OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Classification of Functional Gait Disorders
Venue: arXiv cs.CV 2017
Year: 2017

This article proposes a comprehensive investigation of the automatic classification of functional gait disorders based solely on ground reaction force (GRF) measurements. The aim of the study is twofold: (1) to investigate the suitability of stateof-the-art GRF parameterization techniques (representations) for the discrimination of functional gait disorders; and (2) to provide a first performance baseline for the automated classification of functional gait disorders for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with gait disorders (GDs) and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the "hip", "knee", "ankle", and "calcaneus". Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA)-based representations and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis (LDA). Based on this analysis, two classification experiments are pursued: (1) distinction between healthy and impaired gait (N vs. GD) and (2) multi-class classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step towards the automated classification of gait disorders and a first performance baseline for future developments in this direction.
Detecting Hate Speech in Social Media
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.
HERO: Heterogeneous Embedded Research Platform for Exploring RISC-V Manycore Accelerators on FPGA
Venue: arXiv cs.AR 2017
Year: 2017

Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host processor with programmable manycore accelerators (PMCAs) to combine general-purpose computing with domain-specific, efficient processing capabilities. While leading companies successfully advance their HESoC products, research lags behind due to the challenges of building a prototyping platform that unites an industry-standard host processor with an open research PMCA architecture. In this work we introduce HERO, an FPGA-based research platform that combines a PMCA composed of clusters of RISC-V cores, implemented as soft cores on an FPGA fabric, with a hard ARM Cortex-A multicore host processor. The PMCA architecture mapped on the FPGA is silicon-proven, scalable, configurable, and fully modifiable. HERO includes a complete software stack that consists of a heterogeneous cross-compilation toolchain with support for OpenMP accelerator programming, a Linux driver, and runtime libraries for both host and PMCA. HERO is designed to facilitate rapid exploration on all software and hardware layers: run-time behavior can be accurately analyzed by tracing events, and modifications can be validated through fully automated hard ware and software builds and executed tests. We demonstrate the usefulness of HERO by means of case studies from our research.
A General Technique for Non-blocking Trees
Venue: arXiv cs.DC 2017
Year: 2017

We describe a general technique for obtaining provably correct, non-blocking implementations of a large class of tree data structures where pointers are directed from parents to children. Updates are permitted to modify any contiguous portion of the tree atomically. Our non-blocking algorithms make use of the LLX, SCX and VLX primitives, which are multi-word generalizations of the standard LL, SC and VL primitives and have been implemented from single-word CAS. To illustrate our technique, we describe how it can be used in a fairly straightforward way to obtain a non-blocking implementation of a chromatic tree, which is a relaxed variant of a red-black tree. The height of the tree at any time is $O(c+ \log n)$, where $n$ is the number of keys and $c$ is the number of updates in progress. We provide an experimental performance analysis which demonstrates that our Java implementation of a chromatic tree rivals, and often significantly outperforms, other leading concurrent dictionaries.
Game-Theoretic Electric Vehicle Charging Management Resilient to Non-Ideal User Behavior
Venue: arXiv cs.GT 2017
Year: 2017

In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents' actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect $ε$-Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators.
Estimation of Individual Micro Data from Aggregated Open Data
Venue: arXiv cs.DB 2017
Year: 2017

In this paper, we propose a method of estimating individual micro data from aggregated open data based on semi-supervised learning and conditional probability. Firstly, the proposed method collects aggregated open data and support data, which are related to the individual micro data to be estimated. Then, we perform the locality sensitive hashing (LSH) algorithm to find a subset of the support data that is similar to the aggregated open data and then classify them by using the Ensemble classification model, which is learned by semi-supervised learning. Finally, we use conditional probability to estimate the individual micro data by finding the most suitable record for the probability distribution of the individual micro data among the classification results. To evaluate the performance of the proposed method, we estimated the individual building data where the fire occurred using the aggregated fire open data. According to the experimental results, the micro data estimation performance of the proposed method is 59.41% on average in terms of accuracy.
On Fan-Crossing Graphs
Venue: arXiv cs.DM 2017
Year: 2017

A fan is a set of edges with a single common endpoint. A graph is fan-crossing if it admits a drawing in the plane so that each edge is crossed by edges of a fan. It is fan-planar if, in addition, the common endpoint is on the same side of the crossed edge. A graph is adjacency-crossing if it admits a drawing so that crossing edges are adjacent. Then it excludes independent crossings which are crossings by edges with no common endpoint. Adjacency-crossing allows triangle-crossings in which an edge crosses the edges of a triangle, which is excluded at fan-crossing graphs. We show that every adjacency-crossing graph is fan-crossing. Thus triangle-crossings can be avoided. On the other hand, there are fan-crossing graphs that are not fan-planar, whereas for every fan-crossing graph there is a fan-planar graph on the same set of vertices and with the same number of edges. Hence, fan-crossing and fan-planar graphs are different, but they do not differ in their density with at most 5n - 10 edges for graphs of size n.
An anthropological account of the Vim text editor: features and tweaks after 10 years of usage
Venue: arXiv cs.HC 2017
Year: 2017

The Vim text editor is very rich in capabilities and thus complex. This article is a description of Vim and a set of considerations about its usage and design. It results from more than ten years of experience in using Vim for writing and editing various types of documents, e.g. Python, C++, JavaScript, ChucK programs; \LaTeX, Markdown, HTML, RDF, Make and other markup files; % TTM binary files. It is commonplace, in the Vim users and developers communities, to say that it takes about ten years to master (or start mastering) this text editor, and I find that other experienced users have a different view of Vim and that they use a different set of features. Therefore, this document exposes my understandings in order to confront my usage with that of other Vim users. Another goal is to make available a reference document with which new users can grasp a sound overview by reading it and the discussions that it might generate. Also, it should be useful for users of any degree of experience, including me, as a compendium of commands, namespaces and tweaks. Upon feedback, and maturing of my Vim usage, this document might be enhanced and expanded.
Effect of NBTI/PBTI Aging and Process Variations on Write Failures in MOSFET and FinFET Flip-Flops
Venue: arXiv cs.OH 2017
Year: 2017

The assessment of noise margins and the related probability of failure in digital cells has growingly become essential, as nano-scale CMOS and FinFET technologies are confronting reliability issues caused by aging mechanisms, such as NBTI, and variability in process parameters. The influence of such phenomena is particularly associated to the Write Noise Margins (WNM) in memory elements, since a wrong stored logic value can result in an upset of the system state. In this work, we calculated and compared the effect of process variations and NBTI aging over the years on the actual WNM of various CMOS and FinFET based flip-flop cells. The massive transistor-level Monte Carlo simulations produced both nominal (i.e. mean) values and associated standard deviations of the WNM of the chosen flip-flops. This allowed calculating the consequent write failure probability as a function of an input voltage shift on the flip-flop cells, and assessing a comparison for robustness among different circuit topologies and technologies.
Cache-Aided Private Information Retrieval with Partially Known Uncoded Prefetching: Fundamental Limits
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of private information retrieval (PIR) from $N$ non-colluding and replicated databases, when the user is equipped with a cache that holds an uncoded fraction $r$ of the symbols from each of the $K$ stored messages in the databases. This model operates in a two-phase scheme, namely, the prefetching phase where the user acquires side information and the retrieval phase where the user privately downloads the desired message. In the prefetching phase, the user receives $\frac{r}{N}$ uncoded fraction of each message from the $n$th database. This side information is known only to the $n$th database and unknown to the remaining databases, i.e., the user possesses \emph{partially known} side information. We investigate the optimal normalized download cost $D^*(r)$ in the retrieval phase as a function of $K$, $N$, $r$. We develop lower and upper bounds for the optimal download cost. The bounds match in general for the cases of very low caching ratio ($r \leq \frac{1}{N^{K-1}}$) and very high caching ratio ($r \geq \frac{K-2}{N^2-3N+KN}$). We fully characterize the optimal download cost caching ratio tradeoff for $K=3$. For general $K$, $N$, and $r$, we show that the largest gap between the achievability and the converse bounds is $\frac{5}{32}$.
Detection and classification of masses in mammographic images in a multi-kernel approach
Venue: arXiv cs.CV 2017
Year: 2017

According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high, due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. Herein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases. Classification was performed by using SVM and ELM networks with modified kernels, in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using the best method of the state-of-the-art. As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.
Algebraic lattice codes for linear fading channels
Venue: arXiv cs.IT 2017
Year: 2017

In the decades following Shannon's work, the quest to design codes for the additive white Gaussian noise (AWGN) channel led to the development of a rich theory, revealing a number of beautiful connections between information theory and geometry of numbers. One of the most striking examples is the connection between classical lattice sphere packing and the capacity of the AWGN channel. The main result states that any family of lattice codes with linearly growing Hermite invariant achieves a constant gap to capacity. These classical results and many more can be found in the comprehensive book by Conway and Sloane [5].....
Deep Regression Forests for Age Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is heterogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with heterogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation; Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free and fast-converging update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them.
Disruptive events in high-density cellular networks
Venue: arXiv cs.NI 2017
Year: 2017

Stochastic geometry models are used to study wireless networks, particularly cellular phone networks, but most of the research focuses on the typical user, often ignoring atypical events, which can be highly disruptive and of interest to network operators. We examine atypical events when a unexpected large proportion of users are disconnected or connected by proposing a hybrid approach based on ray launching simulation and point process theory. This work is motivated by recent results using large deviations theory applied to the signal-to-interference ratio. This theory provides a tool for the stochastic analysis of atypical but disruptive events, particularly when the density of transmitters is high. For a section of a European city, we introduce a new stochastic model of a single network cell that uses ray launching data generated with the open source RaLaNS package, giving deterministic path loss values. We collect statistics on the fraction of (dis)connected users in the uplink, and observe that the probability of an unexpected large proportion of disconnected users decreases exponentially when the transmitter density increases. This observation implies that denser networks become more stable in the sense that the probability of the fraction of (dis)connected users deviating from its mean, is exponentially small. We also empirically obtain and illustrate the density of users for network configurations in the disruptive event, which highlights the fact that such bottleneck behaviour not only stems from too many users at the cell boundary, but also from the near-far effect of many users in the immediate vicinity of the base station. We discuss the implications of these findings and outline possible future research directions.
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Venue: arXiv cs.CV 2017
Year: 2017

Gastric cancer is the second leading cause of cancer-related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre-trained or further manual annotation. We eliminate the boundary error of patch-based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions.
On Counting Perfect Matchings in General Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Counting perfect matchings has played a central role in the theory of counting problems. The permanent, corresponding to bipartite graphs, was shown to be #P-complete to compute exactly by Valiant (1979), and a fully polynomial randomized approximation scheme (FPRAS) was presented by Jerrum, Sinclair, and Vigoda (2004) using a Markov chain Monte Carlo (MCMC) approach. However, it has remained an open question whether there exists an FPRAS for counting perfect matchings in general graphs. In fact, it was unresolved whether the same Markov chain defined by JSV is rapidly mixing in general. In this paper, we show that it is not. We prove torpid mixing for any weighting scheme on hole patterns in the JSV chain. As a first step toward overcoming this obstacle, we introduce a new algorithm for counting matchings based on the Gallai-Edmonds decomposition of a graph, and give an FPRAS for counting matchings in graphs that are sufficiently close to bipartite. In particular, we obtain a fixed-parameter tractable algorithm for counting matchings in general graphs, parameterized by the greatest "order" of a factor-critical subgraph.
Ethical Questions in NLP Research: The (Mis)-Use of Forensic Linguistics
Venue: arXiv cs.CL 2017
Year: 2017

Ideas from forensic linguistics are now being used frequently in Natural Language Processing (NLP), using machine learning techniques. While the role of forensic linguistics was more benign earlier, it is now being used for purposes which are questionable. Certain methods from forensic linguistics are employed, without considering their scientific limitations and ethical concerns. While we take the specific case of forensic linguistics as an example of such trends in NLP and machine learning, the issue is a larger one and present in many other scientific and data-driven domains. We suggest that such trends indicate that some of the applied sciences are exceeding their legal and scientific briefs. We highlight how carelessly implemented practices are serving to short-circuit the due processes of law as well breach ethical codes.
An Analytical Perspective to Traffic Engineering in Anonymous Communication Systems
Venue: arXiv cs.CR 2017
Year: 2017

Anonymous communication systems (ACS) offer privacy and anonymity through the Internet. They are mostly free tools and are popular among users all over the world. In the recent years, anonymity applications faced many problems regarding traffic engineering methods. Even though they ensure privacy under some conditions, their anonymity will be endangered by high performance processing units. To address these issues, this study is devoted to investigating traffic-engineering methods in anonymous communication systems, and proposes an analytical view of the current issues in ACS privacy and anonymity. Our study also indicates new types of solutions for these current issues with ACS.
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
Venue: arXiv cs.LG 2017
Year: 2017

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).