Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Method Chaining in Java

While some promote method chaining as a good practice for improving code readability, others refer to it as a bad practice that worsens code quality. In this paper, we first investigate whether method chaining is a programming style accepted by real-world programmers. To answer this question, we collected 2,814 Java repositories on GitHub and analyzed historical trends in the frequency of method chaining. The results of our analysis revealed the increasing use of method chaining; 23.1% of method invocations were part of method chains in 2018, whereas only 16.0% were such invocations in 2010. We then explore language features that are helpful to the method-chaining style but have not been supported yet in Java. For this aim, we conducted manual inspections of method chains that are randomly sampled from the collected repositories. We also estimated how effective they are to encourage the method-chaining style if they are adopted in Java.
Learning to Segment When Experts Disagree

Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS.
On the robustness of skeleton detection against adversarial attacks

Human perception of an object's skeletal structure is particularly robust to diverse perturbations of shape. This skeleton representation possesses substantial advantages for parts-based and invariant shape encoding, which is essential for object recognition. Multiple deep learning-based skeleton detection models have been proposed, while their robustness to adversarial attacks remains unclear. (1) This paper is the first work to study the robustness of deep learning-based skeleton detection against adversarial attacks, which are only slightly unlike the original data but still imperceptible to humans. We systematically analyze the robustness of skeleton detection models through exhaustive adversarial attacking experiments. (2) We propose a novel Frequency attack, which can directly exploit the regular and interpretable perturbations to sharply disrupt skeleton detection models. Frequency attack consists of an excitatory-inhibition waveform with high frequency attribution, which confuses edge-sensitive convolutional filters due to the sudden contrast between crests and troughs. Our comprehensive results verify that skeleton detection models are also vulnerable to adversarial attacks. The meaningful findings will inspire researchers to explore more potential robust models by involving explicit skeleton features.
Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot

In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure.
Generalized and Discriminative Collaborative Representation for Multiclass Classification

This article presents a generalized collaborative representation-based classification (GCRC) framework, which includes many existing representation-based classification (RC) methods, such as collaborative RC (CRC) and sparse RC (SRC) as special cases. This article also advances the GCRC theory by exploring theoretical conditions on the general regularization matrix. A key drawback of CRC and SRC is that they fail to use the label information of training data and are essentially unsupervised in computing the representation vector. This largely compromises the discriminative ability of the learned representation vector and impedes the classification performance. Guided by the GCRC theory, we propose a novel RC method referred to as discriminative RC (DRC). The proposed DRC method has the following three desirable properties: 1) discriminability: DRC can leverage the label information of training data and is supervised in both representation and classification, thus improving the discriminative ability of the representation vector; 2) efficiency: it has a closed-form solution and is efficient in computing the representation vector and performing classification; and 3) theory: it also has theoretical guarantees for classification. Experimental results on benchmark databases demonstrate both the efficacy and efficiency of DRC for multiclass classification.
High Resolution Demoire Network

Photographing LCD screens for information recording has become a prevalent behavior, but moiré artifact will appear in screen-shot pictures with different colors and shapes. In order to remove such artifacts, previous work mostly used multi-scale framework to identify the complex frequencies of moiré, but the relationship between different scales used to be ignored. In this paper, we proposed a novel High-Resolution Demoiré Network(HRDN) to fully explore the relationship between feature maps with different resolutions. It consists of three main components: parallel high resolution network, continuous information exchange modules and final feature fusion layer. Extensive experiments demonstrate our method outperforms the state-of-art both in quantity and quality, the PSNR of demoiré images restored by HRDN reached 28.47, which is 0.72 higher than MopNet [1]. To facilitate research in image demoiréing we have published our code in github.
Real-Time Data Augmentation Based Transfer Learning Model for Breast Cancer Diagnosis Using Histopathological Images

The Real-time automated medical image diagnosis system could assist pathologist for speed-up diagnosis process for confirming the findings. In this paper, various issues involving Brest cancer histopathological image classification are considered. Presently, many models using traditional machine learning algorithms are used on the extracted features from the diagnosis images for the classification task. These models were able to archive high accuracies on a small dataset. However, the performance of classical classification models heavily relies on data representation and feature engineering, which requires prior expert domain knowledge and is extremely time-consuming. Many convolution neural network (CNN) models have been proposed to address this issue, but still, they face the problem of limited availability of authentic medical image diagnosis data. Therefore, it does not allow for highly accurate predictions and even may lead to overfitting on training data, especially when a deep CNN architecture is used. In this paper, a real-time data augmentation based transfer of learning model is proposed to overcome the existing limitations. Two well established and popular image classification models, i.e., InceptionV3 and Xception models were trained on an openly available Breast cancer histopathological image dataset called BreakHis. We first trained the models on learned weights transfer from the supervised training of the model on Imagenet and compared it with the results of the same models when they were trained from scratch. In our findings, it became very obvious that the model that was trained by the transfer of learning is more robust, as they significantly outperformed the same model trained from scratch. Also, the Xception model trained using the transfer of learning gave the best results with 90.86% accuracy on the classification task, exceeding the results obtained previously by the state-of-the-art system on the BreakHis dataset.
TRADER

Recurrent Neural Networks (RNN) can deal with (textual) input with various length and hence have a lot of applications in software systems and software engineering applications. RNNs depend on word embeddings that are usually pre-trained by third parties to encode textual inputs to numerical values. It is well known that problematic word embeddings can lead to low model accuracy. In this paper, we propose a new technique to automatically diagnose how problematic embeddings impact model performance, by comparing model execution traces from correctly and incorrectly executed samples. We then leverage the diagnosis results as guidance to harden/repair the embeddings. Our experiments show that TRADER can consistently and effectively improve accuracy for real world models and datasets by 5.37% on average, which represents substantial improvement in the literature of RNN models.
Case-based Reasoning for Crisis Response: Case Representation and Case Retrieval

With the multiple occurrences of natural and man-made disasters, supporting emergency decision makers (EDMs) in crisis response is primordial. Case-based reasoning (CBR) is a fitting problem-solving paradigm to solve crisis response issues. The performance of the CBR method depends on the steps of case representation and case retrieval. This paper firstly proposes the use of an ontology in order to represent crisis response cases. Then, it develops a two-stage case retrieval method. This latter firstly matches crisis events in order to generate a set of potential similar cases and then matches crisis impacts to obtain a set of the most similar cases. In addition, the proposed case retrieval method integrates cumulative prospect theory (CPT) in similarity measurement for the purpose of taking into account the psychological behavior of the EDM in the process of case retrieval. Finally, a case study using flood crisis real cases is used to illustrate our proposed method.
Incremental learning model inspired in Rehearsal for deep convolutional networks

In Deep Learning, training a model properly with a high quantity and quality of data is crucial in order to achieve a good performance. In some tasks, however, the necessary data is not available at a particular moment and only becomes available over time. In which case, incremental learning is used to train the model correctly. An open problem remains, however, in the form of the stability–plasticity dilemma: how to incrementally train a model that is able to respond well to new data (plasticity) while also retaining previous knowledge (stability). In this paper, an incremental learning model inspired in Rehearsal (recall of past memories based on a subset of data) named CRIF is proposed, and two instances for the framework are employed — one using a random-based selection of representative samples (Naive Incremental Learning, NIL), the other using Crowding Distance and Best vs. Second Best metrics in conjunction for this task (RILBC). The experiments were performed on five datasets — MNIST, Fashion-MNIST, CIFAR-10, Caltech 101, and Tiny ImageNet, in two different incremental scenarios: a strictly class-incremental scenario, and a pseudo class-incremental scenario with unbalanced data. In Caltech 101, Transfer Learning was used, and in this scenario as well as in the other three datasets, the proposed method, NIL, achieved better results in most of the quality metrics than comparison algorithms such as RMSProp Inc (base line) and iCaRL (state-of-the-art proposal) and outperformed the other proposed method, RILBC. NIL also requires less time to achieve these results.
Impact of augmented reality guidance for car repairs on novice users of AR

The use of augmented reality (AR) guidance is seen as an opportunity to address the growing complexity of industrial tasks. Previous research showed benefits of AR for different industrial tasks especially for novice users, while other research suggests that AR was not superior to other means for novices. However, there is not much work that looks at the relation between initial exposure of users to AR (that is, if users have never used AR before) and different types of tasks. In this industry paper, addressing the field of car maintenance and repair, we look into the question of how AR support impacts the performance in familiar and unfamiliar task if the AR user has never used AR before. By running an experiment under field conditions, we investigate whether the familiarity of a specific repair task has an impact on the performance under AR head-mounted display (AR HMD) guidance compared to a traditional repair guideline. Our experiment reveals interesting insights. First, we show that familiarity and routine have an important impact on adherence to (all) repair guidelines, which should be regarded in future studies. Second, despite its novelty and the corresponding added time to deal with an AR HMD, we found that guidance via an AR HMD worked better for unfamiliar tasks. This shows the potential of AR HMDs for guidance of industrial tasks in practice, and it brings up design suggestions for the implementation of this guidance in practice.
Machine Learning based Surveillance System for Detection of Bike Riders without Helmet and Triple Rides

As the number of bikes in India is increasing daily compared to that of the human population. The danger of demise has increased 2.5 times among the riders without using a helmet contrasted with person wearing a helmet. The presented video observation based system may be powerful but still it needs critical human help whose productivity diminishes with time and human biasing additionally comes into the image. This paper plans to unwind this issue by automating the technique for distinguishing the riders with and without helmets. The system takes a video of traffic on an open street as an information and recognizes the moving items inside the scene. This work proposes a system based on the location of individual or different riders taking a trip on bikes with no helmets. Inside the proposed approach, from the beginning stage, bike riders are recognized with the use of YOLOv3 model which is a consistent type of YOLO model, the forefront methodology for object distinguishing helps as such in distinguishing the riders with and without helmet. The vertical projection of binary image is used for counting the number of riders if it exceeds two.
Mineral-resource prediction using advanced data analytics and machine learning of the QUEST-South stream-sediment geochemical data, southwestern British Columbia, Canada

In this study we apply multivariate statistical and predictive classification methods to interpret geochemical data from 8545 stream-sediment samples collected in southern British Columbia, Canada. Data for 35 elements were corrected for laboratory bias and adjusted for values reported below the lower limit of detection. Each sample site was attributed with the closest British Columbia MINFILE occurrence within 2.5 km. MINFILE occurrences were grouped into ‘GroupModels’ based on similarities between the British Columbia Geological Survey mineral deposit models and geochemical signatures. These data were used to create a training dataset of 474 observations, including 100 samples not attributed with a MINFILE occurrence. The training set was used to generate predictions for the mineral deposit models from which posterior probabilities were estimated for the remaining 8071 samples. The data underwent a centred log-ratio transformation and then characterization using either principal component analysis (PCA) or t-distributed stochastic neighbour embedding using 9 dimensions (t-SNE) prior to classification by random forests. The posterior probabilities generated from the t-SNE metric provide a slightly higher level of prediction accuracy compared to the posterior probabilities obtained using the PCA metric. The results are comparable to those obtained using a conventional catchment analysis approach and expert-driven model. The approach presented here provides a repeatable, consistent and defensible methodology for the identification of prospective mineralized terrains and mineral systems.
Industrial Blockchain of Things: A Solution for Trustless Industrial Data Sharing and Beyond

Industrial Internet of Things (IIoT) is regarded as a promising transformation technology for manufacturing industry. We expect the information flow through along the process of material flow, gauging and collective devices feed IIoT platform with essential data. AI and big data analytical techniques can then be used to process the data to support a series of decision-making. Users at both managerial and operational levels can take actions accordingly, which significantly improve management efficiency in one organization, enhance supply chain coordination, and attract investment, etc. Powerful as bigdata and AI technology, they cannot operate without data. In centralized, third-party-managed IIoT platform, few manufactures are willing to share their critical data. This study extends IIoT framework with blockchain technology to provide a solution for trustless data sharing. The solution embeds encrypted ledger to avoid credential data tampering, employs m-in-n-out smart contracts to secure data exchange, and utilizes consensus mechanism to improve cyber security. The proposed framework integrates blockchain with IIoT to operate the entire industrial data exchange in a decentralized network, namely Industrial Blockchain of Things (IBoT).
Uranus

Applications written in Java have strengths to tackle diverse threats in public clouds, but these applications are still prone to privileged attacks when processing plaintext data. Intel SGX is powerful to tackle these attacks, and traditional SGX systems rewrite a Java application's sensitive functions, which process plaintext data, using C/C++ SGX API. Although this code-rewrite approach achieves good efficiency and a small TCB, it requires SGX expert knowledge and can be tedious and error-prone. To tackle the limitations of rewriting Java to C/C++, recent SGX systems propose a code-reuse approach, which runs a default JVM in an SGX enclave to execute the sensitive Java functions. However, both recent study and this paper find that running a default JVM in enclaves incurs two major vulnerabilities, Iago attacks, and control flow leakage of sensitive functions, due to the usage of OS features in JVM. In this paper, Uranus creates easy-to-use Java programming abstractions for application developers to annotate sensitive functions, and Uranus automatically runs these functions in SGX at runtime. Uranus effectively tackles the two major vulnerabilities in the code-reuse approach by presenting two new protocols: 1) a Java bytecode attestation protocol for dynamically loaded functions; and 2) an OS-decoupled, efficient GC protocol optimized for data-handling applications running in enclaves. We implemented Uranus in Linux and applied it to two diverse data-handling applications: Spark and ZooKeeper. Evaluation shows that: 1) Uranus achieves the same security guarantees as two relevant SGX systems for these two applications with only a few annotations; 2) Uranus has reasonable performance overhead compared to the native, insecure applications; and 3) Uranus defends against privileged attacks. Uranus source code and evaluation results are released on https://github.com/hku-systems/uranus.
Cloud Computing Security Issues: a Stakeholder’s Perspective

Cloud computing is a rising technology that has gained significant attention over past decades. It offers various features such as-on-demand access, broad-network access, unlimited resource pool, etc. Despite so many merits, cloud computing has been full several challenges. Security indeed has remained one of the biggest challenges. Researchers, all over world, have been working on designing different types of security mechanism which can work on different levels on cloud environment. However, the literature on cloud security is quite wide and touches several issues pertaining to different stakeholders. The current study addresses the above issue by presenting a detailed review of challenges of security that are faced by different stake holders, such as cloud service provider (CSP), cloud user (CU), etc. This study will help the various researchers to find all security concerns at one place with critical reviews.
Guidance on the usability-privacy tradeoff for utility customer data aggregation

Modern cities, along with their researchers and innovators can benefit from applying " big data" to their sustainability and infrastructure problems and policies, e.g., water and energy consumption. Unfortunately, current utility customer data (UCD) privacy rulemaking fails to ensure safe release of these data for the public benefit and does not currently strike a sound balance between the competing values of usability and privacy. This paper presents a statistical analysis of the tradeoff between usability and privacy for UCD in Los Angeles. The tradeoffs vary by economic sector (residential vs. commercial/industrial) and by utility type (water, electricity, natural gas). This paper provides guidance for safer and more ethically balanced aggregation and release of utility customer data.
Dari Speech Classification Using Deep Convolutional Neural Network

Recently, speech recognition is one of the most advanced research topics in the world. Many recent research papers have proven the power of deep neural networks in speech recognition systems. The main purpose of this paper is to identify isolated words in Dari speech using deep learning algorithms. This research is one of the new studies in Dari speech recognition and focuses on one-word speech recognition. This collection uses our audio files as a database because there were no Dari language databases on the market at that time. In this paper, the Convolutional Neural Network (CNN) is implemented to detect automatically isolated words in Dari. Besides, it uses Mel frequency coefficients (MFCC) to learn the representation of features during training. This model achieved 88.2% in the test set. The results show that the model can predict samples of words seen during training with high accuracy. However, it is somewhat trying to generalize terms outside the scope of training data and very noisy examples.