Reviewerly/exHarmony huggingface dataset:
Learning Time-Series Images of Niacin Skin-Flushing Test for the Diagnosis of Schizophrenia and Affective Disorder

Niacin skin-flushing test is a promising method for fast and objective diagnosis of schizophrenia and affective disorder. Compared to healthy controls, the patients have attenuated flushing extent and reduced flushing rate. The traditional analysis of niacin test relies on manually evaluated flushing extent, while flushing patterns sh own in the sk in test images have not yet been considered. To exploit the potential of raw images for diagnosis and avoid subjective and time-consuming estimation of the flushing levels, we propose a CNN-LSTM hybrid model to learn the time-series of images captured in the niacin skin-flushing test. Moreover, an attention layer is introduced to enhance the model performance and interpretability. We collect a data set of 796 participants, and the experimental results show that the new model outperforms the traditional 4-point scaled measurement and conventional deep learning methods by large margins. Besides, the attention scores yielded by the model reveals key time points during the flushing reaction.
Analysis of Image Edge Extraction Methods

In this paper, the performances of LOG and CANNY edge extraction operators are contrasted through experiments on specific images, and the edge extraction capacities of these two edge extraction methods under different filtering scales and added noise energy (noise level) are studied. The results suggest that the CANNY edge detector is not easily disturbed by noise in comparison with the LOG operator on the same scale, because the LOG edge detector adopts the detection method of second derivative zero cross point; the CANNY operator can more detect the real weak edge better, since the CANNY edge detector adopts two different thresholds to detect strong and weak edges, and when the weak edges are linked with strong edges, the weak edges are included in the output image; the LOG operator is more easily affected by scale; in the case of the properly selected scale, the LOG operator is very accurate in detecting the position of the image edge points, while the CANNY operator has a relatively high detection rate for the image edge, but the organization structure of the edge points is not described in detail enough, and the position also has a small-scale deviation.
Short-term Photovoltaic Power Generation Prediction using Deep Interval Type-2 Fuzzy Model Based on Extended Kalman Filter

A forecasting method using deep interval type-2 fuzzy model (DIT2FM) based on extended Kalman filter (EKF) is proposed to tackle the problem of lower forecasting accuracy in the photovoltaic (PV) power generation prediction which is caused by uncertainties of the characteristics of historical PV power sequences. At first, the DIT2FM is constructed in a way of bottom-up and hierarchical connection of many IT2FISs that are taken as its basic building modules. Then, the EKF algorithm is utilized to train the parameters of DIT2FM recursively for the purpose of reducing computational complexity and improving convergence speed. Besides, the proposed method is applied to the short-term prediction of a PV plant's power generation in an area, and compared with other forecasting methods based on deep fuzzy models and deep neural networks under the same conditions. The results of experiments indicate that the proposed DIT2FM method can further improve the prediction accuracy while meeting the actual necessity of real-time dispatching of high percentage of PV-connected power systems by forecasting the PV power generation in an adaptive and online scheme, which brings potential applications.
Rev: A Video Engine for Object Re-identification at the City Scale

Object re-identification (Re ID) is a key application of city-scale cameras on the edge. It is challenged by the limited accuracy of vision algorithms and the large video volume. We present Rev, a practical ReID engine that builds upon three new techniques. (1) Rev formulates ReID as a spatiotemporal query. Instead of retrieving all the images of a target object, it looks for locations and times in which the target object appeared. (2) Rev makes robust assessment of the target object occurrences by clustering unreliable object features. Each resultant cluster represents the general impression of a distinct object. (3) Rev samples cameras strategically in order to maximize its spatiotemporal coverage at low compute cost. Through an evaluation on 25 hours of videos from 25 cameras, Rev reached a high accuracy of 0.87 (recall at 5) across 70 queries. It runs at 830 × of video realtime in achieving high accuracy.
A study of the Beeclust algorithm for robot swarm aggregation

Swarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at the robot level to accomplish a task at the swarm level. The characteristics and properties of natural swarms have been studied to solve this problem. From these studies, basic behaviors have been defined, one of them being aggregation. This work explores the classical aggregation algorithm known as Beeclust. The Beeclust algorithm was implemented in MATLAB. Test were performed to determine its effectiveness in forming aggregates and the factors that affect its efficiency.
Discovering Onion Services Through Circuit Fingerprinting Attacks

Tor onion services provide anonymous service to clients using the Tor browser without disclosing the real address of the server. But an adversary could use a circuit fingerprinting attack to classify circuit types and discover the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks. But we found that circuits still expose much information to the adversary. In this paper, we present a novel circuit fingerprinting attack, which divides the circuit into the circuit generated by the client and the circuit generated by the onion service. To get a more effective attack, we tried three state-of-the-art classification models called SVM, Random Forest and XG-Boost, respectively. As the best performance, we attain 99.99 % precision and 99.99% recall when using Random Forest and X G Boost classification models, respectively. And we also tried to classify circuit types using our features and the classification model mentioned above, which was first proposed by Kwon. The best performance was achieved with 99.99% precision and 99.99% recall when using the random forest classifier in circuit type classification. The experimental results show that we achieved highly accurate circuit fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor.
A Flexible CNN Architecture for Real-Time FPGA Implementation

In this paper, an optimized and flexible architecture for the convolutional neural networks (CNN) algorithms is proposed for real-time implementation in the field-programmable gate array (FPGA) platforms. In these networks, the convolutional layer is responsible for the high resource usage and the computational complexity. With our approach, the resource and complexity overheads are decreased keeping performance. Moreover, a flexible and fast framework is also introduced and described in this work, in which a hardware description language (HDL) tool is adopted to generate HDL code from the proposed behavioral CNN model. To the best of the author's knowledge, this is the first work in which a CNN architecture is implemented with an HDL tool, decreasing the overhead imposed by software and maintaining the clarity of the CNN structure. The obtained results show the feasibility of the proposed architecture in different scenarios considering the global performance measured by the mean-square error (MSE), resource reports, and timing requirements, in which the resources allocated in the FPGA are considerably reduced compared to a fully parallelized implementation.
Decision Early-Exit: An Efficient Approach to Hasten Offloading in BranchyNets

Many works study partitioning and early exits in Deep Neural Networks (DNNs) to improve the inference time. Early exits allow the inference of samples in advance, based on the fact that some features are learned at DNNs’ initial layers. However, usage of early exits can slightly decrease performance. Partitioning enables the shallowest part of the model to reside at the edge while the deeper layers reside in the cloud. Deciding whether samples must be sent to the cloud at each early exit is time consuming, increasing the total inference time. Hence, reducing this time while maintaining the model performance is currently an open challenge. In this paper, we propose a Decision Early Exit (DEEx), implemented at the first early exit, aiming to reduce the total inference time by skipping unnecessary evaluations at early exits, which may not able to improve the model’s performance. To this end, the DEEx compares a predefined decision threshold with the prediction confidence level for each sample and decides whether the sample must be offloaded. We assess DEEx through a comparative analysis that investigates the influence of different values for the decision threshold on the inference time. Our results show that there is a cost benefit between the inference time and the threshold. Using DEEx in a simulated BranchyNet, we can reduce the inference time by around 20% while maintaining the same accuracy achieved when the samples are offloaded.
Range Estimation of Radar Targets Using a Temporal Convolutional Network

This paper investigates the radar range estimation problem. Using classical signal processing techniques, current radar systems are generally able to accurately detect targets and estimate their ranges. However, radar systems that need to directly sample signals at RF are too computationally intensive to obtain an accurate range estimate in the required time window. In addition, as the amount of data increases the number of computations will also increase. This paper will demonstrate that by using a trained neural network to perform sequence-to-sequence classification on a radar signal, it is possible to detect targets that are within the radar system’s field of vision and estimate their ranges with an overall accuracy of 99% in a fraction of time as compared to conventional radar signal processing.
Federated Learning through Goal Programming: a Computational Study in Cancer Detection

In the era of big data, Federated Learning has emerged as a promising way of learning from scattered nodes. In its essence, Federated Learning is a distributed learning methodology allowing model training on a large corpus of decentralized data. Federated Learning can be a sound solution to the data silos problem in healthcare. However, not all Federated Learning approaches are privacy compliant, resulting in data leakages that prevent its usage in healthcare applications. This computational study investigates whether a privacy-compliant Federated Learning framework is suitable for cancer detection. To this end, we tested the Federated Goal Programming framework in the context of cancer detection. Under such a framework, the models are aggregated based on the cross-performance at each node. Data leakage is prevented since training happens only locally, and only the prediction rules are shared. We tested this approach under three publicly available datasets and different ML models. The results show that the Federated Goal Programming framework is suitable for cancer detection.
Multi-Scale Prototype Constraints with Relation Aggregation for Semi-supervised Medical Image Segmentation

Semi-supervised learning alleviates the problem of lack of labeled data, which has attracted wide attention in the field of medical image segmentation. In this paper, we propose a multi-scale prototype constrained architecture with relation aggregation for semi-supervised medical image segmentation. Concretely, we design the multi-scale prototype constraints such that the ground truth of labeled data can guide the segmentation of unlabeled data through the prototype. Furthermore, to better obtain the related prior knowledge from the annotation information, we designed the relation aggregation module, which can transmit relevant information between the labeled and unlabeled data. Comprehensive quantitative and qualitative evaluations show that our method is a general and efficient method for semi-supervised medical image segmentation.
Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images

Computational Pathology (CPATH) offers the possibility for highly accurate and low-cost automated pathological diagnosis. However, the high time cost of model inference is one of the main issues limiting the application of CPATH methods. Due to the large size of Whole-Slide Image (WSI), commonly used CPATH methods divided a WSI into a large number of image patches at relatively high magnification, then predicted each image patch individually, which is time-consuming. In this paper, we propose a novel Uncertainty-based Model Acceleration (UMA) method for reducing the time cost of model inference, thereby relieving the deployment burden of CPATH applications. Enlightened by the slide-viewing process of pathologists, only a few high-uncertain regions are regarded as “suspicious” regions that need to be predicted at high magnification, and most of the regions in WSI are predicted at low magnification, thereby reducing the times of image patch extraction and prediction. Meanwhile, uncertainty estimation ensures prediction accuracy at low magnification. We take two fundamental CPATH classification tasks (i.e., cancer region detection and subtyping) as examples. Extensive experiments on two large-scale renal cell carcinoma classification datasets demonstrate that our UMA can significantly reduce the time cost of model inference while maintaining competitive classification performance.
Sequential pattern detection for identifying courses of treatment and anomalous claim behaviour in medical insurance

Fraud and waste is a costly problem in medical insurance. Utilising sequence information for anomaly detection is under-explored in this domain. We present a multi-part method employing sequential pattern mining for identifying and grouping comparable courses of treatment, finding patterns within those courses, calculating the cost of possible additional or upcoded claims in unusual patterns, and ranking the providers based on potential recoverable costs. We applied this method to real-world radiation therapy data. Results were assessed by experts at the Australian Government Department of Health, and were found to be interpretable and informative. Previously unknown anomalous claim patterns were discovered, and confirmation of a previously suspected anomalous claim pattern was also obtained. Outlying providers each claimed up to ${\$}$486,617.60 in potentially recoverable costs. Our method was able to identify anomalous claims as well as the patterns in which they were anomalous, making the results easily interpretable. The method is currently being implemented for another problem involving sequential data at the Department of Health.
Development of an Autonomous Mobile Manipulator for Pick and Place Operation Using 3D Point Cloud

Automated Guided Vehicles (AGV) are commonly used for tedious processes such as materials transportation and sorting. However, the function of an AGV is quite limited without a manipulator such as a robotic arm. A mobile manipulator can be formed by pairing up both the robotic arm and AGV, which has higher flexibility to conduct different types of tasks. The navigation of AGV is subjected to a certain degree of inaccuracy that needs to be overcome by applying specific compensation techniques such as 3D point clouds for object segmentation. Therefore, this paper will illustrate a solution to build a ROS2-based autonomous mobile manipulator with autonomous navigation, workpiece detection and robotic arm path planning and manipulation.
A Novel Data Science Approach to Personas' Creation to Study Willingness to Receive Vaccination in the General Population

Vaccines have proved to be effective in reducing mortality of the COVID-19 pandemic. However, a part of the population is still reluctant to be vaccinated. Thus, the aim of this work was to apply a framework to create Personas, fictional representations of real people, to assess the characteristics of the population willing to be vaccinated in order to develop personalized eHealth-based interventions to increase compliance to vaccinations. Data was collected through an online survey at the beginning of 2021. Multiple dimensionality reduction methods were used as input for K-Medoids clustering with PAM algorithm to create Personas. The optimal number of Personas and dimensionality reduction method to be used were evaluated through the average silhouette graph and the percentage of statistically different attributes between Personas. From 1070 respondents, three Personas were identified. Persona 3 showed statistically significant lower trust in institutions, lower education and lower willingness of being vaccinated when compared to the other two Personas. The developed approach to create Personas was deemed able to identify the main characteristics of those more prone not willing to be vaccinated, suggesting that behavioral change techniques should focus on taking advantage of the closer social circle of those reluctant to vaccines.
BioELM: Integrating Biomedical Knowledge into Language Model with Entity-Linking

Pretrained language models have achieved widespread success on various natural language processing tasks. In the biomedical domain, one line of research is to utilize a large amount of in-domain corpus for pre-training.While these models achieved remarkable improvement on in-domain tasks, they do not take into account the positive role of large-scale in-domain knowledge bases. Integrating biomedical knowledge in the knowledge base like the Unified Medical Language System(UMLS) into these models can further benefit in-domain downstream tasks, such as biomedical named entities and relation extraction. To this end, we proposed BioELM, a pre-trained language model based on entity linking that explicitly leverages knowledge from the UMLS knowledge base. We utilize a two-layer entity-linking structure to integrate entity representations. To optimize the pre-training process, we optimized the masked language modeling and added two training objectives as named entity recognition and entity linking. We validate the performance of our BioELM on named entity recognition and relation extraction tasks on the BLURB benchmark. The experimental results demonstrate that the pre-training tasks and entity-linking strategy on BioELM can improve the performance on both biomedical named entity recognition and relation extraction tasks.
AMAT-Net: An Unbiased Network with High Performance for Metabolic Diseases Prediction Using Facial Images

Recent studies have found that human faces not only encode personal identity information, but also related to plenty of human health information. However, for health status prediction, bias remains a challenge which may be introduced by the intrinsic dependency between variables. For example, demographic variables such as gender or age are associated to many diseases, causing model learn to infer the age or gender rather than the real pathological cues. In order to eliminate the bias and guarantee the high performance of health prediction from facial images, we propose a novel unbiased model based on attention mechanism and adversarial technique, named AMAT-Net for health prediction. More specifically, we minimize the target prediction loss and maximize the bias variable prediction loss during training, to encourage extracted features to keep predictive ability while decouple with the bias. At the same time, the tansformer module is introduced to adaptively extract the features of different tasks. We constructed a dataset composed of facial images with demographic information and disease labels. The experimental results demonstrate that our model not only shows superior performance on metabolic disease prediction but also largely reduce the prediction bias on different age groups.
Efficient Biomedical Ontology Meta-matching Based on Interpolation Model Based Hybrid Evolutionary Algorithm

As an advanced biomedical knowledge modeling technology, biomedical ontology models the biomedical domain. However, since the lack of uniform standards for constructing biomedical ontologies, the biomedical ontologies obtained by different construction methods for the same thing may be different, which is known as the biomedical ontology heterogeneity problem. To solve this problem, we need to execute the biomedical ontology matching process, where it is important to integrate different similarity measures to improve the quality of alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the biomedical ontology meta-matching problem. However, the classical EA-based biomedical ontology meta-matching technique needs to traverse reference alignment to evaluate the individuals, which makes the algorithm have high running time. To overcome this drawback, we propose an Interpolation Model (IM) based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to evaluate the individual and execute the local search process. In particular, we use lattice design to divide the feasible domain into several uniform sub-regions, and on this basis, approximately evaluate the fitness of newly generated individual. In addition, to avoid the algorithm falling into the local optimum, we further introduce an IM-based local search process into EA’s evolving process. In the experiment, we test IM-HEA on OAEI’s Benchmark and Anatomy and compared them with classical EA in terms of alignment’s quality and running time. The experimental results show that IM-HEA greatly enhances the efficiency of EA with little sacrifice on the alignment’s quality.
Parallel vs Distributed Edge Detection for Large Medical Image Datasets

The medical routine of the future is strongly influenced by medical information technology. The quality and efficiency of medicine are at higher standards due to the image-based methods and the increase in computation power. Parallel and distributed computing are attractive alternatives to improve processing performance in many areas of study and this paper investigates such solutions for increasing the throughput on large datasets in medical imaging. In this research, different edge detection techniques are used for image processing, as it is the most common and challenging task in the medical domain. The results are relevant and present the effectiveness of the two processing approaches compared to a single CPU for the acceleration of medical image processing. The CUDA processing platform using a hybrid streams-based programming model is the parallel approach. The distributed system is a Linux-based cluster, which uses message passing for communication and efficient load balancing between nodes for high performance. This paper highlights parallel computing as a very good solution for processing large image datasets in the medical domain. Good results were also obtained for the distributed system.