OpenRaiser/Intern-Atlas huggingface dataset:
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
Venue: arXiv cs.CV 2017
Year: 2017

Nowadays, cross-modal retrieval plays an indispensable role to flexibly find information across different modalities of data. Effectively measuring the similarity between different modalities of data is the key of cross-modal retrieval. Different modalities such as image and text have imbalanced and complementary relationships, which contain unequal amount of information when describing the same semantics. For example, images often contain more details that cannot be demonstrated by textual descriptions and vice versa. Existing works based on Deep Neural Network (DNN) mostly construct one common space for different modalities to find the latent alignments between them, which lose their exclusive modality-specific characteristics. Different from the existing works, we propose modality-specific cross-modal similarity measurement (MCSM) approach by constructing independent semantic space for each modality, which adopts end-to-end framework to directly generate modality-specific cross-modal similarity without explicit common representation. For each semantic space, modality-specific characteristics within one modality are fully exploited by recurrent attention network, while the data of another modality is projected into this space with attention based joint embedding to utilize the learned attention weights for guiding the fine-grained cross-modal correlation learning, which can capture the imbalanced and complementary relationships between different modalities. Finally, the complementarity between the semantic spaces for different modalities is explored by adaptive fusion of the modality-specific cross-modal similarities to perform cross-modal retrieval. Experiments on the widely-used Wikipedia and Pascal Sentence datasets as well as our constructed large-scale XMediaNet dataset verify the effectiveness of our proposed approach, outperforming 9 state-of-the-art methods.
Independent Low-Rank Matrix Analysis Based on Complex Student's $t$-Distribution for Blind Audio Source Separation
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, we generalize a source generative model in a state-of-the-art blind source separation (BSS), independent low-rank matrix analysis (ILRMA). ILRMA is a unified method of frequency-domain independent component analysis and nonnegative matrix factorization and can provide better performance for audio BSS tasks. To further improve the performance and stability of the separation, we introduce an isotropic complex Student's $t$-distribution as a source generative model, which includes the isotropic complex Gaussian distribution used in conventional ILRMA. Experiments are conducted using both music and speech BSS tasks, and the results show the validity of the proposed method.
Liquid Marble Interaction Gate for Collision-Based Computing
Venue: arXiv cs.ET 2017
Year: 2017

Liquid marbles are microlitre droplets of liquid, encapsulated by self-organised hydrophobic particles at the liquid/air interface. They offer an efficient approach for manipulating liquid droplets and compartmentalising reactions in droplets. Digital fluidic devices employing liquid marbles might benefit from having embedded computing circuits without electronics and moving mechanical parts (apart from the marbles). We present an experimental implementation of a collision gate with liquid marbles. Mechanics of the gate follows principles of Margolus' soft-sphere collision gate. Boolean values of the inputs are given by the absence (FALSE) or presence (TRUE) of a liquid marble. There are three outputs: two outputs are trajectories of undisturbed marbles (they only report TRUE when just one marble is present at one of the inputs), one output is represented by trajectories of colliding marbles (when two marbles collide they lose their horizontal momentum and fall), this output reports TRUE only when two marbles are present at inputs. Thus the gate implements AND and AND-NOT logical functions. We speculate that by merging trajectories representing AND-NOT output into a single channel one can produce a one-bit half-adder. Potential design of a one-bit full-adder is discussed, and the synthesis of both a pure nickel metal and hybrid nickel/polymer liquid marble is reported.
Energy-Efficient Resource Allocation for Cache-Assisted Mobile Edge Computing
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we jointly consider communication, caching and computation in a multi-user cache-assisted mobile edge computing (MEC) system, consisting of one base station (BS) of caching and computing capabilities and multiple users with computation-intensive and latency-sensitive applications. We propose a joint caching and offloading mechanism which involves task uploading and executing for tasks with uncached computation results as well as computation result downloading for all tasks at the BS, and efficiently utilizes multi-user diversity and multicasting opportunities. Then, we formulate the average total energy minimization problem subject to the caching and deadline constraints to optimally allocate the storage resource at the BS for caching computation results as well as the uploading and downloading time durations. The problem is a challenging mixed discrete-continuous optimization problem. We show that strong duality holds, and obtain an optimal solution using a dual method. To reduce the computational complexity, we further propose a low-complexity suboptimal solution. Finally, numerical results show that the proposed suboptimal solution outperforms existing comparison schemes.
Free Space Estimation using Occupancy Grids and Dynamic Object Detection
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we present an approach to estimate Free Space from a Stereo image pair using stochastic occupancy grids. We do this in the domain of autonomous driving on the famous benchmark dataset KITTI. Later based on the generated occupancy grid we match 2 image sequences to compute the top view representation of the map. We do this to map the environment. We compute a transformation between the occupancy grids of two successive images and use it to compute the top view map. Two issues need to be addressed for mapping are discussed - computing a map and dealing with dynamic objects for computing the map. Dynamic Objects are detected in successive images based on an idea similar to tracking of foreground objects from the background objects based on motion flow. A novel RANSAC based segmentation approach has been proposed here to address this issue.
ConvNet Architecture Search for Spatiotemporal Feature Learning
Venue: arXiv cs.CV 2017
Year: 2017

Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning. Although any image representation can be applied to video frames, a dedicated spatiotemporal representation is still vital in order to incorporate motion patterns that cannot be captured by appearance based models alone. This paper presents an empirical ConvNet architecture search for spatiotemporal feature learning, culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposed architecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51, THUMOS14, and ASLAN while being 2 times faster at inference time, 2 times smaller in model size, and having a more compact representation.
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic
Venue: arXiv cs.CR 2017
Year: 2017

The growing market for smart home IoT devices promises new conveniences for consumers while presenting new challenges for preserving privacy within the home. Many smart home devices have always-on sensors that capture users' offline activities in their living spaces and transmit information about these activities on the Internet. In this paper, we demonstrate that an ISP or other network observer can infer privacy sensitive in-home activities by analyzing Internet traffic from smart homes containing commercially-available IoT devices even when the devices use encryption. We evaluate several strategies for mitigating the privacy risks associated with smart home device traffic, including blocking, tunneling, and rate-shaping. Our experiments show that traffic shaping can effectively and practically mitigate many privacy risks associated with smart home IoT devices. We find that 40KB/s extra bandwidth usage is enough to protect user activities from a passive network adversary. This bandwidth cost is well within the Internet speed limits and data caps for many smart homes.
Natural Language Processing: State of The Art, Current Trends and Challenges
Venue: arXiv cs.CL 2017
Year: 2017

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.
Brain Abnormality Detection by Deep Convolutional Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network. We propose our method to detect high and low-grade glioma, multiple sclerosis, and Alzheimer diseases as well as healthy cases. Our network architecture has ten learning layers that include seven convolutional layers and three fully connected layers. We have achieved a promising result in five categories of brain images (classification task) with 95.7% accuracy.
When data mining meets optimization: A case study on the quadratic assignment problem
Venue: arXiv cs.DM 2017
Year: 2017

This paper presents a hybrid approach called frequent pattern based search that combines data mining and optimization. The proposed method uses a data mining procedure to mine frequent patterns from a set of high-quality solutions collected from previous search, and the mined frequent patterns are then employed to build starting solutions that are improved by an optimization procedure. After presenting the general approach and its composing ingredients, we illustrate its application to solve the well-known and challenging quadratic assignment problem. Computational results on the 21 hardest benchmark instances show that the proposed approach competes favorably with state-of-the-art algorithms both in terms of solution quality and computing time.
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Venue: arXiv cs.CV 2017
Year: 2017

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis. We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.
Privacy Guidelines for Internet of Things: A Cheat Sheet
Venue: arXiv cs.CY 2017
Year: 2017

This document presents 30 different privacy guidelines that can be used to both design and assess IoT applications and IoT middleware platforms. These guidelines can be broadly categorised into eight categories, namely, MINIMIZE ( ), HIDE ( ), SEPARATE ( ), AGGREGATE ( ), INFORM ( ), CONTROL ( ), ENFORCE ( ), DEMONSTRATE ( ). This document uses the following structure to introduce the each privacy guidelines. First, we describe the philosophy behind each guideline in general. Then, we present the questions that software architects need to think about when designing or assessing an IoT platform or application. The questions slightly vary depending on whether the architect is assessing a platform or an application.
Analysis of Static and Dynamic Configurability of Existing Group Communication Systems
Venue: arXiv cs.DC 2017
Year: 2017

Active replication following the state machine replication (SMR) approach is a way to make existing systems and services more reliable and fault-tolerant. The additional communication overhead has a negative impact on the system's throughput and overall request latency. Today's systems should be highly optimized to their execution environment and usage scenario in order to remedy the performance loss introduced by such group communication systems (GCS). In addition to that, systems should be able to adapt to changing environmental conditions. This report analyzes the available configuration options of three existing GCSs. Therefore, it explains the available configuration parameters and describes the given reconfiguration mechanisms. The found parameters are then classified in a parameter scheme.
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Venue: arXiv cs.NE 2017
Year: 2017

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.
Massive BLAST: An Architecture for Realizing Ultra-High Data Rates for Large-Scale MIMO
Venue: arXiv cs.IT 2017
Year: 2017

A detection scheme for uplink massive MIMO, dubbed massive-BLAST or M-BLAST, is proposed. The derived algorithm is an enhancement of the well-known soft parallel interference cancellation. Using computer simulations in massive MIMO application scenarios, M-BLAST is shown to yield a substantially better error performance with reduced complexity, compared to the benchmark alternative of a one-shot linear detector, as well as the original sequential V-BLAST. Hence, M-BLAST may serve as a computationally efficient means to exploit the large number of antennas in massive MIMO.
Serverless Protocols for Inventory and Tracking with a UAV
Venue: arXiv cs.CR 2017
Year: 2017

It is widely acknowledged that the proliferation of Unmanned Aerial Vehicles (UAVs) may lead to serious concerns regarding avionics safety, particularly when end-users are not adhering to air safety regulations. There are, however, domains in which UAVs may help to increase the safety of airplanes and the management of flights and airport resources that often require substantial human resources. For instance, Paris Charles de Gaulle airport (CDG) has more than 7,000 staff and supports 30,000 direct jobs for more than 60 million passengers per year (as of 2016). Indeed, these new systems can be used beneficially for several purposes, even in sensitive areas like airports. Among the considered applications are those that suggest using UAVs to enhance safety of on-ground airplanes; for instance, by collecting (once the aircraft has landed) data recorded by different systems during the flight (like the sensors of the Aircraft Data Networks - ADN) or by examining the state of airplane structure. In this paper, our proposal is to use UAVs, under the control of the airport authorities, to inventory and track various tagged assets, such as luggage, supplies required for the flights, and maintenance tools. The aim of our proposal is to make airport management systems more efficient for operations requiring inventory and tracking, along with increasing safety (sensitive assets such as refueling tanks, or sensitive pieces of luggage can be tracked), thus raising financial profit.
A Heuristic Approach to Protocol Tuning for High Performance Data Transfers
Venue: arXiv cs.DC 2017
Year: 2017

Obtaining optimal data transfer performance is of utmost importance to today's data-intensive distributed applications and wide-area data replication services. Doing so necessitates effectively utilizing available network bandwidth and resources, yet in practice transfers seldom reach the levels of utilization they potentially could. Tuning protocol parameters such as pipelining, parallelism, and concurrency can significantly increase utilization and performance, however determining the best settings for these parameters is a difficult problem, as network conditions can vary greatly between sites and over time. Nevertheless, it is an important problem, since poor tuning can cause either under- or over-utilization of network resources and thus degrade transfer performance. In this paper, we present three algorithms for application-level tuning of different protocol parameters for maximizing transfer throughput in wide-area networks. Our algorithms dynamically tune the number of parallel data streams per file (for large file optimization), the level of control channel pipelining (for small file optimization), and the number of concurrent file transfers to increase I/O throughput (a technique useful for all types of files). The proposed heuristic algorithms improve the transfer throughput up to 10x compared to the baseline and 7x compared to the state of the art solutions.
Towards Algorithmic Typing for DOT
Venue: arXiv cs.PL 2017
Year: 2017

The Dependent Object Types (DOT) calculus formalizes key features of Scala. The D$_{<: }$ calculus is the core of DOT. To date, presentations of D$_{<: }$ have used declarative typing and subtyping rules, as opposed to algorithmic. Unfortunately, algorithmic typing for full D$_{<: }$ is known to be an undecidable problem. We explore the design space for a restricted version of D$_{<: }$ that has decidable typechecking. Even in this simplified D$_{<: }$ , algorithmic typing and subtyping are tricky, due to the "bad bounds" problem. The Scala compiler bypasses bad bounds at the cost of a loss in expressiveness in its type system. Based on the approach taken in the Scala compiler, we present the Step Typing and Step Subtyping relations for D$_{<: }$. We prove these relations sound and decidable. They are not complete with respect to the original D$_{<: }$ rules.
Non-Malleable Codes with Leakage and Applications to Secure Communication
Venue: arXiv cs.CR 2017
Year: 2017

Non-malleable codes are randomized codes that protect coded messages against modification by functions in a tampering function class. These codes are motivated by providing tamper resilience in applications where a cryptographic secret is stored in a tamperable storage device and the protection goal is to ensure that the adversary cannot benefit from their tamperings with the device. In this paper we consider non-malleable codes for protection of secure communication against active physical layer adversaries. We define a class of functions that closely model tampering of communication by adversaries who can eavesdrop on a constant fraction of the transmitted codeword, and use this information to select a vector of tampering functions that will be applied to a second constant fraction of codeword components (possibly overlapping with the first set). We derive rate bounds for non-malleable codes for this function class and give two modular constructions. The first construction adapts and provides new analysis for an existing construction in the new setting. The second construction uses a new approach that results in an explicit construction of non-malleable codes. We show applications of our results in securing message communication against active physical layer adversaries in two settings: wiretap II with active adversaries and Secure Message Transmission (SMT) in networks. We discuss our results and directions for future work.
Weight hierarchy of a class of linear codes relating to non-degenerate quadratic forms
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we discuss the generalized Hamming weights of a class of linear codes associated with non-degenerate quadratic forms. In order to do so, we study the quadratic forms over subspaces of finite field and obtain some interesting results about subspaces and their dual spaces. On this basis, we solve all the generalized Hamming weights of these linear codes.
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
Venue: arXiv cs.IR 2017
Year: 2017

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets.