OpenRaiser/Intern-Atlas huggingface dataset:
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes' performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information - in our case the swimming style of an athlete - and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture.
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
Venue: arXiv cs.CV 2018
Year: 2018

In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow learning methods with predefined features, as well as standard DCNNs. Supervised layerwise trained DCNNs are found to outperform standard shallow learning models such as Support Vector Machines as well as regular DCNNs of similar architecture by achieving error rate of 9.67% thereby setting a new benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%, representing an improvement of nearly 10%.
Representation Learning for Resource Usage Prediction
Venue: arXiv cs.DC 2018
Year: 2018

Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem. Most current systems rely on heuristics and overly simplistic assumptions about the workloads and system statistics. These heuristics are typically a one-size-fits-all solution so as to be applicable in a wide range of applications and systems environments. With this paper, we present our ongoing work of integrating systems telemetry ranging from standard resource usage statistics to kernel and library calls of applications into a machine learning model. Intuitively, such a ML model approximates, at any point in time, the state of a system and allows us to solve tasks such as resource usage prediction and anomaly detection. To achieve this goal, we leverage readily-available information that does not require any changes to the applications run on the system. We train recurrent neural networks to learn a model of the system under consideration. As a proof of concept, we train models specifically to predict future resource usage of running applications.
Hierarchical Aggregation Approach for Distributed clustering of spatial datasets
Venue: arXiv cs.DB 2018
Year: 2018

In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique. This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters. This approach is characterised by the fact that the local clusters are represented in a simple and efficient way. And The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in both response time and memory allocation. We evaluated the approach with different datasets and compared it to well-known clustering techniques. The experimental results show that our approach is very promising and outperforms all those algorithms
AN-aided Secure Transmission in Multi-user MIMO SWIPT Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem is shown to be a single-variable optimization that can be solved by one-dimensional (1-D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. Furthermore, tightness of the relaxation for the 1-D search method is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D search method but with much lower complexity.
Measuring Spark on AWS: A Case Study on Mining Scientific Publications with Annotation Query
Venue: arXiv cs.DC 2018
Year: 2018

Annotation Query (AQ) is a program that provides the ability to query many different types of NLP annotations on a text, as well as the original content and structure of the text. The query results may provide new annotations, or they may select subsets of the content and annotations for deeper processing. Like GATE's Mimir, AQ is based on region algebras. Our AQ is implemented to run on a Spark cluster. In this paper we look at how AQ's runtimes are affected by the size of the collection, the number of nodes in the cluster, the type of node, and the characteristics of the queries. Cluster size, of course, makes a large difference in performance so long as skew can be avoided. We find that there is minimal difference in performance when persisting annotations serialized to local SSD drives as opposed to deserialized into local memory. We also find that if the number of nodes is kept constant, then AWS' storage-optimized instance performs the best. But if we factor in total cost, the compute-optimized nodes provides the best performance relative to cost.
Signal Processing for MIMO-NOMA: Present and Future Challenges
Venue: arXiv cs.IT 2018
Year: 2018

Non-orthogonal multiple access (NOMA), as the newest member of the multiple access family, is envisioned to be an essential component of 5G mobile networks. The combination of NOMA and multi-antenna multi-input multi-output (MIMO) technologies exhibits a significant potential in improving spectral efficiency and providing better wireless services to more users. In this article, we introduce the basic concepts of MIMO-NOMA and summarize the key technical problems in MIMO-NOMA systems. Then, we explore the problem formulation, beamforming, user clustering, and power allocation of single/multi-cluster MIMO-NOMA in the literature along with their limitations. Furthermore, we point out an important issue of the stability of successive interference cancellation (SIC) that arises using achievable rates as performance metrics in practical NOMA/MIMO-NOMA systems. Finally, we discuss incorporating NOMA with massive/millimeter wave MIMO, and identify the main challenges and possible future research directions in this area.
Debloating Software through Piece-Wise Compilation and Loading
Venue: arXiv cs.CR 2018
Year: 2018

Programs are bloated. Our study shows that only 5% of libc is used on average across the Ubuntu Desktop environment (2016 programs); the heaviest user, vlc media player, only needed 18%. In this paper: (1) We present a debloating framework built on a compiler toolchain that can successfully debloat programs (shared/static libraries and executables). Our solution can successfully compile and load most libraries on Ubuntu Desktop 16.04. (2) We demonstrate the elimination of over 79% of code from coreutils and 86% of code from SPEC CPU 2006 benchmark programs without affecting functionality. We show that even complex programs such as Firefox and curl can be debloated without a need to recompile. (3) We demonstrate the security impact of debloating by eliminating over 71% of reusable code gadgets from the coreutils suite and show that unused code that contains real-world vulnerabilities can also be successfully eliminated without adverse effects on the program. (4) We incur a low load time overhead.
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
Venue: arXiv cs.CV 2018
Year: 2018

In the image processing pipeline of almost every digital camera there is a part dedicated to computational color constancy i.e. to removing the influence of illumination on the colors of the image scene. Some of the best known illumination estimation methods are the so called statistics-based methods. They are less accurate than the learning-based illumination estimation methods, but they are faster and simpler to implement in embedded systems, which is one of the reasons for their widespread usage. Although in the relevant literature it often appears as if they require no training, this is not true because they have parameter values that need to be fine-tuned in order to be more accurate. In this paper it is first shown that the accuracy of statistics-based methods reported in most papers was not obtained by means of the necessary cross-validation, but by using the whole benchmark datasets for both training and testing. After that the corrected results are given for the best known benchmark datasets. Finally, the so called green stability assumption is proposed that can be used to fine-tune the values of the parameters of the statistics-based methods by using only non-calibrated images without known ground-truth illumination. The obtained accuracy is practically the same as when using calibrated training images, but the whole process is much faster. The experimental results are presented and discussed. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process. Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challenging. In this paper, we propose VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs. We explore the design space for massive amount of Gaussian variable sampling tasks in BNNs. Specifically, we introduce two high performance Gaussian (pseudo) random number generators: the RAM-based Linear Feedback Gaussian Random Number Generator (RLF-GRNG), which is inspired by the properties of binomial distribution and linear feedback logics; and the Bayesian Neural Network-oriented Wallace Gaussian Random Number Generator. To achieve high scalability and efficient memory access, we propose a deep pipelined accelerator architecture with fast execution and good hardware utilization. Experimental results demonstrate that the proposed VIBNN implementations on an FPGA can achieve throughput of 321,543.4 Images/s and energy efficiency upto 52,694.8 Images/J while maintaining similar accuracy as its software counterpart.
Proportional Representation in Approval-based Committee Voting and Beyond
Venue: arXiv cs.GT 2018
Year: 2018

Proportional representation (PR) is one of the central principles in voting. Elegant rules with compelling PR axiomatic properties have the potential to be adopted for several important collective decision making settings. I survey some recent ideas and results on axioms and rules for proportional representation in committee voting.
Typicality Matching for Pairs of Correlated Graphs
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, web graphs, etc. An achievable region for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks also continue to increase. This will pose a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on FPGA/ASIC have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher-student networks, compact network design and hardware accelerators. Finally, we will introduce and discuss a few possible future directions.
The negative effects of citing with a national orientation in terms of recognition: national and international citations in natural-sciences papers from Germany, the Netherlands, and the UK
Venue: arXiv cs.DL 2018
Year: 2018

Nations can be distinguished in terms of whether domestic or international research is cited. We analyzed the research output in natural sciences of three leading European research economies (Germany, the Netherlands, and the UK) and ask where their researchers look for the knowledge that underpins their most highly-cited papers. Is one internationally oriented or is citation limited to national resources? Do the citation patterns reflect a growing differentiation between the domestic and international research enterprise? To evaluate change over time, we include natural-sciences papers published in the countries from three publication years: 2004, 2009, and 2014. The results show that articles co-authored by researchers from Germany or the Netherlands are less likely to be among the globally most highly-cited articles if they also cite "domestic" research (i.e. research authored by authors from the same country). To put this another way, less well-cited research is more likely to stand on domestic shoulders and research that becomes more highly-cited is more likely to stand on international shoulders. A possible reason for the results is that researchers "over-cite" the papers from their own country - lacking the focus on quality in citing. However, these differences between domestic and international shoulders are not visible for the UK.
TaintAssembly: Taint-Based Information Flow Control Tracking for WebAssembly
Venue: arXiv cs.CR 2018
Year: 2018

WebAssembly (wasm) has recently emerged as a promisingly portable, size-efficient, fast, and safe binary format for the web. As WebAssembly can interact freely with JavaScript libraries, this gives rise to a potential for undesirable behavior to occur. It is therefore important to be able to detect when this might happen. A way to do this is through taint tracking, where we follow the flow of information by applying taint labels to data. In this paper, we describe TaintAssembly, a taint tracking engine for interpreted WebAssembly, that we have created by modifying the V8 JavaScript engine. We implement basic taint tracking functionality, taint in linear memory, and a probabilistic variant of taint. We then benchmark our TaintAssembly engine by incorporating it into a Chromium build and running it on custom test scripts and various real world WebAssembly applications. We find that our modifications to the V8 engine do not incur significant overhead with respect to vanilla V8's interpreted WebAssembly, making TaintAssembly suitable for development and debugging.
Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks
Venue: arXiv cs.SE 2018
Year: 2018

Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present an empirical study that compares how software engineers and machine-learning algorithms perform and reuse tasks. The empirical study involves the synthesis of the control structure of an autonomous streetlight application. Our approach consists of four steps. First, we solved the problem using machine learning to determine specific performance and reuse tasks. Second, we asked software engineers with different domain knowledge levels to provide a solution to the same tasks. Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety. Finally, we analyzed the results to understand which tasks are better performed by either humans or algorithms so that they can work together more effectively. Such an understanding and the resulting human-in-the-loop approaches, which take into account the strengths and weaknesses of humans and machine-learning algorithms, are fundamental not only to provide a basis for cooperative work in support of software engineering, but also, in other areas.
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
Venue: arXiv cs.CV 2018
Year: 2018

We introduce End2You -- the Imperial College London toolkit for multimodal profiling by end-to-end deep learning. End2You is an open-source toolkit implemented in Python and is based on Tensorflow. It provides capabilities to train and evaluate models in an end-to-end manner, i.e., using raw input. It supports input from raw audio, visual, physiological or other types of information or combination of those, and the output can be of an arbitrary representation, for either classification or regression tasks. To our knowledge, this is the first toolkit that provides generic end-to-end learning for profiling capabilities in either unimodal or multimodal cases. To test our toolkit, we utilise the RECOLA database as was used in the AVEC 2016 challenge. Experimental results indicate that End2You can provide comparable results to state-of-the-art methods despite no need of expert-alike feature representations, but self-learning these from the data "end to end".
A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications
Venue: arXiv cs.DL 2018
Year: 2018

Creating scientific publications is a complex process, typically composed of a number of different activities, such as designing the experiments, data preparation, programming software and writing and editing the manuscript. The information about the contributions of individual authors of a paper is important in the context of assessing authors' scientific achievements. Some publications in biomedical disciplines contain a description of authors' roles in the form of a short section written in natural language, typically entitled "Authors' contributions". In this paper, we present an analysis of roles commonly appearing in the content of these sections, and propose an algorithm for automatic extraction of authors' roles from natural language text in scientific publications. During the first part of the study, we used clustering techniques, as well as Open Information Extraction (OpenIE), to semi-automatically discover the most popular roles within a corpus of 2,000 contributions sections obtained from PubMed Central resources. The roles discovered by our approach include: experimenting (1,743 instances, 17% of the entire role set within the corpus), analysis (1,343, 16%), study design (1,132, 13%), interpretation (879, 10%), conceptualization (865, 10%), paper reading (823, 10%), paper writing (724, 8%), paper review (501, 6%), paper drafting (351, 4%), coordination (319, 4%), data collection (76, 1%), paper review (41, 0.5%) and literature review (41, 0.5%). Discovered roles were then used to automatically build a training set for the supervised role extractor, based on Naive Bayes algorithm. According to the evaluation we performed, the proposed role extraction algorithm is able to extract the roles from the text with precision 0.71, recall 0.49 and F1 0.58.
The Effect Race in Fine-Grained Concurrency
Venue: arXiv cs.PL 2018
Year: 2018

Most existed work require knowledge about the effect of program instructions (or statements) to analyze and verify algorithms. In this paper, by revealing some findings on executions of object programs, we define two basic concepts -- effect equivalence relation and effect race relation. Further, we show three effect theorems about the race and histories. The core result is that the effect race relation is the accurate relation to capture the internal steps, of which precedence orders are the reason to cause chaotic histories. In addition, the concept -- linearization points -- widely used in the object verification, is defined formally as the typical effect race relation. These results provide a clear basis for analyzing intricate fine-grained executions. We conduct a lot of experiments on real object algorithms to show the accuracy and efficiency of these definitions in practice. A simple quantitative analysis method for these algorithms is also proposed.