OpenRaiser/Intern-Atlas huggingface dataset:
On building an automated responding system for app reviews: What are the characteristics of reviews and their responses?
Venue: arXiv cs.SE 2019
Year: 2019

Recent studies showed that the dialogs between app developers and app users on app stores are important to increase user satisfaction and app's overall ratings. However, the large volume of reviews and the limitation of resources discourage app developers from engaging with customers through this channel. One solution to this problem is to develop an Automated Responding System for developers to respond to app reviews in a manner that is most similar to a human response. Toward designing such system, we have conducted an empirical study of the characteristics of mobile apps' reviews and their human-written responses. We found that an app reviews can have multiple fragments at sentence level with different topics and intentions. Similarly, a response also can be divided into multiple fragments with unique intentions to answer certain parts of their review (e.g., complaints, requests, or information seeking). We have also identified several characteristics of review (rating, topics, intentions, quantitative text feature) that can be used to rank review by their priority of need for response. In addition, we identified the degree of re-usability of past responses is based on their context (single app, apps of the same category, and their common features). Last but not least, a responses can be reused in another review if some parts of it can be replaced by a placeholder that is either a named-entity or a hyperlink. Based on those findings, we discuss the implications of developing an Automated Responding System to help mobile apps' developers write the responses for users reviews more effectively.
$X$-secure $T$-private Information Retrieval from MDS Coded Storage with Byzantine and Unresponsive Servers
Venue: arXiv cs.IT 2019
Year: 2019

The problem of $X$-secure $T$-private information retrieval from MDS coded storage is studied in this paper, where the user wishes to privately retrieve one out of $K$ independent messages that are distributed over $N$ servers according to an MDS code. It is guaranteed that any group of up to $X$ colluding servers learn nothing about the messages and that any group of up to $T$ colluding servers learn nothing about the identity of desired message. A lower bound of achievable rates is proved by presenting a novel scheme based on \emph{cross-subspace alignment} and a successive decoding with interference cancellation strategy. For large number of messages $(K\rightarrow\infty)$ the achieved rate, which we conjecture to be optimal, improves upon the best known rates previously reported in the literature by Raviv and Karpuk, and generalizes an achievable rate for MDS-TPIR previously found by Freij-Hollanti et al. that is also conjectured to be asymptotically optimal. The setting is then expanded to allow unresponsive and Byzantine servers. Finally, the scheme is applied to find a new lower convex hull of (download, upload) pairs of secure and private distributed matrix multiplication that generalizes, and in certain asymptotic settings strictly improves upon the best known previous results.
Analyzing Customer Feedback for Product Fit Prediction
Venue: arXiv cs.CL 2019
Year: 2019

One of the biggest hurdles for customers when purchasing fashion online, is the difficulty of finding products with the right fit. In order to provide a better online shopping experience, platforms need to find ways to recommend the right product sizes and the best fitting products to their customers. These recommendation systems, however, require customer feedback in order to estimate the most suitable sizing options. Such feedback is rare and often only available as natural text. In this paper, we examine the extraction of product fit feedback from customer reviews using natural language processing techniques. In particular, we compare traditional methods with more recent transfer learning techniques for text classification, and analyze their results. Our evaluation shows, that the transfer learning approach ULMFit is not only comparatively fast to train, but also achieves highest accuracy on this task. The integration of the extracted information with actual size recommendation systems is left for future work.
Performance Analysis of Zippers
Venue: arXiv cs.PL 2019
Year: 2019

A zipper is a powerful technique of representing a purely functional data structure in a way that allows fast access to a specific element. It is often used in cases where the imperative data structures would use a mutable pointer. However, the efficiency of zippers as a replacement for mutable pointers is not sufficiently explored. We attempt to address this issue by comparing the performance of zippers and mutable pointers in two common scenarios and three different languages: C++, C#, and Haskell.
Immutable Log Storage as a Service
Venue: arXiv cs.CR 2019
Year: 2019

Logs contain critical information about the quality of the rendered services on the Cloud and can be used as digital evidence. Hence, we argue that the critical nature of logs calls for immutability and verification mechanism without the presence of a single trusted party. In this paper, we propose a blockchain-based log system, called Logchain, which can be integrated with existing private and public blockchains. To validate the mechanism, we create Logchain as a Service (LCaaS) by integrating it with Ethereum public blockchain network. We show that the solution is scalable (being able to process 100 log files per second) and fast (being able to "seal" a log file in 23 seconds, on average).
A Multiple Source Hourglass Deep Network for Multi-Focus Image Fusion
Venue: arXiv cs.CV 2019
Year: 2019

Multi-Focus Image Fusion seeks to improve the quality of an acquired burst of images with different focus planes. For solving the task, an activity level measurement and a fusion rule are typically established to select and fuse the most relevant information from the sources. However, the design of this kind of method by hand is really hard and sometimes restricted to solution spaces where the optimal all-in-focus images are not contained. Then, we propose here two fast and straightforward approaches for image fusion based on deep neural networks. Our solution uses a multiple source Hourglass architecture trained in an end-to-end fashion. Models are data-driven and can be easily generalized for other kinds of fusion problems. A segmentation approach is used for recognition of the focus map, while the weighted average rule is used for fusion. We designed a training loss function for our regression-based fusion function, which allows the network to learn both the activity level measurement and the fusion rule. Experimental results show our approach has comparable results to the state-of-the-art methods with a 60X increase of computational efficiency for 520X520 resolution images.
Cyber security insights into self-proclaimed virtual world hackers
Venue: arXiv cs.CR 2019
Year: 2019

Virtual worlds have become highly popular in recent years with reports of over a billion people accessing these environments and the virtual goods market growing to near 50 billion US dollars. An undesirable outcome to this popularity and market value is thriving criminal activity in these worlds. The most profitable cyber security problem in virtual worlds is named Virtual Property Theft. The aim of this study is to use an online survey to gain insight into how hackers (n=100) in these synthetic worlds conduct their criminal activity. This survey is the first to report an insight into the criminal mind of hackers (virtual thieves). Results showed a clear-cut profile of a virtual property thief, they appear to be mainly aged 20-24 years of age, live in the United States of America, while using virtual worlds for 5-7 hours a day. These and the other key results of this study will provide a pathway for designing an effective anti-theft framework capable of abolishing this cyber security issue.
Towards Ethical Content-Based Detection of Online Influence Campaigns
Venue: arXiv cs.CY 2019
Year: 2019

The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development. We demonstrate that features derived from the text of user comments are useful for identifying suspect activity, but lead to increased erroneous identifications when keywords over-represented in past influence campaigns are present. Drawing on research in native language identification (NLI), we use "named entity masking" (NEM) to create sentence features robust to this shortcoming, while maintaining comparable classification accuracy. We demonstrate that while NEM consistently reduces false positives when key named entities are mentioned, both masked and unmasked models exhibit increased false positive rates on English sentences by Russian native speakers, raising ethical considerations that should be addressed in future research.
3D Face Pose and Animation Tracking via Eigen-Decomposition based Bayesian Approach
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors, extracted around a few given landmarks (26 selected vertices of CANDIDE model) with a Bayesian approach. The training phase is performed on a synthetic database generated from the first video frame. At each current frame, the face pose and animation parameters are estimated via a Bayesian approach, with a Gaussian prior and a Gaussian likelihood function whose the mean and the covariance matrix eigenvalues are updated from the previous frame using eigen decomposition. Numerical results on pose estimation and landmark locations are reported using the Boston University Face Tracking (BUFT) database and Talking Face video. They show that our approach, compared to six other published algorithms, provides a very good compromise and presents a promising perspective due to the good results in terms of landmark localization.
Texture Retrieval in the Wild through detection-based attributes
Venue: arXiv cs.CV 2019
Year: 2019

Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest). In this paper we show that a texel-based representation fits well with this task. In particular, we refer to Texel-Att, a recent texel-based descriptor which has shown to capture fine grained variations of a texture, for retrieval purposes. After a brief explanation of Texel-Att, we will show in our experiments that this descriptor is robust to distortions resulting from acquisitions in the wild by setting up an experiment in which textures from the ElBa (an Element-Based texture dataset) are artificially distorted and then used to retrieve the original image. We compare our approach with existing descriptors using a simple ranking framework based on distance functions. Results show that even under extreme conditions (such as a down-sampling with a factor of 10), we perform better than alternative approaches.
Improving the Performance of the Paisley Pattern-Matching EDSL by Staged Combinatorial Compilation
Venue: arXiv cs.PL 2019
Year: 2019

Paisley is a declarative lightweight embedded domain-specific language for expressive, non-deterministic, non-invasive pattern matching on arbitrary data structures in Java applications. As such, it comes as a pure Java library of pattern-matching combinators and corresponding programming idioms. While the combinators support a basic form of self-optimization based on heuristic metadata, overall performance is limited by the distributed and compositional implementation that impedes non-local code optimization. In this paper, we describe a technique for improving the performance of Paisley transparently, without compromising the flexible and extensible combinatorial design. By means of distributed bytecode generation, dynamic class loading and just-in-time compilation of patterns, the run-time overhead of the combinatorial approach can be reduced significantly, without requiring any technology other than a standard Java virtual machine and our LLJava bytecode framework. We evaluate the impact by comparison to earlier benchmarking results on interpreted Paisley. The key ideas of our compilation technique are fairly general, and apply in principle to any kind of combinator language running on any jit-compiling host.
DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. DeepDistance uses the inner distances estimated by this FCN in a detection algorithm to locate individual cells in a given image. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model. This extended model includes an auxiliary classification task and learns it in parallel to the two regression tasks by sharing feature representations with them. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify cell locations, even for the cell line that was not used in training, and improve the results of the previous deep learning methods.
VeriSmart: A Highly Precise Safety Verifier for Ethereum Smart Contracts
Venue: arXiv cs.PL 2019
Year: 2019

We present VeriSmart, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a single flaw can cause huge financial damage. In particular, ensuring that arithmetic operations are safe is one of the most important and common security concerns of Ethereum smart contracts nowadays. In response, several safety analyzers have been proposed over the past few years, but state-of-the-art is still unsatisfactory; no existing tools achieve high precision and recall at the same time, inherently limited to producing annoying false alarms or missing critical bugs. By contrast, VeriSmart aims for an uncompromising analyzer that performs exhaustive verification without compromising precision or scalability, thereby greatly reducing the burden of manually checking undiscovered or incorrectly-reported issues. To achieve this goal, we present a new domain-specific algorithm for verifying smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts. Evaluation with real-world smart contracts shows that VeriSmart can detect all arithmetic bugs with a negligible number of false alarms, far outperforming existing analyzers.
Efficient Implementation of Color Coding Algorithm for Subgraph Isomorphism Problem
Venue: arXiv cs.DS 2019
Year: 2019

We consider the subgraph isomorphism problem where, given two graphs G (source graph) and F (pattern graph), one is to decide whether there is a (not necessarily induced) subgraph of G isomorphic to F. While many practical heuristic algorithms have been developed for the problem, as pointed out by McCreesh et al. [JAIR 2018], for each of them there are rather small instances which they cannot cope. Therefore, developing an alternative approach that could possibly cope with these hard instances would be of interest. A seminal paper by Alon, Yuster and Zwick [J. ACM 1995] introduced the color coding approach to solve the problem, where the main part is a dynamic programming over color subsets and partial mappings. As with many exponential-time dynamic programming algorithms, the memory requirements constitute the main limiting factor for its usage. Because these requirements grow exponentially with the treewidth of the pattern graph, all existing implementations based on the color coding principle restrict themselves to specific pattern graphs, e.g., paths or trees. In contrast, we provide an efficient implementation of the algorithm significantly reducing its memory requirements so that it can be used for pattern graphs of larger treewidth. Moreover, our implementation not only decides the existence of an isomorphic subgraph, but it also enumerates all such subgraphs (or given number of them). We provide an extensive experimental comparison of our implementation to other available solvers for the problem.
Factorization and Normalization, Essentially
Venue: arXiv cs.LO 2019
Year: 2019

Lambda-calculi come with no fixed evaluation strategy. Different strategies may then be considered, and it is important that they satisfy some abstract rewriting property, such as factorization or normalization theorems. In this paper we provide simple proof techniques for these theorems. Our starting point is a revisitation of Takahashi's technique to prove factorization for head reduction. Our technique is both simpler and more powerful, as it works in cases where Takahishi's does not. We then pair factorization with two other abstract properties, defining \emph{essential systems}, and show that normalization follows. Concretely, we apply the technique to four case studies, two classic ones, head and the leftmost-outermost reductions, and two less classic ones, non-deterministic weak call-by-value and least-level reductions.
Memorizing All for Implicit Discourse Relation Recognition
Venue: arXiv cs.CL 2019
Year: 2019

Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clue from explicit connectives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the semantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data. Thus in this paper, we propose a novel memory mechanism to tackle the challenges for further performance improvement. The memory mechanism is adequately memorizing information by pairing representations and discourse relations of all training instances, which right fills the slot of the data-hungry issue in the current implicit discourse relation recognizer. Our experiments show that our full model with memorizing the entire training set reaches new state-of-the-art against strong baselines, which especially for the first time exceeds the milestone of 60% accuracy in the 4-way task.
3D Anchor-Free Lesion Detector on Computed Tomography Scans
Venue: arXiv cs.CV 2019
Year: 2019

Lesions are injuries and abnormal tissues in the human body. Detecting lesions in 3D Computed Tomography (CT) scans can be time-consuming even for very experienced physicians and radiologists. In recent years, CNN based lesion detectors have demonstrated huge potentials. Most of current state-of-the-art lesion detectors employ anchors to enumerate all possible bounding boxes with respect to the dataset in process. This anchor mechanism greatly improves the detection performance while also constraining the generalization ability of detectors. In this paper, we propose an anchor-free lesion detector. The anchor mechanism is removed and lesions are formalized as single keypoints. By doing so, we witness a considerable performance gain in terms of both accuracy and inference speed compared with the anchor-based baseline
Ordered Sets for Data Analysis
Venue: arXiv cs.LO 2019
Year: 2019

This book dwells on mathematical and algorithmic issues of data analysis based on generality order of descriptions and respective precision. To speak of these topics correctly, we have to go some way getting acquainted with the important notions of relation and order theory. On the one hand, data often have a complex structure with natural order on it. On the other hand, many symbolic methods of data analysis and machine learning allow to compare the obtained classifiers w.r.t. their generality, which is also an order relation. Efficient algorithms are very important in data analysis, especially when one deals with big data, so scalability is a real issue. That is why we analyze the computational complexity of algorithms and problems of data analysis. We start from the basic definitions and facts of algorithmic complexity theory and analyze the complexity of various tools of data analysis we consider. The tools and methods of data analysis, like computing taxonomies, groups of similar objects (concepts and n-clusters), dependencies in data, classification, etc., are illustrated with applications in particular subject domains, from chemoinformatics to text mining and natural language processing.
Improved Storage for Efficient Private Information Retrieval
Venue: arXiv cs.IT 2019
Year: 2019

We consider the problem of private information retrieval from $N$ \emph{storage-constrained} databases. In this problem, a user wishes to retrieve a single message out of $M$ messages (of size $L$) without revealing any information about the identity of the message to individual databases. Each database stores $μML$ symbols, i.e., a $μ$ fraction of the entire library, where $\frac{1}{N} \leq μ\leq 1$. Our goal is to characterize the optimal tradeoff curve for the storage cost (captured by $μ$) and the normalized download cost ($D/L$). We show that the download cost can be reduced by employing a hybrid storage scheme that combines \emph{MDS coding} ideas with \emph{uncoded partial replication} ideas. When there is no coding, our scheme reduces to Attia-Kumar-Tandon storage scheme, which was initially introduced by Maddah-Ali-Niesen in the context of the caching problem, and when there is no uncoded partial replication, our scheme reduces to Banawan-Ulukus storage scheme; in general, our scheme outperforms both.