OpenRaiser/Intern-Atlas huggingface dataset:
Action Classification and Highlighting in Videos
Venue: arXiv cs.CV 2017
Year: 2017

Inspired by recent advances in neural machine translation, that jointly align and translate using encoder-decoder networks equipped with attention, we propose an attentionbased LSTM model for human activity recognition. Our model jointly learns to classify actions and highlight frames associated with the action, by attending to salient visual information through a jointly learned soft-attention networks. We explore attention informed by various forms of visual semantic features, including those encoding actions, objects and scenes. We qualitatively show that soft-attention can learn to effectively attend to important objects and scene information correlated with specific human actions. Further, we show that, quantitatively, our attention-based LSTM outperforms the vanilla LSTM and CNN models used by stateof-the-art methods. On a large-scale youtube video dataset, ActivityNet, our model outperforms competing methods in action classification.
DolphinAtack: Inaudible Voice Commands
Venue: arXiv cs.CR 2017
Year: 2017

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems(VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though hidden, are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.
Virtual Reality: Blessings and Risk Assessment
Venue: arXiv cs.HC 2017
Year: 2017

Objectives: This paper presents an up-to-date overview of research performed in the Virtual Reality (VR) environment ranging from definitions, its presence in the various fields, and existing market players and their projects in the VR technology. Further an attempt is made to gain an insight on the psychological mechanism underlying experience in using VR device. Methods: Our literature survey is based on the research articles, analysis of the projects of various companies and their findings for different areas of interest. Findings: In our literature survey we observed that the recent advances in virtual reality enabling technologies have led to variety of virtual devices that facilitate people to interact with the digital world. In fact in the past two decades researchers have tried to integrate reality and VR in the form of intuitive computer interface. Improvements: This has led to variety of potential benefits of VR in many applications such as News, Healthcare, Entertainment, Tourism, Military and Defence etc. However despite the extensive research efforts in creating virtual system environments it is yet to become apparent in normal daily life.
Social Network Analysis Using Coordination Games
Venue: arXiv cs.GT 2017
Year: 2017

Communities typically capture homophily as people of the same community share many common features. This paper is motivated by the problem of community detection in social networks, as it can help improve our understanding of the network topology and the spread of information. Given the selfish nature of humans to align with like-minded people, we employ game theoretic models and algorithms to detect communities in this paper. Specifically, we employ coordination games to represent interactions between individuals in a social network. We represent the problem of community detection as a graph coordination game. We provide a novel and scalable two phased approach to compute an accurate overlapping community structure in the given network. We evaluate our algorithm against the best existing methods for community detection and show that our algorithm improves significantly on benchmark networks (real and synthetic) with respect to standard Normalised Mutual Information measure.
Generating Video Descriptions with Topic Guidance
Venue: arXiv cs.CV 2017
Year: 2017

Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.
Human and Machine Judgements for Russian Semantic Relatedness
Venue: arXiv cs.CL 2017
Year: 2017

Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgments, and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (word_i, word_j, relatedness_ij). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organize a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings
Venue: arXiv cs.IR 2017
Year: 2017

Cloud computing is emerging as a revolutionary computing paradigm which pro-vides a flexible and economic strategy for data management and resource sharing. Security and privacy become major concerns in the cloud scenario, for which Searchable Encryption (SE) technology is proposed to support efficient retrieval of encrypted data. However, the absence of lightweight ranked search is still a typical shortage in existing SE schemes. In this paper, we propose a Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings (LRSE) scheme that supports top-k retrieval in the known background model. For the first time, we formulate the privacy issue and design goals for lightweight ranked search in SE. We employ word embedding trained on the whole English Wikipedia using word2vec to replace the general dictionary, afterwards we make use of Dual Embedding Space Model (DESM) to substitute traditional Vector Space Model (VSM), based on which we achieve the goal of lightweight ranked search with higher precision and solve the challenging prob-lems caused by updating the traditional dictionary in existing SE schemes. In LRSE, we employ an improved secure kNN scheme to guarantee sufficient pri-vacy protection. Our security analysis shows that LRSE satisfies our formulated privacy requirements and extensive experiments performed on real-world datasets demonstrate that LRSE indeed accords with our proposed design goals.
Blockchain Based Intelligent Vehicle Data sharing Framework
Venue: arXiv cs.CR 2017
Year: 2017

The Intelligent vehicle (IV) is experiencing revolutionary growth in research and industry, but it still suffers from many security vulnerabilities. Traditional security methods are incapable to provide secure IV data sharing. The major issues in IV data sharing are trust, data accuracy and reliability of data sharing data in the communication channel. Blockchain technology works for the crypto currency, Bit-coin, which is recently used to build trust and reliability in peer-to-peer networks having similar topologies as IV Data sharing. In this paper, we have proposed Intelligent Vehicle data sharing we are proposing a trust environment based Intelligent Vehicle framework. In proposed framework, we have use the blockchain technology as backbone of the IV data-sharing environment. The blockchain technology is provide the trust environment between the vehicles with the based on proof of driving.
Drawing bobbin lace graphs, or, Fundamental cycles for a subclass of periodic graphs
Venue: arXiv cs.CG 2017
Year: 2017

In this paper, we study a class of graph drawings that arise from bobbin lace patterns. The drawings are periodic and require a combinatorial embedding with specific properties which we outline and demonstrate can be verified in linear time. In addition, a lace graph drawing has a topological requirement: it contains a set of non-contractible directed cycles which must be homotopic to $(1,0)$, that is, when drawn on a torus, each cycle wraps once around the minor meridian axis and zero times around the major longitude axis. We provide an algorithm for finding the two fundamental cycles of a canonical rectangular schema in a supergraph that enforces this topological constraint. The polygonal schema is then used to produce a straight-line drawing of the lace graph inside a rectangular frame. We argue that such a polygonal schema always exists for combinatorial embeddings satisfying the conditions of bobbin lace patterns, and that we can therefore create a pattern, given a graph with a fixed combinatorial embedding of genus one.
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

We present a simple method to improve neural translation of a low-resource language pair using parallel data from a related, also low-resource, language pair. The method is based on the transfer method of Zoph et al., but whereas their method ignores any source vocabulary overlap, ours exploits it. First, we split words using Byte Pair Encoding (BPE) to increase vocabulary overlap. Then, we train a model on the first language pair and transfer its parameters, including its source word embeddings, to another model and continue training on the second language pair. Our experiments show that transfer learning helps word-based translation only slightly, but when used on top of a much stronger BPE baseline, it yields larger improvements of up to 4.3 BLEU.
Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we investigate the joint optimization of base station (BS) location, its density, and transmit power allocation to minimize the overall network operational cost required to meet an underlying coverage constraint at each user equipment (UE), which is randomly deployed following the binomial point process (BPP). As this joint optimization problem is nonconvex and combinatorial in nature, we propose a non-trivial solution methodology that effectively decouples it into three individual optimization problems. Firstly, by using the distance distribution of the farthest UE from the BS, we present novel insights on optimal BS location in an optimal sectoring type for a given number of BSs. After that we provide a tight approximation for the optimal transmit power allocation to each BS. Lastly, using the latter two results, the optimal number of BSs that minimize the operational cost is obtained. Also, we have investigated both circular and square field deployments. Numerical results validate the analysis and provide practical insights on optimal BS deployment. We observe that the proposed joint optimization framework, that solves the coverage probability versus operational cost tradeoff, can yield a significant reduction of about $65\%$ in the operational cost as compared to the benchmark fixed allocation scheme.
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in images can be difficult because of the wide variety of features, patterns, colors, values, shapes in real data. In this paper, we use deep learning, a machine learning technique that learns its own features, to discover new knowledge from retinal fundus images. Using models trained on data from 284,335 patients, and validated on two independent datasets of 12,026 and 999 patients, we predict cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as such as age (within 3.26 years), gender (0.97 AUC), smoking status (0.71 AUC), HbA1c (within 1.39%), systolic blood pressure (within 11.23mmHg) as well as major adverse cardiac events (0.70 AUC). We further show that our models used distinct aspects of the anatomy to generate each prediction, such as the optic disc or blood vessels, opening avenues of further research.
Glyph-aware Embedding of Chinese Characters
Venue: arXiv cs.CL 2017
Year: 2017

Given the advantage and recent success of English character-level and subword-unit models in several NLP tasks, we consider the equivalent modeling problem for Chinese. Chinese script is logographic and many Chinese logograms are composed of common substructures that provide semantic, phonetic and syntactic hints. In this work, we propose to explicitly incorporate the visual appearance of a character's glyph in its representation, resulting in a novel glyph-aware embedding of Chinese characters. Being inspired by the success of convolutional neural networks in computer vision, we use them to incorporate the spatio-structural patterns of Chinese glyphs as rendered in raw pixels. In the context of two basic Chinese NLP tasks of language modeling and word segmentation, the model learns to represent each character's task-relevant semantic and syntactic information in the character-level embedding.
On the Edge-length Ratio of Outerplanar Graphs
Venue: arXiv cs.CG 2017
Year: 2017

We show that any outerplanar graph admits a planar straightline drawing such that the length ratio of the longest to the shortest edges is strictly less than 2. This result is tight in the sense that for any $ε> 0$ there are outerplanar graphs that cannot be drawn with an edge-length ratio smaller than $2 - ε$. We also show that every bipartite outerplanar graph has a planar straight-line drawing with edge-length ratio 1, and that, for any $k \geq 1$, there exists an outerplanar graph with a given combinatorial embedding such that any planar straight-line drawing has edge-length ratio greater than k.
Order-Planning Neural Text Generation From Structured Data
Venue: arXiv cs.CL 2017
Year: 2017

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches do not model the order of contents during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In a biography, for example, the nationality of a person is typically mentioned before occupation in a biography. In this paper, we propose an order-planning text generation model to capture the relationship between different fields and use such relationship to make the generated text more fluent and smooth. We conducted experiments on the WikiBio dataset and achieve significantly higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores.
MILP and Max-Clique based heuristics for the Eternity II puzzle
Venue: arXiv cs.DS 2017
Year: 2017

The present paper considers a hybrid local search approach to the Eternity II puzzle and to unsigned, rectangular, edge matching puzzles in general. Both an original mixed-integer linear programming (MILP) formulation and a novel Max-Clique formulation are presented for this NP-hard problem. Although the presented formulations remain computationally intractable for medium and large sized instances, they can serve as the basis for developing heuristic decompositions and very large scale neighbourhoods. As a side product of the Max-Clique formulation, new hard-to-solve instances are published for the academic research community. Two reasonably well performing MILP-based constructive methods are presented and used for determining the initial solution of a multi-neighbourhood local search approach. Experimental results confirm that this local search can further improve the results obtained by the constructive heuristics and is quite competitive with the state of the art procedures.
Revisited Experimental Comparison of Node-Link and Matrix Representations
Venue: arXiv cs.HC 2017
Year: 2017

Visualizing network data is applicable in domains such as biology, engineering, and social sciences. We report the results of a study comparing the effectiveness of the two primary techniques for showing network data: node-link diagrams and adjacency matrices. Specifically, an evaluation with a large number of online participants revealed statistically significant differences between the two visualizations. Our work adds to existing research in several ways. First, we explore a broad spectrum of network tasks, many of which had not been previously evaluated. Second, our study uses a large dataset, typical of many real-life networks not explored by previous studies. Third, we leverage crowdsourcing to evaluate many tasks with many participants.
Inferring Networked Device Categories from Low-Level Activity Indicators
Venue: arXiv cs.NI 2017
Year: 2017

We study the problem of inferring the type of a networked device in a home network by leveraging low level traffic activity indicators seen at commodity home gateways. We analyze a dataset of detailed device network activity obtained from 240 subscriber homes of a large European ISP and extract a number of traffic and spatial fingerprints for individual devices. We develop a two level taxonomy to describe devices onto which we map individual devices using a number of heuristics. We leverage the heuristically derived labels to train classifiers that distinguish device classes based on the traffic and spatial fingerprints of a device. Our results show an accuracy level up to 91% for the coarse level category and up to 84% for the fine grained category. By incorporating information from other sources (e.g., MAC OUI), we are able to further improve accuracy to above 97% and 92%, respectively. Finally, we also extract a set of simple and human-readable rules that concisely capture the behaviour of these distinct device categories.
Online Time Sharing Policy in Energy Harvesting Cognitive Radio Network with Channel Uncertainty
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers an energy harvesting underlay cognitive radio network operating in a slotted fashion. The secondary transmitter scavenges energy from environmental sources in half duplex fashion and stores it in finite capacity rechargeable battery. It splits each slot into two phases: harvesting phase and transmission phase. We model the energy availability at the secondary user as first order stationary Markov process. We propose a robust online transmission policy by jointly optimizing the time sharing between the two phases and transmit power of secondary user, which maximizes its average throughput by a given time deadline.We show the comparison of our proposed policy with the offline and myopic policies.
My Home is My Post-Office: Evaluation of a decentralized email architecture on Internet-of-Things low-end device
Venue: arXiv cs.DC 2017
Year: 2017

Users predominantly access their email via mobile devices. This presents a two-fold challenge to the email applications. First, email's update from multiple devices has to be eventually reconciled with the server. Prioritization of updates is difficult and maybe undesirable. Solving this problem requires a data store with the complete history of email changes. Second, legacy email protocols don't provide an optimal email synchronization and access in mobile environment. In this paper we are proposing to take advantage of the Internet of Things (IoT) phenomena. In IoT environment a user may have multiple interconnected in-home low-end devices with publicly accessible address. In this architecture we move the email application from the central service into user's in-home and mobile devices, store complete email history on each device, and replace legacy IMAP and SMTP protocols with a synchronization protocol found in Distributed Version Control Systems(DVCS). This addresses the email reconciliation issue, optimizes the bandwidth usage, and intrinsically puts the user in control of her data. We analyze a number of stores and synchronization implementations and compare them with the open source Dovecot email server.