Reviewerly/exHarmony huggingface dataset:
A Fault Resilient Consensus Protocol for Large Permissioned Blockchain Networks

Permissioned blockchains have evolved as an alternative to permissionless blockchains for various closed business environments. In this paper, we develop FRChain, a scalable and high-performant consensus protocol for permissioned blockchains, which is resilient to different types of node and network failures. FRChain uses collective signing over multicast trees for block propagation and block validation. The protocol ensures safety and liveness as long as a majority of the nodes can participate in the protocol correctly. Further, we also demonstrate a technique for replacing failed nodes with correct ones. We have implemented and tested FRChain spread across a 5000 node blockchain network over two different data centers - (a) 35 Softlayer Cloud VMs located across Melbourne, Milan and San Jose, (b) Amazon VPC with 30 Amazon EC2 instances spread across Mumbai, Singapore, Tokyo and Frankfurt. Our experiments show that FRChain is scalable in terms of both transaction throughput and network size.
An Empirical Study of Multiple Names and Email Addresses in OSS Version Control Repositories

Data produced by version control systems are widely used in software research and development. Version control data users always use the name or email address field to identify the committer or author of a modification. However, developers may use multiple names and email addresses, which brings difficulties for identification of distinct developers. In this paper, we sample 450 Git repositories from GitHub to study the multiple names and email addresses of developers. We conduct a conservative estimation of its prevalence and impact on related measurements. We merge the multiple names and email addresses of a developer through a method of high precision. With the merged identities, we obtain a number of interesting findings, e.g., about 6% of the developers used multiple names or email addresses in more than 60% of the repositories, and they contributed about half of all the commits. Our impact analysis shows that the multiple names and email addresses issue cannot be ignored for the basic related measurements, e.g., the number of developers in a repository. Our results could help researchers and practitioners have a more clear understanding of multiple names and email addresses in practice to improve the accuracy of related measurements.
The Design of a Novel Multiple-Parameter Fractional Number-Theoretic Transform and Its Application to Image Encryption

In this paper, we describe an image encryption scheme based on the multiple-parameter fractional number-theoretic transform (MFrNTT). In order to define the MFrNTT used in our proposal, we introduce a systematic procedure to construct a number-theoretic transform eigenbasis from chosen parameters. Our results indicate that the proposed scheme can resist to cryptographical attacks usually considered in the related literature; at the same time, our method is very competitive in terms of encryption/decryption speed when compared with other existing approaches. We also demonstrate that our scheme can be easily adjusted to deal with different types of images and that it involves a larger number of free parameters than other NTT-based image encryption techniques.
Single-shot phase measuring profilometry based on color binary grating with intervals

A single-shot phase measuring profilometry (PMP) based on color binary grating with intervals is proposed. In traditional PMP based on binary grating, the width for non-zero transmittance of the binary grating is always 1/2 periods rigorously. In our proposed method, a color binary grating is introduced in which three binary gratings with the same period and the same width for non-zero transmittance less than 1/3 periods are misaligned 1/3 periods one another and programmed in red (R), green (G) and blue (B) channels respectively. Because the widths for non-zero transmittance in R, G and B channels of the introduced color binary grating are all less than 1/3 periods and misaligned 1/3 periods one another, the color overlap can be avoided from the source. When measuring the object, only a single-shot deformed pattern needs to be captured and three nearly unbroken sinusoidal deformed patterns with an equivalent phase-shifting of 2π∕3 can be extracted from R, G and B components of the captured color deformed pattern. So the 3D shape of the measured object can be successfully reconstructed with three-step PMP. Experiment results show the feasibility of the proposed method. Due to its single-shot feature, it is promising to measure the dynamic objects.
Multi-Level Location Privacy Protection Based on Differential Privacy Strategy in VANETs

Location-based service (LBS) has been widely used and brings convenience to people's lives. In order to obtain the desired services, a user must report its current location information to the LBS provider. If this information falls into the hands of malicious adversaries, users may even face serious threats. Researchers have proposed some LBS-based vehicle location privacy protection methods. However, these methods are vulnerable to attackers with background knowledge. In this paper, we propose a privacy protection method based on the background knowledge of reliable servers, and use correlation probabilities and correlation transition probabilities to achieve ε-differential privacy geography indistinguishability. The Laplace scheme is used to add noises to the query results. At the same time, this method provides different levels of privacy protection. The simulation results compare and explain the incompleteness of the consideration of this algorithm.
Whole-Body Vibration Associated with Dozer Operation at an Australian Surface Coal Mine

Bulldozers are used extensively on surface mine sites and have been previously identified as being associated with high amplitude whole-body vibration exposures. Previous investigations of this equipment have involved either a very small number of measurements, or measurements of very short duration (or both); or the data obtained were incompletely reported. This research reports 69 measurements (median duration 440 min) obtained from 15 different dozers during operation at a surface coal mine. More than one-third of vertical vibration measurements exceeded the ISO2631.1 Health Guidance Caution Zone when expressed as VDV(8). Considerable variability in measurement amplitudes was found. This was also true within measurements obtained from the same dozers on different shifts suggesting, by a process of elimination, that the remaining variability in whole-body vibration amplitude is a function of some combination of task characteristics, geology, and operator behaviour; rather than equipment-related variability, such as maintenance, suspension, seating, or track design. Short-comings in the evaluation methods provided by ISO2631.1 are highlighted.
Breast Cancer Histopathological Image Classification Utilizing Convolutional Neural Network

Breast cancer is a significant health concern prevailing in both developing and advanced countries where early and precise diagnosis of the disease receives significant attention from the scientific community. In this work, we proposed a deep learning approach using Convolutional Neural Network (CNN) to address the problem of classifying breast cancer using the public histopathological image dataset BreakHis. We propose a CNN model that takes input as preprocessed and augmented images from the available dataset and finally evaluate the classification performance of the model based on accuracy. The result shows that data preprocessing and augmentation significantly improve the performance of the model and help avoid overfitting due to class imbalance from the raw image set. The performance of our model also indicates the high capability of CNN in learning the representation that substantially improves the overall classifying accuracy of cancerous breast tissue.
Interventions for Software Security: Creating a Lightweight Program of Assurance Techniques for Developers

Though some software development teams are highly effective at delivering security, others either do not care or do not have access to security experts to teach them how. Unfortunately, these latter teams are still responsible for the security of the systems they build: systems that are ever more important to ever more people. We propose that a series of lightweight in-terventions, six hours of facilitated workshops delivered over three months, can improve a team's motivation to consider security and awareness of assurance techniques, changing its security culture even when no security experts are involved. The interventions were developed after an Appreciative Inquiry and Grounded Theory survey of security professionals to find out what approaches work best. They were then validated in fieldwork with a Participatory Action Research study that de-livered the workshops to three development organizations. This approach has the potential to be applied by many development teams, improving the security of software worldwide.
Attention in Software Maintenance: An Eye Tracking Study

In software projects, people often continue working on programs that others created. This is especially the case in agile development. Therefore, developers often need to understand unfamiliar code in order to expand, change, fix or review it. Navigating through code and searching for relevant information requires a lot of developer time. This paper introduces an approach to use eye tracking to record and transfer developers' attention during software maintenance. We visualize the recorded data with heatmaps and display switches between Java classes by coloring filenames. These attention representations aim to help developers finding and navigating to relevant code sections faster. We conducted an eye tracking experiment to investigate whether these attention visualizations help developers during software maintenance. The results show that both representations helped some of the participants for orientation and code finding purposes. The heatmaps slightly decreased the cognitive load as well. However, most participants, especially the more experienced ones, rated the attention visualizations as barely helpful or not helpful. Instead, the representations increased their visual effort, reduced the code's clarity and made the overall understanding of the program's functionality, as well as finding relevant areas, more difficult. By testing this specific implementation of the approach of attention transfer, we gained important lessons learned and provide several improvement suggestions.
Do Developers Discover New Tools On The Toilet?

Maintaining awareness of useful tools is a substantial challenge for developers. Physical newsletters are a simple technique to inform developers about tools. In this paper, we evaluate such a technique, called Testing on the Toilet, by performing a mixed-methods case study. We first quantitatively evaluate how effective this technique is by applying statistical causal inference over six years of data about tools used by thousands of developers. We then qualitatively contextualize these results by interviewing and surveying 382 developers, from authors to editors to readers. We found that the technique was generally effective at increasing software development tool use, although the increase varied depending on factors such as the breadth of applicability of the tool, the extent to which the tool has reached saturation, and the memorability of the tool name.
The Internet-of-Things based hand gestures using wearable sensors for human machine interaction

Hand gestures for human machine interaction using wearable sensors have more potentiality than ambient sensing thanks to its low-cost, light weight and mostly scalablity every where at anytime. Despite the fact of existing works on human hand gestures using wearable sensors, each focuses on a specific application and difficult to be generalized. In addition, it still lacks an available benchmark of hand gestures in the context of human machine interaction. This paper introduces a new human hand gesture dataset which could be suitable for controlling home appliances. The dataset is captured with a low-cost and sensor plugable Internet of Things (IoT) device which is currently embedded with accelerometer and gyroscope sensors. We then investigate various features extracted from multiple sensor data for training several machine learning models. Furthermore, we propose a simple yet effective late fusion model from multimodal data for enhancing the recognition rate. In our preliminary experiments on the collected dataset, we demonstrated that the proposed late fusion schema considerably improves the accuracy of gesture classification. The highest accuracy achieved with late fusion technique is 87.61%. These results are highly promising for practical applications that utilize human gestures such as controlling of the appliances at homes and human machine interaction.
Efficient discovery of sequence outlier patterns

Modern Internet of Things ( IoT ) applications generate massive amounts of time-stamped data, much of it in the form of discrete, symbolic sequences. In this work, we present a new system called TOP that de&lt;u&gt;T&lt;/u&gt;ects &lt;u&gt;O&lt;/u&gt;utlier &lt;u&gt;P&lt;/u&gt;atterns from these sequences. To solve the fundamental limitation of existing pattern mining semantics that miss outlier patterns hidden inside of larger frequent patterns, TOP offers new pattern semantics based on contextual patterns that distinguish the independent occurrence of a pattern from its occurrence as part of its super-pattern. We present efficient algorithms for the mining of this new class of contextual patterns. In particular, in contrast to the bottom-up strategy for state-of-the-art pattern mining techniques, our top-down Reduce strategy piggy backs pattern detection with the detection of the context in which a pattern occurs. Our approach achieves linear time complexity in the length of the input sequence. Effective optimization techniques such as context-driven search space pruning and inverted index-based outlier pattern detection are also proposed to further speed up contextual pattern mining. Our experimental evaluation demonstrates the effectiveness of TOP at capturing meaningful outlier patterns in several real-world IoT use cases. We also demonstrate the efficiency of TOP, showing it to be up to 2 orders of magnitude faster than adapting state-of-the-art mining to produce this new class of contextual outlier patterns, allowing us to scale outlier pattern mining to large sequence datasets.
Compact and Low-Complexity Binary Feature Descriptor and Fisher Vectors for Video Analytics

In this paper, we propose a compact and low-complexity binary feature descriptor for video analytics. Our binary descriptor encodes the motion information of a spatio-temporal support region into a low-dimensional binary string. The descriptor is based on a binning strategy and a construction that binarizes separately the horizontal and vertical motion components of the spatio-temporal support region. We pair our descriptor with a novel Fisher Vector (FV) scheme for binary data to project a set of binary features into a fixed length vector in order to evaluate the similarity between feature sets. We test the effectiveness of our binary feature descriptor with FVs for action recognition, which is one of the most challenging tasks in computer vision, as well as gait recognition and animal behavior clustering. Several experiments on the KTH, UCF50, UCF101, CASIA-B, and TIGdog datasets show that the proposed binary feature descriptor outperforms the state-of-the-art feature descriptors in terms of computational time and memory and storage requirements. When paired with FVs, the proposed feature descriptor attains a very competitive performance, outperforming several state-of-the-art feature descriptors and some methods based on convolutional neural networks.
Sensitivity Analysis and Minimal Measurement Geometry for the Target-Based Calibration of High-End Panoramic Terrestrial Laser Scanners

Target-based calibration is the most widely used calibration procedure for terrestrial laser scanners. Nevertheless, the current implementations of this procedure are suboptimal due to the high complexity of their calibration fields. Therefore, designing calibration fields, by selecting optimal target locations with respect to the instrument, is a pending research question. Answering this question would lead to a more efficient and user-friendly calibration, as well as improved calibration results. Herein, we conduct a detailed analysis of the most sensitive target-to-instrument measurement configuration to estimate individual calibration parameters. Based on this analysis, we derive a minimal measurement geometry, sensitive to all mechanical misalignments, relevant for high-end panoramic terrestrial laser scanners. We support our sensitivity analysis by a simulation and empirical experiments followed by a discussion about the advantages and shortcomings of the proposed minimal measurement geometry.
Using Low-Rank Approximations to Speed Up Kernel Logistic Regression Algorithm

Logistic regression as a classic classification algorithm has limitations that can only be applied to linearly separable data.For linearly indivisible data, we use a kernel trick to map it to a higher dimensional space, making it easier to separate and structure in this space.However, with the increasing scale of data, the use of kernel trick is becoming more and more restricted.When the data reaches a certain scale, the cost of storage and computing kernel matrix is very expensive.To mitigate the problem of kernel matrix overload, we employ the low-rank approximate kernel matrix to speed up the solution of kernel logistic regression (KLR) and propose a framework for quickly solving the KLR algorithm.We use a fast iterative algorithm similar to the sequential minimal optimization (SMO) algorithm to solve the dual problem in the KLR.In addition, in this framework, the low-rank approximation is combined with gradient descent and Newton iteration algorithm, respectively.The low-rank approximation is used to reduce the redundant information in the data, which not only speeds up the solution of the KLR but also improves the accuracy of classification.Finally, the extensive experiments show that the KLR optimization algorithms based on our proposed framework outperform the state-of-the-art algorithms.
Similarity-Based Synthetic Document Representations for Meta-Feature Generation in Text Classification

We propose new solutions that enhance and extend the already very successful application of meta-features to text classification. Our newly proposed meta-features are capable of: (1) improving the correlation of small pieces of evidence shared by neighbors with labeled categories by means of synthetic document representations and (local and global) hyperplane distances; and (2) estimating the level of error introduced by these newly proposed and the existing meta-features in the literature, specially for hard-to-classify regions of the feature space. Our experiments with large and representative number of datasets show that our new solutions produce the best results in all tested scenarios, achieving gains of up to 12% over the strongest meta-feature proposal of the literature.
Molar element ratio analysis of lithogeochemical data: a toolbox for use in mineral exploration and mining

Molar element ratio analysis of element concentrations consists of four basic tools that provide substantial insight into the lithogeochemistry (and mineralogy) of rocks under examination. These tools consist of: (1) conserved element ratio analysis; (2) Pearce element ratio analysis; (3) general element ratio analysis; and (4) lithogeochemical mineral mode analysis. Conserved element ratio analysis is useful in creating a chemostratigraphic model for the host rocks to mineral deposits, whereas Pearce element ratio analysis and general element ratio analysis are primarily used to identify mineralogical and metasomatic controls on rock compositions and to investigate and quantify the extent of the material transfers that formed the host rocks and mineralization. Lithogeochemical mineral mode analysis converts element concentrations into mineral concentrations using a matrix-based change-of-basis operation, allowing lithogeochemical data to be interpreted in terms of mineral modes. It can be used to provide proper names to rocks, an important activity for an exploration geologist because of the implications that rock names have on genetic processes and mineral deposit models. This paper provides a review of the theoretical foundations of each of these four tools and then illustrates how these techniques have been used in a variety of exploration applications to assist in the search for, evaluation and planning of, and the mining of mineral deposits. Examples include the evaluation of total digestion lithogeochemical datasets from mineral deposits hosted by igneous and sedimentary rocks and formed by hydrothermal and igneous processes. In addition, this paper illustrates a more recent geometallurgical application of these methods, whereby the mineral proportions determined by lithogeochemical mineral mode analysis are used to predict rock properties and obtain the ore body knowledge critical for resource evaluation, mine planning, mining and mine remediation. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17
Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection

Road traffic congestion is one of the most significant problems in the world, especially in large cities. In Saudi Arabia, accidents and traffic jams have increased in many major roads due to the lack of public transportation, increasing number of vehicles, and an enormous number of pilgrim visitors all year round. Twitter has emerged as an important source of information on various topics including road traffic. A large number of tweets are posted every day by users who wish to inform their followers about traffic conditions. Moreover, big data processing technologies provide unprecedented data analysis opportunities for addressing transportation problems. In this paper, we introduce a methodology for preprocessing and analyzing traffic-related tweets in the Arabic language, particularly the Saudi dialect using a big data processing platform (SAP HANA). Furthermore, we propose a technique for sentiment classification using lexicon-based approach to understand driver’s feelings. We collect tweets from Jeddah and Makkah cities and identify the most congested roads in the cities. We also detect events of multiple types: accidents, roadworks, fire, weather conditions, and others. The causes for the congestion in the cities are also identified.