Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey on Medical Image Blob Detection and Classification Models

In Health Care, the medical image processing becomes a necessary marathon for the Computer Aided Diagnostic (CAD) systems. Blobs are the heterogeneous small parts of an image, which helps in finding and locating the abnormalities and damages of the body organs from the digital medical images. For the disease early diagnosis and staging purpose, the blobs’ location, size, shape, center and radius like properties should be calculated from the medical images. As on, many former researchers were proposed several blob detections models and algorithms to accurately find the blobs from the medical images. Although many researchers proposed various blob detection models, they are still facing several limitations in detection, due to the indistinct boundaries and the diversity in shape and distribution of the blobs. As part of the research on blob detection, in this survey paper we are presenting the essential information regarding the medical image blob detection and classification models.
Automated Framework for the Augmentation of Missing Anatomical Structures and Generation of Personalized Atrial Models from Clinical Data

Personalized computational models are often generated from electroanatomical maps, which might lack important anatomical structures like the appendages, or from imaging data which are potentially affected by segmentation uncertainty. A bi-atrial statistical shape model (SSM) was shown to cover atrial shape variability. We hypothesized that it could, therefore, also be used to infer the shape of missing structures and deliver ready-to-use models to assess atrial fibrillation vulnerability in silico. We implemented a highly automatized pipeline to generate a personalized computational model by fitting the SSM to the clinically acquired geometries. We applied our framework to a geometry coming from an electroanatomical map and one derived from magnetic resonance images (MRI). Only landmarks belonging to the left atrium and no information from the right atrium were used in the fitting process. The left atrium surface-to-surface distance between electroanatomical map and a fitted instance of the SSM was 2.26&#x00B1;1.95mm. The distance between MRI segmentation and SSM was 2.07&#x00B1;1.56 mm and 3.59&#x00B1;2.84mm in the left and right atrium, respectively. Our semi-automatic pipeline provides personalized computational models representing the original anatomy well by fitting a SSM. We were able to infer the shape ofthe right atrium even in the case ofusing information only from the left atrium.
Deep Learning-Assisted Demodulation for TeraHertz Communications Under Hybrid Distortions

TeraHertz wireless communication has been regarded as an effective technology to satisfy the ever-increasing requirements of high-rate services, where the imperfections of TeraHertz devices, including in-phase/quadrature imbalance, phase noise and nonlinearity, regarded as hybrid distortions, have to be investigated. Due to the hybrid distortions, the explicit system model could not be mathematically derived, which limits the performance of the state-of-the-art demodulation methods. Here, a deep learning-assisted demodulation methodology is proposed to improve the bit error rate performance. Specifically, a multiple-output deep feedforward neural network is designed to fit the mapping between the received signal and likelihood information, where the number of outputs equals to the size of the modulation set, thus enabling the demodulation of the overlapped received signals corresponding to different constellation points. Besides, a training set generation method is proposed to generate the training examples without the prior knowledge of likelihood information. Simulation results validate that the proposed learning-assisted methodology could improve the demodulation performance of the TeraHertz wireless systems under severe hybrid distortions.
Bodily Expression of Emotions in Animated Agents

The goal of this research is to identify key affective body gestures that can clearly convey four emotions, namely happy, content, bored, and frustrated, in animated characters that lack facial features. Two studies were conducted, a first to identify affective body gestures from a series of videos, and a second to validate the gestures as representative of the four emotions. Videos were created using motion capture data of four actors portraying the four targeted emotions and mapping the data to two 3D character models, one male and one female. In the first study the researchers identified body gestures that are commonly produced by individuals when they experience each of the four emotions. In the second study the researchers tested four sets of identified body gestures, one set for each emotion. The animated gestures were mapped to the 3D character models and 91 participants were asked to identify the emotional state conveyed by the characters through the body gestures. The study identified six gestures that were shown to have an acceptable recognition rate of at least 80% for three of the four emotions tested. Contentment was the only emotion which was not conveyed clearly by the identified body gestures. The gender of the character had a significant effect on recognition rates across all emotions.
Efficient Attendance Management System Based on Face Recognition

Every education institution nowadays is concerned about student attendance and performance. In the current academic system, consistent class attendance of students plays a significant role in their performance, assessment, and quality monitoring. The traditional approach of taking attendance in several institutions is by calling the names of students one after the other or each student manually signs on the papers. In existing approaches, taking and tracking student's attendance manually, losing attendance sheets, dishonesty of students, and high error scales are open challenges face by the faculties. It is a complex process, requires time, and causes manual paperwork. We proposed an efficient and robust approach for an attendance monitoring system using the face of a human. The proposed algorithm first detects and recognizes the student's faces from videos or images. Second, mark the attendance using a neural network model and texture features. Experiments were conducted on the commercially available datasets. The proposed approach is compared with the traditional attendance marking system in terms of time and accuracy. Our approach resolves the state-of-the-art approach challenges and saves time to monitor the students.
Semantic segmentation method of 3D liver image based on contextual attention model

Aiming at the problems of difficult segmentation, time-consuming and low precision of 3D liver medical images, 3D liver image semantic segmentation method based on context attention strategy is proposed. This method combines the three-dimensional spatial boundary information on the upper liver features map and the overall channel information on the lower liver features map. The model used the LiTS data set for ablation experiments in comparison with the previous model. Experimental results show that the Dice similarity coefficient (DICE) is increased by 1.6%, and the volume overlap error (VOE) is reduced by 2.49% in comparison with Channel-Unet. The relative volume difference (RVD), the average symmetric surface distance (ASD) and the root mean square symmetric surface distance (RMSD) which indicates that the algorithm improves the performance of 3D liver medical images segmentation. Extended experiments were carried out on the Sliver07 data set, the CT data set in CHAOS and the clinical MRI liver medical imaging data set of a hospital, and the results indicated that the proposed method has strong generalization ability.
Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne Sensing

In the domain of smart city applications, anomalistic crowd investigation in public places is a crucial task for the efficient management of unforeseen crowds. Many of the current approaches for crowd monitoring utilize physical sensor-based tracking (e.g., cellular signals, cameras, RFID beacons) to detect abrupt crowds. Unfortunately, these solutions are highly application-specific and are able to only detect the mere presence of crowds without determining their causes that may reveal the actual story behind gatherings that occur suddenly. In this paper, we explore the opportunity to meld two complementary sensing paradigms: social sensing and unmanned aerial vehicle (UAV)-based physical sensing to scrutinize abrupt crowd events occurring in public places. However, two technical challenges exist in developing our solution: i) handling the data sparsity across social sensing platforms and within video footage from UAVs; and ii) the accurate inference of the cause of crowd events from the collected data. To address these challenges, we develop Unravel, an intelligent social airborne sensing (SAS) framework that leverages interdisciplinary techniques from natural language processing (NLP), object recognition, and estimation theory for a comprehensive anomalistic crowd investigation. The evaluation results with a real-world anomalous crowd instance dataset show that the Unravel framework outperforms the state-of-the-art baselines in accurately validating the existence of anomalistic crowd instances in public areas and deriving their causes.
UAV-VQG: Visual Question Generation Framework on UAV Images

Visual Question Generation (VQG) is one of the most challenging problems since it aims to produce relevant and meaningful questions from images. As the VQG process is able to generate a diverse set of questions that do not exist in the training set, various models (e.g., visual question answering) can be beneficial from this question generation task by evaluating the model’s performance in unknown settings. In this paper, we explored the visual question generation task on images collected by an unmanned aerial vehicle (UAV). We highlight the significant role of the question generation task and present a variational attention-based model that focuses on creating diversified and meaningful questions from images. In comparison to baseline approaches, our presented method has demonstrated the ability to create a broad and meaningful set of questions.
CACS: A Context-Aware and Anonymous Communication Framework for an Enterprise Network Using SDN

The emergence of software-defined networking (SDN) has revolutionized the management of an enterprise network. The SDN-based design provides flexibility in network management, which spans over multiple applications, e.g., routing, switching, forwarding, and controlling. It reduces the reliance on vendor-specific devices and middlebox solutions, such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), etc. Furthermore, due to the integration of different technologies, privacy is one of the core issues faced by the enterprise. Host anonymity is one of the techniques to safeguard against privacy attacks; however, the existing anonymization solutions provide better anonymity, but at the cost of higher latency and are most suited for Internet traffic. To tackle this issue in an enterprise network, we propose an SDN-based communication framework using enterprise integration patterns (EIPs) that offers anonymous communication in an enterprise environment. Host anonymity is achieved by replacing the real IP address with the spoofed IP address during the transmission of data packets inside the network. Unlike the traditional networks, SDN can modify the header fields of packets as they traverse in the network from the source to the destination. In addition to the host anonymity, this framework also provides context-aware communication by leveraging the SDN global visibility characteristic, where application services are discoverable on the network without disclosing the addresses of the application servers. Moreover, context-aware services enable network traffic to be routed based on the application-layer services rather than the network-layer information. In the end, evaluation of the proposed framework is carried out with respect to the performance of anonymous communication, computational complexity, and security of the complete proposed framework. In addition, we also highlighted that the proposed framework is more suitable for heterogeneous network environments such as Internet of Things-based solutions.
Fake Reviewer Group Detection in Online Review Systems

Online review systems are important components in influencing customers’ purchase decisions. To manipulate a product’s reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews. Fake reviewer group detection, however, is more challenging due to the difficulties in capturing the underlying relationships in groups. In this work, we present an unsupervised and end-to-end approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures. First, cohensive groups are detected with modularity-based graph convolutional networks. Then the suspiciousness of each group is measured by several anomaly indicators from both individual and group levels. The fake reviewer groups can be finally detected through suspiciousness. Extensive experiments are conducted on real-world datasets, and the results show that our proposed method is effective in detecting fake reviewer groups compared with the state-of-the-art baselines.
Plenoptic Point Cloud Compression Using Multiview Extension of High Efficiency Video Coding

Plenoptic point clouds are more complete representations of three-dimensional (3-D) objects than single-color point clouds, as they can have multiple colors per spatial point, representing colors of each point as seen from different view angles. They are more realistic but also involve a larger volume of data in need of compression. Therefore, in this paper, a multiview-video-based framework is proposed to exploit the correlations in color across different viewpoints to compress plenoptic point clouds efficiently. To the best of the authors' knowledge, this is the first work to exploit correlations in color across different viewpoints using a multiview-video-based framework. In addition, it is observed that some unoccupied pixels, which do not have corresponding points in plenoptic point clouds and are of no use to the quality of the reconstructed plenoptic point cloud colors, may cost many bits. To address this problem, a block-based group smoothing and a combined occupancy-map-based rate distortion optimization and four-neighbor average residual padding are further proposed to reduce the bit cost of unoccupied color pixels. The proposed algorithms are implemented in the moving pictures experts group (MPEG) video-based point cloud compression (V-PCC) and multiview extension of High Efficiency Video Coding (MV-HEVC) reference software. Compared with the V-PCC independently applied to each view direction, the proposed algorithms can provide a BD-rate reduction of over 70%.
Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext

In recent days, with increased population and traffic on roadways, vehicle collision is one of the leading causes of death worldwide. The automotive industry is motivated on developing techniques to use sensors and advancements in the field of computer vision to build collision detection and collision prevention systems to assist drivers. In this article, a deep-learning-based model comprising of ResNext architecture with SENet blocks is proposed. The performance of the model is compared to popular deep learning models like VGG16, VGG19, Resnet50, and stand-alone ResNext. The proposed model outperforms the existing baseline models achieving a ROC-AUC of 0.91 using a significantly less proportion of the GTACrash synthetic data for training, thus reducing the computational overhead.
Simultaneous Human-Robot Matching and Routing for Multi-Robot Tour Guiding Under Time Uncertainty

Abstract This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their selected places of interest (POIs) and generate the routes and schedules for the robots according to uncertain spatial and time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal low-cost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally under different numbers of humans, robots, and POIs. The largest case tested involves 50 robots, 250 humans, and 50 POIs. Then, a photo-realistic multi-robot simulation platform was developed based on Habitat-AI to verify the tour guiding performance in an uncertain indoor environment. Results demonstrate that the proposed centralized tour planner is scalable, makes a smooth tradeoff in the plans under different environmental constraints, and can lead to robust performance with inaccurate uncertainty estimations (within a certain margin).
The Impact of Drone Technology on The Accounting Profession: The Case of Revenue Recognition in Long-Term Construction Contracts

The accounting profession has gone through radical changes due to recent technological advancements in AI, blockchain technologies, big data, etc. More recently, the accounting literature discussed the possibility of using the drone innovative technology in conducting inventory observation as well as internal and external auditing. This study is a visionary paper which investigates the applicability of a remotely auditing process using drone technology in real-estate accounting. The drone will be used to conduct site inspection to assess and monitor the construction progress through applying the percentage of completion method to recognize revenues from long-term contracts. This innovative technology has the ability to collect better data quality, saving cost and saving time with improved site safety which will help improve the auditor tasks.
Visual search in naturalistic scenes from foveal to peripheral vision: A comparison between dynamic and static displays

How important foveal, parafoveal, and peripheral vision are depends on the task. For object search and letter search in static images of real-world scenes, peripheral vision is crucial for efficient search guidance, whereas foveal vision is relatively unimportant. Extending this research, we used gaze-contingent Blindspots and Spotlights to investigate visual search in complex dynamic and static naturalistic scenes. In Experiment 1, we used dynamic scenes only, whereas in Experiments 2 and 3, we directly compared dynamic and static scenes. Each scene contained a static, contextually irrelevant target (i.e., a gray annulus). Scene motion was not predictive of target location. For dynamic scenes, the search-time results from all three experiments converge on the novel finding that neither foveal nor central vision was necessary to attain normal search proficiency. Since motion is known to attract attention and gaze, we explored whether guidance to the target was equally efficient in dynamic as compared to static scenes. We found that the very first saccade was guided by motion in the scene. This was not the case for subsequent saccades made during the scanning epoch, representing the actual search process. Thus, effects of task-irrelevant motion were fast-acting and short-lived. Furthermore, when motion was potentially present (Spotlights) or absent (Blindspots) in foveal or central vision only, we observed differences in verification times for dynamic and static scenes (Experiment 2). When using scenes with greater visual complexity and more motion (Experiment 3), however, the differences between dynamic and static scenes were much reduced.
Mobile and adaptive User interface for human robot collaboration in assembly tasks

The manufacturing sector is constantly looking for more efficient ways of production. The Industry 4.0 related technologies such as augmented and mixed reality, connectivity and digitalisation as well as the current trend of robotisation have resulted a number of technical solutions to support the production in factories. The combination of human-robot collaboration and augmented reality shows good promises. The challenges in this case come from the need to reconfigure the physical production layout and how to deliver the digital instructions to the operator. This paper introduces a model for collaborative assembly tasks that uses a mobile user interface based on the depth sensors and a projector. The novelty of this research comes from the adaptivity of the user interface, as it can be freely moved between the tasks around the workstation based on the operator needs and requirements of the tasks. The ability to move projection surface is achieved by detecting the surface position using Aruco markers and computing required transformation of the projector image.
Automated Testing for Machine Translation via Constituency Invariance

With the development of deep neural networks, machine translation has achieved significant progress and integrated with people’s daily lives to assist in various tasks. However, machine translators, which are essentially one kind of software, also suffer from software defects. Translation errors might cause misunderstanding or even lead to marketing blunders, and political crisis. Thus, almost all translation service providers have feedback channels of incorrect translations to collect training data and improve product performance. Inspired by the syntax structure analysis, we introduce the constituency invariance, which reflects the structural similarity between a simple sentence and sentences derived from it, to test machine translators. We implement it into an automated tool CIT to detect translation errors by checking the constituency invariance relation between the translation results. CIT adopts constituency parse trees to represent the syntactic structures of sentences and employs an efficient data augmentation method to derive multiple new sentences based on one sentence. To validate CIT, we experiment with three widely-used machine translators, i.e., Bing Microsoft Translator, Google Translate, and Youdao Translator. With 600 seed sentences as input, CIT detects 2212, 1910, and 1590 translation errors with around 77% precision. We have submitted detected errors to the development teams. Until we submit this paper, Google, Bing, and Youdao have fixed 15.4%, 32.0%, 14.3% of reported errors, respectively.
Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids

“Source ID Mix” spoofing emerged as a new type of cyber-attack on Distribution Synchrophasors (DS) where adversaries have the capability to swap the source information of DS without changing the measurement values. Accurate detection of such a highly-deceptive attack is a challenging task especially when the spoofing attack happens on short fragments of DS recorded within a relatively small geographical scale. This letter proposes an effective approach to detect this cyber-attack by realizing the multifractal characteristics of DS measurements. First, the multifractal cross-correlation of DS measured at multiple intra-state locations is revealed. Then the derived correlation is integrated with weighted two-dimensional multifractal surface interpolation to reconstruct quasi high-resolution signals. Finally, informative location-specific signatures are extracted from the high-resolution DS and they are integrated with advanced machine learning techniques for source authentication. Experiments using the real-life DS are performed to verify the proposed method.