OpenRaiser/Intern-Atlas huggingface dataset:
Are Computer Science and Engineering Graduates Ready for the Software Industry? Experiences from an Industrial Student Training Program
Venue: arXiv cs.CY 2018
Year: 2018

It has been 50 years since the term software engineering was coined in 1968 at a NATO conference. The field should be relatively mature by now, with most established universities covering core software engineering topics in their Computer Science programs and others offering specialized degrees. However, still many practitioners lament a lack of skills in new software engineering hires. With the growing demand for software engineers from the industry, this apparent gap becomes more and more pronounced. One corporate strategy to address this gap is for the industry to develop supplementary training programs before the hiring process, which could also help them screen viable candidates. In this paper, we report on our experiences and lessons learned in conducting a summer school program aimed at screening new graduates, introducing them to core skills relevant to the organization and the industry, and assessing their attitudes toward mastering those skills before the hiring process begins. Our experience suggests that such initiatives can be mutually beneficial for new hires and companies. We support this insight with pre- and post-training data collected from the participants during the first edition of such a summer school and a follow-up questionnaire conducted after a year with the graduates, 50% of whom was hired by the company shortly after the summer school.
Energy Sustainable IoT with Individual QoS Constraints Through MISO SWIPT Multicasting
Venue: arXiv cs.IT 2018
Year: 2018

Enabling technologies for energy sustainable Internet of Things (IoT) are of paramount importance since the proliferation of high data communication demands of low power network devices. In this paper, we consider a Multiple Input Single Output (MISO) multicasting IoT system comprising of a multiantenna Transmitter (TX) simultaneously transferring information and power to low power and data hungry IoT Receivers (RXs). Each IoT device is assumed to be equipped with Power Splitting (PS) hardware that enables Energy Harvesting (EH) and imposes an individual Quality of Service (QoS) constraint to the downlink communication. We study the joint design of TX precoding and IoT PS ratios for the considered MISO Simultaneous Wireless Information and Power Transfer (SWIPT) multicasting IoT system with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements. In our novel EH fairness maximization formulation, we adopt a generic Radio Frequency (RF) EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem. For this problem, we first present an equivalent semi-definite relaxation formulation and then prove it possesses unique global optimality. We also derive tight upper and lower bounds on the globally optimal solution that are exploited in obtaining low complexity algorithmic implementations for the targeted joint design. Analytical expressions for the optimal TX beamforming directions, power allocation, and IoT PS ratios are also presented. Our representative numerical results including comparisons with benchmark designs corroborate the usefulness of proposed framework and provide useful insights on the interplay of critical system parameters.
ICADx: Interpretable computer aided diagnosis of breast masses
Venue: arXiv cs.CV 2018
Year: 2018

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.
Cloud Brokerage: A Systematic Survey
Venue: arXiv cs.DC 2018
Year: 2018

Background: The proliferation of cloud providers and provisioning levels has opened a space for cloud brokerage services. Brokers intermediate between cloud customers and providers to assist the customer in selecting the most suitable cloud service, helping to manage the dimensionality, heterogeneity, and uncertainty associated with cloud services. Objective: This paper identifies and classifies approaches to realise cloud brokerage. By doing so, this paper presents an understanding of the state of the art and a novel taxonomy to characterise cloud brokers. Method: We conducted a systematic literature survey to compile studies related to cloud brokerage and explore how cloud brokers are engineered. We analysed the studies from multiple perspectives, such as motivation, functionality, engineering approach, and evaluation methodology. Results: The survey resulted in a knowledge base of current proposals for realising cloud brokers. The survey identified surprising differences between the studies' implementations, with engineering efforts directed at combinations of market-based solutions, middlewares, toolkits, algorithms, semantic frameworks, and conceptual frameworks. Conclusion: Our comprehensive meta-analysis shows that cloud brokerage is still a formative field. There is no doubt that progress has been achieved in the field but considerable challenges remain to be addressed. This survey identifies such challenges and directions for future research.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Venue: arXiv cs.IR 2018
Year: 2018

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.
Quantum error-correcting codes: the unit-derived strategy
Venue: arXiv cs.IT 2018
Year: 2018

Series of maximum distance quantum error-correcting codes are developed and analysed. For a given rate and given error-correction capability, quantum error-correcting codes with these specifications are constructed. The codes are explicit with efficient decoding algorithms. For a given field maximum length quantum codes are constructed.
Visual-Inertial Target Tracking and Motion Planning for UAV-based Radiation Detection
Venue: arXiv cs.RO 2018
Year: 2018

This paper addresses the problem of detecting radioactive material in transit using an UAV of minimal sensing capability, where the objective is to classify the target's radioactivity as the vehicle plans its paths through the workspace while tracking the target for a short time interval. To this end, we propose a motion planning framework that integrates tightly-coupled visual-inertial localization and target tracking. In this framework,the 3D workspace is known, and this information together with the UAV dynamics, is used to construct a navigation function that generates dynamically feasible, safe paths which avoid obstacles and provably converge to the moving target. The efficacy of the proposed approach is validated through realistic simulations in Gazebo.
Topological design of an asymmetric 3-translational parallel mechanism with zero coupling degree and motion decoupling
Venue: arXiv cs.RO 2018
Year: 2018

In this paper a new asymmetric 3-translational (3T) parallel manipulator, i.e., RPa(3R) 2R+RPa, with zero coupling degree and decoupled motion is firstly proposed according to topology design theory of parallel mechanism (PM) based on position and orientation characteristics (POC) equations. The main topological characteristics such as POC, degree of freedom and coupling degree are calculated. Then, the analytical formula for the direct and inverse kinematic are directly derived since coupling degree of the PM is zero. The study of singular configurations is simple because of the independence of the kinematic chains.
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
Venue: arXiv cs.LG 2018
Year: 2018

We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y$ as $[\X; \Y]$ and applied a matrix completion (MC) method to fill the missing entries, under the assumption that $[\X; \Y]$ is of low-rank. However, since entries of $\Y$ take binary values in the multi-label setting, it is unlikely that $\Y$ is of low-rank. Moreover, such assumption implies a linear relationship between $\X$ and $\Y$ which may not hold in practice. In this paper, we consider a latent matrix $\Z$ that produces the probability $σ(Z_{ij})$ of generating label $Y_{ij}$, where $σ(\cdot)$ is nonlinear. Considering label correlation, we assume $[\X; \Z]$ is of low-rank, and propose an MC algorithm based on subgradient descent named co-completion (COCO) motivated by elastic net and one-bit MC. We give a theoretical bound on the recovery effect of COCO and demonstrate its practical usefulness through experiments.
A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory
Venue: arXiv cs.AI 2018
Year: 2018

Mathematics has played a substantial role to revolutionize the medical science. Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper, we used an expert system based on soft set theory and fuzzy set theory named as a soft expert system to diagnose tropical disease dengue. The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.
Wayfinding through an unfamiliar environment
Venue: arXiv cs.HC 2018
Year: 2018

Strategies for finding one's way through an unfamiliar environment may be helped by computer generated 2D maps, 3D virtual environments, or other navigation aids. The relative effectiveness of 2D and 3D virtual navigation aids was investigated. The wayfinding experiments (navigation tests) were conducted in a large, park-like environment. 24 participants (12 men, 12 women; age range = 22-50 years; M=32, SD = 7.4) were divided into three groups of four individuals per gender, who 1) explored a computer generated 2D map of the given route prior to navigation, 2) received a silent guided tour by means of an interactive 3D virtual representation, or 3) acquired direct experience of the real-world route through a silent guided tour where they were accompanied by a human individual who had expert knowledge of all the routes in the park. Participants from the different preparation groups then had to find the same route again on their own. 12 observers (six men and six women) were given a "simple" route with only one critical turn, and the other 12 a "complex" route with six critical turns. Navigation performances were compared with those of three experts who were highly familiar with all the routes of the park. Those among the naive participants who had benefitted from a direct experience (guided tour) prior to navigation, all found their way again on the simple and complex routes. Those who had explored the interactive 3D virtual environment were all unable to find their way on the complex route. The relative scale representation in the virtual 3D environment may have given incorrect impressions of relative distances between salient objects along the itinerary, rendering important landmark information useless.
Attributes in Multiple Facial Images
Venue: arXiv cs.CV 2018
Year: 2018

Facial attribute recognition is conventionally computed from a single image. In practice, each subject may have multiple face images. Taking the eye size as an example, it should not change, but it may have different estimation in multiple images, which would make a negative impact on face recognition. Thus, how to compute these attributes corresponding to each subject rather than each single image is a profound work. To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image. Then, we develop two approaches to address the inconsistency issue. Experimental results show that the proposed methods can handle facial attribute estimation on either multiple still images or video frames, and can correct the incorrectly annotated labels. The experiments are conducted on two large public databases with annotations of facial attributes.
V2X Downlink Coverage Analysis with a Realistic Urban Vehicular Model
Venue: arXiv cs.NI 2018
Year: 2018

As the realization of vehicular communication such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) is imperative for the autonomous driving cars, the understanding of realistic vehicle-to-everything (V2X) models is needed. While previous research has mostly targeted vehicular models in which vehicles are randomly distributed and the variable of carrier frequency was not considered, a more realistic analysis of the V2X model is proposed in this paper. We use a one-dimensional (1D) Poisson cluster process (PCP) to model a realistic scenario of vehicle distribution in a perpendicular cross line road urban area and compare the coverage results with the previous research that distributed vehicles randomly by Poisson Point Process (PPP). Moreover, we incorporate the effect of different carrier frequencies, mmWave and sub-6 GHz, to our analysis by altering the antenna radiation pattern accordingly. Results indicated that while the effect of clustering led to lower outage, using mmWave had even more significance in leading to lower outage. Moreover, line-of-sight (LoS) interference links are shown to be more dominant in lowering the outage than the non-line-of-sight (NLoS) links even though they are less in number. The analytical results give insight into designing and analyzing the urban V2X channels, and are verified by actual urban area three-dimensional (3D) ray-tracing simulation.
Reworked Second Order Blind Identification and Support Vector Machine technique towards imagery movement identification from EEG signals
Venue: arXiv cs.HC 2018
Year: 2018

During imagery motor movements tasks, the so called mu and beta event related desynchronization (ERD) and synchronization (ERS) are taking place, allowing us to determine human patient imagery movement. However, initial recordings of electroencephalography (EEG) signals contain system and environmental noise as well as interference that must be ejected in order to separate the ERS/ERD events from the rest of the signal. This paper presents a new technique based on a reworked Second Order Blind Identification (SOBI) algorithm for noise removal while imagery movement classification is implemented using Support Vector Machine (SVM) technique.
Non-convex non-local flows for saliency detection
Venue: arXiv cs.CV 2018
Year: 2018

We propose and numerically solve a new variational model for automatic saliency detection in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0 < p < 1). The related non-convex non-local reactive flows are then considered and applied for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) images. A fast convolutional kernel based approximated solution is computed. The numerical experiments show how the non-convexity related to the hyperLaplacian operators provides monotonically better results in terms of the standard metrics.
Non-Gaussian Hyperplane Tessellations and Robust One-Bit Compressed Sensing
Venue: arXiv cs.IT 2018
Year: 2018

We show that a tessellation generated by a small number of random affine hyperplanes can be used to approximate Euclidean distances between any two points in an arbitrary bounded set $T$, where the random hyperplanes are generated by subgaussian or heavy-tailed normal vectors and uniformly distributed shifts. We derive quantitative bounds on the number of hyperplanes needed for constructing such tessellations in terms of natural metric complexity measures of $T$ and the desired approximation error. Our work extends significantly prior results in this direction, which were restricted to Gaussian hyperplane tessellations of subsets of the Euclidean unit sphere. As an application, we obtain new reconstruction results in memoryless one-bit compressed sensing with non-Gaussian measurement matrices. We show that by quantizing at uniformly distributed thresholds, it is possible to accurately reconstruct low-complexity signals from a small number of one-bit quantized measurements, even if the measurement vectors are drawn from a heavy-tailed distribution. Our reconstruction results are uniform in nature and robust in the presence of pre-quantization noise on the analog measurements as well as adversarial bit corruptions in the quantization process. Moreover we show that if the measurement matrix is subgaussian then accurate recovery can be achieved via a convex program.
Use of symmetric kernels for convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

At this work we introduce horizontally symmetric convolutional kernels for CNNs which make the network output invariant to horizontal flips of the image. We also study other types of symmetric kernels which lead to vertical flip invariance, and approximate rotational invariance. We show that usage of such kernels acts as regularizer, and improves generalization of the convolutional neural networks at the cost of more complicated training process.
Optimal Hashing in External Memory
Venue: arXiv cs.DS 2018
Year: 2018

Hash tables are a ubiquitous class of dictionary data structures. However, standard hash table implementations do not translate well into the external memory model, because they do not incorporate locality for insertions. Iacono and Patracsu established an update/query tradeoff curve for external hash tables: a hash table that performs insertions in $O(λ/B)$ amortized IOs requires $Ω(\log_λN)$ expected IOs for queries, where $N$ is the number of items that can be stored in the data structure, $B$ is the size of a memory transfer, $M$ is the size of memory, and $λ$ is a tuning parameter. They provide a hashing data structure that meets this curve for $λ$ that is $Ω(\log\log M + \log_M N)$. Their data structure, which we call an \defn{IP hash table}, is complicated and, to the best of our knowledge, has not been implemented. In this paper, we present a new and much simpler optimal external memory hash table, the \defn{Bundle of Arrays Hash Table} (BOA). BOAs are based on size-tiered LSMs, a well-studied data structure, and are almost as easy to implement. The BOA is optimal for a narrower range of $λ$. However, the simplicity of BOAs allows them to be readily modified to achieve the following results: \begin{itemize} \item A new external memory data structure, the \defn{Bundle of Trees Hash Table} (BOT), that matches the performance of the IP hash table, while retaining some of the simplicity of the BOAs. \item The \defn{cache-oblivious Bundle of Trees Hash Table} (COBOT), the first cache-oblivious hash table. This data structure matches the optimality of BOTs and IP hash tables over the same range of $λ$. \end{itemize}
Implicit Language Model in LSTM for OCR
Venue: arXiv cs.CV 2018
Year: 2018

Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown. One key difference between current neural network techniques using LSTMs and the previous state-of-the-art HMM systems is that HMM systems have a strong independence assumption. In comparison LSTMs have no explicit constraints on the amount of context that can be considered during decoding. In this paper we show that they learn an implicit LM and attempt to characterize the strength of the LM in terms of equivalent n-gram context. We show that this implicitly learned language model provides a 2.4\% CER improvement on our synthetic test set when compared against a test set of random characters (i.e. not naturally occurring sequences), and that the LSTM learns to use up to 5 characters of context (which is roughly 88 frames in our configuration). We believe that this is the first ever attempt at characterizing the strength of the implicit LM in LSTM based OCR systems.