OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Refactoring Detection Techniques Based on Change History Analysis
Venue: arXiv cs.SE 2018
Year: 2018

Refactoring is the process of changing a software system in such a way that it does not alter the external behavior of the code yet improves its internal structure. Not only researchers, but also practitioners, need to know about past refactoring instances performed in a software development project. So far, a number of techniques have been proposed for automatic detection of refactoring instances. Those techniques have been presented in various international conferences and journals, however, it is difficult for researchers and practitioners to grasp the current status of studies on refactoring detection techniques. In this survey paper, we review various refactoring detection techniques, especially techniques based on change history analysis. First, we give the definition and categorization of refactoring detection methods in this paper, and then introduce refactoring detection techniques based on change history analysis. Finally, we discuss possible future research directions for refactoring detection.
A Framework for Creative-Visualization Opportunities Workshops
Venue: arXiv cs.HC 2018
Year: 2018

Applied visualization researchers often work closely with domain collaborators to explore new and useful applications of visualization. The early stages of collaborations are typically time consuming for all stakeholders as researchers piece together an understanding of domain challenges from disparate discussions and meetings. A number of recent projects, however, report on the use of creative visualization-opportunities (CVO) workshops to accelerate the early stages of applied work, eliciting a wealth of requirements in a few days of focused work. Yet, there is no established guidance for how to use such workshops effectively. In this paper, we present the results of a 2-year collaboration in which we analyzed the use of 17 workshops in 10 visualization contexts. Its primary contribution is a framework for CVO workshops that: 1) identifies a process model for using workshops; 2) describes a structure of what happens within effective workshops; 3) recommends 25 actionable guidelines for future workshops; and 4) presents an example workshop and workshop methods. The creation of this framework exemplifies the use of critical reflection to learn about visualization in practice from diverse studies and experience.
Sprintz: Time Series Compression for the Internet of Things
Venue: arXiv cs.PF 2018
Year: 2018

Thanks to the rapid proliferation of connected devices, sensor-generated time series constitute a large and growing portion of the world's data. Often, this data is collected from distributed, resource-constrained devices and centralized at one or more servers. A key challenge in this setup is reducing the size of the transmitted data without sacrificing its quality. Lower quality reduces the data's utility, but smaller size enables both reduced network and storage costs at the servers and reduced power consumption in sensing devices. A natural solution is to compress the data at the sensing devices. Unfortunately, existing compression algorithms either violate the memory and latency constraints common for these devices or, as we show experimentally, perform poorly on sensor-generated time series. We introduce a time series compression algorithm that achieves state-of-the-art compression ratios while requiring less than 1KB of memory and adding virtually no latency. This method is suitable not only for low-power devices collecting data, but also for servers storing and querying data; in the latter context, it can decompress at over 3GB/s in a single thread, even faster than many algorithms with much lower compression ratios. A key component of our method is a high-speed forecasting algorithm that can be trained online and significantly outperforms alternatives such as delta coding. Extensive experiments on datasets from many domains show that these results hold not only for sensor data but also across a wide array of other time series.
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Venue: arXiv cs.CV 2018
Year: 2018

Quality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time-consuming and error-prone. In order to meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this article, a system is proposed for the identification of casting defects in X-ray images, based on the Mask Region-based CNN architecture. The proposed defect detection system simultaneously performs defect detection and segmentation on input images, making it suitable for a range of defect detection tasks. It is shown that training the network to simultaneously perform defect detection and defect instance segmentation, results in a higher defect detection accuracy than training on defect detection alone. Transfer learning is leveraged to reduce the training data demands and increase the prediction accuracy of the trained model. More specifically, the model is first trained with two large openly-available image datasets before finetuning on a relatively small metal casting X-ray dataset. The accuracy of the trained model exceeds state-of-the art performance on the GRIMA database of X-ray images (GDXray) Castings dataset and is fast enough to be used in a production setting. The system also performs well on the GDXray Welds dataset. A number of in-depth studies are conducted to explore how transfer learning, multi-task learning, and multi-class learning influence the performance of the trained system.
SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e. varying illumination levels and camera viewpoints) at the patients' homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients - this is almost 3 times the number of patients in other studies - and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results.
Statistics of the Distance Traveled until Connectivity for Unmanned Vehicles
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we consider a scenario where a robot needs to establish connectivity with a remote operator or another robot, as it moves along a path. We are interested in answering the following question: what is the distance traveled by the robot along the path before it finds a connected spot? More specifically, we are interested in characterizing the statistics of the distance traveled along the path before it gets connected, in realistic channel environments experiencing path loss, shadowing and multipath effects. We develop an exact mathematical analysis of these statistics for straight-line paths and also mathematically characterize a more general space of paths (beyond straight paths) for which the analysis holds, based on the properties of the path such as its curvature. Finally, we confirm our theoretical analysis using extensive numerical results with real channel parameters from downtown San Francisco.
A Tutorial on Network Embeddings
Venue: arXiv cs.SI 2018
Year: 2018

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.
Cognitive system to achieve human-level accuracy in automated assignment of helpdesk email tickets
Venue: arXiv cs.AI 2018
Year: 2018

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution. The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources. Our system has been deployed in production of three major service providers and currently assigning over 40,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of about 23000 man-hours of effort per annum.
Age of Information Upon Decisions
Venue: arXiv cs.IT 2018
Year: 2018

We consider an M/M/1 update-and-decide system where Poisson distributed decisions are made based on the received updates. We propose to characterize the freshness of the received updates at decision epochs with Age upon Decisions (AuD). Under the first-come-first-served policy (FCFS), the closed form average AuD is derived. We show that the average AuD of the system is determined by the arrival rate and the service rate, and is independent of the decision rate. Thus, merely increasing the decision rate does not improve the timeliness of decisions. Nevertheless, increasing the arrival rate and the service rate simultaneously can decrease the average AuD efficiently.
On the Monitoring of Decentralized Specifications Semantics, Properties, Analysis, and Simulation
Venue: arXiv cs.SE 2018
Year: 2018

We define two complementary approaches to monitor decentralized systems. The first relies on those with a centralized specification, i.e, when the specification is written for the behavior of the entire system. To do so, our approach introduces a data-structure that i) keeps track of the execution of an automaton, ii) has predictable parameters and size, and iii) guarantees strong eventual consistency. The second approach defines decentralized specifications wherein multiple specifications are provided for separate parts of the system. We study two properties of decentralized specifications pertaining to monitorability and compatibility between specification and architecture. We also present a general algorithm for monitoring decentralized specifications. We map three existing algorithms to our approaches and provide a framework for analyzing their behavior. Furthermore, we introduce THEMIS, a framework for designing such decentralized algorithms and simulating their behavior. We show the usage of THEMIS to compare multiple algorithms and verify the trends predicted by the analysis by studying two scenarios: a synthetic benchmark and a real example.
The roll call interpretation of the Shapley value
Venue: arXiv cs.GT 2018
Year: 2018

The Shapley value is commonly illustrated by roll call votes in which players support or reject a proposal in sequence. If all sequences are equiprobable, a voter's Shapley value can be interpreted as the probability of being pivotal, i.e., to bring about the required majority or to make this impossible for others. We characterize the joint probability distributions over cooperation patterns that permit this roll call interpretation: individual votes may be interdependent but must be exchangeable.
A simple analysis of flying capacitor converter
Venue: arXiv cs.CE 2018
Year: 2018

The paper aims to emphasise how switched systems can be analysed with elementary techniques which require only undergraduate-level linear algebra and differential equations. It is also emphasised how math software can become useful for simplifying analytic complications. The time domain voltage balance methodology is used for stability analysis. As for deriving formulas for the asymptotic average of both capacitor voltage and inductor current, a new simple analytic method is introduced. It was shown analytically that the time average of capacitor voltage converges to half of the source voltage. A formula for the time average of the current of the inductor is also computed. As a by-product it was discovered that the period of the current is half of the switching period. Numerical simulations are obtained to illustrate the accuracy of the results. Higher dimensional generalisations could become a bit complicated as stability analysis of higher dimensional exponential matrices are not so easy to handle. On the other hand, the new discovery on the period of the current is more likely to give new insights in handling higher dimensional systems. Analytical formulas are exact and it helps in accurately modelling flying capacitor converts in practice. FCC is well studied in engineering society. However, not much is done in obtaining exact formulas using analysis. Also, math software is much used in computation of numerical results and obtaining simulations. In this paper, one more important aspect of math software is emphasised, namely, use symbolic computations in analysis.
Multiband SAS Imagery
Venue: arXiv cs.CV 2018
Year: 2018

Advances in unmanned synthetic aperture sonar (SAS) imaging platforms allow for the simultaneous collection of multiband SAS imagery. The imagery is collected over several octaves and the phenomenology's interactions with the sea floor vary greatly over this range -- higher frequencies resolve proud and fine structure of the seafloor while lower frequencies resolve subsurface features and often induce internal resonance in man-made objects. Currently, analysts examine multiband imagery by viewing a single band at a time. This method makes it difficult to ascertain correlations between any pair of bands collected over the same location. To mitigate this issue, we propose methods which ingest high frequency (HF) and low frequency (LF) SAS imagery and generates a color composite creating what we call a multiband SAS (MSAS) image. The MSAS image contains the relevant portions of the HF and LF images required by an analyst to interpret the scene and are defined using a spatial saliency metric computed for each image. We then combine the saliency and acoustic backscatter measures to form the final MSAS image.
Efficient Continuous Top-$k$ Geo-Image Search on Road Network
Venue: arXiv cs.MM 2018
Year: 2018

With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-$k$ geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.
Separators for Planar Graphs that are Almost Trees
Venue: arXiv cs.DS 2018
Year: 2018

We prove that a connected planar graph with $n$ vertices and $n+μ$ edges has a vertex separator of size $O( \sqrtμ + 1)$, and this separator can be computed in linear time.
Random Walk Laplacian and Network Centrality Measures
Venue: arXiv cs.NA 2018
Year: 2018

Random walks over directed graphs are used to model activities in many domains, such as social networks, influence propagation, and Bayesian graphical models. They are often used to compute the importance or centrality of individual nodes according to a variety of different criteria. Here we show how the pseudoinverse of the "random walk" Laplacian can be used to quickly compute measures such as the average number of visits to a given node and various centrality and betweenness measures for individual nodes, both for the network in general and in the case a subset of nodes is to be avoided. We show that with a single matrix inversion it is possible to rapidly compute many such quantities.
Characterizing Co-located Datacenter Workloads: An Alibaba Case Study
Venue: arXiv cs.DC 2018
Year: 2018

Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges in managing such intricate, heterogeneous workloads while providing quality-assured resource orchestration and user experience, we analyze Alibaba's co-located workload trace, the first publicly available dataset with precise information about the category of each job. Two types of workload---long-running, user-facing, containerized production jobs, and transient, highly dynamic, non-containerized, and non-production batch jobs---are running on a shared cluster of 1313 machines. Our multifaceted analysis reveals insights that we believe are useful for system designers and IT practitioners working on cluster management systems.
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study
Venue: arXiv cs.LG 2018
Year: 2018

Affective computing has become a very important research area in human-machine interaction. However, affects are subjective, subtle, and uncertain. So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract. Thus, dimensionality reduction is critical in affective computing. This paper presents our preliminary study on dimensionality reduction for affect classification. Five popular dimensionality reduction approaches are introduced and compared. Experiments on the DEAP dataset showed that no approach can universally outperform others, and performing classification using the raw features directly may not always be a bad choice.
Radon Inversion via Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health examination. The Radon inversion or image reconstruction is challenging due to the potentially defective radon projections. Conventionally, the reconstruction process contains several ad hoc stages to approximate the corresponding Radon inversion. Each of the stages is highly dependent on the results of the previous stage. In this paper, we propose a novel unified framework for Radon inversion via deep learning (DL). The Radon inversion can be approximated by the proposed framework with an end-to-end fashion instead of processing step-by-step with multiple stages. For simplicity, the proposed framework is short as iRadonMap (inverse Radon transform approximation). Specifically, we implement the iRadonMap as an appropriative neural network, of which the architecture can be divided into two segments. In the first segment, a learnable fully-connected filtering layer is used to filter the radon projections along the view-angle direction, which is followed by a learnable sinusoidal back-projection layer to transfer the filtered radon projections into an image. The second segment is a common neural network architecture to further improve the reconstruction performance in the image domain. The iRadonMap is overall optimized by training a large number of generic images from ImageNet database. To evaluate the performance of the iRadonMap, clinical patient data is used. Qualitative results show promising reconstruction performance of the iRadonMap.
Simulation of Mixed Critical In-vehicular Networks
Venue: arXiv cs.NI 2018
Year: 2018

Future automotive applications ranging from advanced driver assistance to autonomous driving will largely increase demands on in-vehicular networks. Data flows of high bandwidth or low latency requirements, but in particular many additional communication relations will introduce a new level of complexity to the in-car communication system. It is expected that future communication backbones which interconnect sensors and actuators with ECU in cars will be built on Ethernet technologies. However, signalling from different application domains demands for network services of tailored attributes, including real-time transmission protocols as defined in the TSN Ethernet extensions. These QoS constraints will increase network complexity even further. Event-based simulation is a key technology to master the challenges of an in-car network design. This chapter introduces the domain-specific aspects and simulation models for in-vehicular networks and presents an overview of the car-centric network design process. Starting from a domain specific description language, we cover the corresponding simulation models with their workflows and apply our approach to a related case study for an in-car network of a premium car.