Reviewerly/exHarmony huggingface dataset:
Target‐tracking algorithm based on improved probabilistic data association

Abstract When tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified.
Basics of Stochastic Processes

Stochastic processes are probability models that describe the evolution of systems that evolve randomly in time and hence are useful in modeling a wide variety of phenomena that occur over time. After a brief introduction in Sect. 1, the notions of a stochastic process and its family of finite dimensional distribution functions are defined and illustrated with examples in Sect. 2. These examples provide a good understanding of the Kolmogorov compatibility conditions as well as the four major types of stochastic processes. Section 3 is devoted to a discussion on processes with stationary and independent increments. Apart from the usual results, a characterization of such processes in terms of characteristic functions is also given. A brief discussion on stationary processes is given in Sect. 4. R software is used throughout the book to illustrate the concepts with numerical examples. In order to facilitate an understanding of R codes, a preliminary introduction to the R software is given in Sect. 5 of this chapter.
Explainable convolutional neural networks for assessing head and neck cancer histopathology

Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI.Here, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology. To this end, we manually annotated 101 histopathological slides of locally advanced head and neck squamous cell carcinoma. We trained a CNN to classify tumor and non-tumor tissue, and another CNN to semantically segment four classes - tumor, non-tumor, non-specified tissue, and background. We applied Explainable AI techniques, namely Grad-CAM and HR-CAM, to both networks and explored important features that contributed to their decisions.The classification network achieved an accuracy of 89.9% on previously unseen data. Our segmentation network achieved a class-averaged Intersection over Union score of 0.690, and 0.782 for tumor tissue in particular. Explainable AI methods demonstrated that both networks rely on features agreeing with the pathologist's expert opinion.Our work suggests that CNNs can predict head and neck cancer with high accuracy. Especially if accompanied by visual explanations, CNNs seem promising for assisting pathologists in the assessment of cancer sections.
A crowd-sourcing recommendation algorithm OPCA-CF using outer-product co-attention mechanism

With the rapid development of information technology, crowd-sourcing technology is increasingly used in non-invasive monitoring in smart cities. Applying recommendation algorithms in crowd-sourcing can optimise resource allocation, improve task-matching accuracy and enhance participant satisfaction, whereas existing recommendation algorithms cannot be directly applied in crowd-sourcing, as such scenarios have unique features, such as task timeliness and multi-role users. Designed explicitly for crowd-sourcing scenarios, our OPCA-CF (Outer-product Co-attention Collaborative Filtering) algorithm is formed by an upgraded ItemCF (Item-based Collaborative Filtering) algorithm as main-network and OPCA (Outer-product Co-attention) mechanism as a sub-network. Firstly, ItemCF is improved through attribute-level task feature learning, new-role feature and weighted cross-entropy in the loss function. Most importantly, we propose OPCA using outer-product, while the existing co-attention mechanism only uses inner-product. Compared with the best existing algorithm using real-world datasets, OPCA-CF's performance is proved to be superior by 1.24%, 4.25% and 5.35%, with binary classification indicators AUC (Area under Curve), recommended Lists related indicators HR (Hit Ratio) and MRR (Mean Reciprocal Rank), respectively. All the performance indicators verified the effectiveness of the OPCA-CF algorithm.
Early and fair COVID-19 outcome risk assessment using robust feature selection

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
Use of Mobile/Smart Phones and Students' Mathematics Learning: A Case of Basic Schools in Nepal

This study is mainly focused on the students' mobile using habit and its impact on teaching and learning mathematics. The study method was survey with 119 students of public and institutional schools in the Chandragiri Municipality of Kathmandu. Questionnaire is use for identifying the access and use of mobile for learning and achievement test for measuring the students' achievement as tools for the study. The study found that 89.9% students had access of smartphones at their home including personal as well as their own. The utilization of mobile smart phone is the genuine concern for the proper utilization for learning. The results indicated that, the use of mobile in planned learning for a specified time enhanced the mathematics performance of students. Adversely, those students who engaged on mobile/smart phones higher in hours and not using mobile for learning purpose decreased their achievement in mathematics. Gender difference observed in using mobile that boys used mobile more hours than the girls. Further, entertainment type of application/programme such as music videos, song were the favorite program for girls whereas games related programme was the favorite programme for boys in the use of mobile/smart phones. Two hours/day was found the standard time to use mobile/smart phones for better performance in mathematics.
Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

AI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.In this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.The Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.In this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.
A q-ROF Based Intelligent Framework for Exploring the Interface Among the Variables of Culture Shock and Adoption Toward Organizational Effectiveness

Nowadays, Culture shock is very much common to work life and this is an important issue in identification of the variables to develop morale of the employees towards an aim to increase productivity. The term ‘cultural shock’ refers the emotional state of uncertainty, confusion, anxiety that people may experience when transforming to a new state of affairs or feeling a new work culture or social status. So, it happens when an individual is censored from acquainted surroundings and culture after transferring to a new environment. It brings the logic that Culture shock tends to mean a bustle of emotions, including excitement, emotional labor, job stress, and job satisfaction and helplessness. Social scientists are of the opinion that culture shock is treated as ‘mental illness’ as common men are suffering as they are distracted from the cultural environment. In this context, it is pertinent to identify the impact of cultural shock during the pandemic time especially in the service sector management as people are constrained to work from home and that is also to some extent detrimental to their mental health. In this present study, the researchers are trying to explore the possible attributes that are responsible for cultural shock and also try to measure the possible impact of the same. This study will provide a working model for the stakeholders to frame a strategy to get rid of the crisis and develop an employee retention policy with respect to stress coping behavior as well, in the days of New Normal. The study is essentially focused on tech-based gaming industry. The present chapter proposes a new q-Rung Orthopair Fuzzy (qROF) based computational intelligence framework of psychological assessment. The procedural steps of forced field analysis (FFA) is followed in this chapter wherein Level Based Weight Assessment (LBWA) is applied for calculating the weights of the attributes. Further, it carries out stability analysis of the results obtained. Finally, it puts forth policy recommendations for formulating effective employee motivation and retention strategy.
Functional Programming of Intelligent Systems

An intelligent system is a system that uses intelligence to shape its behavior in its environment. This intelligence depends on the following main factors: the first is the ability of the system to model the environment with which the system interacts. The second factor: using this model to successfully plan and solve problems that determine the behavior of the system in the environment in order to achieve the goals set for the system. Modeling of the environment is based on the use of knowledge about the environment and its components, which the system collects using its sensors and organs, as well as the knowledge base, which stores information previously collected or incorporated during the development of the system. This information in the form of knowledge uses various forms of mathematical structures that form the basis of the model, logic and ontology that are part of the knowledge representation. Problem solving applies either previous experience, or uses a logical conclusion, based on the logic embedded in the system during its development, or ontological description, included in the representation of knowledge and relationships between elements. When developing an intelligent system, you can apply the methods and tools of functional programming as a way to represent the development of a particular system, and analyze its capabilities and efficiency at the computational level.
Context-aware fusion of transformers and CNNs for medical image segmentation

Localization and screening of the target tissue is a main prerequisite of numerous medical procedures, including capsule endoscopy, colonoscopy and histology. Convolutional Neural Networks (CNNs), by stacking convolutional, down-sampling, and up-sampling operators in an encoder-decoder fashion, were the de-facto standard and has shown great promise in recent years. The main deficiency of these models is their local convolutional operators, which degrade accuracy, especially for targets with long-range dependencies. While CNNs excel at local feature extraction, Transformers are known for their ability to capture long-range dependencies. Also, CNN-Transformer models employ complex attention mechanisms for fusion that could increase model complexity and the potential for overfitting and underfitting in many datasets. In this paper, we propose an efficient context-aware CNN-Transformer fusion mechanism based on Semi-supervised Spatial and Global Attention mechanism (SSG-Att). Our model is designed to combine the strengths of both models and overcome their limitations. High-level features that are extracted from the two parallel branches are combined and fused using the proposed SSG-Att mechanism. A hybrid loss function is also employed, which is better adapted to the introduced fusion system. We evaluated the performance of our proposed model on the Kvasir-SEG, a polyp segmentation and detection dataset, and the Gland segmentation dataset. The experimental results confirmed that the improvements yield a top-performing yet efficient deep fused CNN-Transformer architecture. The proposed model outperformed the best-reported accuracies, achieving improved dice scores of 92.11 ± 1.10 % and 91.16 ± 0.81 on the Kvasir-SEG and GlaS datasets, respectively. We concluded that the proposed context-aware fusion mechanism has the potential to be used in screening and localization applications in a more reliable and accurate operation compared to other state-of-the-art methods.
Conflict-based negotiation strategy for human-agent negotiation

Abstract Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.
Definition of guidelines for virtual reality application design based on visual attention

Abstract In virtual reality applications, head-mounted displays allow users to explore virtual surroundings, thus creating a high sense of immersion. However, due to the novelty of the technology and the possibility of freely enjoying a $$360^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>360</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> virtual world, users can get distracted and divert their attention from the content of the application. In this work, we define a set of guidelines for the design of virtual reality applications for enhancing the users’ attention. To the best of our knowledge, this is one of the first attempts to provide general guidelines for virtual application design based on visual attention. More specifically, we analyze the different categories of factors that contribute to the user’s responsiveness and define a set of experiments for measuring the user’s promptness with respect to visual stimuli with different features and in the presence of audio/visual distractions. Experimental tests have been carried out with 36 volunteers. The users’ reaction time has been recorded and the performed analysis allowed the definition of a set of guidelines based on individual, operational, and technological factors for the design of virtual reality applications optimized in terms of user attention. In particular, statistical tests demonstrated that the presence of distractions leads to significantly different reaction times with respect to the case of no distractions, and that users belonging to different age intervals have significantly different behaviors. Moreover, the optimal placement of objects has been identified and the impact of cybersickness has been analyzed.
Spectral warping based data augmentation for low resource children’s speaker verification

Abstract In this paper, we present our effort to develop an automatic speaker verification (ASV) system for low resources children’s data. For the children’s speakers, very limited amount of speech data is available in majority of the languages for training the ASV system. Developing an ASV system under low resource conditions is a very challenging problem. To develop the robust baseline system, we merged out of domain adults’ data with children’s data to train the ASV system and tested with children’s speech. This kind of system leads to acoustic mismatches between training and testing data. To overcome this issue, we have proposed spectral warping based data augmentation. We modified adult speech data using spectral warping method (to simulate like children’s speech) and added it to the training data to overcome data scarcity and mismatch between adults’ and children’s speech. The proposed data augmentation gives 20.46% and 52.52% relative improvement (in equal error rate) for Indian Punjabi and British English speech databases, respectively. We compared our proposed method with well known data augmentation methods: SpecAugment, speed perturbation (SP) and vocal tract length perturbation (VTLP), and found that the proposed method performed best. The proposed spectral warping method is publicly available at https://github.com/kathania/Speaker-Verification-spectral-warping .
SpaTiaL: monitoring and planning of robotic tasks using spatio-temporal logic specifications

Abstract Many tasks require robots to manipulate objects while satisfying a complex interplay of spatial and temporal constraints. For instance, a table setting robot first needs to place a mug and then fill it with coffee, while satisfying spatial relations such as forks need to placed left of plates. We propose the spatio-temporal framework SpaTiaL that unifies the specification, monitoring, and planning of object-oriented robotic tasks in a robot-agnostic fashion. SpaTiaL is able to specify diverse spatial relations between objects and temporal task patterns. Our experiments with recorded data, simulations, and real robots demonstrate how SpaTiaL provides real-time monitoring and facilitates online planning. SpaTiaL is open source and easily expandable to new object relations and robotic applications.
Phenotropic Interaction

Traditional interactive systems are generally built to cater to the needs of the average user, resulting in interfaces that may exclude individuals with different abilities or needs. Moreover, these systems often lack adaptability once deployed, forcing users to conform to strict protocols. In contrast, human interactions are characterized by iterative adaptation, leading to conversational alignment and successful information exchange. This chapter argues that adopting a similar natural interaction paradigm in human–machine interfaces can improve the adaptability and understanding of artificial agents. This chapter presents a set of design principles based on observations from human interactions, leading to the concept of phenotropics. Phenotropic interaction aims to enable artificial systems to understand and adapt to user needs, promoting collaboration in a more natural way. By increasing the naturalness of human–machine interaction, cognitive effort and communication ambiguity can be reduced, aligning with the media naturalness theory. The chapter introduces phenotropics and its origins in software engineering, followed by the design principles and a framework for the practical inclusion of the concepts of phenotropics in interactive systems. It concludes with a summary, critical outlook, and potential implications of phenotropic interaction in interactive system design.
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks

Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation for the target distribution depends on the width and depth (capacity) of the generators and discriminators and the number of samples in training. A quantified generalization bound is established for the Wasserstein distance between the generated and target distributions. According to the theoretical results, WGANs have a higher requirement for the capacity of discriminators than that of generators, which is consistent with some existing results. More importantly, the results with overly deep and wide (high-capacity) generators may be worse than those with low-capacity generators if discriminators are insufficiently strong. Numerical results obtained using Swiss roll and MNIST datasets confirm the theoretical results.