Reviewerly/exHarmony huggingface dataset:
Adventitia segmentation in intravascular ultrasound images based on improved Snake algorithm

Segmentation of coronary artery intravascular ultrasound (IVUS) images is the first step in assessing blood vessel morphology and detecting possible atherosclerotic lesions. To overcome the disadvantage of manual acquisition of initial contour in traditional Snake algorithm and construction of external force only from gradient information, an improved Snake algorithm is proposed for automatic outer membrane segmentation in intravascular ultrasound images. Firstly, we design a basic segmentation algorithm to automatically extract the approximate contour of the first frame, and this is iterated as the initial contour of the Snake algorithm. Next, the extended structure tensor is combined with the Snake algorithm to make it part of the external force of the Snake algorithm, and then the curve is evolved to complete the segmentation. To verify the effectiveness of the proposed method, this paper not only shows the experimental results qualitatively, but also quantitatively measures the RMSE and RDD indices. Comparative experiments show that the proposed method not only has higher accuracy, but also has better timeliness.
Logistic Sine Map (LSM) Based Partial Image Encryption

Information security is the biggest concern in today's world i.e. how to secure the digital images while storing on the hard disks or while transmitting online through a network. To provide the privacy and protect the confidential information in the images, encryption of the image is the best way. Now, to encrypt the full image, computational time will be more, which will be a limitation in widely used real time applications. Partial image encryption is an approach used to encrypt only the required part of the image that to be protected, by doing this the computational time will reduce significantly. Here we propose a block based partial image encryption technique. Logistic sine map (LSM) used to select and encrypt the image block randomly based the percentage of encryption requirements. Amount of encryption easily controlled by varying the control threshold value. The Experimental results of the proposed approach have proved that the encryption time has been significantly reduced compared to that of full image encryption. Hence we can consider this method as an alternative for full image encryption for real time applications which improves the security and also reduces the computational time.
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main actor's pose and thus tend to record only parts of the movement when focusing on manipulation tasks. Thus, the amount of information about the action itself is limited, making crucial the understanding of the manipulated objects and their context. Many previous works addressed this issue with two-stream architectures, where one stream is dedicated to modeling the appearance of objects involved in the action, and another to extracting motion features from optical flow. In this paper, we argue that learning features jointly from these two information channels is beneficial to capture the spatio-temporal correlations between the two better. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion prediction task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach.
Improving Multivariate Microaggregation through Hamiltonian Paths and Optimal Univariate Microaggregation

The collection of personal data is exponentially growing and, as a result, individual privacy is endangered accordingly. With the aim to lessen privacy risks whilst maintaining high degrees of data utility, a variety of techniques have been proposed, being microaggregation a very popular one. Microaggregation is a family of perturbation methods, in which its principle is to aggregate personal data records (i.e., microdata) in groups so as to preserve privacy through k-anonymity. The multivariate microaggregation problem is known to be NP-Hard; however, its univariate version could be optimally solved in polynomial time using the Hansen-Mukherjee (HM) algorithm. In this article, we propose a heuristic solution to the multivariate microaggregation problem inspired by the Traveling Salesman Problem (TSP) and the optimal univariate microaggregation solution. Given a multivariate dataset, first, we apply a TSP-tour construction heuristic to generate a Hamiltonian path through all dataset records. Next, we use the order provided by this Hamiltonian path (i.e., a given permutation of the records) as input to the Hansen-Mukherjee algorithm, virtually transforming it into a multivariate microaggregation solver we call Multivariate Hansen-Mukherjee (MHM). Our intuition is that good solutions to the TSP would yield Hamiltonian paths allowing the Hansen-Mukherjee algorithm to find good solutions to the multivariate microaggregation problem. We have tested our method with well-known benchmark datasets. Moreover, with the aim to show the usefulness of our approach to protecting location privacy, we have tested our solution with real-life trajectories datasets, too. We have compared the results of our algorithm with those of the best performing solutions, and we show that our proposal reduces the information loss resulting from the microaggregation. Overall, results suggest that transforming the multivariate microaggregation problem into its univariate counterpart by ordering microdata records with a proper Hamiltonian path and applying an optimal univariate solution leads to a reduction of the perturbation error whilst keeping the same privacy guarantees.
Class Imbalanced Fault Diagnosis via Combining K-Means Clustering Algorithm with Generative Adversarial Networks

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k -means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k -means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k -means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples.
Multilingual Sentiment Analysis for Web Text Based on Word to Word Translation

People's sentiments are known to have a large impact on changes in stock prices, products sales, and trends. Since web users generally state their opinion in various languages, it is important to develop a method of multilingual sentiment analysis for web texts. In this research, we design a multilingual sentiment analysis method based on word to word translation using a sentiment dictionary in arbitrary native language. This method consists of three phases: morphological analysis of text, a sentiment extraction of each word with sentiment dictionary, and a sentiment extraction of text based on words sentiments. We conduct a sentiment classification experiment for tweets in English, German, French, and Spanish. In the experiment, we evaluate our classifier's performance by comparing the classifier with the other previous classifiers based on the evaluation standards "Accuracy", "Precision", "Recall", and "F1 score". The experimental results show that our classifier has an applicability to sentiment analysis for multilingual, because our classifier's performance is independent of the differences languages.
Semi-Centralized Blockchain Based Distributed System for Secure and Private Sharing of Electronic Health Records

An Electronic Health Record (EHR) is an electronic, official health record that contains a systematic collection of health information of a patient, which is maintained by a healthcare provider. EHRs have always been considered as the technology that has the potential to revolutionize healthcare, however, there are some drawbacks that prevents healthcare institutions from adopting them, and patients from supporting them. The main concerns are patients' privacy and their data security, numerous data breaches of hospitals have caused the loss of patients' EHRs. Multiple solutions have been proposed to allow the patients to take control of their EHR data access, but a secure, and private system that would allow for flawless sharing of EHRs does not exist. In this paper, a hybrid distributed system architecture that addresses these drawbacks is proposed. The proposed system utilizes the decentralized systems data storage model in a centralized system to allow for the high data reproducibility and availability of decentralized systems, while allowing for the high security by authorization and authentication of centralized systems; it also utilizes the blockchain which allows for security, patients pseudo-anonymity, patients' EHRs usage consent requirement, and eventual consistency of data among peers, while keeping a ledger of all shared EHRs among healthcare providers.
Profiling SLAs for cloud system infrastructures and user interactions

Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this paper, we propose the CloudCost UML profile, which allows the modeling of cloud architectures and the users’ behavior when they interact with the cloud to request resources. We then investigate how to increase the profits of cloud infrastructures by using price schemes. For this purpose, we distinguish between two types of users in the SLAs: regular and high-priority users . Regular users do not require a continuous service, so they can wait to be attended to. In contrast, high-priority users require a constant and immediate service, so they pay a greater price for their services. In addition, a computer-aided design tool, called MSCC (Modeling SLAs Cost Cloud), has been implemented to support the CloudCost profile, which enables the creation of specific cloud scenarios, as well as their edition and validation. Finally, we present a complete case study to illustrate the applicability of the CloudCost profile, thus making it possible to draw conclusions about how to increase the profits of the cloud infrastructures studied by adjusting the different cloud parameters and the resource configuration.
End-to-End Text-to-Speech Using Latent Duration Based on VQ-VAE

Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS). We propose a new TTS framework using explicit duration modeling that incorporates duration as a discrete latent variable to TTS and enables joint optimization of whole modules from scratch. We formulate our method based on conditional VQ-VAE to handle discrete duration in a variational autoencoder and provide a theoretical explanation to justify our method. In our framework, a connectionist temporal classification (CTC) -based force aligner acts as the approximate posterior, and text-to-duration works as the prior in the variational autoencoder. We evaluated our proposed method with a listening test and compared it with other TTS methods based on soft-attention or explicit duration modeling. The results showed that our systems rated between soft-attention-based methods (Transformer-TTS, Tacotron2) and explicit duration modeling-based methods (Fastspeech).
Blockchain Development Platforms: Performance Comparison

In this paper, two of the main Blockchain development platforms, Ethereum and EOS.IO are compared. The objective is to help developers select the most appropriate platform as the back-end Blockchain for their apps. A decentralized application was implemented on each of the platforms triggering basic operations and timing them. The simulations were performed on Microsoft's Azure cloud, running up to 150 Blockchain nodes while recording the user response time, the CPU utilization, and the totally used memory in Mbytes. The results in this study show that although recognized as a major competitor to Ethereum, EOS.IO fails to outperform the Ethereum platform in this experiment, recording a very high response time in comparison to Ethereum.
Mixed Precision Quantization of Transformer Language Models for Speech Recognition

State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantization techniques provides a powerful solution to dramatically reduce their model size. Current low-bit quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of the system to quantization errors. To this end, novel mixed precision DNN quantization methods are proposed in this paper. The optimal local precision settings are automatically learned using two techniques. The first is based on a quantization sensitivity metric in the form of Hessian trace weighted quantization perturbation. The second is based on mixed precision Transformer architecture search. Alternating direction methods of multipliers (ADMM) are used to efficiently train mixed precision quantized DNN systems. Experiments conducted on Penn Treebank (PTB) and a Switchboard corpus trained LF-MMI TDNN system suggest the proposed mixed precision Transformer quantization techniques achieved model size compression ratios of up to 16 times over the full precision baseline with no recognition performance degradation. When being used to compress a larger full precision Transformer LM with more layers, overall word error rate (WER) reductions up to 1.7% absolute (18% relative) were obtained.
MiniCon: Automatic Enforcement of a Minimal Capability Set for Security-Enhanced Containers

Nowadays, containers have been widely adopted not only for clouds but also for individual users. On the one hand, containers provide much more light-weight virtualized infrastructure, but on the other hand, it is unavoidable to handle security issues since the isolation of containers is relatively weak, compared to the legacy VMs. Under the container architecture, adversaries are able to exploit kernel vulnerabilities to escalate privilege to gain root privilege, and leak system, privacy critical information. Although previous solutions provide strong security protection, unfortunately, none of them do provide a way to apply policies. Therefore, in this paper, we present an eBPF-based capability enforcement system, MiniCon, that automatically generates and enforces a minimal capability set by using Seccomp Filter. It monitors capability requests from containers, merges those requests to create a minimal capability set, and enforces capability policies through Seccomp Filter.
Depression Detection by Analysing Eye Movements on Emotional Images

To achieve an objective and efficient depression detection system, we propose a cognitive psychology experimental paradigm based on the attentional bias theory and eye movements in this paper. We select images of three different emotions (positive, neutral, and negative) as experimental stimulus. Comparing with the traditional free viewing paradigm, the paradigm we proposed adds a stage of frame tracking to analyse the process of attention disengagement. Based on extracted psychological features from eye movement data, we train a mental state classifier of Support Vector Machine to classify people with depression and normal controls, and the model achieves 77.0% of accuracy, which achieve state-of-the-art under the same data condition. Our model is interpretable and our results demonstrate the theory of attention bias.
Tea, Earl Grey, Hot: Designing Speech Interactions from the Imagined Ideal of Star Trek

Speech is now common in daily interactions with our devices, thanks to voice user interfaces (VUIs) like Alexa. Despite their seeming ubiquity, designs often do not match users' expectations. Science fiction, which is known to influence design of new technologies, has included VUIs for decades. Star Trek: The Next Generation is a prime example of how people envisioned ideal VUIs. Understanding how current VUIs live up to Star Trek's utopian technologies reveals mismatches between current designs and user expectations, as informed by popular fiction. Combining conversational analysis and VUI user analysis, we study voice interactions with the Enterprise's computer and compare them to current interactions. Independent of futuristic computing power, we find key design-based differences: Star Trek interactions are brief and functional, not conversational, they are highly multimodal and context-driven, and there is often no spoken computer response. From this, we suggest paths to better align VUIs with user expectations.
Matching as Color Images: Thermal Image Local Feature Detection and Description

Feature detection and extraction is considered to be one of the most important aspects when it comes to any computer vision application, especially the autonomous driving field that is highly dependent on it. Thermal imaging is less explored in the field of autonomous driving mainly due to the high cost of the cameras and inferior techniques available for detection. Due to advances in technology the former does not hold true anymore and there lies tremendous scope for improvement in the latter. Autonomous driving relies heavily on multiple and sometimes redundant sensors, for which thermal sensors are a preferred addition. Thermal sensors being completely dependent on the infrared radiation emitted are able to frame and recognize objects even in the complete absence of light. However detecting features persistently through subsequent frames is difficult due to the lack of textures in thermal images. Motivated by this challenge, we propose a triplet based Siamese CNN for feature detection and extraction for any given thermal image. Our architecture is able to detect larger number of good feature points on thermal images than other best performed feature detection algorithms with superb matching performance based on our extracted descriptors.
Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients' medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a major breakthrough in this type of detection. There has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient screening. The purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis (ELA) by verifying the noise pattern in the data. The proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon schemes. The proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data tampering. The results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.
ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel

Highly configurable systems are highly complex systems. The Linux kernel is arguably one of the most well-known examples. Given its vast configuration space, researchers have used it to conduct many empirical studies as well as to build dedicated methods and tools for analyzing, configuring, testing, optimizing, and maintaining the kernel. However, despite a large body of work, mainly bug fixes that were the result of such research made it back into the kernel's source tree. Unfortunately, Linux users still struggle with kernel configuration and resolving configuration conflicts, since the kernel largely lacks automated support. Additionally, there are technical and community requirements for supporting automated conflict resolution in the kernel, for example, using a pure C-based solution that uses only compatible third-party libraries (if any). With the aim of contributing back to the Linux community, we present ConfigFix, a tooling that we integrated with the Linux kernel configurator, that is purely implemented in C, and that is finally a working solution able to produce fixes for configuration conflicts. We describe our experiences of building upon the large body of research done on the kernel configuration mechanisms as well as how we designed and realized ConfigFix while adhering to the Linux kernel's community requirements and standards. ConfigFix not only helps Linux kernel users obtain their desired configuration, but our implemented semantic abstraction provides the basis for many of the above techniques supporting kernel configuration.