Reviewerly/exHarmony huggingface dataset:
Segmentation of Breast Ultrasound Images using Densely Connected Deep Convolutional Neural Network and Attention Gates

Ultrasound imagining modality is a popular complementary technique for diagnosing breast cancer. A standardized reporting process called Breast imaging reporting and data system (BI-RADS) is used to categorize breast cancer. The BI-RADS scale uses several features of lesions based on the ultrasound images, which makes the quality of the diagnosis highly dependent on the experience of the radiologist. Radiologists use Computer-Aided Diagnosis (CAD) system to help in the detection of lesions. The accuracy of a CAD system depends greatly on the segmentation stage of the system. To increase the reliability of the diagnosis, we propose a solution based on a densely connected deep convolutional neural network and attention gates, called Attention U-DenseNet. Attention U-DenseNet is an architecture to do semantic segmentation of the lesions from Breast Ultrasound (BUS) images based on the U-Net, DenseNet, and attention gates. Convolutional layers of the U-Net are made densely connected using dense blocks to help to learn complex patterns of the BUS image which is usually noisy and contaminated with speckles. This architecture (U-DenseNet) produced an F-score of 0.63 compared to the U-Net model with an F-score of 0.49. Furthermore, to localize the segmentation by learning salient features, attention gates are added to the U-DenseNet architecture (Attention U-DenseNet). Attention U-DenseNet performed even better compared to U-DenseNet, by improving the F-score to 0.75. Finally, a per-image regularised binary cross-entropy is employed to penalize false negatives more than false positives, since the region of interest is small.
A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.
Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning

Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data and investigate how to transfer information to real-world datasets while reducing the need for manual annotation. CNN-based crowd-counting algorithms usually consist of feature extraction, density estimation, and count regression. To improve the domain adaptation in feature extraction, we propose an adaptive domain-invariant feature extracting module. Meanwhile, after taking inspiration from recent innovative meta-learning, we present a dynamic-β MAML algorithm to generate a density map in unseen novel scenes and render the density estimation model more universal. Finally, we use a counting map refiner to optimize the coarse density map transformation into a fine density map and then regress the crowd number. Extensive experiments show that our proposed domain adaptation- and model-generalization methods can effectively suppress domain gaps and produce elaborate density maps in cross-domain crowd-counting scenarios. We demonstrate that the proposals in our paper outperform current state-of-the-art techniques.
Gesture estimation for 3D martial arts based on neural network

A 3D martial arts gestures estimation method is proposed, for it is difficult to accurately describe the gestures, which is caused by the high degree of freedom and high similarity of 3D martial arts gestures. This method is combined with finger kinematics analysis model. And beyond that, it is based on the morphological topological structure of hand and combined with the CNN neural network. Firstly, the CNN is used to extract and classify 3D gesture features of martial arts. Then, using the morphological topological structure of hands to simulate the dependence of hand joints, and the 3D coordinates of hand joints were obtained. Finally, the attitude regression module is used to realize the attitude estimation of martial arts gesture action. Simulation results show that the accuracy of gesture estimation can be improved through cascade splicing in this research. Compared with existing 3D gesture estimation methods such as V2V, Pose-REN and CrossInfoNet, the proposed method performs better in MSE and FS indicators. It has lower estimation errors and an inference speed of 220.7 frames per second. In addition, the three-dimensional visualization results show that the predicted joint points obtained by the proposed estimation method coincide with the labeled joint points, and there is no occlusion. So it is proved that the proposed method is feasible, and it can be used to estimate martial arts gestures in the future.
Improving Relation Extraction by Knowledge Representation Learning

Relation extraction is an important NLP task to extract the semantic relationship between two entities. Recently, large-scale pre-training language models have achieved excellent performance in many NLP applications. Most of the existing relation extraction models mainly rely on context information, but entity information is also very important for relation extraction, especially domain knowledge of entity and the direction between entity pairs. In this paper, based on the pre-trained BERT model, we propose a multi-task joint relation extraction model incorporating knowledge representation learning(KRL). The experimental results on the SemEval 2010 task 8 dataset and the KBP37 dataset show that our proposed model outperforms most of state-of-the-art methods. The results on the larger dataset FewRel80 refined from FewRel also indicate that increasing the knowledge representation learning as an auxiliary objective is helpful for the relation extraction task.
Blockchain-Based Solutions for Effective and Secure Management of Electronic Health Records

Traditional medical systems are vulnerable to attacks, leaks, and loss of data. The centralized structure of medical systems makes them more susceptible to attacks. Blockchain can be used as a solution to this issue. In particular, using blockchain to track patient medical history and vaccination records can guarantee privacy and security, which are critical requirements for healthcare systems. In this paper, we propose a blockchain-based application to maintain and share medical data and vaccination records using Hyperledger Fabric and Ethereum platforms to ensure data integrity and immutability. Our vaccination tracking system based on the Ethereum platform allows easy verification for people who want to travel abroad. For this system, we employ Ganache to test these vaccination records in a safe and deterministic environment. We have evaluated the performance of our implementation using Hyperledger Caliper and Explorer. The results show that our blockchain-based systems can increase the efficiency and transparency of tracking medical and vaccination records. We believe our proposal is especially valuable in the context of the ongoing COVID-19 pandemic because it providesa secure way to update, share and verify vaccination records.
Deep Learning-based fault prediction in cloud system

A cloud system typically contains a huge number of computing nodes with numerous services running on. Nodes can fail in practice, leading to the downtime of services, hence, the reliability of cloud service is a crucial issue for cloud service providers. To make the cloud environment more reliable, predicting the failure before it already happens is important. The ability to predict faulty nodes enables the migration of service to the healthy nodes, therefore improving service availability. Proactive fault prediction techniques with deep learning methods that are based on historical metrics (such as CPU usage, memory usage, etc) to predict future failures can be used to solve effectively this problem. In this paper, we propose to use the Bidirectional Long Short Term Memory (Bi-LSTM) model for predicting the failure-proneness of nodes in a cluster based on time series data.
CLASS: A Novel Method for Chinese Legal Judgments Summarization

We propose a novel method to generate abstractive summarization of Chinese legal judgments named CLASS (Chinese LegAl judgmentS Summarization) which exploits the element structure of the legal judgments. Firstly, we extract sentences with high importance from the legal judgments. Secondly, the extracted sentences along with its summaries are split into different source-target element pairs that are used for training an abstractive model to summarize different elements of the judgments separately. Finally, a complete summary is generated by combining the summaries of each element. We conduct comparative experiments on Chinese legal judgments dataset and the results show that CLASS can generate more faithful summaries with less information lost, which shows the effectiveness of CLASS on capturing the deep contextualized information.
Machine Learning Based Dynamic Risk Assessment for Autonomous Vehicles

Autonomous vehicles (AVs) are complex safety-critical systems that operate in an uncertain and dynamic environment. To ensure safety, Hazard Analysis and Risk Assessment (HARA) is recommended in ISO 26262. The entire process, however, is based on the very premise that a human driver is responsible for the safety of the vehicle. On the contrary, AVs function without any human intervention. Therefore, to ensure safe behavior in all possible situations, Dynamic Risk Assessment (DRA) at runtime is a necessity to make AVs aware of themselves about the current risk and take decisions accordingly; instead of relying on static worst-case assumptions. In this paper, we present a novel approach to identify and classify the severity and controllability rating class based on the measured data from the on-board sensors. Support Vector Machine (SVM) learning technique was used to train, test, and validate the model with diverse feature sets. We illustrate the presented approach by employing an example of adaptive cruise control and discuss the case study with initial findings.
Social Media Big Data Analytics for Demand Forecasting

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.
On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud

Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.
Cadxela: An educational tool for supporting the global software engineering education at undergraduate level

Abstract The Global Software Development (GSD) strategy has introduced many benefits into the modern software industry, but it has also created many risks that can negatively affect the quality of the final product. Moreover, this approach is still in constant evolution and, therefore, diverse issues and challenges related to geographical, temporal, and sociocultural distances are also present. Therefore, many universities around the world are increasing their efforts in creating programs to prepare more skilled graduates to work in globally distributed environments. However, providing GSD courses among universities distributed in different locations is not so profitable and normally involves a high number of collaborations among various teachers and students that traditional teaching approaches cannot support. In this study, the Cadxela tool is presented to enable undergraduate students to acquire practical experience in GSD and improve their communication and teamwork skills. An empirical evaluation was conducted on Cadxela involving students and teachers from five universities in Mexico, Spain, Chile, Cuba, and Peru. The participants developed a total of 15 projects to experience different problems related to geographical, temporal, and sociocultural distances. The obtained results from pre and post‐evaluations showed that Cadxela can contribute to facilitating the development of practical projects on GSD undergraduate courses, while students develop their knowledge, social skills associated with this topic as well as teamwork and communication skills. Finally, the data collected on the teachers' perceptions suggested that Cadxela can also be a useful tool for introducing the GSD approach at the undergraduate level.
Adaptive Time-Frequency Synthesis for Waveform Discernment in Wireless Communications

The discernment of waveforms for the purpose of identifying the underlying wireless technologies and validating if observed transmissions are legitimate or not remains a challenge within the communications sector and beyond. Conventional techniques struggle to robustly process Signals under Test (SuTs) in real-time. A particular difficulty relates to the selection of an appropriate window size for the processed data when pertinent contextual information on SuTs is not known a priori. The disadvantage of applying a predetermined fixed window size is that of length and shape (i.e., coarse resolution). In contrast, an adaptive window size offers more optimally tuned resolution. Towards this end, we propose a novel approach that uses an Adaptive Resolution Transform (ART) to either maintain a constant (prespecified) resolution, via a Variable Window Size and Shape (VWSS), or adjust the resolution (again using the VWSS technique) to match latency requirements. Central to this approach is the utilization of Continuous Wavelet Transforms (CWTs), which do not substantively suffer from those energy leakage issues found in more commonly used transforms such as Discrete Wavelet Transforms (DWT). A robust numerical implementation of CWTs is presented via a particular class of Convolutional Neural Networks (CNNs) called Robust Convex Relaxation (RCR)-based Convolutional Long Short-Term Memory Deep Neural Networks (a.k.a., CLSTMDNNs or CLNNs). By employing small convolutional filters, this class leverages deeper cascade learning, which nicely emulates CWTs. In addition to its use for convex relaxation adversarial training, the RCR framework also improves the bound tightening for the successive convolutional layers (which contain the cascading of ever smaller “CWT-like” convolutional filters). In this paper, we explore this particular architecture for its discernment capability among the SuT time series being compared. To operationalize this architectural paradigm, non-conventional Nonnegative Matrix Factorization (NMF) and Multiresolution Matrix Factorization (MMF) is used in conjunction to facilitate the capture of the structure and content of the involved matrices so as to achieve higher resolution and enhanced discernment accuracy. The desired WT (a.k.a., Corresponding WT or CORWT) resulting from the MMF is implemented as a translation-invariant CWT PyWavelet to better illuminate the intricate structural characteristics of the SuT and facilitate the analysis/discernment of their constituent Waveforms of Interest (WoIs). A precomputed hash and lookup table is utilized to facilitate WoI classification and discernment in quasi-real-time.
CNN-based Invariant Extended Kalman Filter for projectile trajectory estimation using IMU only

This paper presents a new method to estimate the position, velocity and orientation of a projectile using only accelerometers, gyrometers and magnetometers. This algorithm is composed of an Imperfect Right-Invariant Extended Kalman Filter (R-IEKF) and a Convolutional Neural Network (CNN) to dynamically optimize the measurement noise covariance matrix. This time-varying covariance matrix is always adjusted to each phase of the projectile flight. To evaluate the algorithm performances, mortar fire simulations are performed and a comparison between the estimated trajectory and the reference is shown. The estimates obtained with an Imperfect R-IEKF outperform Dead Reckoning estimates. In addition, the joint use of an Imperfect R-IEKF and a CNN enables to significantly reduce the estimation errors of the projectile trajectory.
MiCS-P:Parallel mutual-information computation of big categorical data on spark

Mutual information can effectively measure the correlation between categorical attributes. However, it is found to be quite computationally intensive and time consuming process for enormous size and different distribution data sets. It involves steps for computation of marginal entropies, probability distribution and so on. Spark is a fast, general-purpose parallel framework designed specifically for large-scale data processing. Main motivation of this paper is to provide an intelligent method for parallel mutual information calculation based on Spark computing environment with maintaining the synchronization between different computing nodes. Proposed method named MiCS-P has been able to execute with different number of computing nodes, and gives significant speedup working with different dimensions and sizes of data sets. The MiCS-P algorithm adopts column-wise transformation scheme, which is conducive to the calculation of mutual information between a large number of feature pairs. And to alleviate imbalanced load causing long execution times, we implement a two-phase virtual partitioning scheme running on Spark.
Comparison of Active Vulnerability Scanning vs. Passive Vulnerability Detection

Vulnerability analysis is an integral part of an overall security program. Through identifying known security flaws and weaknesses, vulnerability identification tools help security practitioners to remediate the existing vulnerabilities on the networks. Thus, it is crucial that the results of such tools are complete, accurate, timely and they produce vulnerability results with minimum or no side-effects on the networks. To achieve these goals, Active Vulnerability Scanning (AVS) or Passive Vulnerability Detection (PVD) approaches can be used by network-based vulnerability scanners. In this work, we evaluate these two approaches with respect to efficiency and effectiveness. For the effectiveness analysis, we compare these two approaches empirically on a test environment and evaluate their outcomes. According to total amount of accuracy and precision, the PVD results are higher than AVS. As a result of our analysis, we conclude that PVD returns more complete and accurate results with considerably shorter scanning periods and with no side-effects on networks, compared to the AVS.
Spatial Action Maps Augmented with Visit Frequency Maps for Exploration Tasks

Reinforcement learning has been widely applied in exploration, navigation, manipulation, and other fields. Most of the relevant techniques generate kinematic commands (e.g., move, stop, turn) for agents based on the current state information. However, recent dense action representations based research, such as spatial action maps, pointing way-points to the agent in the same domain as its observation of the state shows great promise in mobile manipulation tasks. Inspired by that, we make the first step towards using a spatial action maps based method to effectively explore novel environmental spaces. To reduce the chance of redundant exploration, the visit frequency map (VFM) and its corresponding reward function are introduced to direct the agent to actively search previously unexplored areas. In the experimental section, our work was compared to the same method without VFM and the method based on traditional steering commands with the same input data in various environments. The results show conclusively that our method is more efficient than other methods. The project page is: https://github.com/zxwang96/sam-exploration
Scaling Resilient Adversarial Patch

Deep neural networks are easily affected by adversarial patches, causing prediction errors. However, existing adversarial patches are trained with specific model-dataset pairs and only effective for the images with the predetermined size in the dataset. The semantic information of the patch will be distorted when scaling the image, which is a common preprocessing process in practical applications. In this paper, we propose SRA Patch (Scaling-Resilient Adversarial Patch), a new adversarial patch resilient to image scaling. Specifically, we generate the patch in a block-wise way and utilize the superpixel method to resist the loss of semantic information during scaling. Further, we introduce the ensemble model as a Black-Box indicator to address the noise space shrinking issue, which is caused by the small size and the block operation of SRA patch. Finally, we leverage Class Activation Mapping to extract the region with salient features as the final patch to improve the ratio of effective semantic features on the patch to remain during scaling. Extensive experiments have demonstrated that our SRA patch has much stronger attack capability and scaling robustness than existing methods.