipeline method which splits the processing element (PE) array is proposed, further improves the efficiency and optimizes the memory of the accelerator. Moreover, the data of the original model are processed at a low fixed point, reducing the resource consumption of the FPGA. Our proposed architecture can ensure the accuracy of network testing while reducing the power consumption required by the hardware.
Modelling, Simulation and Navigation of a Two-Wheel Mobile Robot using Pure Pursuit Controller

Recently, the field of mobile robotics have witnessed an unprecedented expansion due to their vast applications that range from surveillance and construction to planetary exploration and emergency rescue operations. Their tasks became more and more complex that their autonomy became essential in the majority of the applications which means that devising controllers that suit different scenarios and situations is of importance. In this project, we aim to control the velocity of a modelled 2-wheel differential drive robot by using a PSO optimized PID controller. In addition to speed control, in order for a robot to move to specific waypoints by itself, a path planning algorithm has to be implemented. Pure pursuit, a simple yet efficient path tracking method for nonholonomic ground vehicles is utilized.
Implementing scripted conversations by means of smart assistants

Abstract Smart assistants are among the most popular technological devices at home. With a built‐in voice‐based user interface, they provide access to a broad portfolio of online services and information, and constitute the central element of state‐of‐the‐art home automation systems. This work discusses the challenges addressed and the solutions adopted for the design and implementation of scripted conversations by means of off‐the‐shelf smart assistants. Scripted conversations play a fundamental role in many application fields, such as call center facilities, retail customer services, rapid prototyping, role‐based training or the management of neuropsychiatric disorders. To illustrate this proposal, an actual implementation of the phone version of the Montreal cognitive assessment test as an Amazon's Alexa skill is described as a proof‐of‐concept.
Active Learning Methodology for Expert-Assisted Anomaly Detection in Mobile Communications

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is becoming an increasingly important challenge in the operation of new generations of mobile communications. In many cases, the underlying relationships between the multiplicity of parameters and factors that can cause anomalous behavior are only determined by human expert knowledge. On the other hand, although automatic algorithms have a great capacity to process multiple sources of information, they are not always able to correctly signal such abnormalities. In this sense, this paper proposes the integration of both components in a framework based on Active Learning that enables enhanced performance in anomaly detection tasks. A series of tests have been conducted using an online anomaly detection algorithm comparing the proposed solution with a method based on the algorithm output alone. The obtained results demonstrate that a hybrid anomaly detection model that automates part of the process and includes the knowledge of an expert following the described methodology yields increased performance.
Reduction between Categorical Syllogisms Based on the Syllogism EIO-2

Syllogism reasoning is a common and important form of reasoning in human thinking from Aristotle onwards. To overcome the shortcomings of previous studies, this article makes full use of set theory and classical propositional logic, and deduces the remaining 23 valid syllogisms only on the basis of the syllogism EIO-2 from the perspective of mathematical structuralism, and then successfully establishes a concise formal axiom system for categorical syllogistic logic. More specifically, the article takes advantage of the trisection structure of categorical propositions such as Q(a, b), the transformation relations between an Aristotelian quantifier and its inner and outer negation, the symmetry of the two Aristotelian quantifier (that is, no and some), and some inference rules in classical propositional logic, and derives the remaining 23 valid syllogisms from the syllogism EIO-2, so as to realize the reduction between different valid categorical syllogisms.
XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.
Optimal path planning using a continuous anisotropic model for navigation on irregular terrains

Abstract Mobile robots usually need to minimize energy when they are traversing uneven terrains. To reach a location of interest, one strategy consists of making the robot follow the path that demands the least possible amount of energy. Yet, its calculation is not trivial with irregular surfaces. Gravity makes the energy consumption of a robot change according to its heading. Such a variation is subject to the terramechanic characteristics of the surface. This paper introduces a cost function that addresses this variation when traversing slopes. This function presents direction-dependency (anisotropic) and returns the cost for all directions (continuous).. Moreover, it is compatible with the Ordered Upwind Method, which allows finding globally optimal paths in a deterministic way. Besides, the segments of these paths are not restricted to the shape of a grid. Finally, this paper also introduces the description and discussion of a simulation experiment. It served to analyse what kinds of terrain motivate the use of anisotropy. The Ordered Upwind Method was executed on a virtual crater with different terrain parameter configurations, using both isotropic (direction-non-dependent) and anisotropic cost functions. The results evince how in certain situations the use of an anisotropic cost function instead of an isotropic one produces a path that reduces the accumulated cost by up to 20%.
A comparative study of adversarial training methods for neural models of source code

Adversarial training has been employed by researchers to protect AI models of source code. However, it is still unknown how adversarial training methods in this field compare to each other in effectiveness and robustness. This study surveys and investigates existing adversarial training methods, and conducts experiments to evaluate these neural models' performance in the domain of source code. First, we examine the process of adversarial training to identify four dimensions that could be used to classify different adversarial training methods into five categories, which are Mixing Directly, Composite Loss, Adversarial Fine-tuning, Min–max + Composite Loss, and Min–max. Second, we conduct empirical evaluations of these classified adversarial training methods under two tasks (i.e., code summarization and code authorship attribution) to determine their performance of effectiveness and robustness. Experimental results indicate that the performance of certain combinations of adversarial training techniques (i.e., min–max with composite loss, or directly-sample with ordinary loss) would be much better than other combinations or other techniques used alone. Our experiments also reveal that the model's robustness of defensive methods can be enhanced by using diverse input data for adversarial training, and that the number of fine-tuning epochs has little or no impact on model's performance.
Edge detection and graph neural networks to classify mammograms: A case study with a dataset from Vietnamese patients

Mammograms are breast X-ray images and they are used by doctors, among other purposes, as an effective means of detecting breast cancer. Screening mammography is crucial since it allows doctors to understand better the situation and have suitable intervention. The classification of medical modalities is a prerequisite for development of computer-aided diagnosis tools in healthcare, and various techniques have been proposed to automatically classify from mammography images. Though there have been several tools developed, they have been mostly validated with data collected from Western women. Based on our initial investigations, breast anatomy in Vietnamese women differs from that of Western women, due to denser breast tissue. In this paper, we propose MammoGNN – a practical solution to the classification of mammograms using the synergy between image processing techniques and graph neural networks. First, a well-founded edge detection algorithm was applied to provide input for the recommendation engine. Afterward, we empirically experimented to select suitable graph neural networks to manage the training and prediction. A mammogram dataset was curated from 2,351 Vietnamese women to validate the conceived tool. By several testing instances, MammoGNN obtains a maximum accuracy of 100%, precision and recall of 1.0 on independent and shuffle test sets for both classification of BI-RADS scores and breast density types. The experimental results also demonstrate that our proposed approach obtains an optimal prediction performance on the considered datasets, outperforming different baselines. We anticipate that the proposed approach can be deployed as a non-invasive pre-screening tool to assist doctors in performing their diagnosis activities.
MISSING PULSE DETECTOR

As a signal goes through a rapid shift from base to higher value and after some period returns back to base value from higher value. It is considered as a pulse signal. A Missing Pulse Detector Circuit is needed to identify such missed pulses when any of the pulses in this pulse chain do not occur that have the predefined interval duration. The detector circuit is capable of delivering notification of missed pulses.
Real-Time Detection of Objects on Roads for Autonomous Vehicles Using Deep Learning

Autonomous driving has received much attention in academia and industries in recent years. Researchers incorporate different perceptions to detect vehicles, objects, routes, and lighting. However, few publications focus on road signs on pavement surface. This paper aims at creating a dataset with 10,000 images of U.S. road markings from similar datasets, web scraping, and raw driving footage in California. In addition, this paper proposes an ensemble deep learning model as a component to autonomous driving systems to allow real time detection of road markings and other objects. The proposed ensemble model is based on four modified models, including improved CenterNet, improved EfficientDet-D1, improved SSD and improved YOLOv4. Finally, the report experiments show that the proposed ensemble model classification of mobile objects on roads for autonomous vehicles.
Explainable Interactive Projections for Image Data

Making sense of large collections of images is difficult. Dimension reductions (DR) assist by organizing images in a 2D space based on similarities, but provide little support for explaining why images were placed together or apart in the 2D space. Additionally, they do not provide support for modifying and updating the 2D space to explore new relationships and organizations of images. To address these problems, we present an interactive DR method for images that uses visual features extracted by a deep neural network to project the images into 2D space and provides visual explanations of image features that contributed to the 2D location. In addition, it allows people to directly manipulate the 2D projection space to define alternative relationships and explore subsequent projections of the images. With an iterative cycle of semantic interaction and explainable-AI feedback, people can explore complex visual relationships in image data. Our approach to human-AI interaction integrates visual knowledge from both human mental models and pre-trained deep neural models to explore image data. We demonstrate our method through examples with collaborators in agricultural science.