Reviewerly/exHarmony huggingface dataset:
Hardware accelerator for an accurate local stereo matching algorithm using binary neural network

Convolutional neural networks (CNNs) have shown appealing performance on stereo matching tasks in recent years. However, existing deep neural networks (DNNs) based matching algorithms use semi-global matching (SGM) to aggregate the matching costs, which limit the processing speed. In this study, we present a novel binarized CNN stereo matching hardware acceleration using local methods on an FPGA, which can provide high accuracy stereo estimation and achieve high-throughput at the same time. To reduce the consumption of hardware resource and improve the processing speed, we propose pipelined architecture for BNN, and sparse local aggregation to optimize the implementation on an FPGA. We evaluated the proposed implementation on a challenging stereo dataset with a Stratix V FPGA. From the experimental results, our binarized CNN stereo matching implementation shows significant improvement in the real-time performance and the energy efficiency over other computing platforms with a 6.95% error rate on the KITTI2015 dataset.
Enhancing the Intelligibility of Cleft Lip and Palate Speech Using Cycle-Consistent Adversarial Networks

Cleft lip and palate (CLP) refer to a congenital craniofacial condition that causes various speech-related disorders. As a result of structural and functional deformities, the affected subjects' speech intelligibility is significantly degraded, limiting the accessibility and usability of speech-controlled devices. Towards addressing this problem, it is desirable to improve the CLP speech intelligibility. Moreover, it would be useful during speech therapy. In this study, the cycle-consistent adversarial network (CycleGAN) method is exploited for improving CLP speech intelligibility. The model is trained on native Kannada-speaking childrens' speech data. The effectiveness of the proposed approach is also measured using automatic speech recognition performance. Further, subjective evaluation is performed, and those results also confirm the intelligibility improvement in the enhanced speech over the original.
Analyzing Stance and Topic of E-Cigarette Conversations on Twitter: Case Study in Indonesia

To control the use of e-cigarette, Indonesia plan to establish a regulation that embodies all the concerns, sentiments, and opinions of public This study aims to identify public opinions in social media Twitter by classifying tweets into group in favor or against e-cigarette and explore dominant topics of each group. This research obtained 15,373 tweets between June 2019 - May 2020 that is classified into 4 labels: Against, Favor, Neutral, and Irrelevant. The best model was selected with specification: 3 features (Count, Unigram, and Bigram), Logistic Regression algorithm, and three-stage classification pipeline ( F1-score=0.807). As for topic modelling, corpus Against and Favor are used to retrieve dominant topics. We chose Non-negative Matrix Factorization algorithm with k=6 and achieve high coherence scores, which are 0.962004 for corpus Against and 0.999736 for corpus Favor.
On the error in Laplace approximations of high‐dimensional integrals

Laplace approximations are commonly used to approximate high‐dimensional integrals in statistical applications, but the quality of such approximations as the dimension of the integral grows is not well understood. In this paper, we provide a new result on the size of the error in first‐ and higher‐order Laplace approximations, in terms of the rate of growth of information about each of the integrated variables. By contrast with many existing results, we allow for variation in the rate of information growth among the different integrated variables. We apply our results to investigate the quality of Laplace approximations to the likelihood in some generalized linear mixed models.
HackerScope: The Dynamics of a Massive Hacker Online Ecosystem

Authors of malicious software are not hiding as much as one would assume: they have a visible online footprint. Apart from online forums, this footprint appears in software development platforms, where authors create publicly-accessible malware repositories to share and collaborate. With the exception of a few recent efforts, the existence and the dynamics of this community has received surprisingly limited attention. The goal of our work is to analyze this ecosystem of hackers in order to: (a) understand their collaborative patterns, and (b) identify and profile its most influential authors. We develop HackerScope, a systematic approach for analyzing the dynamics of this hacker ecosystem. Leveraging our targeted data collection, we conduct an extensive study of 7389 authors of malware repositories on GitHub, which we combine with their activity on four security forums. From a modeling point of view, we study the ecosystem using three network representations: (a) the author-author network, (b) the author-repository network, and (c) cross-platform egonets. Our analysis leads to the following key observations: (a) the ecosystem is growing at an accelerating rate as the number of new malware authors per year triples every 2 years, (b) it is highly collaborative, more so than the rest of GitHub authors, and (c) it includes influential and professional hackers. We find 30 authors maintain an online “brand” across GitHub and our security forums. Our study is a significant step towards using public online information for understanding the malicious hacker community.
A deep multimodal feature learning network for RGB-D salient object detection

In this paper, we propose a deep multimodal feature learning (DMFL) network for RGB-D salient object detection. The color and depth features are firstly extracted from low level to high level feature using CNN. Then the features at the high layer are shared and concatenated to construct joint feature representation of multi-modalities. The fused features are embedded to a high dimension metric space to express the salient and non-salient parts. And also a new objective function, consisting of cross-entropy and metric loss, is proposed to optimize the model. Both pixel and attribute level discriminative features are learned for semantical grouping to detect the salient objects. Experimental results show that the proposed model achieves promising performance and has about 1% to 2% improvement to conventional methods.
Investigating Crossover Operators in Genetic Algorithms for High-Utility Itemset Mining

Genetic Algorithms (GAs) are an excellent approach for mining high-utility itemsets (HUIs) as they can discover most of the HUIs in a fraction of the time spent by exact algorithms. A key feature of GAs is crossover operators, which allow individuals in a population to communicate and exchange information with each other. However, the usefulness of crossover operator in the overall progress of GAs for high-utility itemset mining (HUIM) has not been investigated. In this paper, the headless chicken test is used to analyze four GAs for HUIM. In that test, crossover operators in the original GAs for HUIM are first replaced with randomized crossover operators. Then, the performance of original GAs with normal crossover are compared with GAs with random crossover. This allows evaluating the overall usefulness of crossover operators in the progress that GAs make during the search and evolution process. Through this test, we found that one GA for HUIM performed poorly, which indicates the absence of well-defined building blocks and that crossover in that GA was indeed working as a macromutation.
Intelligent Monitoring of IoT Devices using Neural Networks

The Internet of Things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. Through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. The application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. This will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.
Evaluation of scanning transmission X-ray microscopy at the Mn L2,3-edges as a potential probe for manganese redox state in natural silicates

Determining the Mn valence variation at the nanometer scale will be an important advance in the study of heterogeneous natural silicates. Here, the potential of the scanning transmission X-ray microscopy at the Mn L2,3-edges (640–655 eV) as a probe for manganese redox state is evaluated. For this purpose, several natural Mn-silicates (rhodonite, ardennite, piemontite, Mn4+-silicate, jacobsite) were analysed to identify the spectral parameters most sensitive to the Mn valence, regardless of the coordination environment, the crystal field strength, the nature and the length of the metal–ligand bonds, and the intra-atomic Coulomb and spin–orbit interactions. Two suitable spectral empirical calibrations are thus proposed, linking the Mn valence to two peak intensity ratios: one ratio of intensities from two energy points of the L2 peak (at 651.7 and 655.2 eV), and one ratio of intensities from one energy point of the L2 peak (at 655.2 eV) and one of the L3 peak (at 641.6 eV). Thank to them, the first quantitative Mn valence maps are constructed, with a high spatial resolution (<40 nm pixel size), opening the way to exhaustive crystallochemical studies of silicates containing Mn with different valences.
Deep learning based pectoral muscle segmentation on Mammographic Image Analysis Society (MIAS) mammograms

Purpose The purpose of this study was to propose a deep learning-based method for automated detection of the pectoral muscle, in order to reduce misdetection in a computer- aided diagnosis (CAD) system for diagnosing breast cancer in mammography. This study also aimed to assess the performance of the deep learning method for pectoral muscle detection by comparing it to an image processing-based method using the random sample consensus (RANSAC) algorithm. Methods Using the 322 images in the Mammographic Image Analysis Society (MIAS) database, the pectoral muscle detection model was trained with the U-Net architecture. Of the total data, 80% was allocated as training data and 20% was allocated as test data, and the performance of the deep learning model was tested by 5-fold cross validation. Results The image processing-based method for pectoral muscle detection using RANSAC showed 92% detection accuracy. Using the 5-fold cross validation, the deep learning-based method showed a mean sensitivity of 95.55%, mean specificity of 99.88%, mean accuracy of 99.67%, and mean dice similarity coefficient of 95.88%. Conclusion The proposed deep learning-based method of pectoral muscle detection performed better than an existing image processing-based method. In the future, by collecting data from various medical institutions and devices to further train the model and improve its reliability, we expect that this model could greatly reduce misdetection rates by CAD systems for breast cancer diagnosis. Keywords: Deep learning; Mammography; Pectoralis muscles
Learning to Count Words in Fluent Speech Enables Online Speech Recognition

Sequence to Sequence models, in particular the Transformer, achieve state of the art results in Automatic Speech Recognition. Practical usage is however limited to cases where full utterance latency is acceptable. In this work we introduce Taris, a Transformer-based online speech recognition system aided by an auxiliary task of incremental word counting. We use the cumulative word sum to dynamically segment speech and enable its eager decoding into words. Experiments performed on the LRS2, LibriSpeech, and Aishell-1 datasets of English and Mandarin speech show that the online system performs comparable with the offline one when having a dynamic algorithmic delay of 5 segments. Furthermore, we show that the estimated segment length distribution resembles the word length distribution obtained with forced alignment, although our system does not require an exact segment-to-word equivalence. Taris introduces a negligible overhead compared to a standard Transformer, while the local relationship modelling between inputs and outputs grants invariance to sequence length by design.
Development of Qur’an Memorization Learning Model Based on Mobile Learning

Abstract Memorizing the Qur’an is the Sunnah of Prophet Muhammad, may peace be upon him. The most crucial part of remembering the Quran is the process of learning. In general, this activity is conducted privately memorizing, one teacher for one student. One obstacle of memorizing the Quran is the limited number of teachers and many students. This private model undoubtedly ineffective. The teacher is difficult to handle the activities. Also, the teacher cannot control the student to memorize more verses because teachers must hear one by one to the student’s reading voice. Information technology has the potential benefit to innovate the learning models of memorizing the Quran. In this study, we develop a learning model using mobile technology. We design mobile learning that students can memorize independently; however teachers can still listen to reviews their reading sample, evaluate, and give guidance. The student module comes with a paragraph of verse view, including the translations. The student module also features a reading reference from international Qori. Besides that, the student module comes with a voice recording facility so students can compare with the reference reader. Teachers can assess every student until students good in reading and more verses to memorize. The primary findings of this study indicate that the teacher can handle more with better student performance. Students are highly motivated to memorize and self-evaluate their reading because they always feel under supervising by the teacher.
An Adversarial Attack Based on Incremental Learning Techniques for Unmanned in 6G Scenes

With the development of artificial intelligence(AI), unmanned vehicles can relieve traffic jamming and decrease the risk of traffic accidents, where deep neural networks (DNNs) play an important role and have become one of the most critical technologies. Nevertheless, DNNs are still susceptible to adversarial examples. Even worse, they also show severe performance degradation when the system needs DNNs to learn new knowledge without forgetting the old one. As unmanned vehicles travel on the road, they need to frequently learn new categories and different representations. Learning all data after the new sample arrives will expend a lot of time and space. As a result, it will affect the deployment of artificial intelligence in unmanned scenes. In recent years, it has been observed that incremental learning technology can solve the above challenges. However, previously reported works mainly focused on batch learning. It is not clear how much impact the adversarial attack will have on the deep learning model when performing incremental learning tasks. This issue exposes the hidden safety risks of unmanned driving and increases discuss opportunities. Therefore, we propose an adversarial attack based on incremental learning techniques for unmanned scenes in this paper. Specifically, it can retain information previously learned by the model. At the same time, it can renew the old model to learn new model, thereby continually adding small perturbation to legitimate examples. A couple of experiments on the Pascal VOC 2012 dataset has been conducted, and the experiment results show that the adversarial attack based on incremental learning techniques has a higher attack success rate. Further, it can improve the successful attack rate by 8.43%.
A Blockchain-based secure PHR data storage and sharing framework

The appearance of Covid-19 proves the deficiency of world health care systems to handle exponentially infected-patients and to deal with lack of personal health records (PHR). As it is known, data is valuable in such situation. Current solutions for storing and sharing PHR data adopt centralized solutions, such as cloud-based totally centralized data centers, which requires a fully-trusted third party. Therefore, it suffers from single point of failure, data deleting and network delay. To overcome these issues, we propose in this paper a Blockchain-based secure PHR data storage and sharing framework that leverages the benefits of IPFS (Inter Planetary File System). Our aim is to ensure privacy with patient full control over his data and to enhance scalability. To this end, we use steganography to hide sensitive data within PHR data, and then it is uploaded to IPFS network. However, IPFS Hash delivered by IPFS network is divided into n secret shares using Shamir's Secret Sharing (SSS) algorithm, which ensures security. Moreover, Ethereum smart contract automates the execution of access control strategies defined by the owner, as well as, traceability and auditability insurance.
An Online Cost Optimization Algorithm for IaaS Instance Releasing in Cloud Environments

Benefiting from the development of cloud computing, service trades among IaaS (Infrastructure-as-a-Service) providers, SaaS (Software-as-a-Service) providers, and users are more and more common, where SaaS providers purchase on-demand instances in a pay-per-use way from IaaS providers to execute users' jobs. Given the pay-as-you-go mode of IaaS instances, SaaS providers can acquire and release instances whenever they need. However, considering the preparation time of acquiring new instances and the penalty functions of users, from the perspective of cost optimization, SaaS providers need to decide whether and when to release an idle instance. To make optimal decisions, future information about job arrivals and execution time is generally required, which is actually difficult to accurately predict. To address this problem, we propose an online cost optimization algorithm for SaaS providers to help them make better decisions on whether to release instances. Theoretical analysis shows that our online algorithm achieves a competitive ratio of $2-\alpha$ for different penalty functions, where $\alpha\in(0,1)$ . Extensive experiments on account of realistic Google cluster data demonstrate the effectiveness and efficiency of our online algorithm.
Email Spam Filtering Technique from Various Perspectives Using Machine Learning Algorithms

The upsurge in the number of emails send for varying purposes and the dependency of life on them in some way or the other, has given rise to the detection of fraud email and also to the inventors of these emails. The work helps us to prove the efficiency in the detection of spams and prevents the users from opening such emails. The various perspective that are taken into consideration in the work includes parameter tuning, the division of data set on various test train numbers or ratio. Further, the formulation of the study related to parameters and test train set of problem, comparison by both the popular algorithm is discussed. This work has been done after preprocessing and feature extraction of the data. As many of these spam hampers our security, privacy, and often leads in stealing critical information so the categorization of these in an effective manner keeping into mind various parameters is of utmost importance.
Direct Prediction of BRAFV600E Mutation from Histopathological Images in Papillary Thyroid Carcinoma with a Deep Learning Workflow

Papillary Thyroid Carcinoma (PTC) is the most common type of thyroid cancer. BRAFV600E is a prominent oncogenic mutation and has been found to have strong associations with the mortality and recurrence of PTC. In this paper, we propose a workflow to show that BRAFV600E mutation status can be directly predicted from histopathological images with deep learning. Our method mainly consists of two steps, tumor detection and mutation classification; each of them contains a Convolutional Neural Network (CNN). The information derived from the two steps are combined to predict mutation. We propose three different strategies and build a PTC dataset of 6,541,586 512×512-pixel patches from 439 H&E stained Whole Slide Images (WSIs) to perform our experiments. In the PTC-V600E strategy, we use patches from 50 PTC WSIs and 202 WSIs of 200 cases to train the two networks, respectively, and get an AUC of 0.884 on the test of 187 WSIs of 186 cases. In PCam-V600E and PAIP-V600E strategies, we use public datasets, PCam and PAIP 2019, of other cancer types instead of 50 PTC WSIs and get AUCs of 0.884 and 0.860. All the three strategies separate mutation-positive and negative cases successfully in our experiments, demonstrating the availability and feasibility of our work and its potential in further research and applications.
Savitzky–Golay Filtering-Based Fusion of Multiple Exposure Images for High Dynamic Range Imaging

The problem of compositing multiple exposure images has attracted lots of researchers, over the past years. It all began with the problem of High Dynamic Range (HDR) imaging, for capturing scenes with vast differences in their dynamic range. Fine details in all the areas in these scenes cannot be captured with one single exposure setting of the camera aperture. This leads to multiple exposure images with each image containing accurate representation of different regions dimly lit, well lit and brightly lit in the scenes. One can make a combined HDR image out of these multiple exposure shots. This combination of multiple exposure shots leads to an image of a higher dynamic range in a different image format which cannot be represented in the traditional Low Dynamic Range (LDR) formats. Moreover HDR images cannot be displayed in traditional display devices suitable for LDR. So these images have to undergo a process called as tone mapping for further converting them to be suitable enough to be represented on usual LDR displays. An approach based on Savitzky–Golay parametric filtering which preserves edges, is proposed which uses filtered multiple exposure images to generate the alpha matte coefficients required for fusing the input multiple exposure set. The coefficients generated in the proposed approach helps in retaining the weak edges and the fine textures which are lost as a result of the under and over exposures. The proposed approach is similar in nature to the bilateral filter-based compositing approach for multiple exposure images in the literature but it is novel, in exploring the possibility of compositing using a parametric filtering approach. The proposed approach performs the fusion in the LDR domain and the fused output can also be displayed using standard LDR image formats on standard LDR displays. A brief comparison of the results generated by the proposed method and various other approaches, including the traditional exposure fusion, tone mapping-based techniques and bilateral filter-based approach is presented where in the proposed method compares well and fares better in majority of the test cases.