OpenRaiser/Intern-Atlas huggingface dataset:
Four-Dimensional Usability Investigation of Image CAPTCHA
Venue: arXiv cs.HC 2016
Year: 2016

Image CAPTCHA, aiming at effectively distinguishing human users from malicious script attacks, has been an important mechanism to protect online systems from spams and abuses. Despite the increasing interests in developing and deploying image CAPTCHAs, the usability aspect of those CAPTCHAs has hardly been explored systematically. In this paper, the universal design factors of image CAPTCHAs, such as image layouts, quantities, sizes, tilting angles and colors were experimentally evaluated through the following four dimensions: eye-tracking, efficiency, effectiveness and satisfaction. The cognitive processes revealed by eye-tracking indicate that the distribution of eye gaze is equally assigned to each candidate image and irrelevant to the variation of image contents. In addition, the gazing plot suggests that more than 70% of the participants inspected CAPTCHA images row-by-row, which is more efficient than scanning randomly. Those four-dimensional evaluations essentially suggest that square and horizontal rectangle are the preferred layout; image quantities may not exceed 16 while the image color is insignificant. Meanwhile, the image size and tilting angle are suggested to be larger than 55 pixels x 55 pixels and within -45~45 degrees, respectively. Basing on those usability experiment results, we proposed a design guideline that is expected to be useful for developing more usable image CAPTCHAs.
A Mathematical Proof of the Superiority of NOMA Compared to Conventional OMA
Venue: arXiv cs.IT 2016
Year: 2016

While existing works about non-orthogonal multiple access (NOMA) have indicated that NOMA can yield a significant performance gain over orthogonal multiple access (OMA) with fixed resource allocation, it is not clear whether such a performance gain will diminish when optimal resource (Time/Frequency/Power) allocation is carried out. In this paper, the performance comparison between NOMA and conventional OMA systems is investigated, from an optimization point of view. Firstly, by using the idea of power splitting, a closed-form expression for the optimum sum rate of NOMA systems is derived. Then, with rigorous mathematical proofs, we reveal the fact that NOMA can always outperform conventional OMA systems, even if both are equipped with the optimal resource allocation policies. Finally, computer simulations are conducted to validate the accuracy of the analytical results.
Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining
Venue: arXiv cs.DB 2016
Year: 2016

Our main goal is to discover the main factors influencing students' academic trajectory and students' academic evolution within such environment. Our results indicate strong correlation in this virtual learning environment between student average and some factors like: student's English level (despite the fact that Arabic language is the teaching language), student's age, student's gender, student's over-stay and student's place of residence (inside or outside Syria). Our results indicate also a need to modify the academic trajectory of students by changing the prerequisites of few courses delivered as a part of BIT diploma like Advanced DBA II, Data Security. In this research, the results also highlight the effect of the Syrian Crisis on students. Finally, we've suggested some future recommendations based on our observations and results to develop the current information system in SVU in order to help us to deduce some indicators more easily.
Support vector regression model for BigData systems
Venue: arXiv cs.DC 2016
Year: 2016

Nowadays Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. In such systems, capacity allocation, which is the ability to optimally size minimal resources for achieve a certain level of performance, is a key challenge to enhance performance for MapReduce jobs and minimize cloud resource costs. In order to do so, one of the biggest challenge is to build an accurate performance model to estimate job execution time of MapReduce systems. Previous works applied simulation based models for modeling such systems. Although this approach can accurately describe the behavior of Big Data clusters, it is too computationally expensive and does not scale to large system. We try to overcome these issues by applying machine learning techniques. More precisely we focus on Support Vector Regression (SVR) which is intrinsically more robust w.r.t other techniques, like, e.g., neural networks, and less sensitive to outliers in the training set. To better investigate these benefits, we compare SVR to linear regression.
Authoring image decompositions with generative models
Venue: arXiv cs.CV 2016
Year: 2016

We show how to extend traditional intrinsic image decompositions to incorporate further layers above albedo and shading. It is hard to obtain data to learn a multi-layer decomposition. Instead, we can learn to decompose an image into layers that are "like this" by authoring generative models for each layer using proxy examples that capture the Platonic ideal (Mondrian images for albedo; rendered 3D primitives for shading; material swatches for shading detail). Our method then generates image layers, one from each model, that explain the image. Our approach rests on innovation in generative models for images. We introduce a Convolutional Variational Auto Encoder (conv-VAE), a novel VAE architecture that can reconstruct high fidelity images. The approach is general, and does not require that layers admit a physical interpretation.
Enabling Bio-Plausible Multi-level STDP using CMOS Neurons with Dendrites and Bistable RRAMs
Venue: arXiv cs.ET 2016
Year: 2016

Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such hybrid CMOS-RRAM neuromorphic architectures will result in several orders of magnitude reduction in energy consumption at a very small form factor, and herald autonomous learning machines capable of self-adapting to their environment. However, the progress in this area has been impeded from the realization that the actual memory devices fall well short of their expected behavior. In this work, we discuss the challenges associated with these memory devices and their use in neuromorphic computing circuits, and propose pathways to overcome these limitations by introducing 'dendritic learning'.
A System Architecture for the Detection of Insider Attacks in Big Data Systems
Venue: arXiv cs.CR 2016
Year: 2016

In big data systems, the infrastructure is such that large amounts of data are hosted away from the users. In such a system information security is considered as a major challenge. From a customer perspective, one of the big risks in adopting big data systems is in trusting the provider who designs and owns the infrastructure from accessing user data. Yet there does not exist much in the literature on detection of insider attacks. In this work, we propose a new system architecture in which insider attacks can be detected by utilizing the replication of data on various nodes in the system. The proposed system uses a two-step attack detection algorithm and a secure communication protocol to analyze processes executing in the system. The first step involves the construction of control instruction sequences for each process in the system. The second step involves the matching of these instruction sequences among the replica nodes. Initial experiments on real-world hadoop and spark tests show that the proposed system needs to consider only 20% of the code to analyze a program and incurs 3.28% time overhead. The proposed security system can be implemented and built for any big data system due to its extrinsic workflow.
Automatic Event Detection for Signal-based Surveillance
Venue: arXiv cs.CV 2016
Year: 2016

Signal-based Surveillance systems such as Closed Circuits Televisions (CCTV) have been widely installed in public places. Those systems are normally used to find the events with security interest, and play a significant role in public safety. Though such systems are still heavily reliant on human labour to monitor the captured information, there have been a number of automatic techniques proposed to analysing the data. This article provides an overview of automatic surveillance event detection techniques . Despite it's popularity in research, it is still too challenging a problem to be realised in a real world deployment. The challenges come from not only the detection techniques such as signal processing and machine learning, but also the experimental design with factors such as data collection, evaluation protocols, and ground-truth annotation. Finally, this article propose that multi-disciplinary research is the path towards a solution to this problem.
Automated Inference of Software Library Usage Patterns
Venue: arXiv cs.SE 2016
Year: 2016

Modern software systems are increasingly dependent on third-party libraries. It is widely recognized that using mature and well-tested third-party libraries can improve developers' productivity, reduce time-to-market, and produce more reliable software. Today's open-source repositories provide a wide range of libraries that can be freely downloaded and used. However, as software libraries are documented separately but intended to be used together, developers are unlikely to fully take advantage of these reuse opportunities. In this paper, we present a novel approach to automatically identify third-party library usage patterns, i.e., collections of libraries that are commonly used together by developers. Our approach employs hierarchical clustering technique to group together software libraries based on external client usage. To evaluate our approach, we mined a large set of over 6,000 popular libraries from Maven Central Repository and investigated their usage by over 38,000 client systems from the Github repository. Our experiments show that our technique is able to detect the majority (77%) of highly consistent and cohesive library usage patterns across a considerable number of client systems.
Towards a Step Semantics for Story-Driven Modelling
Venue: arXiv cs.SE 2016
Year: 2016

Graph Transformation (GraTra) provides a formal, declarative means of specifying model transformation. In practice, GraTra rule applications are often programmed via an additional language with which the order of rule applications can be suitably controlled. Story-Driven Modelling (SDM) is a dialect of programmed GraTra, originally developed as part of the Fujaba CASE tool suite. Using an intuitive, UML-inspired visual syntax, SDM provides usual imperative control flow constructs such as sequences, conditionals and loops that are fairly simple, but whose interaction with individual GraTra rules is nonetheless non-trivial. In this paper, we present the first results of our ongoing work towards providing a formal step semantics for SDM, which focuses on the execution of an SDM specification.
On High-Order Capacity Statistics of Spectrum Aggregation Systems over $κ$-$μ$ and $κ$-$μ$ shadowed Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

The frequency scarcity imposed by fast growing demand for mobile data service requires promising spectrum aggregation systems. The so-called higher-order statistics (HOS) of the channel capacity is a suitable metric on the system performance. While prior relevant works have improved our knowledge on the HOS characterization of spectrum aggregation systems, an analytical framework encompassing generalized fading models of interest is not yet available. In this paper, we pursue a detailed HOS analysis of $κ$-$μ$ and $κ$-$μ$ shadowed fading channels by deriving novel and exact expressions. Furthermore, the simplified HOS expressions for the asymptotically low and high signal-to-noise regimes are derived. Several important statistical measures, such as amount of fading, amount of dispersion, reliability, skewness, and kurtosis, are obtained by using the HOS results. More importantly, the useful implications of system and fading parameters on spectrum aggregation systems are investigated for channel selection. Finally, all derived expressions are validated via Monte-Carlo simulations.
How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data
Venue: arXiv cs.DL 2016
Year: 2016

In this short communication, we provide an overview of a relatively newly provided source of altmetrics data which could possibly be used for societal impact measurements in scientometrics. Recently, Altmetric - a start-up providing publication level metrics - started to make data for publications available which have been mentioned in policy-related documents. Using data from Altmetric, we study how many papers indexed in the Web of Science (WoS) are mentioned in policy-related documents. We find that less than 0.5% of the papers published in different subject categories are mentioned at least once in policy-related documents. Based on our results, we recommend that the analysis of (WoS) publications with at least one policy-related mention is repeated regularly (annually). Mentions in policy-related documents should not be used for impact measurement until new policy-related sites are tracked.
An Improved One-to-All Broadcasting in Higher Dimensional Eisenstein-Jacobi Networks
Venue: arXiv cs.DC 2016
Year: 2016

Recently, a higher dimensional Eisenstein-Jacobi networks, has been proposed in [22], which is shown that they have better average distance with more number of nodes than a single dimensional EJ networks. Some communication algorithms such as one-to-all and all-to-all communications are well known and used in interconnection networks. In one-to-all communication, a source node sends a message to every other node in the network. Whereas, in all-to-all communication, every node is considered as a source node and sends its message to every other node in the network. In this paper, an improved one-to-all communication algorithm in higher dimensional EJ networks is presented. The paper shows that the proposed algorithm achieves a lower average number of steps to receiving the broadcasted message. In addition, since the links are assumed to be half-duplex, the all-to-all broadcasting algorithm is divided into three phases. The simulation results are discussed and showed that the improved one-to-all algorithm achieves better traffic performance than the well-known one-to-all algorithm and has 2.7% less total number of senders
On the computational power of affine automata
Venue: arXiv cs.FL 2016
Year: 2016

We investigate the computational power of affine automata (AfAs) introduced in [4]. In particular, we present a simpler proof for how to change the cutpoint for any affine language and a method how to reduce error in bounded error case. Moreover, we address to the question of [4] by showing that any affine language can be recognized by an AfA with certain limitation on the entries of affine states and transition matrices. Lastly, we present the first languages shown to be not recognized by AfAs with bounded-error.
Consistency in the face of change: an adaptive approach to physical layer cooperation
Venue: arXiv cs.NI 2016
Year: 2016

Most existing works on physical-layer (PHY) cooperation (beyond routing) focus on how to best use a given, static relay network--while wireless networks are anything but static. In this paper, we pose a different set of questions: given that we have multiple devices within range, which relay(s) do we use for PHY cooperation, to maintain a consistent target performance? How can we efficiently adapt, as network conditions change? And how important is it, in terms of performance, to adapt? Although adapting to the best path when routing is a well understood problem, how to do so over PHY cooperation networks is an open question. Our contributions are: (1) We demonstrate via theoretical evaluation, a diminishing returns trend as the number of deployed relays increases. (2) Using a simple algorithm based on network metrics, we efficiently select the sub-network to use at any given time to maintain a target reliability. (3) When streaming video from Netflix, we experimentally show (using measurements from a WARP radio testbed employing DIQIF relaying) that our adaptive PHY cooperation scheme provides a throughput gain of 2x over nonadaptive PHY schemes, and a gain of 6x over genie-aided IP-level adaptive routing.
Knowledge Space Framework: An API for representation, persistence and visualization of knowledge spaces
Venue: arXiv cs.CY 2016
Year: 2016

This paper will discuss the challenges in tooling around the management and utilization of knowledge space structures, via standardized APIs for external Adaptive Learning Systems (ALS) to consume. It then describes how these challenges are addressed in a graph based knowledge management framework application designed for external ALSs.
A Unified Linear Precoding Design for Multi-user MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

We address the problem of the bit error rate (BER) performance gap between the sub-optimal and optimal linear precoder (LP) for a multiuser (MU) multiple input and multiple output (MIMO) broadcast systems in this paper. Particularly, mobile users suffer noise enhancement effect due to a sub-optimal LP that can be suppressed by an optimal LP matrix. A sub-optimal LP matrix such as a linear zero-forcing (LZF) precoder performs in high signal to noise ratio (SNR) regime only, in contrast, an optimal precoder for instance a linear minimum mean-square-error (LMMSE) precoder outperforms in both low and high SNR scenarios. These kinds of precoder illustrates the BER gap distance at least 0.1 when it is used in itself in a MU MIMO systems. Thus, we propose and design a unified linear precoding (ULP) matrix using a precoding selection technique that combines the sub-optimal and optimal LP matrix for a multi-user MIMO systems to ensure zero BER performance gap in this paper. The numerical results show that our proposed ULP technique offers significant performance in both low and high SNR scenarios.
Towards computer-assisted understanding of dynamics in symphonic music
Venue: arXiv cs.SD 2016
Year: 2016

Many people enjoy classical symphonic music. Its diverse instrumentation makes for a rich listening experience. This diversity adds to the conductor's expressive freedom to shape the sound according to their imagination. As a result, the same piece may sound quite differently from one conductor to another. Differences in interpretation may be noticeable subjectively to listeners, but they are sometimes hard to pinpoint, presumably because of the acoustic complexity of the sound. We describe a computational model that interprets dynamics---expressive loudness variations in performances---in terms of the musical score, highlighting differences between performances of the same piece. We demonstrate experimentally that the model has predictive power, and give examples of conductor ideosyncrasies found by using the model as an explanatory tool. Although the present model is still in active development, it may pave the road for a consumer-oriented companion to interactive classical music understanding.
An Energy Efficiency Perspective on Massive MIMO Quantization
Venue: arXiv cs.IT 2016
Year: 2016

One of the basic aspects of Massive MIMO (MaMi) that is in the focus of current investigations is its potential of using low-cost and energy-efficient hardware. It is often claimed that MaMi will allow for using analog-to-digital converters (ADCs) with very low resolutions and that this will result in overall improvement of energy efficiency. In this contribution, we perform a parametric energy efficiency analysis of MaMi uplink for the entire base station receiver system with varying ADC resolutions. The analysis shows that, for a wide variety of system parameters, ADCs with intermediate bit resolutions (4 - 10 bits) are optimal in energy efficiency sense, and that using very low bit resolutions results in degradation of energy efficiency.
Multiscale Computing in the Exascale Era
Venue: arXiv cs.DC 2016
Year: 2016

We expect that multiscale simulations will be one of the main high performance computing workloads in the exascale era. We propose multiscale computing patterns as a generic vehicle to realise load balanced, fault tolerant and energy aware high performance multiscale computing. Multiscale computing patterns should lead to a separation of concerns, whereby application developers can compose multiscale models and execute multiscale simulations, while pattern software realises optimized, fault tolerant and energy aware multiscale computing. We introduce three multiscale computing patterns, present an example of the extreme scaling pattern, and discuss our vision of how this may shape multiscale computing in the exascale era.
Discrete Schroedinger Transform For Texture Recognition
Venue: arXiv cs.CV 2016
Year: 2016

This work presents a new procedure to extract features of grey-level texture images based on the discrete Schroedinger transform. This is a non-linear transform where the image is mapped as the initial probability distribution of a wave function and such distribution evolves in time following the Schroedinger equation from Quantum Mechanics. The features are provided by statistical moments of the distribution measured at different times. The proposed method is applied to the classification of three databases of textures used for benchmark and compared to other well-known texture descriptors in the literature, such as textons, local binary patterns, multifractals, among others. All of them are outperformed by the proposed method in terms of percentage of images correctly classified. The proposal is also applied to the identification of plant species using scanned images of leaves and again it outperforms other texture methods. A test with images affected by Gaussian and "salt \& pepper" noise is also carried out, also with the best performance achieved by the Schroedinger descriptors.
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
Venue: arXiv cs.CV 2016
Year: 2016

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.