OpenRaiser/Intern-Atlas huggingface dataset:
Compiler-Level Matrix Multiplication Optimization for Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

An important linear algebra routine, GEneral Matrix Multiplication (GEMM), is a fundamental operator in deep learning. Compilers need to translate these routines into low-level code optimized for specific hardware. Compiler-level optimization of GEMM has significant performance impact on training and executing deep learning models. However, most deep learning frameworks rely on hardware-specific operator libraries in which GEMM optimization has been mostly achieved by manual tuning, which restricts the performance on different target hardware. In this paper, we propose two novel algorithms for GEMM optimization based on the TVM framework, a lightweight Greedy Best First Search (G-BFS) method based on heuristic search, and a Neighborhood Actor Advantage Critic (N-A2C) method based on reinforcement learning. Experimental results show significant performance improvement of the proposed methods, in both the optimality of the solution and the cost of search in terms of time and fraction of the search space explored. Specifically, the proposed methods achieve 24% and 40% savings in GEMM computation time over state-of-the-art XGBoost and RNN methods, respectively, while exploring only 0.1% of the search space. The proposed approaches have potential to be applied to other operator-level optimizations.
A characterization of graph properties testable for general planar graphs with one-sided error (It is all about forbidden subgraphs)
Venue: arXiv cs.DS 2019
Year: 2019

The problem of characterizing testable graph properties (properties that can be tested with a number of queries independent of the input size) is a fundamental problem in the area of property testing. While there has been some extensive prior research characterizing testable graph properties in the dense graphs model and we have good understanding of the bounded degree graphs model, no similar characterization has been known for general graphs, with no degree bounds. In this paper we take on this major challenge and consider the problem of characterizing all testable graph properties in general planar graphs. We consider the model in which a general planar graph can be accessed by the random neighbor oracle that allows access to any given vertex and access to a random neighbor of a given vertex. We show that, informally, a graph property $P$ is testable with one-sided error for general planar graphs if and only if testing $P$ can be reduced to testing for a finite family of finite forbidden subgraphs. While our presentation focuses on planar graphs, our approach extends easily to general minor-free graphs. Our analysis of the necessary condition relies on a recent construction of canonical testers in the random neighbor oracle model that is applied here to the one-sided error model for testing in planar graphs. The sufficient condition in the characterization reduces the problem to the task of testing $H$-freeness in planar graphs, and is the main and most challenging technical contribution of the paper: we show that for planar graphs (with arbitrary degrees), the property of being $H$-free is testable with one-sided error for every finite graph $H$, in the random neighbor oracle model.
Computation Offloading for IoT in C-RAN: Optimization and Deep Learning
Venue: arXiv cs.NI 2019
Year: 2019

We consider computation offloading for Internet-of-things (IoT) applications in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN). Due to the limited battery life and computational capability in the IoT devices (IoTDs), the computational tasks of the IoTDs are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks, and find that the problem is non-convex. To obtain a feasible solution, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, we leverage the alternating optimization framework for joint optimization on the residual variables at the BBU, where the baseband combiner is obtained in a closed-form, the resource allocation sub-problem is solved through successive inner convexification, and the number of quantization bits is obtained by a line-search method. As a low-complexity approach, we deploy a supervised deep learning method, which is trained with the solutions to our optimization algorithm. Numerical results validate the effectiveness of the proposed algorithm and the deep learning method.
LitGen: Genetic Literature Recommendation Guided by Human Explanations
Venue: arXiv cs.CL 2019
Year: 2019

As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical interpretation is the manual curation of evidence in the genetic literature by highly trained biocurators. What makes curation particularly time-consuming is that the curator needs to identify papers that study variant pathogenicity using different types of approaches and evidences---e.g. biochemical assays or case control analysis. In collaboration with the Clinical Genomic Resource (ClinGen)---the flagship NIH program for clinical curation---we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity. LitGen uses semi-supervised deep learning to predict the type of evidence provided by each paper. It is trained on papers annotated by ClinGen curators and systematically evaluated on new test data collected by ClinGen. LitGen further leverages rich human explanations and unlabeled data to gain 7.9%-12.6% relative performance improvement over models learned only on the annotated papers. It is a useful framework to improve clinical variant curation.
A Systematic Literature Review of Test Breakage Prevention and Repair Techniques
Venue: arXiv cs.SE 2019
Year: 2019

Context: When an application evolves, some of the developed test cases break. Discarding broken test cases causes a significant waste of effort and leads to test suites that are less effective and have lower coverage. Test repair approaches evolve test suites along with applications by repairing the broken test cases. Objective: Numerous studies are published on test repair approaches every year. It is important to summarise and consolidate the existing knowledge in the area to provide directions to researchers and practitioners. This research work provides a systematic literature review in the area of test case repair and breakage prevention, aiming to guide researchers and practitioners in the field of software testing. Method: We followed the standard protocol for conducting a systematic literature review. First, research goals were defined using the Goal Question Metric (GQM). Then we formulate research questions corresponding to each goal. Finally, metrics are extracted from the included papers. Based on the defined selection criteria a final set of 41 primary studies are included for analysis. Results: The selection process resulted in 5 journal papers, and 36 conference papers. We present a taxonomy that lists the causes of test case breakages extracted from the literature. We found that only four proposed test repair tools are publicly available. Most studies evaluated their approaches on open-source case studies. Conclusion: There is significant room for future research on test repair techniques. Despite the positive trend of evaluating approaches on large scale open-source studies, there is a clear lack of results from studies done in a real industrial context. Few tools are publicly available which lowers the potential of adaption by industry practitioners.
Landscaping Systematic Mapping Studies in Software Engineering: A Tertiary Study
Venue: arXiv cs.SE 2019
Year: 2019

Context: A number of Systematic Mapping Studies (SMSs) that cover Software Engineering (SE) are reported in literature. Tertiary studies synthesize the secondary studies to provide a holistic view of an area. Objectives: We synthesize SMSs in SE to provide insights into existing SE areas and to investigate the trends and quality of SMSs. Methodology: We use Systematic Literature Review protocol to analyze and map the SMSs in SE, till August 2017, to SE Body of Knowledge (SWEBOK). Results: We analyze 210 SMSs and results show that: (1) Software design and construction are most active areas in SE; (2) Some areas lack SMSs, including mathematical foundations, software configuration management, and SE tools; (3) The quality of SMSs is improving with time; (4) SMSs in journals have higher quality than SMSs in conferences and are cited more often; (5) Low quality in SMSs can be attributed to a lack of quality assessment in SMSs and not reporting information about the primary studies. Conclusion: There is a potential for more SMSs in some SE areas. A number of SMSs do not provide the required information for an SMS, which leads to a low quality score.
How to use Software Heritage for archiving and referencing your source code: guidelines and walkthrough
Venue: arXiv cs.DL 2019
Year: 2019

Software source code is an essential research output, and many research communities strongly encourage making the source code of the artefact available by archiving it in publicly-accessible long-term archives.Software Heritage is a non profit, long term universal archive specifically designed for software source code, and able to store not only a software artifact, but also its full development history. It provides the ideal place to preserve research software artifacts, and offers powerful mechanisms to enhance research articles with precise references to relevant fragments of your source code.Using Software Heritage for your research software artifacts is straightforward and involves three simple steps. This document details each of these three steps, providing guidelines for making the most out of Software Heritage for your research.
Towards S-NAMO: Socially-aware Navigation Among Movable Obstacles
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we present an in-depth analysis of Navigation Among Movable Obstacles (NAMO) literature, notably highlighting that social acceptability remains an unadressed problem in this robotics navigation domain. The objectives of a Socially-Aware NAMO are defined and a first set of algorithmic propositions is built upon existing work. We developed a simulator allowing to test our propositions of social movability evaluation for obstacle selection, and social placement of objects with a semantic map layer. Preliminary pushing tests are done with a Pepper robot, the standard platform for the Robocup@home Social Standard Platform League, in the context of our participation (LyonTech Team).
Deep Text Mining of Instagram Data Without Strong Supervision
Venue: arXiv cs.CL 2019
Year: 2019

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.
Security analysis of two lightweight certificateless signature schemes
Venue: arXiv cs.CR 2019
Year: 2019

Certificateless cryptography can be considered as an intermediate solution to overcome the issues in traditional public key infrastructure (PKI) and identity-based public key cryptography (ID-PKC). There exist a vast number of certificateless signature (CLS) schemes in the literature; however, most of them are not efficient enough to be utilized in limited resources environments such as Internet of things (IoT) or Healthcare Wireless Sensor Networks (HWSN). Recently, two lightweight CLS schemes have been proposed by Karati et al. and Kumar et al. to be employed in IoT and HWSNs, respectively. While both schemes are claimed to be existentially unforgeable, in this paper, we show that both these signatures can easily be forged. More specifically, it is shown that 1) in Karati et al.'s scheme, a type 1 adversary, considered in certificateless cryptography, can generate a valid partial private key corresponding to any user of its choice and as a consequence, it can forge any users' signature on any message of its choice, and 2) in Kumar et al.'s scheme, both types of adversaries which are considered in certificateless cryptography are able to forge any signer's signature on an arbitrary message.
A high-level characterisation and generalisation of communication-avoiding programming techniques
Venue: arXiv cs.DC 2019
Year: 2019

Today's hardware's explosion of concurrency plus the explosion of data we build upon in both machine learning and scientific simulations have multifaceted impact on how we write our codes. They have changed our notion of performance and, hence, of what a good code is: Good code has, first of all, to be able to exploit the unprecedented levels of parallelism. To do so, it has to manage to move the compute data into the compute facilities on time. As communication and memory bandwidth cannot keep pace with the growth in compute capabilities and as latency increases---at least relative to what the hardware could do---communication-avoiding techniques gain importance. We characterise and classify the field of communication-avoiding algorithms. A review of some examples of communication-avoiding programming by means of our new terminology shows that we are well-advised to broaden our notion of "communication-avoiding" and to look beyond numerical linear algebra. An abstraction, generalisation and weakening of the term enriches our toolset of how to tackle the data movement challenges. Through this, we eventually gain access to a richer set of tools that we can use to deliver proper code for current and upcoming hardware generations.
Multi-Person 3D Human Pose Estimation from Monocular Images
Venue: arXiv cs.CV 2019
Year: 2019

Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose HG-RCNN, a Mask-RCNN based network that also leverages the benefits of the Hourglass architecture for multi-person 3D Human Pose Estimation. A two-staged approach is presented that first estimates the 2D keypoints in every Region of Interest (RoI) and then lifts the estimated keypoints to 3D. Finally, the estimated 3D poses are placed in camera-coordinates using weak-perspective projection assumption and joint optimization of focal length and root translations. The result is a simple and modular network for multi-person 3D human pose estimation that does not require any multi-person 3D pose dataset. Despite its simple formulation, HG-RCNN achieves the state-of-the-art results on MuPoTS-3D while also approximating the 3D pose in the camera-coordinate system.
Data Interpretation Support in Rescue Operations: Application for French Firefighters
Venue: arXiv cs.AI 2019
Year: 2019

This work aims at developing a system that supports French firefighters in data interpretation during rescue operations. An application ontology is proposed based on existing crisis management ones and operational expertise collection. After that, a knowledge-based system will be developed and integrated in firefighters' environment. Our first studies are shown in this paper.
Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing
Venue: arXiv cs.DC 2019
Year: 2019

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.
Private Aggregation from Fewer Anonymous Messages
Venue: arXiv cs.CR 2019
Year: 2019

Consider the setup where $n$ parties are each given a number $x_i \in \mathbb{F}_q$ and the goal is to compute the sum $\sum_i x_i$ in a secure fashion and with as little communication as possible. We study this problem in the anonymized model of Ishai et al. (FOCS 2006) where each party may broadcast anonymous messages on an insecure channel. We present a new analysis of the one-round "split and mix" protocol of Ishai et al. In order to achieve the same security parameter, our analysis reduces the required number of messages by a $Θ(\log n)$ multiplicative factor. We complement our positive result with lower bounds showing that the dependence of the number of messages on the domain size, the number of parties, and the security parameter is essentially tight. Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, δ\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $δ= \frac{1}{\mathrm{poly}(n)}$, incurs only a constant error and requires only a constant number of messages per party. Previously, such a protocol was known only for $Ω(\log n)$ messages per party.
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches
Venue: arXiv cs.IT 2019
Year: 2019

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users' collaboration, and thus, avoids many complicated issues such as users' privacy and security, selfishness, etc. In order to optimize users' quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived, making this method simple and flexible to the system context. The second one is based on the deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.
Energy Efficiency Maximization Via Joint Sub-Carrier Assignment and Power Control for OFDMA Full Duplex Networks
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system.
P2FAAS: Toward Privacy-Preserving Fuzzing as a Service
Venue: arXiv cs.CR 2019
Year: 2019

Global corporations (e.g., Google and Microsoft) have recently introduced a new model of cloud services, fuzzing-as-a-service (FaaS). Despite effectively alleviating the cost of fuzzing, the model comes with privacy concerns. For example, the end user has to trust both cloud and service providers who have access to the application to be fuzzed. Such concerns are due to the platform is under the control of its provider and the application and the fuzzer are highly coupled. In this paper, we propose P2FaaS, a new ecosystem that preserves end user's privacy while providing FaaS in the cloud. The key idea of P2FaaS is to utilize Intel SGX for preventing cloud and service providers from learning information about the application. Our preliminary evaluation shows that P2FaaS imposes 45% runtime overhead to the fuzzing compared to the baseline. In addition, P2FaaS demonstrates that, with recently introduced hardware, Intel SGX Card, the fuzzing service can be scaled up to multiple servers without native SGX support.