OpenRaiser/Intern-Atlas huggingface dataset:
Correction of "A Comparative Study to Benchmark Cross-project Defect Prediction Approaches"
Venue: arXiv cs.SE 2017
Year: 2017

Unfortunately, the article "A Comparative Study to Benchmark Cross-project Defect Prediction Approaches" has a problem in the statistical analysis which was pointed out almost immediately after the pre-print of the article appeared online. While the problem does not negate the contribution of the the article and all key findings remain the same, it does alter some rankings of approaches used in the study. Within this correction, we will explain the problem, how we resolved it, and present the updated results.
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Venue: arXiv cs.DC 2017
Year: 2017

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.
Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
Venue: arXiv cs.OH 2017
Year: 2017

Because of vast volume of data being produced by today's scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific datasets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this paper, we present a survey of existing lossy compressors. Then we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties of any dataset to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality, provide various global distortion analysis comparing the original data with the decompressed data and statistical analysis of the compression error. Z-checker can perform the analysis with either coarse granularity or fine granularity, such that the users and developers can select the best-fit, adaptive compressors for different parts of the dataset. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the datasets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific datasets.
Identifying the potential of Near Data Computing for Apache Spark
Venue: arXiv cs.DC 2017
Year: 2017

While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. There is also a renewed interest is Near Data Computing (NDC) due to technological advancement in the last decade. However, it is not known if NDC architectures can improve the performance of big data processing frameworks such as Apache Spark. In this position paper, we hypothesize in favour of NDC architecture comprising programmable logic based hybrid 2D integrated processing-in-memory and in-storage processing for Apache Spark, by extensive profiling of Apache Spark based workloads on Ivy Bridge Server.
Interference Modeling in Cognitive Radio Networks: A Survey
Venue: arXiv cs.NI 2017
Year: 2017

One of the fundamental elements impacting the performance of a wireless system is interference, which has been a long-term issue in wireless networks. In the case of cognitive radio (CR) networks, the problem of interference is tremendously crucial. In other words, CR keeps the important promise of not producing any harmful interference to the primary user (PU) system. Thus, it is essential to investigate the impact of interference caused to the PUs so that its detrimental effect on the performance of the PU system performance is reduced. Study of cognitive interference generally includes developing a model to statistically demonstrate the power of cognitive interference at the PUs, which then can be utilized to examine different performance measures. Having inspected the different models for channel interference present in the literature, it can be obviously seen that interference models have been gradually evolved in terms of complication and sophistication. Although numerous papers can be found in the literature that have investigated different models for interference, to the best of our knowledge, very few publications are available that provide a review of all models and their comparisons. This paper is a collection of state-of-the-art in interference modeling which overviews and compares different models in the literature to provide the valuable insights for researchers when modeling the interference in a specific scenario.
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
Venue: arXiv cs.LG 2017
Year: 2017

This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state spaces efficiently. We test the proposed method in a simulated environment, and show that it is more accurate than existing methods and significantly better in scalability. We also show that the proposed method can extend many existing methods to high-dimensional state spaces. We then apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.
Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Objective: To apply deep learning pose estimation algorithms for vision-based assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine participants with Parkinson's disease (PD) and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used to extract movement trajectories from videos of PD assessments. Features of the movement trajectories were used to detect and estimate the severity of parkinsonism and LID using random forest. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. Results: For LID, the communication task yielded the best results for dyskinesia (severity estimation: r = 0.661, detection: AUC = 0.930). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. Conclusion: This paper presents the first application of deep learning for vision-based assessment of parkinsonism and LID and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Significance: The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD.
Probabilistic Model Counting with Short XORs
Venue: arXiv cs.LO 2017
Year: 2017

The idea of counting the number of satisfying truth assignments (models) of a formula by adding random parity constraints can be traced back to the seminal work of Valiant and Vazirani, showing that NP is as easy as detecting unique solutions. While theoretically sound, the random parity constraints in that construction have the following drawback: each constraint, on average, involves half of all variables. As a result, the branching factor associated with searching for models that also satisfy the parity constraints quickly gets out of hand. In this work we prove that one can work with much shorter parity constraints and still get rigorous mathematical guarantees, especially when the number of models is large so that many constraints need to be added. Our work is based on the realization that the essential feature for random systems of parity constraints to be useful in probabilistic model counting is that the geometry of their set of solutions resembles an error-correcting code.
Cloud Computing - Architecture and Applications
Venue: arXiv cs.DC 2017
Year: 2017

In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.
Benchmarking Multimodal Sentiment Analysis
Venue: arXiv cs.MM 2017
Year: 2017

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal sentiment analysis research: the role of speaker-independent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal sentiment analysis and also demonstrates the different facets of analysis to be considered while performing such tasks.
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Venue: arXiv cs.CV 2017
Year: 2017

Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years, computerized histology diagnosis has become one of the most rapidly expanding fields in medical image computing, which alleviates pathologists' workload and reduces misdiagnosis rate. However, automatic detection of lymph node metastases from whole slide images remains a challenging problem, due to the large-scale data with enormous resolutions and existence of hard mimics. In this paper, we propose a novel framework by leveraging fully convolutional networks for efficient inference to meet the speed requirement for clinical practice, while reconstructing dense predictions under different offsets for ensuring accurate detection on both micro- and macro-metastases. Incorporating with the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge corroborated the efficacy of our method. Compared with the state-of-the-art methods, our method achieved superior performance with a faster speed on the tumor localization task and surpassed human performance on the WSI classification task.
Dimensional Affect and Expression in Natural and Mediated Interaction
Venue: arXiv cs.HC 2017
Year: 2017

There is a perceived controversy as to whether the cognitive representation of affect is better modelled using a dimensional or categorical theory. This paper first suggests that these views are, in fact, compatible. The paper then discusses this theme and related issues in reference to a commonly stated application domain of research on human affect and expression: human computer interaction (HCI). The novel suggestion here is that a more realistic framing of studies of human affect in expression with reference to HCI and, particularly HCHI (Human-Computer-Human Interaction) entails some re-formulation of the approach to the basic phenomena themselves. This theme is illustrated with several examples from several recent research projects.
Robust stability conditions for feedback interconnections of distributed-parameter negative imaginary systems
Venue: arXiv cs.SY 2017
Year: 2017

Sufficient and necessary conditions for the stability of positive feedback interconnections of negative imaginary systems are derived via an integral quadratic constraint (IQC) approach. The IQC framework accommodates distributed-parameter systems with irrational transfer function representations, while generalising existing results in the literature and allowing exploitation of flexibility at zero and infinite frequencies to reduce conservatism in the analysis. The main results manifest the important property that the negative imaginariness of systems gives rise to a certain form of IQCs on positive frequencies that are bounded away from zero and infinity. Two additional sets of IQCs on the DC and instantaneous gains of the systems are shown to be sufficient and necessary for closed-loop stability along a homotopy of systems.
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
Venue: arXiv cs.CV 2017
Year: 2017

Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-to-end fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.
Adaptive Delivery in Caching Networks
Venue: arXiv cs.IT 2017
Year: 2017

The problem of content delivery in caching networks is investigated for scenarios where multiple users request identical files. Redundant user demands are likely when the file popularity distribution is highly non-uniform or the user demands are positively correlated. An adaptive method is proposed for the delivery of redundant demands in caching networks. Based on the redundancy pattern in the current demand vector, the proposed method decides between the transmission of uncoded messages or the coded messages of [1] for delivery. Moreover, a lower bound on the delivery rate of redundant requests is derived based on a cutset bound argument. The performance of the adaptive method is investigated through numerical examples of the delivery rate of several specific demand vectors as well as the average delivery rate of a caching network with correlated requests. The adaptive method is shown to considerably reduce the gap between the non-adaptive delivery rate and the lower bound. In some specific cases, using the adaptive method, this gap shrinks by almost 50% for the average rate.
On Some Exponential Sums Related to the Coulter's Polynomial
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, the formulas of some exponential sums over finite field, related to the Coulter's polynomial, are settled based on the Coulter's theorems on Weil sums, which may have potential application in the construction of linear codes with few weights.
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
Venue: arXiv cs.SY 2017
Year: 2017

This paper addresses the problem of crack detection which is essential for health monitoring of built infrastructure. Our approach includes two stages, data collection using unmanned aerial vehicles (UAVs) and crack detection using histogram analysis. For the data collection, a 3D model of the structure is first created by using laser scanners. Based on the model, geometric properties are extracted to generate way points necessary for navigating the UAV to take images of the structure. Then, our next step is to stick together those obtained images from the overlapped field of view. The resulting image is then clustered by histogram analysis and peak detection. Potential cracks are finally identified by using locally adaptive thresholds. The whole process is automatically carried out so that the inspection time is significantly improved while safety hazards can be minimised. A prototypical system has been developed for evaluation and experimental results are included.
Coordination of Dynamic Software Components with JavaBIP
Venue: arXiv cs.SE 2017
Year: 2017

JavaBIP allows the coordination of software components by clearly separating the functional and coordination aspects of the system behavior. JavaBIP implements the principles of the BIP component framework rooted in rigorous operational semantics. Recent work both on BIP and JavaBIP allows the coordination of static components defined prior to system deployment, i.e., the architecture of the coordinated system is fixed in terms of its component instances. Nevertheless, modern systems, often make use of components that can register and deregister dynamically during system execution. In this paper, we present an extension of JavaBIP that can handle this type of dynamicity. We use first-order interaction logic to define synchronization constraints based on component types. Additionally, we use directed graphs with edge coloring to model dependencies among components that determine the validity of an online system. We present the software architecture of our implementation, provide and discuss performance evaluation results.
Joint Sum Rate And Error Probability Optimization: Finite Blocklength Analysis
Venue: arXiv cs.IT 2017
Year: 2017

We study the tradeoff between the sum rate and the error probability in downlink of wireless networks. Using the recent results on the achievable rates of finite-length codewords, the problem is cast as a joint optimization of the network sum rate and the per-user error probability. Moreover, we develop an efficient algorithm based on the divide-and-conquer technique to simultaneously maximize the network sum rate and minimize the maximum users' error probability and to evaluate the effect of the codewords length on the system performance. The results show that, in delay-constrained scenarios, optimizing the per-user error probability plays a key role in achieving high throughput.
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Venue: arXiv cs.CV 2017
Year: 2017

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.
Delay Analysis of Multichannel Parallel Contention Tree Algorithms (MP-CTA)
Venue: arXiv cs.IT 2017
Year: 2017

Contention tree algorithm is initially invented as a solution to improve the stable throughput problem of Slotted ALOHA in multiple access schemes. Even though the throughput is stabilized in tree algorithms, the delay of requests may grow to infinity with respect to the arrival rate of the system. Delay depends heavily on the exploration of the tree structure, i.e., breadth search, or depth search. Breadth search is necessary for faster exploration of tree. The analytical probability distribution of delay, which is available mostly for depth search, is not generalizable to all breadth search. In this paper we fill this gap through though arbitrary grouping of branches and including this in the delay analysis. This enables obtaining the delay analysis of any contention tree algorithm that runs a breadth first search exploration. We show through simulations that the analysis is in agreement with the realizations.
Low-Resource Neural Headline Generation
Venue: arXiv cs.CL 2017
Year: 2017

Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller datasets by the means of pretraining. We propose new methods that enable pre-training all the parameters of the model and utilize all available text, resulting in improvements by up to 32.4% relative in perplexity and 2.84 points in ROUGE.
Speed-up of Self-Organizing Networks for Routing Problems in a Polygonal Domain
Venue: arXiv cs.RO 2017
Year: 2017

Routing problems are optimization problems that consider a set of goals in a graph to be visited by a vehicle (or a fleet of them) in an optimal way, while numerous constraints have to be satisfied. We present a solution based on multidimensional scaling which significantly reduces computational time of a self-organizing neural network solving a typical routing problem -- the Travelling Salesman Problem (TSP) in a polygonal domain, i.e. in a space where obstacles are represented by polygons. The preliminary results show feasibility of the proposed approach and although the results are presented only for TSP, the method is general so it can be used also for other variants of routing problems.