Reviewerly/exHarmony huggingface dataset:
Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging

In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks (CNNs) for segmentation. Medical experts determine the consistency between the proposed ML approach, which is a state-of-the-art deep learning method, and the manual segmentation approach. These methods are compared in terms of performance indicators such as the ventricular area (VA), ventricular maximum diameter (VMXD), ventricular minimum diameter (VMID), and ventricular long axis angle (AVLA) measurements. Furthermore, the Dice similarity coefficient, Jaccard index, and Hausdorff distance are measured to estimate the agreement of the LV segmented results between the automatic and visual approaches. The obtained results indicate that the proposed techniques for LV segmentation are useful and practical. There is no significant difference between the use of AC and CNN in image segmentation; however, the AC method could obtain comparable accuracy as the CNN method using less training data and less run-time.
Comparing Blockchain Standards and Recommendations

Since the introduction of Bitcoin, the term “blockchain” has attracted many start-ups and companies over the years, especially in the financial sector. However, technology is evolving faster than standardization frameworks. This left the industry in the position of having to use this emerging technology, without being backed by any international standards organization regarding for neither the technology itself, nor for a blockchain specific information security framework. In times of the General Data Protection Regulation and growing international trade conflicts, protecting information is more relevant than ever. Standardization of blockchains is an appeal to raise the development of information technologies to the next level. Therefore, this paper shall provide an overview of standardization organization’s publications about blockchains/distributed ledger technologies, a set of comparison criteria for future work and a comparison of the existing standards work itself. With that information, aligning to existing standardization efforts becomes easier, and might even present the possibility to create frameworks where there are none at the moment.
Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data

Machine learning and blockchain technology have been explored for potential applications in medicine with only modest success to date. Focus has shifted to exploring the intersection of these technologies along with other privacy preserving encryption techniques for better utility. This combination applied to federated learning, which allows remote execution of function and analysis without the need to move highly regulated personal health information, seems to be the key to successful applications of these technologies to rapidly advance evidence-based medicine. We give a brief history of these technologies in medicine, outlining some of the challenges with successful use. We then explore a more detailed combination of usage with an emphasis on decentralizing or federating the learning process along with auditability and incentivization blockchain can allow in the machine learning process. Based on the cost-benefit analysis of previous efforts, we provide the framework for an advanced blockchain-orchestrated machine learning system for privacy preserving federated learning in medicine and a new utility in health. Six critical elements for this approach in the future will be:(a) Data and analytic processes discoverable on secure public blockchain while retaining privacy of the data and analytic processes(b) Value fabricated by generating data/compute matches that were previously illegal, unethical and infeasible(c) Compute guarantees provided by federated learning and advanced cryptography(d) Privacy guarantees provided by software (e.g., Homomorphic Encryption, Secure Multi-Party Computation, ...) and hardware (e.g., Intel SGX and AMD SEV-SNP) cryptography(e) Data quality incentivized via tokenized reputation-based rewards(f) Discarding of poor data accomplished via model poisoning attack prevention techniques.
Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach

Blockchain is a technology that ensures data security by verifying database of records established in a decentralized and distributed network. Blockchain-based approaches have been applied to secure data in the fields of the Internet of Things, software engineering, healthcare systems, financial services, and smart power grids. However, the security of the blockchain system is still a major concern. We took the initiative to present a systematic study which sheds light on what defensive strategies are used to secure the blockchain system effectively. Specifically, we focus on blockchain data security that aims to mitigate the two data consistency attacks: double-spend attack and selfish mining attack. We employed the systematic approach to analyze a total of 40 selected studies using the proposed taxonomy of defensive strategies: monitoring, alert forwarding, alert broadcasting, inform, detection, and conceptual research design. It presents a comparison framework for existing and future research on blockchain security. Finally, some recommendations are proposed for blockchain researchers and developers.
Adversarial Attack on Skeleton-Based Human Action Recognition

Deep learning models achieve impressive performance for skeleton-based human action recognition. Graph convolutional networks (GCNs) are particularly suitable for this task due to the graph-structured nature of skeleton data. However, the robustness of these models to adversarial attacks remains largely unexplored due to their complex spatiotemporal nature that must represent sparse and discrete skeleton joints. This work presents the first adversarial attack on skeleton-based action recognition with GCNs. The proposed targeted attack, termed constrained iterative attack for skeleton actions (CIASA), perturbs joint locations in an action sequence such that the resulting adversarial sequence preserves the temporal coherence, spatial integrity, and the anthropomorphic plausibility of the skeletons. CIASA achieves this feat by satisfying multiple physical constraints and employing spatial skeleton realignments for the perturbed skeletons along with regularization of the adversarial skeletons with generative networks. We also explore the possibility of semantically imperceptible localized attacks with CIASA and succeed in fooling the state-of-the-art skeleton action recognition models with high confidence. CIASA perturbations show high transferability in black-box settings. We also show that the perturbed skeleton sequences are able to induce adversarial behavior in the RGB videos created with computer graphics. A comprehensive evaluation with NTU and Kinetics data sets ascertains the effectiveness of CIASA for graph-based skeleton action recognition and reveals the imminent threat to the spatiotemporal deep learning tasks in general.
PersonAut

Personas has been an important support mechanism in interfaces design. They represent, hypothetically, behavior synthesis, main characteristics, motivations and expectations of real users. Through Personas, empathy can be generated between interface designers and future software end-users. However, when these end users are autistic, this challenge is even bigger due to the lack of specific knowledge of some designers / developers on the autism subject. Another reason is the fact that low functioning autistic users (the audience selected for this research) have difficulty expressing themselves verbally. This work presents the PersonAut, which is a specific Personas Model for autistic individuals. Our goal is to support the designer / developer in the interface design activity so that they can create empathy in order to meet the needs of the autistic users. The main contribution of the PersonAut is to make the personas design faster and easier, in the context of software development for autistic people, through the use of diverse and specialized data sources for autistic individuals.
PMBFE: Efficient and Privacy-Preserving Monitoring and Billing Using Functional Encryption for AMI Networks

Preserving the customers' privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.
Copy Move Source-Target Disambiguation Through Multi-Branch CNNs

We propose a method to identify the source and target regions of a copy-move forgery so allow a correct localisation of the tampered area. First, we cast the problem into a hypothesis testing framework whose goal is to decide which region between the two nearly-duplicate regions detected by a generic copy-move detector is the original one. Then we design a multi-branch CNN architecture that solves the hypothesis testing problem by learning a set of features capable to reveal the presence of interpolation artefacts and boundary inconsistencies in the copy-moved area. The proposed architecture, trained on a synthetic dataset explicitly built for this purpose, achieves good results on copy-move forgeries from both synthetic and realistic datasets. Based on our tests, the proposed disambiguation method can reliably reveal the target region even in realistic cases where an approximate version of the copy-move localization mask is provided by a state-of-the-art copy-move detection algorithm.
Multi-criteria tensor model for tourism recommender systems

Many tourism recommender systems have been studied to offer users the items meeting their interests. However, it is a non-trivial task to reflect the multi-criteria ratings and the cultural differences, which significantly influence users’ reviews of tourism facilities, into recommendation services. This paper proposes two “single tensor” models, consisting of users (or countries), items, multi-criteria ratings, and cultural groups, in order to consider simultaneously an inherent structure and interrelations of these factors into recommendation processes. With one Tripadvisor dataset, including 13 K users from 120 countries, experiments demonstrated that, in terms of MAE, the two proposed models for user and country give an improvement of 21.31% and 7.11% than other collaborative filtering and multi-criteria recommendation techniques. Besides, there were the positive influences of multiple-criteria ratings and cultural group factors on recommendation performances. The comparative analysis of several variants of the proposed models showed that considering Western and Eastern cultures is appropriate for improving predictive performances and their stability.
Weakly Supervised Cell Segmentation by Point Annotation

We propose weakly supervised training schemes to train end-to-end cell segmentation networks that only require a single point annotation per cell as the training label and generate a high-quality segmentation mask close to those fully supervised methods using mask annotation on cells. Three training schemes are investigated to train cell segmentation networks, using the point annotation. First, self-training is performed to learn additional information near the annotated points. Next, co-training is applied to learn more cell regions using multiple networks that supervise each other. Finally, a hybrid-training scheme is proposed to leverage the advantages of both self-training and co-training. During the training process, we propose a divergence loss to avoid the overfitting and a consistency loss to enforce the consensus among multiple co-trained networks. Furthermore, we propose weakly supervised learning with human in the loop, aiming at achieving high segmentation accuracy and annotation efficiency simultaneously. Evaluated on two benchmark datasets, our proposal achieves high-quality cell segmentation results comparable to the fully supervised methods, but with much less amount of human annotation effort.
Multi-label semantics preserving based deep cross-modal hashing

Due to the storage and retrieval efficiency of hashing, as well as the highly discriminative feature extraction by deep neural networks, deep cross-modal hashing retrieval has been attracting increasing attention in recent years. However, most of existing deep cross-modal hashing methods simply employ single-label to directly measure the semantic relevance across different modalities, but neglect the potential contributions from multiple category labels. With the aim to improve the accuracy of cross-modal hashing retrieval by fully exploring the semantic relevance based on multiple labels of training data, in this paper, we propose a multi-label semantics preserving based deep cross-modal hashing (MLSPH) method. MLSPH firstly utilizes multi-labels of instances to calculate semantic similarity of the original data. Subsequently, a memory bank mechanism is introduced to preserve the multiple labels semantic similarity constraints and enforce the distinctiveness of learned hash representations over the whole training batch. Extensive experiments on several benchmark datasets reveal that the proposed MLSPH surpasses prominent baselines and reaches the state-of-the-art performance in the field of cross-modal hashing retrieval. Code is available at: https://github.com/SWU-CS-MediaLab/MLSPH.
Estimation of solar radiation using modern methods

It is stated in the present study that extreme learning machines (ELM) will display a greater performance in solar radiation estimation compared to artificial neural networks (ANN). The data acquired from Karaman province during 2010–2018 were used for evaluating the performance of the suggested approach. It was put forth when results were compared that ELM has displayed a greater estimation performance. Moreover, ANN and ELM were tested with different activation functions in order to obtain the best estimation response. While the best estimation result for ANN was obtained with the tansig function as 0.9828, mean square error (MSE) was obtained as 0.000129. The best estimation result for ELM was obtained with the sin function as 0.991 and MSE was calculated as 0.000881. Additionally ELM, training time 0.295 s, test time 0.266 s, MSE time 0.558 s was obtained. ELM displayed a high estimation performance in a very short amount of time. The ELM achieved a root mean square error (RMSE) value of 0.0297. This algorithm has achieved high accuracy with minimal error. Confidence interval estimations were carried out for the acquired correlation coefficients and the results were compared. ELM estimation performance is better than ANN with 95% confidence interval.
Pretraining Techniques for Sequence-to-Sequence Voice Conversion

Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. Nonetheless, without sufficient data, seq2seq VC models can suffer from unstable training and mispronunciation problems in the converted speech, thus far from practical. To tackle these shortcomings, we propose to transfer knowledge from other speech processing tasks where large-scale corpora are easily available, typically text-to-speech (TTS) and automatic speech recognition (ASR). We argue that VC models initialized with such pretrained ASR or TTS model parameters can generate effective hidden representations for high-fidelity, highly intelligible converted speech. In this work, we examine our proposed method in a parallel, one-to-one setting. We employed recurrent neural network (RNN)-based and Transformer based models, and through systematical experiments, we demonstrate the effectiveness of the pretraining scheme and the superiority of Transformer based models over RNN-based models in terms of intelligibility, naturalness, and similarity.
Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.
Driver Gaze Estimation in the Real World: Overcoming the Eyeglass Challenge

A driver's gaze is critical for determining the driver's attention level, state, situational awareness, and readiness to take over control from partially and fully automated vehicles. Tracking both the head and eyes (pupils) can provide a reliable estimation of a driver's gaze using face images under ideal conditions. However, the vehicular environment introduces a variety of challenges that are usually unaccounted for - harsh illumination, nighttime conditions, and reflective/dark eyeglasses. Unfortunately, relying on head pose alone under such conditions can prove to be unreliable owing to significant eye movements. In this study, we offer solutions to address these problems encountered in the real world. To solve issues with lighting, we demonstrate that using an infrared camera with suitable equalization and normalization usually suffices. To handle eyeglasses and their corresponding artifacts, we adopt the idea of image-to-image translation using generative adversarial networks (GANs) to pre-process images prior to gaze estimation. To this end, we propose the Gaze Preserving CycleGAN (GPCycleGAN). This network preserves the driver's gaze while removing potential eyeglasses from infrared face images. Our approach exhibits improved performance and robustness on challenging real-world data spanning 13 subjects and a variety of driving conditions.
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection

Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects. In this paper, we propose a novel anchor-free method, called FII-CenterNet, which introduces the foreground information to eliminate the interference of the complex background information in traffic scenes. The foreground region proposal network segments the foreground based on boxes-induced segmentation annotation, and midground is proposed to provide rich edge information of the objects. In addition to foreground location, scale information is also introduced to improve the regression performance. Extensive experimental results on two public datasets verify the benefits of the introduction of the foreground information, and demonstrate that our FII-CenterNet achieves the state-of-the-art performance in both accuracy and efficiency.
Improving Video Captioning with Temporal Composition of a Visual-Syntactic Embedding

Video captioning is the task of predicting a semantic and syntactically correct sequence of words given some context video. The most successful methods for video captioning have a strong dependency on the effectiveness of semantic representations learned from visual models, but often produce syntactically incorrect sentences which harms their performance on standard datasets. In this paper, we address this limitation by considering syntactic representation learning as an essential component of video captioning. We construct a visual-syntactic embedding by mapping into a common vector space a visual representation, that depends only on the video, with a syntactic representation that depends only on Part-of-Speech (POS) tagging structures of the video description. We integrate this joint representation into an encoder-decoder architecture that we call Visual-Semantic-Syntactic Aligned Network (SemSynAN), which guides the decoder (text generation stage) by aligning temporal compositions of visual, semantic, and syntactic representations. We tested our proposed architecture obtaining state-of-the-art results on two widely used video captioning datasets: the Microsoft Video Description (MSVD) dataset and the Microsoft Research Video-to-Text (MSR-VTT) dataset.
Decentralized E-voting system based on Smart Contract by using Blockchain Technology

Nowadays the use of the Internet is growing; E-voting system has been used by different countries because it reduces the cost and the time which used to consumed by using traditional voting. When the voter wants to access the E-voting system through the web application, there are requirements such as a web browser and a server. The voter uses the web browser to reach to a centralized database. The use of a centralized database for the voting system has some security issues such as Data modification through the third party in the network due to the use of the central database system as well as the result of the voting is not shown in real-time. However, this paper aims to provide an E-voting system with high security by using blockchain. Blockchain provides a decentralized model that makes the network Reliable, safe, flexible, and able to support real-time services.