OpenRaiser/Intern-Atlas huggingface dataset:
Deep Residual Learning for Image Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Predicting proximity with ambient mobile sensors for non-invasive health diagnostics
Venue: arXiv cs.CY 2015
Year: 2015

Modern smart phones are becoming helpful in the areas of Internet-Of-Things (IoT) and ambient health intelligence. By learning data from several mobile sensors, we detect nearness of the human body to a mobile device in a three-dimensional space with no physical contact with the device for non-invasive health diagnostics. We show that the human body generates wave patterns that interact with other naturally occurring ambient signals that could be measured by mobile sensors, such as, temperature, humidity, magnetic field, acceleration, gravity, and light. This interaction consequentially alters the patterns of the naturally occurring signals, and thus, exhibits characteristics that could be learned to predict the nearness of the human body to a mobile device, hence provide diagnostic information for medical practitioners. Our prediction technique achieved 88.75% accuracy and 88.3% specificity.
A Secure Database System using Homomorphic Encryption Schemes
Venue: arXiv cs.CR 2015
Year: 2015

Cloud computing emerges as an attractive solution that can be delegated to store and process confidential data. However, several security risks are encountered with such a system as the securely encrypted data should be decrypted before processing them. Therefore, the decrypted data is susceptible to reading and alterations. As a result, processing encrypted data has been a research subject since the publication of the RSA encryption scheme in 1978. In this paper we present a relational database system based on homomorphic encryption schemes to preserve the integrity and confidentiality of the data. Our system executes SQL queries over encrypted data. We tested our system with a recently developed homomorphic scheme that enables the execution of arithmetic operations on ciphertexts. We show that the proposed system performs accurate SQL operations, yet its performance discourages a practical implementation of this system.
ClusPath: A Temporal-driven Clustering to Infer Typical Evolution Paths
Venue: arXiv cs.DB 2015
Year: 2015

We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in an economical dataset) can be inferred from low-level descriptive features. Such high-level regularities in the evolution of entities are detected by combining spatial and temporal features into a spatio-temporal dissimilarity measure and using semi-supervised clustering techniques. The relations between the evolution phases are modeled using a graph structure, inferred simultaneously with the partition, by using a "slow changing world" assumption. The idea is to ensure a smooth passage for entities along their evolution paths, which catches the long-term trends in the dataset. Additionally, we also provide a method, based on an evolutionary algorithm, to tune the parameters of ClusPath to new, unseen datasets. This method assesses the fitness of a solution using four opposed quality measures and proposes a balanced compromise.
Evolution of Privacy Loss in Wikipedia
Venue: arXiv cs.SI 2015
Year: 2015

The cumulative effect of collective online participation has an important and adverse impact on individual privacy. As an online system evolves over time, new digital traces of individual behavior may uncover previously hidden statistical links between an individual's past actions and her private traits. To quantify this effect, we analyze the evolution of individual privacy loss by studying the edit history of Wikipedia over 13 years, including more than 117,523 different users performing 188,805,088 edits. We trace each Wikipedia's contributor using apparently harmless features, such as the number of edits performed on predefined broad categories in a given time period (e.g. Mathematics, Culture or Nature). We show that even at this unspecific level of behavior description, it is possible to use off-the-shelf machine learning algorithms to uncover usually undisclosed personal traits, such as gender, religion or education. We provide empirical evidence that the prediction accuracy for almost all private traits consistently improves over time. Surprisingly, the prediction performance for users who stopped editing after a given time still improves. The activities performed by new users seem to have contributed more to this effect than additional activities from existing (but still active) users. Insights from this work should help users, system designers, and policy makers understand and make long-term design choices in online content creation systems.
Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection
Venue: arXiv cs.CV 2015
Year: 2015

This paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but is also suitable for motion detection in video streams and its use for background subtraction has received little previous investigation. In this study we present a comprehensive evaluation of background subtraction, using the randomized DMD and compare the results with leading robust principal component analysis algorithms. The results are convincing and show the random DMD is an efficient and powerful approach for background modeling, allowing processing of high resolution videos in real-time. Supplementary materials include implementations of the algorithms in Python.
Limitations of sum of products of Read-Once Polynomials
Venue: arXiv cs.CC 2015
Year: 2015

We study limitations of polynomials computed by depth two circuits built over read-once polynomials (ROPs) and depth three syntactically multi-linear formulas. We prove an exponential lower bound for the size of the $ΣΠ^{[N^{1/30}]}$ arithmetic circuits built over syntactically multi-linear $ΣΠΣ^{[N^{8/15}]}$ arithmetic circuits computing a product of variable disjoint linear forms on $N$ variables. We extend the result to the case of $ΣΠ^{[N^{1/30}]}$ arithmetic circuits built over ROPs of unbounded depth, where the number of variables with $+$ gates as a parent in an proper sub formula is bounded by $N^{1/2+1/30}$. We show that the same lower bound holds for the permanent polynomial. Finally we obtain an exponential lower bound for the sum of ROPs computing a polynomial in ${\sf VP}$ defined by Raz and Yehudayoff. Our results demonstrate a class of formulas of unbounded depth with exponential size lower bound against the permanent and can be seen as an exponential improvement over the multilinear formula size lower bounds given by Raz for a sub-class of multi-linear and non-multi-linear formulas. Our proof techniques are built on the one developed by Raz and later extended by Kumar et. al.\cite{KMS13} and are based on non-trivial analysis of ROPs under random partitions. Further, our results exhibit strengths and limitations of the lower bound techniques introduced by Raz\cite{Raz04a}.
Improving BitTorrent's Peer Selection For Multimedia Content On-Demand Delivery
Venue: arXiv cs.NI 2015
Year: 2015

The great efficiency achieved by the BitTorrent protocol for the distribution of large amounts of data inspired its adoption to provide multimedia content on-demand delivery over the Internet. As it is not designed for this purpose, some adjustments have been proposed in order to meet the related QoS requirements like low startup delay and smooth playback continuity. Accordingly, this paper introduces a BitTorrent-like proposal named as Quota-Based Peer Selection (QBPS). This proposal is mainly based on the adaptation of the original peer-selection policy of the BitTorrent protocol. Its validation is achieved by means of simulations and competitive analysis. The final results show that QBPS outperforms other recent proposals of the literature. For instance, it achieves a throughput optimization of up to 48.0% in low-provision capacity scenarios where users are very interactive.
Efficient Deep Feature Learning and Extraction via StochasticNets
Venue: arXiv cs.LG 2015
Year: 2015

Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data. One area worth exploring in feature learning and extraction using deep neural networks is efficient neural connectivity formation for faster feature learning and extraction. Motivated by findings of stochastic synaptic connectivity formation in the brain as well as the brain's uncanny ability to efficiently represent information, we propose the efficient learning and extraction of features via StochasticNets, where sparsely-connected deep neural networks can be formed via stochastic connectivity between neurons. To evaluate the feasibility of such a deep neural network architecture for feature learning and extraction, we train deep convolutional StochasticNets to learn abstract features using the CIFAR-10 dataset, and extract the learned features from images to perform classification on the SVHN and STL-10 datasets. Experimental results show that features learned using deep convolutional StochasticNets, with fewer neural connections than conventional deep convolutional neural networks, can allow for better or comparable classification accuracy than conventional deep neural networks: relative test error decrease of ~4.5% for classification on the STL-10 dataset and ~1% for classification on the SVHN dataset. Furthermore, it was shown that the deep features extracted using deep convolutional StochasticNets can provide comparable classification accuracy even when only 10% of the training data is used for feature learning. Finally, it was also shown that significant gains in feature extraction speed can be achieved in embedded applications using StochasticNets. As such, StochasticNets allow for faster feature learning and extraction performance while facilitate for better or comparable accuracy performances.
Verifying Temporal Properties of Reactive Systems by Transformation
Venue: arXiv cs.LO 2015
Year: 2015

We show how program transformation techniques can be used for the verification of both safety and liveness properties of reactive systems. In particular, we show how the program transformation technique distillation can be used to transform reactive systems specified in a functional language into a simplified form that can subsequently be analysed to verify temporal properties of the systems. Example systems which are intended to model mutual exclusion are analysed using these techniques with respect to both safety (mutual exclusion) and liveness (non-starvation), with the errors they contain being correctly identified.
A Class of Linear Codes With Three Weights
Venue: arXiv cs.IT 2015
Year: 2015

Linear codes have been an interesting subject of study for many years. Recently, linear codes with few weights have been constructed and extensively studied. In this paper, for an odd prime p, a class of three-weight linear codes over Fp are constructed. The weight distributions of the linear codes are settled. These codes have applications in authentication codes, association schemes and data storage systems.
Correlating Features and Code by Dynamic and Semantic Analysis
Venue: arXiv cs.SE 2015
Year: 2015

One major problem in maintaining a software system is to understand how many functional features in the system and how these features are implemented. In this paper a novel approach for locating features in code by semantic and dynamic analysis is proposed. The method process consists of three steps: The first uses the execution traces as text corpus and the method calls involved in the traces as terms of document. The second ranks the method calls in order to filter out omnipresent methods by setting a threshold. And the third step treats feature-traces as first class entities and extracts identifiers from the rest method source code and a trace-by-identifier matrix is generated. Then a semantic analysis model-LDA is applied on the matrix to extract topics, which act as functional features. Through building several corresponding matrices, the relations between features and code can be obtained for comprehending the system functional intents. A case study is presented and the execution results of this approach can be used to guide future research.
Coding for classical-quantum channels with rate limited side information at the encoder: An information-spectrum approach
Venue: arXiv cs.IT 2015
Year: 2015

We study the hybrid classical-quantum version of the channel coding problem for the famous Gel'fand-Pinsker channel. In the classical setting for this channel the conditional distribution of the channel output given the channel input is a function of a random parameter called the channel state. We study this problem when a rate limited version of the channel state is available at the encoder for the classical-quantum Gel'fand-Pinsker channel. We establish the capacity region for this problem in the information-spectrum setting. The capacity region is quantified in terms of spectral-sup classical mutual information rate and spectral-inf quantum mutual information rate.
SharesSkew: An Algorithm to Handle Skew for Joins in MapReduce
Venue: arXiv cs.DB 2015
Year: 2015

In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identify join attributes values that appear very frequently, Heavy Hitters (HH). We distribute HH valued records to reducers avoiding skew by using an adaptation of the Shares~\cite{AfUl} algorithm to achieve minimum communication cost. Our algorithm is implemented for experimentation and is offered as open source software. Furthermore, we investigate a class of multiway joins for which a simpler variant of the algorithm can handle skew. We offer closed forms for computing the parameters of the algorithm for chain and symmetric joins.
Active Distance-Based Clustering using K-medoids
Venue: arXiv cs.LG 2015
Year: 2015

k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upper-bound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to cluster objects and to choose some points actively from each bunch of data and acquire the distances between these prominent points from oracle. Experimental results show that the proposed method using only a small subset of the distances can find proper clustering on many real-world and synthetic datasets.
Tracking Idea Flows between Social Groups
Venue: arXiv cs.SI 2015
Year: 2015

In many applications, ideas that are described by a set of words often flow between different groups. To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups. In particular, an improved Bayesian conditional cointegration based on dynamic time warping is employed to learn links between words in different groups. A tensor-based technique is developed to cluster these linked words into different clusters (ideas) and track the flow of ideas. The main feature of the tensor representation is that we introduce two additional dimensions to represent both time and lead-lag relationships. Experiments on both synthetic and real datasets show that our method is more effective than methods based on traditional clustering techniques and achieves better accuracy. A case study was conducted to demonstrate the usefulness of our method in helping users understand the flow of ideas between different user groups on social media
A Person Re-Identification System For Mobile Devices
Venue: arXiv cs.CV 2015
Year: 2015

Person re-identification is a critical security task for recognizing a person across spatially disjoint sensors. Previous work can be computationally intensive and is mainly based on low-level cues extracted from RGB data and implemented on a PC for a fixed sensor network (such as traditional CCTV). We present a practical and efficient framework for mobile devices (such as smart phones and robots) where high-level semantic soft biometrics are extracted from RGB and depth data. By combining these cues, our approach attempts to provide robustness to noise, illumination, and minor variations in clothing. This mobile approach may be particularly useful for the identification of persons in areas ill-served by fixed sensors or for tasks where the sensor position and direction need to dynamically adapt to a target. Results on the BIWI dataset are preliminary but encouraging. Further evaluation and demonstration of the system will be available on our website.
Parameterized Algorithms on Perfect Graphs for deletion to $(r,\ell)$-graphs
Venue: arXiv cs.DS 2015
Year: 2015

For fixed integers $r,\ell \geq 0$, a graph $G$ is called an {\em $(r,\ell)$-graph} if the vertex set $V(G)$ can be partitioned into $r$ independent sets and $\ell$ cliques. The class of $(r, \ell)$ graphs generalizes $r$-colourable graphs (when $\ell =0)$ and hence not surprisingly, determining whether a given graph is an $(r, \ell)$-graph is \NP-hard even when $r \geq 3$ or $\ell \geq 3$ in general graphs. When $r$ and $\ell$ are part of the input, then the recognition problem is NP-hard even if the input graph is a perfect graph (where the {\sc Chromatic Number} problem is solvable in polynomial time). It is also known to be fixed-parameter tractable (FPT) on perfect graphs when parameterized by $r$ and $\ell$. I.e. there is an $f(r+\ell) \cdot n^{\Oh(1)}$ algorithm on perfect graphs on $n$ vertices where $f$ is some (exponential) function of $r$ and $\ell$. In this paper, we consider the parameterized complexity of the following problem, which we call {\sc Vertex Partization}. Given a perfect graph $G$ and positive integers $r,\ell,k$ decide whether there exists a set $S\subseteq V(G)$ of size at most $k$ such that the deletion of $S$ from $G$ results in an $(r,\ell)$-graph. We obtain the following results: \begin{enumerate} \item {\sc Vertex Partization} on perfect graphs is FPT when parameterized by $k+r+\ell$. \item The problem does not admit any polynomial sized kernel when parameterized by $k+r+\ell$. In other words, in polynomial time, the input graph can not be compressed to an equivalent instance of size polynomial in $k+r+\ell$. In fact, our result holds even when $k=0$. \item When $r,\ell$ are universal constants, then {\sc Vertex Partization} on perfect graphs, parameterized by $k$, has a polynomial sized kernel. \end{enumerate}
Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions
Venue: arXiv cs.RO 2015
Year: 2015

We present a robotic system that watches a human using a Kinect v2 RGB-D sensor, detects what he forgot to do while performing an activity, and if necessary reminds the person using a laser pointer to point out the related object. Our simple setup can be easily deployed on any assistive robot. Our approach is based on a learning algorithm trained in a purely unsupervised setting, which does not require any human annotations. This makes our approach scalable and applicable to variant scenarios. Our model learns the action/object co-occurrence and action temporal relations in the activity, and uses the learned rich relationships to infer the forgotten action and the related object. We show that our approach not only improves the unsupervised action segmentation and action cluster assignment performance, but also effectively detects the forgotten actions on a challenging human activity RGB-D video dataset. In robotic experiments, we show that our robot is able to remind people of forgotten actions successfully.
The Globalization of Academic Entrepreneurship? The Recent Growth (2009-2014) in University Patenting Decomposed
Venue: arXiv cs.DL 2015
Year: 2015

The contribution of academia to US patents has become increasingly global. Following a pause, with a relatively flat rate, from 1998 to 2008, the long-term trend of university patenting rising as a share of all patenting has resumed, driven by the internationalization of academic entrepreneurship and the persistence of US university technology transfer. We disaggregate this recent growth in university patenting at the US Patent and Trademark Organization (USPTO) in terms of nations and patent classes. Foreign patenting in the US has almost doubled during the period 2009-2014, mainly due to patenting by universities in Taiwan, Korea, China, and Japan. These nations compete with the US in terms of patent portfolios, whereas most European countries--with the exception of the UK--have more specific portfolios, mainly in the bio-medical fields. In the case of China, Tsinghua University holds 63% of the university patents in USPTO, followed by King Fahd University with 55.2% of the national portfolio.
Modeling the power consumption of a Wifibot and studying the role of communication cost in operation time
Venue: arXiv cs.RO 2015
Year: 2015

Mobile robots are becoming part of our every day living at home, work or entertainment. Due to their limited power capabilities, the development of new energy consumption models can lead to energy conservation and energy efficient designs. In this paper, we carry out a number of experiments and we focus on the motors power consumption of a specific robot called Wifibot. Based on the experimentation results, we build models for different speed and acceleration levels. We compare the motors power consumption to other robot running modes. We, also, create a simple robot network scenario and we investigate whether forwarding data through a closer node could lead to longer operation times. We assess the effect energy capacity, traveling distance and data rate on the operation time.