Reviewerly/exHarmony huggingface dataset:
EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis

This paper describes the implementation of a comprehensive clinical decision support system (CDSS) for the risk factors prediction of comorbidities related to obesity and for the characterization of indirect connections between such comorbidities and non-communicable diseases. In particular, the direct correlation between obesity, diabetes, cardiovascular, and heart disease is analyzed by using machine learning (ML) predictive models, while the connection of the co-occurring disorders to the numerous additional non-communicable diseases is analyzed via a graph-based user interface. The CDSS here proposed is, therefore, structured with three main components: ML predictive models based on publicly available datasets, explainable artificial intelligence (XAI) local and global model interpretation, and graph-based representation of non-communicable disease connections. Multiple ML models are presented for risk assessment and a comparison is carried out based on performance key performance indicators. The best-performing model for each disease was proved to be: the multi-layer perceptron for diabetes and heart disease, and extreme gradient boosting for cardiovascular disease. Comorbidities risk factor prediction and a XAI local model explanation is performed on significant case studies. In addition, XAI global model interpretation is given for the entire dataset providing insights on the features’ contribution to the models’ implementation. Moreover, the graph-based visualization of indirect disease co-occurrence is performed by filtering connections according to different relative risk factor thresholds. This interface can be exploited by healthcare professionals to obtain, according to the needs and the clinical approach, a global perspective on obesity and its associated pathologies prevention as well as long-term treatment and care provision.
Semantic Policy Network for Zero-Shot Object Goal Visual Navigation

The task of zero-shot object goal visual navigation (ZSON) aims to enable robots to locate previously “unseen” objects by visual observations. This task presents a significant challenge since the robot must transfer the navigation policy learned from “seen” objects to “unseen” objects through auxiliary semantic information without training samples, a process known as zero-shot learning. In order to address this challenge, we propose a novel approach termed the Semantic Policy Network (SPNet). The SPNet consists of two modules that are deeply integrated with semantic embeddings: the Semantic Actor Policy (SAP) module and the Semantic Trajectory (ST) module. The SAP module generates actor network weight bias based on semantic embeddings, creating unique navigation policies for different target classes. The ST module records the robot's actions, visual features, and semantic embeddings at each step, and aggregates information in both the spatial and temporal dimensions. To evaluate our approach, we conducted extensive experiments using MP3D dataset, HM3D dataset, and RoboTHOR. Experimental results indicate that the proposed method outperforms other ZSON methods for both seen and unseen target classes.
D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration

For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that the collected information is efficiently shared among robots in order to support exploration and navigation tasks. Practical constraints of wireless channels, such as limited bandwidth, urge robots to carefully select information to be transmitted. In this letter, we consider the case where environmental information is modeled using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D Scene Graph</i> , a hierarchical map representation that describes both geometric and semantic aspects of the environment. Then, we leverage graph-theoretic tools, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph spanners</i> , to design greedy algorithms that efficiently compress 3D Scene Graphs with the aim of enabling communication between robots under bandwidth constraints. Our compression algorithms are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">navigation-oriented</i> in that they are designed to approximately preserve shortest paths between locations of interest while meeting a user-specified communication budget constraint. The effectiveness of the proposed algorithms is demonstrated in robot navigation experiments in a realistic simulator.
Online 3D Edge Reconstruction of Wiry Structures From Monocular Image Sequences

Three-dimensional (3D) reconstruction of wiry structures from vision suffers from thin geometry, lack of texture, and severe self-occlusions. We propose an online 3D edge reconstruction framework that uses monocular image sequences to reconstruct the wiry structures whose skeletons are mainly straight as commonly found in the real world. To reconstruct such structures in an efficient manner, we employ straight edges constructed from points as underlying primitives of the representation. This is to address the harsh geometric nature of wiry objects (e.g., severe self-occlusion) and also to avoid a typically expensive line matching process. Specifically, we first construct sparse 3D points by tracking feature points, while simultaneously refining the camera poses via a robust maximum a posteriori (MAP) inference. These sparse points are then used to generate edge candidates and the belief of each candidate is updated in a Bayesian fashion using a likelihood evaluated on the image observation. Finally, we take the set of 3D edges with beliefs greater than a threshold and apply a post-processing step to reject false edges. We experimentally validate our framework using real-world wiry objects and demonstrate a manipulation task using the reconstruction. The proposed framework exhibits superior performance over state-of-the-art algorithms for the class of wiry structures and the potential to be easily used for subsequent robotic tasks.
State-business relations for entrepreneurial takeoff in Africa: institutional analysis

Purpose Reports and experiences suggest that several developing African economies are faced with entrepreneurial-impeding forces such as lengthy bureaucratic processes and poor regulatory space. The study examines a general trend in “doing business performance” among selected African countries and uses the case of Ghana to explore how particular indicators or forces affect the development and deployment of small and medium-sized enterprise (SME) policies. Design/methodology/approach Comparative analysis of six African economies on their ease of doing business score. This is followed by a critical review of the literature to develop a six-point explanatory framework to explore the relative position of the six countries on the ease of doing business scores. Using Ghana as a critical case study, the authors deploy an in-depth case study analysis via in-depth interviews of relevant stakeholders to validate the information from secondary sources. Findings The study observes that the nature of leadership, socio-cultural imperatives, economic structure and policy and the role of domestic institutional players and international players have implications for the extent to which the state creates an enabling environment for SMEs and entrepreneurial activities. The role of supportive cultural software that will help drive SME and entrepreneurial growth has been established. The study contends that different aspects of national culture do have implications for the tendency for people to be business-minded or to have the ability to take risks. The demand and supply sides are crucial in promoting SME growth. Originality/value The study develops a framework that helps explore elements to help explain ease of doing business scores and the viability of SMEs in Africa. These elements were validated through qualitative interviews as well.
Multimodal representative answer extraction in community question answering

To solve the information overload problem of multimodal answers in community question answering (CQA), this paper proposes a multimodal representative answer extraction method. First, the method of similarity calculation between multimodal answers is constructed, and multimodal clustering is used to cluster answers. Then, a binary multi-objective optimization model with three objective functions including multimodal answer coverage, multimodal answer redundancy, and multimodal answer consistency is constructed to extract a representative subset of answers. The improved Beluga whale optimization algorithm (MTRL-BWO), based on tent mapping, reinforcement learning, and multiple swarm strategy, is designed to increase the diversity of the population while avoiding local optima to improve the search capability and solution accuracy of the algorithm. Experimental results show the feasibility and superior performance of the proposed method.
HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets

The PSO-PARSIMONY methodology (a heuristic for finding accurate and low-complexity models with particle swarm optimization (PSO)) allows obtaining machine learning models with a good balance between accuracy and complexity. However, when the datasets are of high dimensionality, the methodology does not sufficiently reduce the complexity of the models. This paper presents a new hybrid methodology, called HYB-PARSIMONY, that combines PSO with genetic algorithm (GA) based methods. In the early stages of the optimization process, GA methods have a preponderance to accelerate the search for parsimony. Later, PSO becomes more relevant to improve accuracy. This new methodology obtains significant improvements in the search for more accurate and low-complexity models in high-dimensional datasets.
Does familiarity with the attraction matter? Antecedents of satisfaction with virtual reality for heritage tourism

Abstract Virtual reality (VR) is a topic of growing interest. While many researchers have identified factors that influence satisfaction with VR, additional important factors remain uninvestigated. In our research model, system quality, presence, and authenticity influence two mediating variables of enjoyment and usefulness of information. Enjoyment (a hedonic aspect of the VR experience) and usefulness of information (a utilitarian aspect), in turn, influence satisfaction, with familiarity moderating both of these relationships. PLS analysis of survey data collected in a heritage tourism context finds relationships that have not been previously identified. While system quality and authenticity are associated with enjoyment and usefulness of information, presence is positively associated only with enjoyment. Familiarity negatively moderates the relationship between enjoyment and satisfaction with VR. This study thus extends prior research on a key metaverse technology, VR, by identifying and explicating the roles of authenticity and familiarity—and also extends prior research by focusing on the hedonic and utilitarian dimensions of the VR experience.
DB-Tracker: Multi-Object Tracking for Drone Aerial Video Based on Box-MeMBer and MB-OSNet

Drone aerial videos offer a promising future in modern digital media and remote sensing applications, but effectively tracking several objects in these recordings is difficult. Drone aerial footage typically includes complicated sceneries with moving objects, such as people, vehicles, and animals. Complicated scenarios such as large-scale viewing angle shifts and object crossings may occur simultaneously. Random finite sets are mixed in a detection-based tracking framework, taking the object’s location and appearance into account. It maintains the detection box information of the detected object and constructs the Box-MeMBer object position prediction framework based on the MeMBer random finite set point object tracking. We develop a hierarchical connection structure in the OSNet network, build MB-OSNet to get the object appearance information, and connect feature maps of different levels through the hierarchy such that the network may obtain rich semantic information at different sizes. Similarity measurements are computed and collected for all detections and trajectories in a cost matrix that estimates the likelihood of all possible matches. The cost matrix entries compare the similarity of tracks and detections in terms of position and appearance. The DB-Tracker algorithm performs excellently in multi-target tracking of drone aerial videos, achieving MOTA of 37.4% and 46.2% on the VisDrone and UAVDT data sets, respectively. DB-Tracker achieves high robustness by comprehensively considering the object position and appearance information, especially in handling complex scenes and target occlusion. This makes DB-Tracker a powerful tool in challenging applications such as drone aerial videos.
Formalstyler: GPT-Based Model for Formal Style Transfer with Meaning Preservation

Abstract Style transfer is a natural language processing generation task, it consists of substituting one given writing style for another one. In this work, we seek to perform informal-to-formal style transfers in the English language by using a style transfer model that takes advantage of the GPT-2. This process is shown in our web interface where the user input a informal message by text or voice. Our target audience are students and professionals in the need to improve the quality of their work by formalizing their texts. A style transfer is considered successful when the original semantic meaning of the message is preserved after the independent style has been replaced with a formal one with a high degree of grammatical correctness. This task is hindered by the scarcity of training and evaluation datasets alongside the lack of metrics. To accomplish this task, we opted to utilize OpenAI’s GPT-2 Transformer-based pre-trained model. To adapt the GPT-2 to our research, we fine-tuned the model with a parallel corpus containing informal text entries paired with the equivalent formal ones. We evaluate the fine-tuned model results with two specific metrics, formality and meaning preservation. To further fine-tune the model, we integrate a human-based feedback system where the user selects the best formal sentence out of the ones generated by the model. The resulting evaluations of our solution exhibit similar to improved scores in formality and meaning preservation to state-of-the-art approaches.
Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE

Infrared sensors capture thermal radiation emitted by objects. They can operate in all weather conditions and are thus employed in fields such as military surveillance, autonomous driving, and medical diagnostics. However, infrared imagery poses challenges such as low contrast and indistinct textures due to the long wavelength of infrared radiation and susceptibility to interference. In addition, complex enhancement algorithms make real-time processing challenging. To address these problems and improve visual quality, in this paper, we propose a multi-scale FPGA-based method for real-time enhancement of infrared images by using rolling guidance filter (RGF) and contrast-limited adaptive histogram equalization (CLAHE). Specifically, the original image is first decomposed into various scales of detail layers and a base layer using RGF. Secondly, we fuse detail layers of diverse scales, then enhance the detail information by using gain coefficients and employ CLAHE to improve the contrast of the base layer. Thirdly, we fuse the detail layers and base layer to obtain the image with global details of the input image. Finally, the proposed algorithm is implemented on an FPGA using advanced high-level synthesis tools. Comprehensive testing of our proposed method on the AXU15EG board demonstrates its effectiveness in significantly improving image contrast and enhancing detail information. At the same time, real-time enhancement at a speed of 147 FPS is achieved for infrared images with a resolution of 640 × 480.
Analysis of Facial Expressions for the Estimation of Concentration on Online Lectures

The present study aimed to develop a method to estimate the state of attention from facial expressions while students are in online lectures. We con-ducted an experiment to measure the level of attention based on reaction time to detect an auditory target, which was the disappearance of noise sound, while watching lecture videos, assuming that reaction time for the detection of contents-irrelevant noise is longer when learners are focusing attention more to the contents of the videos. We sought facial features that are useful for predicting the reaction time and found that reaction time can be estimated in some amount from facial features. This result indicates that facial expressions are useful for predicting attention state, or concentration level while attending lectures.
Weak texture remote sensing image matching based on hybrid domain features and adaptive description method

Abstract Weak texture remote sensing image (WTRSI) has characteristics such as low reflectivity, high similarity of neighbouring pixels and insignificant differences between regions. These factors cause difficulties in feature extraction and description, which lead to unsuccessful matching. Therefore, this paper proposes a novel hybrid‐domain features and adaptive description (HFAD) approach to perform WTRSI matching. This approach mainly provides two contributions: (1) a new feature extractor that combines both the spatial domain scale space and the frequency domain scale space is established, where a weighted least square filter combined with a phase consistency filter is used to establish the frequency domain scale space; and (2) a new log‐polar descriptor of adaptive neighbourhood (LDAN) is established, where the neighbourhood window size of each descriptor is calculated according to the log‐normalised intensity value of feature points. This article prepares some remote sensing images under weak texture scenes which include deserts, dense forests, waters, ice and snow, and shadows. The data set contains 50 typical image pairs, on which the proposed HFAD was demonstrated and compared with state‐of‐the‐art matching algorithms (RIFT, HOWP, KAZE, POS‐SIFT and SIFT). The statistical results of the comparative experiment show that the HFAD can achieve the accuracy of matching within two pixels and confirm that the proposed algorithm is robust and effective.
A robust watermarking approach against high‐density salt and pepper noise (RWSPN) to enhance medical image security

Abstract This paper proposes a robust watermarking method against high‐density salt and pepper noise attacks. Automatic region of interest (ROI) detection, embedding encoded data, removing different densities of noise, data extraction by omitting, and labeling the noisy pixels of Region of Non‐Interest (RONI), and decoding the extracted data using ROI pixel information as a key, are various steps of the presented scheme. The automatic ROI detection method separates the RONI from ROI with four vertices of the smallest rectangle, for the embedding process. The encoded watermark data is embedded into the least significant bits of RONI in four neighbour pixels. Adaptive Removal of high‐density Salt and pepper Noise method can enhance image quality and reduce the effect of salt and pepper noise attacks. The embedded information is preserved from destruction if the host image is impaired through the power of robustness. The best results are obtained through the action of extracting the watermark from RONI pixels, utilizing the same ROI detection method. Omitting and labeling the noisy pixels of the RONI will ensure healthy extracted watermark data, leading to decreased Bit Error Rate (BER) values. Finally, these data are interpreted using the key of ROI pixels, and the watermark data is decoded and retrieved. Due to salt and pepper noise obliterating pixel bits and their corresponding transform coefficients in the transform domain, the spatial domain is employed to enhance robustness against such attacks. The results show the high performance of the presented scheme. The average BER value for five MRI databases in a 97% salt and pepper noise attack is 38.6.
Neutralizations, Altercasting, and Online Romance Fraud Victimizations

ABSTRACTIn the present study we draw from data collected from 87 online romance fraudsters to explore whether and how offenders may suggest neutralizations to victims as a means to facilitate their crimes. Through thematic analysis of a series of e-mails exchanged with each fraudster we find that they encourage victims to neutralize their misgivings about sending them money in four overlapping ways. First, fraudsters appeal to vicarious necessity or implore victims to consider the fraudsters' needs. Second, they also appeal to an intimate relationship or suggest victims view their relationship as intimate in nature. Third, they deny susceptibility or subtly persuade victims to see themselves as holding more power in the interaction. Finally, fraudsters appeal to religious identity or encourage victims to consider their own religious identities. Our study contributes to understanding of neutralizations by explicating how neutralizations can be used by offenders as tools to assist their offending. It also adds to knowledge of the relationship between neutralizations and altercasting. In addition, we expand prior work illustrating the grooming process of online romance fraud and other crimes. We discuss implications for victim-blaming as it occurs among online romance fraud victims. Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The fraudsters' names are pseudonyms assigned by the researchers.2 The fraudsters' e-mails are presented as written and have thus not been grammatically corrected.3 Ellipses represent words or phrases that have been removed for the sake of clarity.Additional informationNotes on contributorsTimothy DickinsonTimothy Dickinson is an assistant professor in the Department of Criminology and Criminal Justice at the University of Alabama. His work focuses on exploring how various elements influence the decisions, actions, and identities of persons involved in criminal activity.Fangzhou WangFangzhou Wang is an assistant professor in the Department of Criminology and Criminal Justice at the University of Texas-Arlington. Her main research interests involve online fraud, romance scams, and human factors in cybercrime and criminological theory.