OpenRaiser/Intern-Atlas huggingface dataset:
The system of cloud oriented learning tools as an element of educational and scientific environment of high school
Venue: arXiv cs.OH 2018
Year: 2018

The aim of this research is to design and implementation of cloud based learning environment for separate division of the university. The analysis of existing approaches to the construction of cloud based learning environments, the formation of requirements cloud based learning tools, the selection on the basis of these requirements, cloud ICT training and pilot their use for building cloud based learning environment for separate division of the university with the use of open source software and resources its own IT infrastructure of the institution. Results of the study is planned to generalize to develop recommendations for the design of cloud based environment of high school.
DCert: Find the Leak in Your Pocket
Venue: arXiv cs.SE 2018
Year: 2018

Static data-flow analysis has proven its effectiveness in assessing security of applications. One major challenge it faces is scalability to large software. This issue is even exacerbated when additional limitations on computing and storage resources are imposed, as is the case for mobile devices. In such cases the analysis is performed on a conventional computer. This poses two problems. First, a man-in-the-middle attack can tamper with an analyzed application. So once on the mobile device, what guarantees that the actual version is not corrupt. Second, the analysis itself might be broken leading to an erroneous result. As a solution, we present DCert a tool for checking and certifying data-flow properties that consists of two components: a (heavy- weight) analyzer and a (lightweight) checker. The analyzer is deployed on a conventional computer. It verifies the conformance of a given application to a specified policy and generates a certificate attesting the validity of the analysis result. It suffices then for the checker, on a mobile device, to perform a linear pass in the application size to validate or refute the certificate as well as the policy. This allows us to separate the verification and the checking process while ensuring a trust relationship between them via the certificate. We describe DCert and report on experimental results obtained for real-world applications.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard and novel data sets demonstrates the superiority of our approach over other methods for learning RLR.
Hierarchical Clustering better than Average-Linkage
Venue: arXiv cs.DS 2018
Year: 2018

Hierarchical Clustering (HC) is a widely studied problem in exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage or complete-linkage. In this paper we focus on two objectives, introduced recently to give insight into the performance of average-linkage clustering: a similarity based HC objective proposed by [Moseley and Wang, 2017] and a dissimilarity based HC objective proposed by [Cohen-Addad et al., 2018]. In both cases, we present tight counterexamples showing that average-linkage cannot obtain better than 1/3 and 2/3 approximations respectively (in the worst-case), settling an open question raised in [Moseley and Wang, 2017]. This matches the approximation ratio of a random solution, raising a natural question: can we beat average-linkage for these objectives? We answer this in the affirmative, giving two new algorithms based on semidefinite programming with provably better guarantees.
Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Modern machine learning algorithms have been adopted in a range of signal-processing applications spanning computer vision, natural language processing, and artificial intelligence. Many relevant problems involve subspace-structured features, orthogonality constrained or low-rank constrained objective functions, or subspace distances. These mathematical characteristics are expressed naturally using the Grassmann manifold. Unfortunately, this fact is not yet explored in many traditional learning algorithms. In the last few years, there have been growing interests in studying Grassmann manifold to tackle new learning problems. Such attempts have been reassured by substantial performance improvements in both classic learning and learning using deep neural networks. We term the former as shallow and the latter deep Grassmannian learning. The aim of this paper is to introduce the emerging area of Grassmannian learning by surveying common mathematical problems and primary solution approaches, and overviewing various applications. We hope to inspire practitioners in different fields to adopt the powerful tool of Grassmannian learning in their research.
Performance-Aware Management of Cloud Resources: A Taxonomy and Future Directions
Venue: arXiv cs.DC 2018
Year: 2018

Dynamic nature of the cloud environment has made distributed resource management process a challenge for cloud service providers. The importance of maintaining the quality of service in accordance with customer expectations as well as the highly dynamic nature of cloud-hosted applications add new levels of complexity to the process. Advances to the big data learning approaches have shifted conventional static capacity planning solutions to complex performance-aware resource management methods. It is shown that the process of decision making for resource adjustment is closely related to the behaviour of the system including the utilization of resources and application components. Therefore, a continuous monitoring of system attributes and performance metrics provide the raw data for the analysis of problems affecting the performance of the application. Data analytic methods such as statistical and machine learning approaches offer the required concepts, models and tools to dig into the data, find general rules, patterns and characteristics that define the functionality of the system. Obtained knowledge form the data analysis process helps to find out about the changes in the workloads, faulty components or problems that can cause system performance to degrade. A timely reaction to performance degradations can avoid violations of the service level agreements by performing proper corrective actions including auto-scaling or other resource adjustment solutions. In this paper, we investigate the main requirements and limitations in cloud resource management including a study of the approaches in workload and anomaly analysis in the context of the performance management in the cloud. A taxonomy of the works on this problem is presented which identifies the main approaches in existing researches from data analysis side to resource adjustment techniques.
Partially perfect hash functions for intersecting families
Venue: arXiv cs.DM 2018
Year: 2018

Consider a large network with unknown number of nodes. Some of these nodes coordinate to perform tasks. The number of such coordination groups is also unknown. The only information about the network available is that any two coordinating groups share at least $t$ nodes. To complete a particular task in a day, at least $p$ nodes of the corresponding coordinating group must get different time slots out of the $r$ available slots per day. Is there a way of estimating the number of days required such that every coordinating group gets at least one day where it can complete the task? As it turns out, this problem is a special case of \textit{partially} perfect hash functions for intersecting families.
Approximations of Schatten Norms via Taylor Expansions
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we consider symmetric, positive semidefinite (SPSD) matrix $A$ and present two algorithms for computing the $p$-Schatten norm $\|A\|_p$. The first algorithm works for any SPSD matrix $A$. The second algorithm works for non-singular SPSD matrices and runs in time that depends on $κ= {λ_1(A)\over λ_n(A)}$, where $λ_i(A)$ is the $i$-th eigenvalue of $A$. Our methods are simple and easy to implement and can be extended to general matrices. Our algorithms improve, for a range of parameters, recent results of Musco, Netrapalli, Sidford, Ubaru and Woodruff (ITCS 2018) and match the running time of the methods by Han, Malioutov, Avron, and Shin (SISC 2017) while avoiding computations of coefficients of Chebyshev polynomials.
Low Delay Scheduling of Objects Over Multiple Wireless Paths
Venue: arXiv cs.NI 2018
Year: 2018

In this paper we consider the task of scheduling packet transmissions amongst multiple paths with uncertain, time-varying delay. We make the observation that the requirement is usually to transmit application layer objects (web pages, images, video frames etc) with low latency, and so it is the object delay rather than the per packet delay which is important. This has fundamental implications for multipath scheduler design. We introduce SOS (Stochastic Object-aware Scheduler), the first multipath scheduler that considers application layer object sizes and their relationship to link uncertainty. We demonstrate that SOS reduces the 95% percentile object delivery delay by 50-100% over production WiFi and LTE links compared to state-of-the art schedulers. We extend SOS to utilize FEC and to handle the scheduling multiple objects in parallel. We show that judicious priority scheduling of HTTP objects can lead to a 2-3x improvement in page load times
Overhead Detection: Beyond 8-bits and RGB
Venue: arXiv cs.CV 2018
Year: 2018

This study uses the challenging and publicly available SpaceNet dataset to establish a performance baseline for a state-of-the-art object detector in satellite imagery. Specifically, we examine how various features of the data affect building detection accuracy with respect to the Intersection over Union metric. We demonstrate that the performance of the R-FCN detection algorithm on imagery with a 1.5 meter ground sample distance and three spectral bands increases by over 32% by using 13-bit data, as opposed to 8-bit data at the same spatial and spectral resolution. We also establish accuracy trends with respect to building size and scene density. Finally, we propose and evaluate multiple methods for integrating additional spectral information into off-the-shelf deep learning architectures. Interestingly, our methods are robust to the choice of spectral bands and we note no significant performance improvement when adding additional bands.
Data augmentation using synthetic data for time series classification with deep residual networks
Venue: arXiv cs.CV 2018
Year: 2018

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN's accuracy on some datasets and significantly improve the deep model's accuracy when the method is used in an ensemble approach.
A practical Single Source Shortest Path algorithm for random directed graphs with arbitrary weight in expecting linear time
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, I present an algorithm called Raffica algorithm for Single-Source Shortest Path(SSSP). On random graph, this algorithm has linear time complexity(in expect). More precisely, the random graph uses configuration model, and the weights are distributed mostly positively. It is also linear for random grid graphs. Despite I made an assumption on the weights of the random graph, this algorithm is able to solve SSSP with arbitrary weights; when a negative cycle exists, this algorithm can find it out once traversed. The algorithm has a lot of appliances.
A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. small data), such as human pose and behavior estimation/tracking which could be highly personalized. In this paper, we present a semi-supervised data augmentation approach that can synthesize large scale labeled training datasets using 3D graphical engines based on a physically-valid low dimensional pose descriptor. To evaluate the performance of our synthesized datasets in training deep learning-based models, we generated a large synthetic human pose dataset, called ScanAva using 3D scans of only 7 individuals based on our proposed augmentation approach. A state-of-the-art human pose estimation deep learning model then was trained from scratch using our ScanAva dataset and could achieve the pose estimation accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain adaptation on the synthetic images, in which its pose estimation accuracy was comparable to the same model trained on large scale pose data from real humans such as MPII dataset and much higher than the model trained on other synthetic human dataset such as SURREAL.
Using Applied Behavior Analysis in Software to help Tutor Individuals with Autism Spectrum Disorder
Venue: arXiv cs.CY 2018
Year: 2018

There are currently many tutoring software systems which have been designed for neurotypical children. These systems cover academic topics such as reading and math, and are made available through various technological mediums. The majority of these systems were not designed for use by children with special needs, in particular those who are diagnosed with Autism Spectrum Disorder. Since the 1970's, studies have been conducted on the use of Applied Behavior Analysis to help autistic children learn [1]. This teaching methodology is proven to be very effective, with many patients having their diagnosis of autism dropped after a few years of treatment. With the advent of ubiquitous technologies such as mobile devices, it has become apparent that these devices could also be used to help tutor autistic children on academic subjects such as reading and math. Though the delivery of tutoring material must be made using Applied Behavior Analysis techniques, given that ABA therapy is currently the only form of treatment for Autism Spectrum Disorder endorsed by the US Surgeon General [2], which further makes the case for incorporating it into an academics tutoring system tailored for autistic children. In this paper, we present a mobile software system which can be utilized to tutor children who are diagnosed with Autism Spectrum Disorder in the subjects of reading and math. The software makes use of Applied Behavior Analysis techniques such as a Token Economy system, visual and audible reinforcers, and generalization. Furthermore, we explore how combining Applied Behavior Analysis and technology, could help extend the reach of tutoring systems to these children.
Against Marrying a Stranger Marital Matchmaking Technologies in Saudi Arabia
Venue: arXiv cs.HC 2018
Year: 2018

Websites and applications that match and connect individuals for romantic purposes are commonly used in the Western world. However, there have not been many previous investigations focusing on cultural factors that affect the adoption of similar technologies in religiously conservative non-Western cultures. In this study, we examine the socio-technical and cultural factors that influence the perceptions and use of matchmaking technologies in Saudi Arabia. We report the methods and findings of interviews with 18 Saudi nationals (nine males and nine females) with diverse demographics and backgrounds. We provide qualitatively generated insights into the major themes reported by our participants related to the common approaches to matchmaking, the current role of technology, and concerns regarding matchmaking technologies in this cultural con-text. We relate these themes to specific implications for designing marital matchmaking technologies in Saudi Arabia and we outline opportunities for future investigations.
Steiner Point Removal with distortion $O(\log k)$, using the Noisy-Voronoi algorithm
Venue: arXiv cs.DS 2018
Year: 2018

In the Steiner Point Removal (SPR) problem, we are given a weighted graph $G=(V,E)$ and a set of terminals $K\subset V$ of size $k$. The objective is to find a minor $M$ of $G$ with only the terminals as its vertex set, such that distances between the terminals will be preserved up to a small multiplicative distortion. Kamma, Krauthgamer and Nguyen [SICOMP2015] devised a ball-growing algorithm with exponential distributions to show that the distortion is at most $O(\log^5 k)$. Cheung [SODA2018] improved the analysis of the same algorithm, bounding the distortion by $O(\log^2 k)$. We devise a novel and simpler algorithm (called the Noisy Voronoi algorithm) which incurs distortion $O(\log k)$. This algorithm can be implemented in almost linear time ($O(|E|\log |V|)$).
Debunking Fake News One Feature at a Time
Venue: arXiv cs.CL 2018
Year: 2018

Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection. In this paper, we introduce a 2-stage ensemble model to solve the stance detection task. By using only hand-crafted features as input to a gradient boosting classifier, we are able to achieve a score of 9161.5 out of 11651.25 (78.63%) on the official Fake News Challenge (Stage 1) dataset. We identify the most useful features for detecting fake news and discuss how sampling techniques can be used to improve recall accuracy on a highly imbalanced dataset.
Cinematic Visualization of Multiresolution Data: Ytini for Adaptive Mesh Refinement in Houdini
Venue: arXiv cs.GR 2018
Year: 2018

We have entered the era of large multidimensional datasets represented by increasingly complex data structures. Current tools for scientific visualization are not optimized to efficiently and intuitively create cinematic production quality, time-evolving representations of numerical data for broad impact science communication via film, media, or journalism. To present such data in a cinematic environment, it is advantageous to develop methods that integrate these complex data structures into industry standard visual effects software packages, which provide a myriad of control features otherwise unavailable in traditional scientific visualization software. In this paper, we present the general methodology for the import and visualization of nested multiresolution datasets into commercially available visual effects software. We further provide a specific example of importing Adaptive Mesh Refinement data into the software Houdini. This paper builds on our previous work, which describes a method for using Houdini to visualize uniform Cartesian datasets. We summarize a tutorial available on the website www.ytini.com, which includes sample data downloads, Python code, and various other resources to simplify the process of importing and rendering multiresolution data.
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
Venue: arXiv cs.CV 2018
Year: 2018

Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability. In addition, class activation map (CAM), a visualization technique for CNNs, is applied for providing an interpretation of the results.
A Secure Multiple Elliptic Curves Digital Signature Algorithm for Blockchain
Venue: arXiv cs.CR 2018
Year: 2018

Most cryptocurrency systems or systems based on blockchain technology are currently using the elliptic curves digital signature algorithm (ECDSA) on the secp256k1 curve, which is susceptible to backdoors implemented by the curve creator (secp256k1). The paper proposes a multiple elliptic curves digi-tal signature algorithm (MECDSA), which allows not only for setting the number of elliptic curves according to practical security requirements, but also for editing the parameters of each elliptic curve. The performance analy-sis proves that the scheme is secure and efficient, and can avoid any back-doors implemented by curve creators. We suggest that the systems based on blockchain should operate in two elliptic curves considering the contradic-tion between security and efficiency.
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Venue: arXiv cs.CV 2018
Year: 2018

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.