Reviewerly/exHarmony huggingface dataset:
An intelligent tracking system for surgical instruments in complex surgical environment

In recent years, the problem of visual occlusion in optical surgical tracking system has attracted widespread attention. To solve this problem, this paper proposes a tracking system for surgical instruments based on data fusion of multicamera modules. Multicamera modules are used to track surgical instruments from different angles to solve the problem of tracking occlusion in monocular tracking. Firstly, occlusion of monocular tracking based on different fiducial marker is analyzed, and a fast calibration method of tracking system for surgical instrument based on multicamera module is proposed. The calibration of the tracking system is mainly divided into the calibration of surgical instruments and the fast calibration between multicamera modules. On this basis, the occlusion detection mechanism is proposed to screen out the data with large errors, and the fuzzy control theory is used to calculate the support degree between different modules. Finally, the serviceable data is fused based on the least square method and the position of surgical instruments is estimated. The average accuracy of static positioning and dynamic trajectory tracking is mm and mm respectively. The real-time performance of the proposed system is 35 frames per second (fps). Compared with the existing optical tracking system, the method proposed in this paper has stronger anti-occlusion and simpler operation.
Photon blockade in the Jaynes-Cummings model with two-photon dissipation

We propose a scheme to generate a single-photon source based on photon blockade in the Jaynes-Cummings (J-C) model with a two-photon dissipation (TPD) process. We present the optimal conditions for conventional/unconventional photon blockade via the wave function method with an effective Hamiltonian involving TPD. The results show that the second-order correlation function for the J-C model with TPD is considerably less than that of the J-C model with single-photon dissipation. Additionally, the average photon number can reach 0.5 in the large atomic detuning regime. This feature makes the J-C model with TPD a high-quality single photon source.
Distinct neural signatures of multimodal resizing illusions

Illusory body resizing typically uses multisensory integration to change the perceived size of a body part. Previous studies associate these multisensory body illusions with frontal theta oscillations and parietal gamma oscillations for dis-integration and integration of multisensory signals, respectively. However, recent studies also support illusory changes of embodiment from unimodal visual stimuli. This preregistered study (N = 48) investigated differences between multisensory visuo-tactile and unimodal visual resizing illusions using EEG, to gain a more comprehensive understanding of the neural underpinnings of resizing illusions in a healthy population. We hypothesised (1) stronger illusion in multisensory compared to unimodal, and unimodal compared to incongruent (dis-integration) conditions, (2) greater parietal gamma during multisensory compared to unimodal, and (3) greater frontal theta during incongruent compared to baseline conditions. Subjective Illusory results partially support Hypothesis 1, showing a stronger illusion in multisensory compared to unimodal conditions, but finding no significant difference comparing unimodal to incongruent conditions. Results partially supported EEG hypotheses, finding increased parietal gamma activity comparing multisensory to unimodal visual conditions, happening at a later stage of the illusion when compared to previous rubber hand illusion EEG findings, whilst also finding increased parietal theta activity when comparing incongruent to non-illusion conditions. While results demonstrated that only 27% of participants experienced the stretching illusion with unimodal visual stimuli compared to 73% of participants experiencing the stretching illusion in the multisensory condition, further analysis suggested that those who experience visual-only illusions exhibit a different neural signature to those who do not, with activity focussed around frontal and parietal regions early on in the illusory manipulation, compared to activity focussed more over parietal regions and at a later point in the illusory manipulation for the full sample of participants. Our results replicate previous subjective experience findings and support the importance of multisensory integration for illusory changes in perceived body size, whilst adding to our understanding of the temporal onset of multisensory integration within resizing illusions, differing from that of rubber hand illusions.
Elliptical4VR: An Interactive Exergame Authoring Tool for Personalized Elliptical Workout Experience in VR

In this paper, we propose Elliptical4VR, an interactive exergame authoring tool that synthesizes virtual environments and training programs for personalized elliptical workout experiences in Virtual Reality (VR) by exploring the possibility to combine functionality, aesthetics, and human activities as multi-dimensional interactions between humans, computers, and elliptical machines. Considering several key features of elliptical training such as Relative Rate of Perceived Exertion (Relative RPE) scales, target energy expenditure, total walking distance, etc., we optimize the elliptical workout profile to deliver expected training effects while at the same time considering the aesthetics of the virtual environment and the immersiveness delivered to the players by providing with the compatibility between the visual experience and haptic feedback during the gym activities enhanced by VR. In the end, we validate the effectiveness of our approach through a series of numerical experiments and preliminary user studies.
Towards Safe Cyber Practices: Developing a Proactive Cyber-Threat Intelligence System for Dark Web Forum Content by Identifying Cybercrimes

The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements.
Intuitionistic Fuzzy Kernel Random Vector Functional Link Classifier

Random vector functional link (RVFL) and its kernelized version, i.e., kernel-based RVFL (K-RVFL) are popular machine learning models for classification as well as regression. This work presents a new K-RVFL-based classification model with intuitionistic fuzzy membership called the intuitionistic fuzzy K-RVFL (IFK-RVFL) classifier. In IFK-RVFL, an intuitionistic fuzzy number is linked with each training data point which can be either membership or non-membership. IFK-RVFL uses the least-squares approach for finding a solution rather than solving quadratic programming problems (QPPs) unlike support vector machine (SVM) and twin SVM (TWSVM). In IFK-RVFL, the kernel function is introduced to handle the nonlinearity. The proposed IFK-RVFL is novel and has a low computational cost. Experimental simulations have been carried out on a few popular benchmark datasets. The obtained classification accuracies of IFK-RVFL are statistically compared with intuitionistic fuzzy SVM (IFSVM), intuitionistic fuzzy twin SVM (IFTSVM), RVFL, and K-RVFL models. The experimental outcomes portray the efficacy and applicability of the proposed IFK-RVFL model.
Digital Image Transformation Using Outer Totality Cellular Automata

In the recent technology, digital images play an important role to implement various applications, i.e., pattern recognition, object detection, quality enhancement, object tracking, face identification, etc. The image applications may accept input of different types, and some applications have to perform various internal operations to achieve desired results. The proposed research provides best methods, which could be used as internal operations of image-based applications. The methods of proposed research target various features of image like brightness, sharpness, colors and intensity of pixels, and by analyzing these features, the proposed methods can increase efficiency and accuracy of the image-based applications.
Mobile robot path planning based on hybrid ant colony optimization

To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm.
Optimizing the impact of data augmentation for low-resource grammatical error correction

Grammatical Error Correction (GEC) refers to the automatic identification and amendment of grammatical, spelling, punctuation, and word-positioning errors in monolingual texts. Neural Machine Translation (NMT) is nowadays one of the most valuable techniques used for GEC but it may suffer from scarcity of training data and domain shift, depending on the addressed language. However, current techniques (e.g., tuning pre-trained language models or developing spell-confusion methods without focusing on language diversity) tackling the data sparsity problem associated with NMT create mismatched data distributions. This paper proposes new aggressive transformation approaches to augment data during training that extend the distribution of authentic data. In particular, it uses augmented data as auxiliary tasks to provide new contexts when the target prefix is not helpful for the next word prediction. This enhances the encoder and steadily increases its contribution by forcing the GEC model to pay more attention to the text representations of the encoder during decoding. The impact of these approaches was investigated using the Transformer-based for low-resource GEC task, and Arabic GEC was used as a case study. GEC models trained with our data tend more to source information, are more domain shift robustness, and have less hallucinations with tiny training datasets and domain shift. Experimental results showed that the proposed approaches outperformed the baseline, the most common data augmentation methods, and classical synthetic data approaches. In addition, a combination of the three best approaches Misspelling, Swap, and Reverse achieved the best F1 score in two benchmarks and outperformed previous Arabic GEC approaches.
Efficient parallel implementation of Gaussian Mixture Model background subtraction algorithm on an embedded multi-core Digital Signal Processor

Scene segmentation is crucial in video processing applications, such as vehicle detection. The Gaussian Mixture Model (GMM) based background subtraction (BS) is a widely employed technique for scene segmentation. However, due to its high computational demands, an efficient parallel implementation is necessary. This paper proposes a parallelized implementation of the GMM algorithm on the C6678 digital signal processor (DSP), utilizing its eight cores. Three different parallelization approaches are presented: Open Multiprocessing (OpenMP), manually optimized method, and manually optimized method involving Direct Memory Access (DMA) engine. To evaluate their effectiveness, a metrics comparison is conducted using background truth images provided by the literature dataset references. The experimental results demonstrate that the proposed manually optimized method surpasses the current state-of-the-art. It achieves a remarkable parallel efficiency of approximately 99% for low and medium frame sizes and 83% and 79% for high-definition (HD) and full-HD resolution frames, respectively, when all eight cores are enabled. In contrast, the parallel efficiency obtained using OpenMP is only 45%.
MyWSL: Malaysian words sign language dataset

Deaf and hard-of-hearing individuals use sign language as a means of communication. However, those around them, especially family members like the children of deaf adults, may face communication challenges if they are unfamiliar with sign language. This issue has prompted numerous researchers to conduct studies on sign language translation and recognition. However, there is currently no publicly available dataset specifically for Malaysian sign language. This article introduces an image dataset of the Malaysian Sign Language (MySL) hand gestures used in everyday situations. The dataset, named MyWSL2023, comprises 3,500 images of ten static Malaysian sign language words collected from five participants (two males and three females) aged between 20 and 21 years old. The data collection took place indoors under normal lighting conditions. The MyWSL2023 dataset, which has been made freely accessible to all researchers, serves as a valuable resource for not only investigating and developing automated systems for hearing-impaired and deaf individuals but also gesture and sign language recognition using vision-based methods. The dataset can be accessed for free at https://data.mendeley.com/datasets/zvk55p7ktd.
Defense R&amp;D Technology Trend Analysis Using Patent Information: Focusing on Topic Modeling and ARM Analysis

Korea reached the world’s highest level in terms of national R&amp;D investment, but analysis has been conducted mainly through qualitative methods such as literature research and Delphi techniques rather than objective analysis of technology trends in the R&amp;D planning stage by technology sector. In the case of the defense field, in particular, few studies have been conducted on quantitative trend analysis due to security and closeness, and existing previous studies have been analyzed limited to specific technologies. Therefore, this study attempted to improve the efficiency of analysis by applying patent data containing technical information to defense technology analysis using big data analysis methods and processes for quantitative analysis of technology trends. To carry out this, after collecting patent data from the Agency for Defense Development for 20 years, major R&amp;D fields were derived using topic modeling techniques, and based on the results, the trend of technological change over time was examined. In addition, in order to find out the interrelationship between the derived topics, the association rule mining(ARM) was performed to identify which fields are being studied together and visualize the connection network. It is expected that the technology trends and analysis methods presented through this study will be effectively used to establish R&amp;D technology policies.
FASTDIAGP: An Algorithm for Parallelized Direct Diagnosis

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the "no solution could be found" dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without predetermining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm's runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.
Do Disease Stories Need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease

Abstract Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand‐crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data‐driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character‐centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self‐referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character‐driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.
Redesigning Multi-Scale Neural Network for Crowd Counting

Perspective distortions and crowd variations make crowd counting a challenging task in computer vision. To tackle it, many previous works have used multi-scale architecture in deep neural networks (DNNs). Multi-scale branches can be either directly merged (e.g. by concatenation) or merged through the guidance of proxies (e.g. attentions) in the DNNs. Despite their prevalence, these combination methods are not sophisticated enough to deal with the per-pixel performance discrepancy over multi-scale density maps. In this work, we redesign the multi-scale neural network by introducing a hierarchical mixture of density experts, which hierarchically merges multi-scale density maps for crowd counting. Within the hierarchical structure, an expert competition and collaboration scheme is presented to encourage contributions from all scales; pixel-wise soft gating nets are introduced to provide pixel-wise soft weights for scale combinations in different hierarchies. The network is optimized using both the crowd density map and the local counting map, where the latter is obtained by local integration on the former. Optimizing both can be problematic because of their potential conflicts. We introduce a new relative local counting loss based on relative count differences among hard-predicted local regions in an image, which proves to be complementary to the conventional absolute error loss on the density map. Experiments show that our method achieves the state-of-the-art performance on five public datasets, i.e. ShanghaiTech, UCF_CC_50, JHU-CROWD++, NWPU-Crowd and Trancos. Our codes will be available at https://github.com/ZPDu/Redesigning-Multi-Scale-Neural-Network-for-Crowd-Counting.
Edge-enabled federated sequential recommendation with knowledge-aware Transformer

The increasing ubiquity of recommendation systems in modern applications has brought about significant changes in various aspects of our daily lives. However, the emergence of privacy concerns has highlighted the need for innovative solutions to address the challenges faced by traditional recommender systems. In this regard, federated learning provides an approach to address privacy concerns by training global models based on edge data. However, the integration of the existing federated learning framework with the latest recommendation system method remains insufficiently explored. To address this issue, we present edge-enabled federated Sequential Recommendation with Knowledge-aware Transformer (KG-FedTrans4Rec) model that incorporates knowledge graph information into sequential recommendation tasks while applying federated learning for privacy preservation. The KG extraction module and sequential processing module in KG-FedTrans4Rec are designed to capture item-related information and sequence inner relationships simultaneously. We leverage Graph Convolutional Networks (GCNs) to aggregate item-related information and user preferences in the KG extraction module. Furthermore, sequential recommender systems model users' dynamic preferences based on their behaviors to make customized recommendations. In the sequential processing module, we employ replaced token detection and two-stream self-attention strategies to enhance the Transformer-based model. In summary, our proposed KG-FedTrans4Rec model presents a approach to sequential recommendation tasks by incorporating knowledge graph information while preserving user privacy through federated learning. Our experimental results demonstrate the superior performance of our model compared to various recommendation models. This research is expected to make significant contributions to the development of privacy-preserving recommendation systems, which are increasingly essential in modern applications.