Reviewerly/exHarmony huggingface dataset:
Thermal-Based Pedestrian Detection Using Faster R-CNN and Region Decomposition Branch

In this paper, we present an infrared thermal-based pedestrian detection method that can be applied in nighttime intelligent surveillance systems. Pedestrian detection plays an important role in computer vision and automation industry applications, which include video surveillance, automotive robot, and smart vehicles. Recently, the improvement in deep learning techniques, such as convolutional neural networks (CNNs), have significantly increased the accuracy of pedestrian detection. Normally, the optical cameras, e.g. charge-coupled device cameras, are the device used to capture images. However, considering the dark environments and the luminance variation issues, infrared thermal camera would be an effective alternative solution to nighttime pedestrian detection. On the other hand, occlusion is one of the commonest problems, which makes nighttime pedestrian detection more challenging. To address the abovementioned problems, this work presents a pedestrian detection framework which consists of Faster R-CNN and a region decomposition branch. The proposed region decomposition branch allows us to detect wider range of the pedestrian appearances including partial body poses and occlusions. From the experimental results, this work demonstrates better detection accuracy than the currently developed CNN-based detection method because of combining the multi-region features.
Towards Extract-Transform-Load Operations in a Big Data context

Big Data emerged after a big explosion of data from the Web 2.0, digital sensors, and social media applications such as Facebook, Twitter, etc. In this constant growth of data, many domains are influenced, especially the decisional support system domain, where the integration of processes should be adapted to support this huge amount of data to improve analysis goals. The basic purpose of this research article is to adapt extract-transform-load processes with Big Data technologies, in order to support not only this evolution of data but also the knowledge discovery. In this article, a new approach called Big Dimensional ETL (BigDimETL) is suggested to deal with ETL basic operations and take into account the multidimensional structure. In order to accelerate data handling, the MapReduce paradigm is used to enhance data warehousing capabilities and HBase as a distributed storage mechanism. Experimental results confirm that the ETL operation performs well especially with adapted operations.
Sentiment Analysis on Movie Reviews

Movie reviews help users decide if the movie is worth their time. A summary of all reviews for a movie can help users make this decision by not wasting their time reading all reviews. Movie-rating websites are often used by critics to post comments and rate movies which help viewers decide if the movie is worth watching. Sentiment analysis can determine the attitude of critics depending on their reviews. Sentiment analysis of a movie review can rate how positive or negative a movie review is and hence the overall rating for a movie. Therefore, the process of understanding if a review is positive or negative can be automated as the machine learns through training and testing the data. This project aims to rate reviews using two classifiers and compare which gives better and more accurate results. Classification is a data mining methodology that assigns classes to a collection of data in order to help in more accurate predictions and analysis. Naïve Bayes and decision tree classifications will be used and the results of sentiment analysis compared.
High-Value Prioritized Experience Replay for Off-Policy Reinforcement Learning

In deep reinforcement learning, experience replay has been shown an effective solution to handle sample-inefficiency. Prioritized Experience Replay (PER) uses temporal-difference error (TD error) as replay priority in Deep Q-Networks (DQN), so that agent can learn more effectively from important experiences. However, experiences with large TD error may appear near the edge of state space and these experiences do not help agent learn policy quickly. We present a novel technique called High-Value Prioritized Experience Replay (HVPER), which designs a combination of TD error and value (reward or state-action value) in replay priority. Specifically, we first propose prioritizing replay based on reward and TD error in sparse reward environment. Extendedly, we design prioritizing replay based on state-action value and TD error for more ordinary environment. We design experiments in the gym environment to evaluate the proposed HVPER. First, we verify that the combination of TD error and reward improves the training speed in two problems with sparse rewards compared to DQN algorithm and PER algorithm. In addition, HVPER accelerates the network learning and achieves a better performance in two continuous space problems compared to Deep Deterministic Policy Gradient algorithm.
On the Density and Diversity of Degradation Symptoms in Refactored Classes: A Multi-case Study

Root canal refactoring is a software development activity that is intended to improve dependability-related attributes such as modifiability and reusability. Despite being an activity that contributes to these attributes, deciding when applying root canal refactoring is far from trivial. In fact, finding which elements should be refactored is not a cut-and-dried task. One of the main reasons is the lack of consensus on which characteristics indicate the presence of structural degradation. Thus, we evaluated whether the density and diversity of multiple automatically detected symptoms can be used as consistent indicators of the need for root canal refactoring. To achieve our goal, we conducted a multi-case exploratory study involving 6 open source systems and 2 systems from our industry partners. For each system, we identified the classes that were changed through one or more root canal refactorings. After that, we compared refactored and non-refactored classes with respect to the density and diversity of degradation symptoms. We also investigated if the most recurrent combinations of symptoms in refactored classes can be used as strong indicators of structural degradation. Our results show that refactored classes usually present higher density and diversity of symptoms than non-refactored classes. However, root canal refactorings that are performed by developers in practice may not be enough for reducing degradation, since the vast majority had little to no impact on the density and diversity of symptoms. Finally, we observed that symptom combinations in refactored classes are similar to the combinations in non-refactored classes. Based on our findings, we elicited an initial set of requirements for automatically recommending root canal refactorings.
Single-shot phase calibration of a spatial light modulator using geometric phase interferometry

A vibration-insensitive, single-shot phase-calibration method for phase-only spatial light modulators (SLM) is reported. The proposed technique uses a geometric phase lens to form a phase-shifting radial shearing interferometer to enable common-path measurements. This configuration has several advantages: (a) unlike diffraction-based SLM calibration techniques, this technique is robust against intensity errors due to misalignment; (b) unlike two-beam interferometers, this technique offers a high environmental stability; and (c) unlike intensity-based methods, the phase-shifting capability provides a phase uncertainty routinely in the order of ${2}\pi /100$2π/100. The experimental results show a significantly higher accuracy when compared to the diffraction-based approaches.
Multi-Level Cross-Lingual Transfer Learning With Language Shared and Specific Knowledge for Spoken Language Understanding

Recently conversational agents effectively improve their understanding capabilities by neural networks. Such deep neural models, however, do not apply to most human languages due to the lack of annotated training data for various NLP tasks. In this paper, we propose a multi-level cross-lingual transfer model with language shared and specific knowledge to improve the spoken language understanding of low-resource languages. Our method explicitly separates the model into the language-shared part and language-specific part to transfer cross-lingual knowledge and improve the monolingual slot tagging, especially for low-resource languages. To refine the shared knowledge, we add a language discriminator and employ adversarial training to reinforce information separation. Besides, we adopt novel multi-level knowledge transfer in an incremental and progressive way to acquire multi-granularity shared knowledge rather than a single layer. To mitigate the discrepancies between the feature distributions of language specific and shared knowledge, we propose the neural adapters to fuse knowledge automatically. Experiments show that our proposed model consistently outperforms monolingual baseline with a statistically significant margin up to 2.09%, even higher improvement of 12.21% in the zero-shot setting.
TopicSifter: Interactive Search Space Reduction through Targeted Topic Modeling

Topic modeling is commonly used to analyze and understand large document collections. However, in practice, users want to focus on specific aspects or “targets” rather than the entire corpus. For example, given a large collection of documents, users may want only a smaller subset which more closely aligns with their interests, tasks, and domains. In particular, our paper focuses on large-scale document retrieval with high recall where any missed relevant documents can be critical. A simple keyword matching search is generally not effective nor efficient as 1) it is difficult to find a list of keyword queries that can cover the documents of interest before exploring the dataset, 2) some documents may not contain the exact keywords of interest but may still be highly relevant, and 3) some words have multiple meanings, which would result in irrelevant documents included in the retrieved subset. In this paper, we present TopicSifter, a visual analytics system for interactive search space reduction. Our system utilizes targeted topic modeling based on nonnegative matrix factorization and allows users to give relevance feedback in order to refine their target and guide the topic modeling to the most relevant results.
Empirical Evaluation of Blockchain Smart Contracts

One of the building blocks of our legal and economic systems in society is the indispensable reliance on contracts and trust systems to protect individual rights. Recently smart contracts are becoming prominent parts of various blockchain platforms. The goal of smart contracts is to eliminate the third party and centralized trust systems. Due to recent emergence of smart contracts, there is no well-defined framework that researchers can use to evaluate smart contracts under various blockchain platforms and differentiate between them. In this work, a survey on the prominent smart contract landscape specially those based on blockchain have been conducted. Based on the survey, an evaluation framework to assess smart contracts has been proposed. The framework is a set of criteria based on two major aspects; infrastructure related and development related criteria. The evaluation framework was peer-reviewed for reliability and validity. To measure the applicability of the proposed framework, it has been used to empirically evaluate some of the most prominent smart contract platforms. The results of the empirical evaluation have shown that the Ethereum blockchain smart contract exceeds the others in terms of development tools, resources, and community support. EOS blockchain smart contracts have the best execution speeds, and transaction costs. Lastly, Stellar blockchain has predictability and the best transaction builder to use in smart contract development concerning user friendliness. Recommendations for smart contract developers are provided in light of the research.
A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services.The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas.For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes).However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time.In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks.This paper presents the technical challenges that need to be considered for mining sensor data.It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared.We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area.
High accuracy measurement of heterogeneous deformation field using spatial-temporal subset digital image correlation

Subset-based digital image correlation (DIC) has limitations in subset size selection for heterogeneous deformation field measurement. To maintain good spatial resolution, selecting sufficiently small subsets is necessary, but the measurement resolution is reduced because of poor matching information, inducing large errors in the correlation matching procedure. Spatial-temporal subset digital image correlation (STS-DIC) incorporates the temporal continuity of displacement in the deformation analysis. This can increase the matching information of subset in the temporal dimension. Here, the advantages of STS-DIC for the measurement of heterogeneous deformation fields were investigated through simulations and experiments. The results demonstrated that the measurement resolution was improved while ensuring a good spatial resolution for heterogeneous deformation field measurement by using STS-DIC. Finally, STS-DIC was applied to calculate the displacement field near a precast crack, and the stress intensity factors were calculated accurately from high accuracy displacement fields.
Stochastic Gradient Coding for Flexible Straggler Mitigation in Distributed Learning

We consider distributed gradient descent in the presence of stragglers. Recent work on gradient coding and approximate gradient coding have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are slow or non-responsive. In this work we propose a new type of approximate gradient coding which we call Stochastic Gradient Coding (SGC). The idea of SGC is very simple: we distribute data points redundantly to workers according to a good combinatorial design. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> loss function, and show how the convergence rate can improve with the redundancy. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.
RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

Real time speech recognition technology, as a key cross technology in the field of artificial intelligence in recent years, has been widely used in the fields of intelligent voice toys, industrial control and intelligent rehabilitation. Because the real-time speech recognition technology based on embedded technology has obvious advantages in the volume, power consumption and research and development cost of the system, it has become a hot carrier to achieve efficient speech recognition technology. In order to realize a simple and practical real-time speech recognition system based on embedded system, this paper designs a basic framework of machine learning based on Markov random field theory combined with machine learning theory, and studies the algorithm of real-time speech vocabulary matching recognition based on this framework. In detail, the algorithm proposed in this paper will process the speech signal from the aspects of preprocessing, signal detection, feature extraction and quantization. Finally, this paper will build a real-time speech recognition system based on DSP processor. The experimental results show that the real-time speech recognition algorithm proposed in this paper can improve the real-time recognition speed of the system. The corresponding speed changes from about 12 s to about 200 ms, and the corresponding real-time accuracy rate increases to about 95%.
Green Cloud Computing - A Greener Approach To IT

Cloud Computing is one of the most widely emergent areas in today's IT sector. It helps a number of people to use various services through their own devices with the help of the internet. It provides an environment which has low cost, easy to use and also consumes less power with virtualization. VM's are required to be managed by a number of task scheduling algorithms to make sure that the less amount of energy consumption takes place. This paper deals with the importance of green cloud computing and how it can provide alternatives to the IT sector in terms of the energy consumption, load on data centers, VM average load and the task distribution. All these experiments have been performed by using a green cloud simulator in which three different algorithms are being used such as DENS, Round Robin and Green Schedulers.
Secure Personal Health Records Sharing Based on Blockchain and IPFS

Personal Health Records (PHR) system has attracted intensive attention due to its universal accessibility and low cost in economics. Because of high cost of storing data and access control, most PHR systems adopt centralized management, where an authoritative management center controls the entire system and PHR data is stored in a trusted third-party service provider. However, there are some disadvantages, such as fully trusting to a control center, suffering from a single point of failure, and data deleting. In this paper, we propose a novel distributed framework based on blockchain and IPFS (Inter Planetary File System), and a suite of mechanisms for data access control to PHR data. Smart Contracts are designed on the blockchain, and all data operations are treated as transactions. The symmetric cryptographic algorithm is used to encrypt the PHR data, and then all encrypted data is stored on IPFS nodes securely in distributed environment. The ciphertext-policy attribute-based encryption (CP-ABE) is used to encrypt the symmetric secret keys, and the corresponding ciphertext is stored and published in IPNS (Inter Planetary Name Space), so as to achieve fine-grained access control. Analytical and experimental results are presented, which show that our framework has ability to provide authenticity, confidentiality, fine-grained access control, forward secrecy, and traceability simultaneously.
Exploring Effective Data Augmentation with TDNN-LSTM Neural Network Embedding for Speaker Recognition

The speaker characterization using four different data augmentation methods and time delay neural networks and long short-term memory neural networks (TDNN-LSTM) is proposed in this paper. The proposed data augmentation is used to increase the amount and diversity of the training data including adding speed perturbation, adding volume perturbation, adding room impulse responses, and adding additive noises. The idea of TDNN-LSTM based speaker embedding is better to capture the temporal information in speaker speech than the conventional TDNN based x-vectors. The proposed methods were trained on VoxCeleb dataset and tested with Speakers In The Wild (SITW) dataset in the evaluation core-core condition. We achieved results of EER=1.86% and a minimum decision cost function (DCF) of 0.204 at p-target=0.01, and a minimum DCF of 0.368 at p-target=0.001. The proposed methods outperform the baselines of both i-vector and x-vector.
How do Spanish speakers read words? Insights from a crowdsourced lexical decision megastudy

Vocabulary size seems to be affected by multiple factors, including those that belong to the properties of the words themselves and those that relate to the characteristics of the individuals assessing the words. In this study, we present results from a crowdsourced lexical decision megastudy in which more than 150,000 native speakers from around 20 Spanish-speaking countries performed a lexical decision task to 70 target word items selected from a list of about 45,000 Spanish words. We examined how demographic characteristics such as age, education level, and multilingualism affected participants' vocabulary size. Also, we explored how common factors related to words like frequency, length, and orthographic neighbourhood influenced the knowledge of a particular item. Results indicated important contributions of age to overall vocabulary size, with vocabulary size increasing in a logarithmic fashion with this factor. Furthermore, a contrast between monolingual and bilingual communities within Spain revealed no significant vocabulary size differences between the communities. Additionally, we replicated the standard effects of the words' properties and their interactions, accurately accounting for the estimated knowledge of a particular word. These results highlight the value of crowdsourced approaches to uncover effects that are traditionally masked by small-sampled in-lab factorial experimental designs.
3D Deformable Convolutions for MRI Classification

Deep learning convolution neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolution deep neural network layers for MRI data classification. We propose new 3D deformable convolutions (d-convolutions), implement them in VoxResNet architecture and apply for structural MRI data classification. We show that 3D d-convolutions outperform standard ones and are effective for unprocessed 3D MR images being robust to particular geometrical properties of the data. Firstly proposed dVoxResNet architecture exhibits high potential for the use in MRI data classification.
Challenges in Detecting an “Evasive Spectre”

Spectre attacks exploit serious vulnerabilities in modern CPU design to extract sensitive data through side channels. Completely fixing the problem would require a redesign of the architecture for conditional execution which cannot be backported. Researchers have proposed to detect Spectre with promising accuracy by monitoring deviations in microarchitectural events using existing hardware performance counters. However, the attacker may attempt to evade detection by reshaping the microarchitectural profile of Spectre so as to mimic benign programs. This letter thus identifies the challenges in detecting “Evasive Spectre” attacks by showing that the detection accuracy drops significantly after the attacker inserted carefully chosen instructions in the middle of an attack or periodically put the attack to sleep at a frequency higher than the victim's sampling rate when operating the attack at a lower bandwidth, yet with reasonable success rate.