OpenRaiser/Intern-Atlas huggingface dataset:
Practical Demonstration of a Memristive Fuse
Venue: arXiv cs.ET 2016
Year: 2016

Since its inception the memristive fuse has been a good example of how small numbers of memristors can be combined to obtain useful behaviours unachievable by individual devices. In this work, we link the memristive fuse concept with that of the Complementary Resistive Switch (CRS), exploit that link to experimentally demonstrate a practical memristive fuse using TiOx-based ReRAM cells and explain its basic operational principles. The fuse is stimulated by trains of identical pulses where successive pulse trains feature opposite polarities. In response, we observe a gradual (analogue) drop in resistive state followed by a gradual recovery phase regardless of input stimulus polarity; echoing traditional, binary CRS behaviour. This analogue switching property opens the possibility of operating the memristive fuse as a single-component step change detector. Moreover, we discover that the characteristics of the individual memristors used to demonstrate the memristive fuse concept in this work allow our fuse to be operated in a regime where one of the two constituent devices can be switched largely independently from the other. This property, not present in the traditional CRS, indicates that the inherently analogue memristive fuse architecture may support additional operational flexibility through e.g. allowing finer control over its resistive state.
Incremental Consistency Guarantees for Replicated Objects
Venue: arXiv cs.DC 2016
Year: 2016

Programming with replicated objects is difficult. Developers must face the fundamental trade-off between consistency and performance head on, while struggling with the complexity of distributed storage stacks. We introduce Correctables, a novel abstraction that hides most of this complexity, allowing developers to focus on the task of balancing consistency and performance. To aid developers with this task, Correctables provide incremental consistency guarantees, which capture successive refinements on the result of an ongoing operation on a replicated object. In short, applications receive both a preliminary---fast, possibly inconsistent---result, as well as a final---consistent---result that arrives later. We show how to leverage incremental consistency guarantees by speculating on preliminary values, trading throughput and bandwidth for improved latency. We experiment with two popular storage systems (Cassandra and ZooKeeper) and three applications: a Twissandra-based microblogging service, an ad serving system, and a ticket selling system. Our evaluation on the Amazon EC2 platform with YCSB workloads A, B, and C shows that we can reduce the latency of strongly consistent operations by up to 40% (from 100ms to 60ms) at little cost (10% bandwidth increase, 6% throughput drop) in the ad system. Even if the preliminary result is frequently inconsistent (25% of accesses), incremental consistency incurs a bandwidth overhead of only 27%.
End-to-End Eye Movement Detection Using Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Common computational methods for automated eye movement detection - i.e. the task of detecting different types of eye movement in a continuous stream of gaze data - are limited in that they either involve thresholding on hand-crafted signal features, require individual detectors each only detecting a single movement, or require pre-segmented data. We propose a novel approach for eye movement detection that only involves learning a single detector end-to-end, i.e. directly from the continuous gaze data stream and simultaneously for different eye movements without any manual feature crafting or segmentation. Our method is based on convolutional neural networks (CNN) that recently demonstrated superior performance in a variety of tasks in computer vision, signal processing, and machine learning. We further introduce a novel multi-participant dataset that contains scripted and free-viewing sequences of ground-truth annotated saccades, fixations, and smooth pursuits. We show that our CNN-based method outperforms state-of-the-art baselines by a large margin on this challenging dataset, thereby underlining the significant potential of this approach for holistic, robust, and accurate eye movement protocol analysis.
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Venue: arXiv cs.CL 2016
Year: 2016

Robotic commands in natural language usually contain various spatial descriptions that are semantically similar but syntactically different. Mapping such syntactic variants into semantic concepts that can be understood by robots is challenging due to the high flexibility of natural language expressions. To tackle this problem, we collect robotic commands for navigation and manipulation tasks using crowdsourcing. We further define a robot language and use a generative machine translation model to translate robotic commands from natural language to robot language. The main purpose of this paper is to simulate the interaction process between human and robots using crowdsourcing platforms, and investigate the possibility of translating natural language to robot language with paraphrases.
Some Advances in Role Discovery in Graphs
Venue: arXiv cs.AI 2016
Year: 2016

Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way. In contrast to other graph prob- lems such as community discovery, which finds groups of highly connected nodes, the role discovery problem finds groups of nodes that share similar graph topological structure. However, existing work so far has two severe limitations that prevent its use in some domains. Firstly, it is completely unsupervised which is undesirable for a number of reasons. Secondly, most work is limited to a single relational graph. We address both these lim- itations in an intuitive and easy to implement alternating least squares framework. Our framework allows convex constraints to be placed on the role discovery problem which can provide useful supervision. In par- ticular we explore supervision to enforce i) sparsity, ii) diversity and iii) alternativeness. We then show how to lift this work for multi-relational graphs. A natural representation of a multi-relational graph is an order 3 tensor (rather than a matrix) and that a Tucker decomposition allows us to find complex interactions between collections of entities (E-groups) and the roles they play for a combination of relations (R-groups). Existing Tucker decomposition methods in tensor toolboxes are not suited for our purpose, so we create our own algorithm that we demonstrate is pragmatically useful.
Convex Independence in Permutation Graphs
Venue: arXiv cs.DM 2016
Year: 2016

A set C of vertices of a graph is P_3-convex if every vertex outside C has at most one neighbor in C. The convex hull σ(A) of a set A is the smallest P_3-convex set that contains A. A set M is convexly independent if for every vertex x \in M, x \notin σ(M-x). We show that the maximal number of vertices that a convexly independent set in a permutation graph can have, can be computed in polynomial time.
RACE: A Rate Adaptive Channel Estimation Approach for Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.
Optimal Speed Scaling with a Solar Cell
Venue: arXiv cs.DS 2016
Year: 2016

We consider the setting of a sensor that consists of a speed-scalable processor, a battery, and a solar cell that harvests energy from its environment at a time-invariant recharge rate. The processor must process a collection of jobs of various sizes. Jobs arrive at different times and have different deadlines. The objective is to minimize the *recharge rate*, which is the rate at which the device has to harvest energy in order to feasibly schedule all jobs. The main result is a polynomial-time combinatorial algorithm for processors with a natural set of discrete speed/power pairs.
Identifying Topology of Power Distribution Networks Based on Smart Meter Data
Venue: arXiv cs.SY 2016
Year: 2016

In a power distribution network, the network topology information is essential for an efficient operation of the network. This information of network connectivity is not accurately available, at the low voltage level, due to uninformed changes that happen from time to time. In this paper, we propose a novel data--driven approach to identify the underlying network topology including the load phase connectivity from time series of energy measurements. The proposed method involves the application of Principal Component Analysis (PCA) and its graph-theoretic interpretation to infer the topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated networks and also on IEEE recognized Roy Billinton distribution test system.
Extraction of Layout Entities and Sub-layout Query-based Retrieval of Document Images
Venue: arXiv cs.IR 2016
Year: 2016

Layouts and sub-layouts constitute an important clue while searching a document on the basis of its structure, or when textual content is unknown/irrelevant. A sub-layout specifies the arrangement of document entities within a smaller portion of the document. We propose an efficient graph-based matching algorithm, integrated with hash-based indexing, to prune a possibly large search space. A user can specify a combination of sub-layouts of interest using sketch-based queries. The system supports partial matching for unspecified layout entities. We handle cases of segmentation pre-processing errors (for text/non-text blocks) with a symmetry maximization-based strategy, and accounting for multiple domain-specific plausible segmentation hypotheses. We show promising results of our system on a database of unstructured entities, containing 4776 newspaper images.
Detecting Singleton Review Spammers Using Semantic Similarity
Venue: arXiv cs.CL 2016
Year: 2016

Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).
An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we present an interactive tool that can be used to quantify fat infiltration in lumbar muscles, which is useful in studying fat infiltration and lower back pain (LBP) in adults. Currently, a qualitative assessment by visual grading via a 5-point scale is used to study fat infiltration in lumbar muscles from an axial view of lumbar-spine MR Images. However, a quantitative approach (on a continuous scale of 0-100\%) may provide a greater insight. In this paper, we propose a method to precisely quantify the fat deposition / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis and analysis. The key steps are interactively segmenting the region of interest (ROI) from the lumbar muscles using the well known livewire technique, identifying fatty regions in the segmented region based on variable-selection of threshold and softness levels, automatically detecting the center of the spinal column and fragmenting the lumbar muscles into smaller regions with reference to the center of the spinal column, computing key parameters [such as total and region-wise fat content percentage, total-cross sectional area (TCSA) and functional cross-sectional area (FCSA)] and exporting the computations and associated patient information from the MRI, into a database. A standalone application using MATLAB R2014a was developed to perform the required computations along with an intuitive graphical user interface (GUI).
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment and participate in all subtasks, i.e. two-point, three-point, and five-point scale sentiment classification and two-point and five-point scale sentiment quantification. We achieve competitive results for two-point scale sentiment classification and quantification, ranking fifth and a close fourth (third and second by alternative metrics) respectively despite using only pre-trained embeddings that contain no sentiment information. We achieve good performance on three-point scale sentiment classification, ranking eighth out of 35, while performing poorly on five-point scale sentiment classification and quantification. An error analysis reveals that this is due to low expressiveness of the model to capture negative sentiment as well as an inability to take into account ordinal information. We propose improvements in order to address these and other issues.
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Venue: arXiv cs.CL 2016
Year: 2016

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.
Toward a new tool to extract the Evidence from a Memory Card of Mobile phones
Venue: arXiv cs.CR 2016
Year: 2016

Today, a mobile phone is not just a phone but it is a computer that you can also use for calling someone. Besides, in criminal investigations the importance of evidence from the mobile phone is increasing as more and more phones are seized at the Digital Forensic Department of the police. Indeed, the amount of memory cards of these mobile phones that need to be investigated separately is also increasing. Possible reasons are that the mobile phone investigation software does not support the specific mobile phone or the specific, for that investigation, artefacts. Sometimes the software investigates just the internal memory of the mobile phone and not the data which is written on the memory card. Fact is also that although the mobile phone was investigated by the dedicated software, the possibility that the associated memory card contains additional important information is evident. The current procedure to get all of the usable information from a memory card of a mobile phone is very time-consuming process and not user friendly. In this paper, we present a new single tool to simplify the investigation of a memory card from a mobile phone. We also test our tool with WhatsApp application installed on the memory card from different mobile phones.
Forensics Acquisition of IMVU: A Case Study
Venue: arXiv cs.CR 2016
Year: 2016

There are many applications available for personal computers and mobile devices that facilitate users in meeting potential partners. There is, however, a risk associated with the level of anonymity on using instant message applications, because there exists the potential for predators to attract and lure vulnerable users. Today Instant Messaging within a Virtual Universe (IMVU) combines custom avatars, chat or instant message (IM), community, content creation, commerce, and anonymity. IMVU is also being exploited by criminals to commit a wide variety of offenses. However, there are very few researches on digital forensic acquisition of IMVU applications. In this paper, we discuss first of all on challenges of IMVU forensics. We present a forensic acquisition of an IMVU 3D application as a case study. We also describe and analyse our experiments with this application.
An Empirical Study of Cycle Toggling Based Laplacian Solvers
Venue: arXiv cs.DS 2016
Year: 2016

We study the performance of linear solvers for graph Laplacians based on the combinatorial cycle adjustment methodology proposed by [Kelner-Orecchia-Sidford-Zhu STOC-13]. The approach finds a dual flow solution to this linear system through a sequence of flow adjustments along cycles. We study both data structure oriented and recursive methods for handling these adjustments. The primary difficulty faced by this approach, updating and querying long cycles, motivated us to study an important special case: instances where all cycles are formed by fundamental cycles on a length $n$ path. Our methods demonstrate significant speedups over previous implementations, and are competitive with standard numerical routines.
Numerical Characterization of In Vivo Wireless Communication Channels
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we numerically investigated the in vivo wireless communication channel for the human male torso at 915 MHz. The results show that in vivo channel is different from the classical communication channel, and location dependency is very critical for link budget calculations. A statistical path loss model based on the angle, depth and body region is introduced for near and far field regions. Furthermore, multipath characteristics are investigated using a power delay profile as well.
MP3: A More Efficient Private Presence Protocol
Venue: arXiv cs.CR 2016
Year: 2016

This paper proposes MP3, the second privacy-preserving presence protocol that leaks no information about the graph structure of the social network. Several cryptographic techniques are applied to improve the existing DP5 protocol---the first privacy-preserving presence protocol---while maintaining the same level of privacy. The key contribution of this paper is the use of a dynamic broadcast encryption scheme to reduce the size of the presence database. This enables cheaper registration and lookup required for the protocol. As compared to DP5, MP3 requires on the order of ten times less bandwidth of the servers during registration, and requires on the order of two times less bandwidth for lookup, for a small number of users ($N=10000$). Furthermore, these savings asymptotically increase with the number of users. The client-side latency is also improved significantly in MP3, as compared with DP5. We provide an evaluation of the performance and scalability of both protocols.
New Steps on the Exact Learning of CNF
Venue: arXiv cs.LG 2016
Year: 2016

A major problem in computational learning theory is whether the class of formulas in conjunctive normal form (CNF) is efficiently learnable. Although it is known that this class cannot be polynomially learned using either membership or equivalence queries alone, it is open whether CNF can be polynomially learned using both types of queries. One of the most important results concerning a restriction of the class CNF is that propositional Horn formulas are polynomial time learnable in Angluin's exact learning model with membership and equivalence queries. In this work we push this boundary and show that the class of multivalued dependency formulas (MVDF) is polynomially learnable from interpretations. We then provide a notion of reduction between learning problems in Angluin's model, showing that a transformation of the algorithm suffices to efficiently learn multivalued database dependencies from data relations. We also show via reductions that our main result extends well known previous results and allows us to find alternative solutions for them.
Style-Transfer via Texture-Synthesis
Venue: arXiv cs.CV 2016
Year: 2016

Style-transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image which is an artistic mixture of the two. Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path towards handling the style-transfer task, via generalization of texture-synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared to the CNN ones. In this work we propose a novel style-transfer algorithm that extends the texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized images that get closer in quality to the CNN ones. We modify Kwatra's algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style-transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.