OpenRaiser/Intern-Atlas huggingface dataset:
Did Evolution get it right? An evaluation of Near-Infrared imaging in semantic scene segmentation using deep learning
Venue: arXiv cs.CV 2016
Year: 2016

Animals have evolved to restrict their sensing capabilities to certain region of electromagnetic spectrum. This is surprisingly a very narrow band on a vast scale which makes one think if there is a systematic bias underlying such selective filtration. The situation becomes even more intriguing when we find a sharp cutoff point at Near-infrared point whereby almost all animal vision systems seem to have a lower bound. This brings us to an interesting question: did evolution "intentionally" performed such a restriction in order to evolve higher visual cognition? In this work this question is addressed by experimenting with Near-infrared images for their potential applicability in higher visual processing such as semantic segmentation. A modified version of Fully Convolutional Networks are trained on NIR images and RGB images respectively and compared for their respective effectiveness in the wake of semantic segmentation. The results from the experiments show that visible part of the spectrum alone is sufficient for the robust semantic segmentation of the indoor as well as outdoor scenes.
On the Spatial Separation of Multi-user channels Using 73 GHz Statistical Channel Models
Venue: arXiv cs.IT 2016
Year: 2016

This paper focuses at the investigation of the degree of orthogonality of channels of multiple users in densely populated indoor and outdoor scenarios. For this purpose, a statistical millimeter wave (mmwave) MIMO channel simulator is carefully designed using state of the art channel models. At the mmwave frequencies, human/vehicular mobility around the mobile users may partially or completely block the communication link. This give rise to the consideration of new channel modeling parameter i.e. probability of a user to be in LOS dynamics. Higher line of sight (LOS) probabilities may increase the spatial correlation among the multiuser channels. Therefore, quantification of the spatial separation of users in different scenarios with distinct LOS probabilities is crucial and it is the subject of investigation of this paper. Additionally, the mutual orthogonality of channels by changing the number of base station (BS) antennas and inter antenna element distance (IED) have also been investigated. Analysis shows that LOS blockage of certain scenario has more impact at the degree of orthogonality among users as compared to the number of BS antennas and the spacing between the antenna elements.
Case Studies in Industry: What We Have Learnt
Venue: arXiv cs.SE 2016
Year: 2016

Case study research has become an important research methodology for exploring phenomena in their natural contexts. Case studies have earned a distinct role in the empirical analysis of software engineering phenomena which are difficult to capture in isolation. Such phenomena often appear in the context of methods and development processes for which it is difficult to run large, controlled experiments as they usually have to reduce the scale in several respects and, hence, are detached from the reality of industrial software development. The other side of the medal is that the realistic socio-economic environments where we conduct case studies -- with real-life cases and realistic conditions -- also pose a plethora of practical challenges to planning and conducting case studies. In this experience report, we discuss such practical challenges and the lessons we learnt in conducting case studies in industry. Our goal is to help especially inexperienced researchers facing their first case studies in industry by increasing their awareness for typical obstacles they might face and practical ways to deal with those obstacles.
Predicting drug recalls from Internet search engine queries
Venue: arXiv cs.IR 2016
Year: 2016

Batches of pharmaceutical are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report abnormalities to medical authorities. Here we test the hypothesis that defective production lots can be detected earlier by monitoring queries to Internet search engines. We extracted queries from the USA to the Bing search engine which mentioned one of 5,195 pharmaceutical drugs during 2015 and all recall notifications issued by the Food and Drug Administration (FDA) during that year. By using attributes that quantify the change in query volume at the state level, we attempted to predict if a recall of a specific drug will be ordered by FDA in a time horizon ranging from one to 40 days in future. Our results show that future drug recalls can indeed be identified with an AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall will occur one day ahead. This performance degrades as prediction is made for longer periods ahead. The most indicative attributes for prediction are sudden spikes in query volume about a specific medicine in each state. Recalls of prescription drugs and those estimated to be of medium-risk are more likely to be identified using search query data. These findings suggest that aggregated Internet search engine data can be used to facilitate in early warning of faulty batches of medicines.
Exploiting Unlabeled Data for Neural Grammatical Error Detection
Venue: arXiv cs.CL 2016
Year: 2016

Identifying and correcting grammatical errors in the text written by non-native writers has received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVMs and convolutional networks with fixed-size context window.
Hyperspectral CNN Classification with Limited Training Samples
Venue: arXiv cs.CV 2016
Year: 2016

Hyperspectral imaging sensors are becoming increasingly popular in robotics applications such as agriculture and mining, and allow per-pixel thematic classification of materials in a scene based on their unique spectral signatures. Recently, convolutional neural networks have shown remarkable performance for classification tasks, but require substantial amounts of labelled training data. This data must sufficiently cover the variability expected to be encountered in the environment. For hyperspectral data, one of the main variations encountered outdoors is due to incident illumination, which can change in spectral shape and intensity depending on the scene geometry. For example, regions occluded from the sun have a lower intensity and their incident irradiance skewed towards shorter wavelengths. In this work, a data augmentation strategy based on relighting is used during training of a hyperspectral convolutional neural network. It allows training to occur in the outdoor environment given only a small labelled region, which does not need to sufficiently represent the geometric variability of the entire scene. This is important for applications where obtaining large amounts of training data is labourious, hazardous or difficult, such as labelling pixels within shadows. Radiometric normalisation approaches for pre-processing the hyperspectral data are analysed and it is shown that methods based on the raw pixel data are sufficient to be used as input for the classifier. This removes the need for external hardware such as calibration boards, which can restrict the application of hyperspectral sensors in robotics applications. Experiments to evaluate the classification system are carried out on two datasets captured from a field-based platform.
Awesome Typography: Statistics-Based Text Effects Transfer
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effects for different characters. To address this issue, our key idea is to exploit the analytics on the high regularity of the spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example. Experimental results demonstrate the superiority of our method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation.
Deep, Dense, and Low-Rank Gaussian Conditional Random Fields
Venue: arXiv cs.CV 2016
Year: 2016

In this work we introduce a fully-connected graph structure in the Deep Gaussian Conditional Random Field (G-CRF) model. For this we express the pairwise interactions between pixels as the inner-products of low-dimensional embeddings, delivered by a new subnetwork of a deep architecture. We efficiently minimize the resulting energy by solving the resulting low-rank linear system with conjugate gradients, and derive an analytic expression for the gradient of our embeddings which allows us to train them end-to-end with backpropagation. We demonstrate the merit of our approach by achieving state of the art results on three challenging Computer Vision benchmarks, namely semantic segmentation, human parts segmentation, and saliency estimation. Our implementation is fully GPU based, built on top of the Caffe library, and will be made publicly available.
Benchmarking OODBs with a Generic Tool
Venue: arXiv cs.DB 2016
Year: 2016

We present in this paper a generic object-oriented benchmark (OCB: the Object Clustering Benchmark) that has been designed to evaluate the performances of Object-Oriented Data-bases (OODBs), and more specifically the performances of clustering policies within OODBs. OCB is generic because its sample database may be customized to fit any of the databases in-troduced by the main existing benchmarks, e.g., OO1 (Object Operation 1) or OO7. The first version of OCB was purposely clustering-oriented due to a clustering-oriented workload, but OCB has been thoroughly extended to be able to suit other purposes. Eventually, OCB's code is compact and easily portable. OCB has been validated through two implementations: one within the O2 OODB and another one within the Texas persistent object store. The perfor-mances of a specific clustering policy called DSTC (Dynamic, Statistical, Tunable Clustering) have also been evaluated with OCB.
Robust Variational Inference
Venue: arXiv cs.LG 2016
Year: 2016

Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for the evidence, which is applicable when the majority of the training set samples are random noise objects. We provide experiments for variational autoencoders to show advantage of the objective over the evidence lower bound on synthetic datasets obtained by adding uninformative noise objects to MNIST and OMNIGLOT. Additionally, for the original MNIST and OMNIGLOT datasets we observe a small improvement over the non-robust evidence lower bound.
A Technique to Share Multiple Secret Images
Venue: arXiv cs.CR 2016
Year: 2016

Visual Cryptography comes under cryptography domain. It deals with encrypting and decrypting of visual information like pictures, texts, videos $ etc.$ Multi Secret Image Sharing (MSIS) scheme is a part of visual cryptography that provides a protected method to transmit more than one secret images over a communication channel. Conventionally, transmission of a single secret image is possible over a channel at a time. But as technology grows, there emerge a need for sharing more than one secret image. An $(n, n)$-MSIS scheme is used to encrypt $n$ secret images into $n$ meaningless noisy images that are stored over different servers. To recover $n$ secret images all $n$ noisy images are required. At earlier time, the main problem with secret sharing schemes was that attacker can partially figure out secret images, even by getting access of $n-1$ or fewer noisy images. To tackle with this security issue, there arises a need of secure MSIS scheme, so that attacker can not retrieve any information by using less than $n-1$ noisy images. In this paper, we propose a secure $(n, n+1)$-MSIS scheme using additive modulo operation for grayscale and colored images. For checking the effectiveness of proposed scheme; Correlation, MSE and PSNR techniques are used. The experimental results show that the proposed scheme is highly secured and altering of noisy images will not reveal any partial information about secret images. The proposed $(n, n+1)$-MSIS scheme outperforms the existing MSIS schemes in terms of security.
Dynamic Reductions for Model Checking Concurrent Software
Venue: arXiv cs.LO 2016
Year: 2016

Symbolic model checking of parallel programs stands and falls with effective methods of dealing with the explosion of interleavings. We propose a dynamic reduction technique to avoid unnecessary interleavings. By extending Lipton's original work with a notion of bisimilarity, we accommodate dynamic transactions, and thereby reduce dependence on the accuracy of static analysis, which is a severe bottleneck in other reduction techniques. The combination of symbolic model checking and dynamic reduction techniques has proven to be challenging in the past. Our generic reduction theorem nonetheless enables us to derive an efficient symbolic encoding, which we implemented for IC3 and BMC. The experiments demonstrate the power of dynamic reduction on several case studies and a large set of SVCOMP benchmarks.
Generalization of the de Bruijn's identity to general $ϕ$-entropies and $ϕ$-Fisher informations
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose generalizations of the de Bruijn's identities based on extensions of the Shannon entropy, Fisher information and their associated divergences or relative measures. The foundation of these generalizations are the $ϕ$-entropies and divergences of the Csiszá's class (or Salicrú's class) considered within a multidimensional context, included the monodimensional case, and for several type of noisy channels characterized by a more general probability distribution beyond the well-known Gaussian noise. It is found that the gradient and/or the hessian of these entropies or divergences with respect to the noise parameters give naturally rise to generalized versions of the Fisher information or divergence, which are named as the $ϕ$-Fisher information (divergence). The obtained identities can be viewed as further extensions of the classical de Bruijn's identity. Analogously, it is shown that a similar relation holds between the $ϕ$-divergence and a extended mean-square error, named $ϕ$-mean square error, for the Gaussian channel.
Hierarchical Online Intrusion Detection for SCADA Networks
Venue: arXiv cs.CR 2016
Year: 2016

We propose a novel hierarchical online intrusion detection system (HOIDS) for supervisory control and data acquisition (SCADA) networks based on machine learning algorithms. By utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newton optimization algorithm using the Broyden-Fletcher-Goldfarb-Shanno approach. The detection system is capable of accelerating detection by information gain based feature selection or principle component analysis based dimension reduction. By evaluating our system using the KDD99 dataset and the industrial control system dataset, we demonstrate that HOIDS is highly scalable, efficient and cost effective for securing SCADA infrastructures.
Teaching Functional Patterns through Robotic Applications
Venue: arXiv cs.CY 2016
Year: 2016

We present our approach to teaching functional programming to First Year Computer Science students at Middlesex University through projects in robotics. A holistic approach is taken to the curriculum, emphasising the connections between different subject areas. A key part of the students' learning is through practical projects that draw upon and integrate the taught material. To support these, we developed the Middlesex Robotic plaTfOrm (MIRTO), an open-source platform built using Raspberry Pi, Arduino, HUB-ee wheels and running Racket (a LISP dialect). In this paper we present the motivations for our choices and explain how a number of concepts of functional programming may be employed when programming robotic applications. We present some students' work with robotics projects: we consider the use of robotics projects to have been a success, both for their value in reinforcing students' understanding of programming concepts and for their value in motivating the students.
Domain-Specific Languages of Mathematics: Presenting Mathematical Analysis Using Functional Programming
Venue: arXiv cs.CY 2016
Year: 2016

We present the approach underlying a course on "Domain-Specific Languages of Mathematics", currently being developed at Chalmers in response to difficulties faced by third-year students in learning and applying classical mathematics (mainly real and complex analysis). The main idea is to encourage the students to approach mathematical domains from a functional programming perspective: to identify the main functions and types involved and, when necessary, to introduce new abstractions; to give calculational proofs; to pay attention to the syntax of the mathematical expressions; and, finally, to organise the resulting functions and types in domain-specific languages.
Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany
Venue: arXiv cs.SE 2016
Year: 2016

For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice.
A Fair Relay Selection Scheme for a DF Cooperative Network With Spatially Random Relays
Venue: arXiv cs.NI 2016
Year: 2016

A new, fair relay selection scheme is proposed for a dual-hop decode-and-forward network with randomly-distributed relays. Most of the reported works in the literature achieve fairness at the expense of degrading the outage probability performance. In addition, they often assume that the number and locations of the relays are known. In contrast, the proposed scheme achieves fairness in a random field of relays without deteriorating the outage probability performance. In this scheme, each relay maintains a countdown timer whose initial value is a function of the relay location and a tunable parameter which controls the level of fairness. The optimum value of this parameter is evaluated in an offline manner so as to achieve fairness by making the average powers consumed by the relays as close as possible. An exact analytical expression is derived for the average power consumed by each relay. This expression is then used to show the superiority of the proposed scheme over opportunistic relaying and random relay selection schemes.
Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms
Venue: arXiv cs.SD 2016
Year: 2016

One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transform the audio into spectral representations, as a function of frequency, amplitude and phase transformation. In this work, we take a purely data-driven approach to understand the temporal dynamics of audio at the raw signal level. We maximize the information extracted from the raw signal through a deep convolutional neural network (CNN) model. Our CNN model is trained on the urbansound8k dataset. We discover that salient audio patterns embedded in the raw waveforms can be efficiently extracted through a combination of nonlinear filters learned by the CNN model.