Reviewerly/exHarmony huggingface dataset:
Understanding the relation between repeat developer interactions and bug resolution times in large open source ecosystems: A multisystem study

Abstract Large‐scale software systems are being increasingly built by distributed teams of developers who interact across geographies and time zones. Ensuring smooth knowledge transfer and the percolation of skills within and across such teams remain key challenges for organizations. Towards addressing this challenge, organizations often grapple with questions around whether and how repeat collaborations between members of a team relate to outcomes of important activities. In the context of this paper, the word ‘repeat interaction’ does not imply a greater number of interactions; it refers to repeat interaction between a pair of developers who have collaborated before. In this paper, we empirically examine such a question using real‐world data from three diverse development ecosystems, collectively involving 400,000+ units of work and 600,000+ comments exchanged between numerous developers. Our statistical models consistently establish a counter‐intuitive relation between repeat developer interaction and bug resolution times. Our experimental results show that more instances of repeat developer interactions over bug fixing are associated with more time taken for the bugs to be fixed. Given the expanse and variety of the underlying data, our results offer an unexpected set of insights on a key dynamic of collaboration in software development ecosystems. We discuss how these insights can influence the practice of large‐scale software development at individual, team and organizational levels.
Sequential Pattern Mining Using IDLists

Sequential pattern mining is a practical problem whose objective is to discover helpful informative patterns in a stored database such as market transaction databases. It covers many applications in different areas. Recently, a study that improved the runtime for mining patterns was proposed. It was called pseudo-IDLists and it helps prevent duplicate data from replicating during the mining process. However, the idea only works for the special type of sequential patterns, which are clickstream patterns. Direct applying the idea for sequential pattern mining is not feasible. Hence, we proposed adaptions and changes to the novel idea and proposed SUI (Sequential pattern mining Using IDList), a sequential pattern mining algorithm based on pseudo-IDLists. Via experiments on three test databases, we show that SUI is efficient and effective regarding runtime and memory consumption.
Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation

We propose to use a model-based generative loss for training hand pose estimators on depth images based on a volumetric hand model. This additional loss allows training of a hand pose estimator that accurately infers the entire set of 21 hand keypoints while only using supervision for 6 easy-to-annotate keypoints (fingertips and wrist). We show that our partially-supervised method achieves results that are comparable to those of fully-supervised methods which enforce articulation consistency. Moreover, for the first time we demonstrate that such an approach can be used to train on datasets that have erroneous annotations, i.e. “ground truth” with notable measurement errors, while obtaining predictions that explain the depth images better than the given “ground truth.”
Hard-Label Black-Box Adversarial Attack on Deep Electrocardiogram Classifier

Through aiding the process of diagnosing cardiovascular diseases (CVD) such as arrhythmia, electrocardiograms (ECGs) have progressively improved prospects for an automated diagnosis system in modern healthcare. Recent years have seen the promising applications of deep neural networks (DNNs) in analyzing ECG data, even outperforming cardiovascular experts in identifying certain rhythm irregularities. However, DNNs have shown to be susceptible to adversarial attacks, which intentionally compromise the models by adding perturbations to the inputs. This concept is also applicable to DNN-based ECG classifiers and the prior works generate these adversarial attacks in a white-box setting where the model details are exposed to the attackers. However, the black-box condition, where the classification model's architecture and parameters are unknown to the attackers, remains mostly unexplored. Thus, we aim to fool ECG classifiers in the black-box and hard-label setting where given an input, only the final predicted category is visible to the attacker. Our attack on the DNN classification model for the PhysioNet Computing in Cardiology Challenge 2017 [12] database produced ECG data sets mostly indistinguishable from the white-box version of an adversarial attack on this same database. Our results demonstrate that we can effectively generate the adversarial ECG inputs in this black-box setting, which raises significant concerns regarding the potential applications of DNN-based ECG classifiers in security-critical systems.
Design and Implementation of Rivest Shamir Adleman’s (RSA) Cryptography Algorithm in Text File Data Security

Abstract Security of data text file on a computer can be done by utilizing encryption and decryption techniques. One technique is encryption and decryption of data encryption system text file with cryptography. Cryptography is the science or art to randomize and secure the message to avoid the manipulation of data by performing encryption and decryption on the data of the text file. Cryptographic algorithm consists of several types of one example is the algorithm Rivest Shamir Adleman (RSA). The RSA algorithm is one of asymmetric cryptography technique ie riptografi modern technique that has two keys of the public key used for encryption and a private key techniques to make the process deskripsi. The purpose of this research is to design and implement RSA cryptography algorithm on text file data security. The results of this design is a dekstop based software. This research is expected to provide benefits to the users, individually as well for other organizations.
Online Multiple Object Tracking with Recurrent Neural Networks and Appearance Model

Multiple object tracking suffers from many challenges including huge computation work, crowd scenes. In order to solve these problems, we proposed a novel online multiple object tracking algorithm based on recurrent neural networks (RNNs) and appearance model. Compared to traditional algorithms, the RNNs can handle the motion state of the target well because it is trained with a quantity of data extracted from real world scenes. In addition, RNNs is helpful to improve tracking speed because it predicts the trajectories of objects without complex appearance calculations. The appearance feature is significant for tracking, especially in crowed scenes. The appearance model is extracted by convolutional neural networks trained with MARS dataset which is more targeted for the multi object tracking. In order to balance the speed and accuracy of tracking, a novel simple decision method was proposed to decide which features should be used. Otherwise, the cascade matching is integrated into the data association to solve a lot of subproblems in tracking. The experimental evaluation shows our algorithm is fast and accurate.
PasVis

Public transport maps are typically designed in a way to support route finding tasks for passengers while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We illustrate the usefulness of our interactive visualization by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a user experiment with 20 participants.
Joint detection and tracking of extended stealth targets from image observations based on subrandom matrices

The challenge of joint detection and tracking of multiple extended targets (ETs) arises in many radar applications, especially for extended stealth targets (ESTs) with small signal-to-noise ratio (SNR). Recently, the multi-Bernoulli (MB) filter-based random matrix model (RMM) has been proposed for tracking ellipsoidal ETs. The MB-RMM filter depends on the measurements (position and range rate), which are extracted by thresholding the received signal. It is acceptable if SNR is high. The threshold must be less than the sufficient probability to avoid a high rate of false detections. This assumption is unsuitable for tracking extended-stealth targets (ESTs) with unknown detection probability. To this point, we address a track-before-detect-MB-RMM approach, which is an efficient way to solve the unknown detection profile issue. There is a considerable advantage of using unthresholded data in simultaneous detection and tracking. In this scenario, although the extension ellipsoid is efficient, it may not be accurate enough because of a lack of useful information, such as size, shape, and orientation. Therefore, we introduce a filter composed of subellipses where each one is represented by an RMM. Furthermore, a sequential Monte Carlo implementation is applied to estimate nonlinear kinematic ESTs’ state. The results confirm the effectiveness and robustness of the proposed filter.
Cross-layer metrics sharing for QUICker video streaming

QUIC is marketed to hold many advantages over TCP. However, preliminary experimentation has shown that simply running contemporary HTTP Adaptive Streaming (HAS) implementations over QUIC does not improve but actually hurts streaming performance compared to a traditional TCP deployment. We argue that this behavior can be attributed to the amount of TCP specialization that HAS Adaptive BitRate (ABR) algorithms have received over the years. In contrast to TCP (which can be regarded as a "black box"), QUIC actually encompasses all the necessary tools to empower streaming performance optimization (e.g., definition of custom congestion control algorithms, access to transport-layer metrics in the application layer). This however comes at the expense of added complexity which in turn could lead to misinterpretations of the root causes of suboptimal streaming performance. To facilitate research on HTTP adaptive bitrate streaming over QUIC, in this paper we propose a solution towards jointly visualizing transport- and application-layer metrics to allow for a better understanding of HAS streaming performance over various types of transport protocols (i.e., TCP versus QUIC). We see the work presented in this paper as an important stepping stone towards cross-layer optimization of HAS ABR performance to achieve a better overall Quality of Experience (QoE) for streaming users.
Employee attendance application using location based service (lbs) method based on android

Abstract PT. Brainmatics Cipta Informatika is an IT Training and Consulting company located in Jakarta. The development of a company can not be separated from the role of employees in it. The success assessment of existing human resources apart from the aspect of success in the field of work is also inseparable from the assessment of the conditions including the assessment of attendance. Problems that occur at PT. Cipta Cipta Brainmatics is the process of existing employee attendance, attendance is done by using Odoo’s web based Enterprise Resource Planning system that stores employee data on coming and outgoing hours that can be accessed anywhere without any location or area restrictions, so employees can do attendance at anywhere, even without having to come to the office. Based on these problems, an Android-based attendance application was designed by utilizing the Location Based Service (LBS) feature that can be operated using a smartphone and can be used with a limited area determined for the attendance process. This application was built with the Java programming language using Android Studio, PostgreSQL and Objecbox software as its database. It is expected that the application that is designed can resolve the attendance issues that exist at PT. Brainmatics Cipta Informatika.
Not All Areas Are Equal: A Novel Separation‐Restoration‐Fusion Network for Image Raindrop Removal

Abstract Detecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image‐to‐image translation and propose a separation‐restoration‐fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi‐scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state‐of‐the‐art methods by a large margin on the benchmark datasets in extensive experiments.
Personalized Hand Modeling from Multiple Postures with Multi‐View Color Images

Abstract Personalized hand models can be utilized to synthesize high quality hand datasets, provide more possible training data for deep learning and improve the accuracy of hand pose estimation. In recent years, parameterized hand models, e.g., MANO, are widely used for obtaining personalized hand models. However, due to the low resolution of existing parameterized hand models, it is still hard to obtain high‐fidelity personalized hand models. In this paper, we propose a new method to estimate personalized hand models from multiple hand postures with multi‐view color images. The personalized hand model is represented by a personalized neutral hand, and multiple hand postures. We propose a novel optimization strategy to estimate the neutral hand from multiple hand postures. To demonstrate the performance of our method, we have built a multi‐view system and captured more than 35 people, and each of them has 30 hand postures. We hope the estimated hand models can boost the research of high‐fidelity parameterized hand modeling in the future. All the hand models are publicly available on www.yangangwang.com .
Machine learning for evaluating and improving theories

We summarize our recent work that uses machine learning techniques as a complement to theoretical modeling, rather than a substitute for it. The key concepts are those of the completeness and restrictiveness of a model. A theory's completeness is how much it improves predictions over a naive baseline, relative to how much improvement is possible. When a theory is relatively incomplete, machine learning algorithms can help reveal regularities that the theory doesn't capture, and thus lead to the construction of theories that make more accurate predictions. Restrictiveness measures a theory's ability to match arbitrary hypothetical data: A very unrestrictive theory will be complete on almost any data, so the fact that it is complete on the actual data is not very instructive. We algorithmically quantify restrictiveness by measuring how well the theory approximates randomly generated behaviors. Finally, we propose "algorithmic experimental design" as a method to help select which experiments to run.
IoT Fog-based Image Matching Monitoring System for Physical Access Control through IP Camera Devices

Many people are concerned with physical security and seek support from resources provided by the latest technological advancements, such as security cameras with Internet access associated with biometric people recognition algorithms. However, an isolated camera has a low degree of effectiveness because it has a limited context of information. For instance, detection of missing persons is only effective if multiple cameras are aware of that person is missing. This work proposes a command and control center to allow the coordination of security cameras in a remote and flexible way. This flexibility is achieved because the user can remotely register the actions that each camera must perform, even with the system in operation. It also allows the sharing of a user's information with multiple cameras. Our tests demonstrate that the system can be installed in an environment with few computational resources, as well as it reacts properly according to the configured parameters.
Research and Implementation of Data Extraction Method Based on NLP

In order to accurately extract the data from unstructured Chinese text, this paper proposes a rule-based method based on natural language processing and regular expression. This method makes use of the language expression rules of the data in the text and other related knowledge to form the feature word lists and rule template to match the text. Experimental results show that the accuracy of the designed algorithm is 94.09%.
ARGarden: 3D outdoor landscape design using handheld augmented reality with multi-user interaction

Abstract Augmented Reality (AR) is a technology to display the virtual object by overlaying on the physical marker in the real environment. Nowadays, the handheld device becomes a trend in AR since it has high processing power and compatible with the AR system. However, most of the AR applications did not fully support the interaction from multiple users for the collaborative AR interface. Therefore, this research aim is to develop multi-user interaction for collaborative handheld AR. There are a few phases involved to design and develop the collaborative AR application for the handheld device called ARGarden. All the interaction that has been done for the virtual object transformation such as translation, rotation and scaling can be seen by the other users, and this data is synchronized by using a network protocol. Then, a queue system is implemented for the multi-user interaction to allow the virtual object can only be selected by one user at one time, and the other users must wait for their turn. The significance of this research is all the method that has been implemented for the ARGarden can be applied in different fields such as simulation, modelling, medical and education.
IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image

3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous and challenging as single image depth estimation is already an ill-posed problem. In this paper, we propose an instance-aware approach to aggregate useful information for improving the accuracy of 3D object detection with the following contributions. First, an instance-aware feature aggregation (IAFA) module is proposed to collect local and global features for 3D bounding boxes regression. Second, we empirically find that the spatial attention module can be well learned by taking coarse-level instance annotations as a supervision signal. The proposed module has significantly boosted the performance of the baseline method on both 3D detection and 2D bird-eye’s view of vehicle detection among all three categories. Third, our proposed method outperforms all single image-based approaches (even these methods trained with depth as auxiliary inputs) and achieves state-of-the-art 3D detection performance on the KITTI benchmark.
Virtual Reality on Product Usability Testing: A Systematic Literature Review

Research has shown that virtual reality (VR) technology has significant relevance in the product development process, revealing the user's increasing interactive capacity with the virtual product. This study aims to characterize the scientific knowledge that address the application of usability testing for product development in the automotive sector using virtual reality technology. Based on a systematic literature review (SLR) on virtual reality and product usability evaluations, it was conducted a search in Science Direct, Springer, IEEE Xplore, and Google Scholar knowledge bases, that identified six studies related to this topic. The results indicated that, although the user experience using virtual reality in usability testing has some limitations related to the tactile sense, its use is providing results capable of identifying design errors, bringing benefits in agile corrections, and cost reduction with physical prototypes. Additionally, user experience tends to be more realistic with the adoption of haptic devices. On this basis, virtual reality has the potential to innovate the product development process, which through usability testing, could verify and validate the human-machine interaction within the secure digital environment.
Lymphocyte Annotator: CD3<sup>+</sup> and CD8<sup>+</sup> IHC Stained Patch Image Annotation Tool

In digital pathology, preliminary step for the development of Computer Aided Diagnostics involves defining and labelling an Object of Interest. Contrary to traditional tiresome method of observation and marking of objects under microscope, digital pathology enables the use of annotation tools to capture localization information and morphology for targeted object(s). However, it is quite difficult to capture this information due to high inter and intra class variance. In this paper, we have addressed these two different problems by developing an easy to use and efficient tool "Lymphocyte Annotator" that allows a user to draw bounding box(es) on IHC stained images, and as a result, automatically generates multiple variants of segmentation masks for the targeted object. Lymphocyte Annotator provides multiple options for bounding box selection, with variation in bounding box size and type. The developed tool targets labelling of lymphocytes in histopathological images. This annotator generates five different types of masks by using a) Chan Vese Segmentation Model, b) Morphological Active Contours without Edges Model, c) Morphological Geodesic Active Contours Model, d) Adaptive Thresholding and e) Circular Masks, thus providing user with an opportunity to use a mixture of different types of generated masks. Capturing of lymphocytes' morphology using different models incorporates morphological variance and enables training of robust deep Convolutional Neural Network. The annotations created from this tool are by default exported in PASCAL VOC and JSON format. Lymphocyte annotator is an open-source tool, and can be accessed from source, available at https://github.com/m-mohsin-zafar/lysto_labeltool.