OpenRaiser/Intern-Atlas huggingface dataset:
Breaking Fitness Records without Moving: Reverse Engineering and Spoofing Fitbit
Venue: arXiv cs.CR 2017
Year: 2017

Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors' cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.
Efficient Online Timed Pattern Matching by Automata-Based Skipping
Venue: arXiv cs.FL 2017
Year: 2017

The timed pattern matching problem is an actively studied topic because of its relevance in monitoring of real-time systems. There one is given a log $w$ and a specification $\mathcal{A}$ (given by a timed word and a timed automaton in this paper), and one wishes to return the set of intervals for which the log $w$, when restricted to the interval, satisfies the specification $\mathcal{A}$. In our previous work we presented an efficient timed pattern matching algorithm: it adopts a skipping mechanism inspired by the classic Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem of online timed pattern matching, towards embedded applications where it is vital to process a vast amount of incoming data in a timely manner. Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching algorithm---a recent variant of the BM algorithm---and extend it to timed pattern matching. Our experiments indicate the efficiency of our FJS-type algorithm in online and offline timed pattern matching.
Semantic Web Prefetching Using Semantic Relatedness between Web pages
Venue: arXiv cs.IR 2017
Year: 2017

Internet as become the way of life in the fast growing digital life.Even with the increase in the internet speed, higher latency time is still a challenge. To reduce latency, caching and pre fetching techniques can be used. However, caching fails for dynamic websites which keeps on changing rapidly. Another technique is web prefetching, which prefetches the web pages that the user is likely to request for in the future. Semantic web prefetching makes use of keywords and descriptive texts like anchor text, titles, text surrounding anchor text of the present web pages for predicting users future requests. Semantic information is embedded within the web pages during their designing for the purpose of reflecting the relationship between the web pages. The client can fetch this information from the server. However, this technique involves load on web designers for adding external tags and on server for providing this information along with the desired page, which is not desirable. This paper is an effort to find the semantic relation between web pages using the keywords provided by the user and the anchor texts of the hyperlinks on the present web page.It provides algorithms for sequential and similar semantic relations. These algorithms will be implemented on the client side which will not cause overhead on designers and load on server for semantic information.
CDCL-inspired Word-level Learning for Bit-vector Constraint Solving
Venue: arXiv cs.LO 2017
Year: 2017

The theory of quantifier-free bit-vectors (QF_BV) is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bit-vector problem to a Boolean problem, leveraging the powerful SAT solving techniques and their conflict-driven clause learning (CDCL) mechanisms. Yet, this bit-level approach loses the structure of the initial bit-vector problem. We propose a conflict-driven, word-level, combinable constraints learning for the theory of quantifier-free bit-vectors. This work paves the way to truly word-level decision procedures for bit-vectors, taking full advantage of word-level propagations recently designed in CP and SMT communities.
Accelerations for Graph Isomorphism
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we present two main results. First, by only one conjecture (Conjecture 2.9) for recognizing a vertex symmetric graph, which is the hardest task for our problem, we construct an algorithm for finding an isomorphism between two graphs in polynomial time $ O(n^{3}) $. Second, without that conjecture, we prove the algorithm to be of quasi-polynomial time $ O(n^{1.5\log n}) $. The conjectures in this paper are correct for all graphs of size no larger than $ 5 $ and all graphs we have encountered. At least the conjecture for determining if a graph is vertex symmetric is quite true intuitively. We are not able to prove them by hand, so we have planned to find possible counterexamples by a computer. We also introduce new concepts like collapse pattern and collapse tomography, which play important roles in our algorithms.
Personalized advice for enhancing well-being using automated impulse response analysis --- AIRA
Venue: arXiv cs.CY 2017
Year: 2017

The attention for personalized mental health care is thriving. Research data specific to the individual, such as time series sensor data or data from intensive longitudinal studies, is relevant from a research perspective, as analyses on these data can reveal the heterogeneity among the participants and provide more precise and individualized results than with group-based methods. However, using this data for self-management and to help the individual to improve his or her mental health has proven to be challenging. The present work describes a novel approach to automatically generate personalized advice for the improvement of the well-being of individuals by using time series data from intensive longitudinal studies: Automated Impulse Response Analysis (AIRA). AIRA analyzes vector autoregression models of well-being by generating impulse response functions. These impulse response functions are used in simulations to determine which variables in the model have the largest influence on the other variables and thus on the well-being of the participant. The effects found can be used to support self-management. We demonstrate the practical usefulness of AIRA by performing analysis on longitudinal self-reported data about psychological variables. To evaluate its effectiveness and efficacy, we ran its algorithms on two data sets ($N=4$ and $N=5$), and discuss the results. Furthermore, we compare AIRA's output to the results of a previously published study and show that the results are comparable. By automating Impulse Response Function Analysis, AIRA fulfills the need for accurate individualized models of health outcomes at a low resource cost with the potential for upscaling.
On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a closer match to empirically obtained results of the OMP algorithm.
Sample-based high-dimensional convexity testing
Venue: arXiv cs.CC 2017
Year: 2017

In the problem of high-dimensional convexity testing, there is an unknown set $S \subseteq \mathbb{R}^n$ which is promised to be either convex or $\varepsilon$-far from every convex body with respect to the standard multivariate normal distribution $\mathcal{N}(0, 1)^n$. The job of a testing algorithm is then to distinguish between these two cases while making as few inspections of the set $S$ as possible. In this work we consider sample-based testing algorithms, in which the testing algorithm only has access to labeled samples $(\boldsymbol{x},S(\boldsymbol{x}))$ where each $\boldsymbol{x}$ is independently drawn from $\mathcal{N}(0, 1)^n$. We give nearly matching sample complexity upper and lower bounds for both one-sided and two-sided convexity testing algorithms in this framework. For constant $\varepsilon$, our results show that the sample complexity of one-sided convexity testing is $2^{\tildeΘ(n)}$ samples, while for two-sided convexity testing it is $2^{\tildeΘ(\sqrt{n})}$.
CASCONet: A Conference dataset
Venue: arXiv cs.DL 2017
Year: 2017

Knowledge mobilization and translation describes the process of moving knowledge from research and development (R&D) labs into environments where it can be put to use. There is increasing interest in understanding mechanisms for knowledge mobilization, specifically with respect to academia and industry collaborations. These mechanisms include funding programs, research centers, and conferences, among others. In this paper, we focus on one specific knowledge mobilization mechanism, the CASCON conference, the annual conference of the IBM Centre for Advanced Studies (CAS). The mandate of CAS when it was established in 1990 was to foster collaborative work between the IBM Toronto Lab and university researchers from around the world. The first CAS Conference (CASCON) was held one year after CAS was formed in 1991. The focus of this annual conference was, and continues to be, bringing together academic researchers, industry practitioners, and technology users in a forum for sharing ideas and showcasing the results of the CAS collaborative work. We collected data about CASCON for the past 25 years including information about papers, technology showcase demos, workshops, and keynote presentations. The resulting dataset, called "CASCONet" is available for analysis and integration with related datasets. Using CASCONet, we analyzed interactions between R&D topics and changes in those topics over time. Results of our analysis show how the domain of knowledge being mobilized through CAS had evolved over time. By making CASCONet available to others, we hope that the data can be used in additional ways to understand knowledge mobilization and translation in this unique context.
Toward Inverse Control of Physics-Based Sound Synthesis
Venue: arXiv cs.SD 2017
Year: 2017

Long Short-Term Memory networks (LSTMs) can be trained to realize inverse control of physics-based sound synthesizers. Physics-based sound synthesizers simulate the laws of physics to produce output sound according to input gesture signals. When a user's gestures are measured in real time, she or he can use them to control physics-based sound synthesizers, thereby creating simulated virtual instruments. An intriguing question is how to program a computer to learn to play such physics-based models. This work demonstrates that LSTMs can be trained to accomplish this inverse control task with four physics-based sound synthesizers.
Music Signal Processing Using Vector Product Neural Networks
Venue: arXiv cs.SD 2017
Year: 2017

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
Venue: arXiv cs.CL 2017
Year: 2017

We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10% gains over baselines.
Online Convolutional Dictionary Learning
Venue: arXiv cs.LG 2017
Year: 2017

While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learning from large training sets is the memory requirements, which grow at least linearly with the size of the training set since all existing methods are batch algorithms. The work reported here addresses this limitation by extending online dictionary learning ideas to the convolutional context.
Multi-scale Multi-band DenseNets for Audio Source Separation
Venue: arXiv cs.SD 2017
Year: 2017

This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet), which has shown excellent results on image classification tasks. To deal with the specific problem of audio source separation, an up-sampling layer, block skip connection and band-dedicated dense blocks are incorporated on top of DenseNet. The proposed approach takes advantage of long contextual information and outperforms state-of-the-art results on SiSEC 2016 competition by a large margin in terms of signal-to-distortion ratio. Moreover, the proposed architecture requires significantly fewer parameters and considerably less training time compared with other methods.
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.
SocialStegDisc: Application of steganography in social networks to create a file system
Venue: arXiv cs.CR 2017
Year: 2017

The concept named SocialStegDisc was introduced as an application of the original idea of StegHash method. This new kind of mass-storage was characterized by unlimited space. The design also attempted to improve the operation of StegHash by trade-off between memory requirements and computation time. Applying the mechanism of linked list provided the set of operations on files: creation, reading, deletion and modification. Features, limitations and opportunities were discussed.
Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used to enhance the recognition performance of text-dependent speaker identification in the shouted environment. Our speech database consists of two databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. Our results show that SPHMMs significantly enhance speaker identification performance compared to Second-Order Circular Hidden Markov Models (CHMM2s) in the shouted environment. Using our collected database, speaker identification performance in this environment is 68% and 75% based on CHMM2s and SPHMMs respectively. Using the SUSAS database, speaker identification performance in the same environment is 71% and 79% based on CHMM2s and SPHMMs respectively.
The Modified Optimal Velocity Model: Stability Analyses and Design Guidelines
Venue: arXiv cs.SY 2017
Year: 2017

Reaction delays are important in determining the qualitative dynamical properties of a platoon of vehicles traveling on a straight road. In this paper, we investigate the impact of delayed feedback on the dynamics of the Modified Optimal Velocity Model (MOVM). Specifically, we analyze the MOVM in three regimes -- no delay, small delay and arbitrary delay. In the absence of reaction delays, we show that the MOVM is locally stable. For small delays, we then derive a sufficient condition for the MOVM to be locally stable. Next, for an arbitrary delay, we derive the necessary and sufficient condition for the local stability of the MOVM. We show that the traffic flow transits from the locally stable to the locally unstable regime via a Hopf bifurcation. We also derive the necessary and sufficient condition for non-oscillatory convergence and characterize the rate of convergence of the MOVM. These conditions help ensure smooth traffic flow, good ride quality and quick equilibration to the uniform flow. Further, since a Hopf bifurcation results in the emergence of limit cycles, we provide an analytical framework to characterize the type of the Hopf bifurcation and the asymptotic orbital stability of the resulting non-linear oscillations. Finally, we corroborate our analyses using stability charts, bifurcation diagrams, numerical computations and simulations conducted using MATLAB.
Talking Condition Recognition in Stressful and Emotional Talking Environments Based on CSPHMM2s
Venue: arXiv cs.SD 2017
Year: 2017

This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while the emotional talking environment uses Emotional Prosody Speech and Transcripts (EPST) database. The achieved results of this work using Mel-Frequency Cepstral Coefficients (MFCCs) demonstrate that CSPHMM2s outperform each of Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s), and Suprasegmental Hidden Markov Models (SPHMMs) in enhancing talking condition recognition in the stressful and emotional talking environments. The results also show that the performance of talking condition recognition in stressful talking environments leads that in emotional talking environments by 3.67% based on CSPHMM2s. Our results obtained in subjective evaluation by human judges fall within 2.14% and 3.08% of those obtained, respectively, in stressful and emotional talking environments based on CSPHMM2s.
Stronger Baselines for Trustable Results in Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over "vanilla" NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.
On Sampling Edges Almost Uniformly
Venue: arXiv cs.CC 2017
Year: 2017

We consider the problem of sampling an edge almost uniformly from an unknown graph, $G = (V, E)$. Access to the graph is provided via queries of the following types: (1) uniform vertex queries, (2) degree queries, and (3) neighbor queries. We describe an algorithm that returns a random edge $e \in E$ using $\tilde{O}(n / \sqrt{\varepsilon m})$ queries in expectation, where $n = |V|$ is the number of vertices, and $m = |E|$ is the number of edges, such that each edge $e$ is sampled with probability $(1 \pm \varepsilon)/m$. We prove that our algorithm is optimal in the sense that any algorithm that samples an edge from an almost-uniform distribution must perform $Ω(n / \sqrt{m})$ queries.
Speaker Identification Investigation and Analysis in Unbiased and Biased Emotional Talking Environments
Venue: arXiv cs.SD 2017
Year: 2017

This work aims at investigating and analyzing speaker identification in each unbiased and biased emotional talking environments based on a classifier called Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is unbiased towards any emotion, while the second talking environment is biased towards different emotions. Each of these talking environments is made up of six distinct emotions. These emotions are neutral, angry, sad, happy, disgust and fear. The investigation and analysis of this work show that speaker identification performance in the biased talking environment is superior to that in the unbiased talking environment. The obtained results in this work are close to those achieved in subjective assessment by human judges.
New Lower Bounds on the Generalized Hamming Weights of AG Codes
Venue: arXiv cs.IT 2017
Year: 2017

A sharp upper bound for the maximum integer not belonging to an ideal of a numerical semigroup is given and the ideals attaining this bound are characterized. Then the result is used, through the so-called Feng-Rao numbers, to bound the generalized Hamming weights of algebraic-geometry codes. This is further developed for Hermitian codes and the codes on one of the Garcia-Stichtenoth towers, as well as for some more general families.