OpenRaiser/Intern-Atlas huggingface dataset:
Concerns and Limitations in Agile Software Development: A Survey with Paraguayan Companies
Venue: arXiv cs.SE 2017
Year: 2017

This year, the Agile Manifesto completes seventeen years and, throughout the world, companies and researchers seek to understand their adoption stage, as well as the benefits, barriers, and limitations of agile methods. Although we have some studies and questionnaire data at the global level, we know little about how the Paraguayan software community is adopting agile methods. The present work conducted a research to set up the current stage of adoption, initial concerns and barriers of implementation of agile methods in software development companies in Paraguay. An online survey was sent to representatives of 53 Paraguayan companies. Of these, 9 (17%) companies responded. The concern about adopting more agile methods (44.44% of respondents) was the lack of reliability in product quality if developed using agile methods. The main barrier was the lack of experience (66.66%) of the companies.
Robust non-local means filter for ultrasound image denoising
Venue: arXiv cs.CV 2017
Year: 2017

This paper introduces a new approach to non-local means image denoising. Instead of using all pixels located in the search window for estimating the value of a pixel, we identify the highly corrupted pixels and assign less weight to these pixels. This method is called robust non-local means. Numerical and subjective evaluations using ultrasound images show good performances of the proposed denoising method in recovering the shape of edges and important detailed components, in comparison to traditional ultrasound image denoising methods
Decoding visemes: improving machine lipreading
Venue: arXiv cs.CV 2017
Year: 2017

Machine lipreading (MLR) is speech recognition from visual cues and a niche research problem in speech processing & computer vision. Current challenges fall into two groups: the content of the video, such as rate of speech or; the parameters of the video recording e.g, video resolution. We show that HD video is not needed to successfully lipread with a computer. The term "viseme" is used in machine lipreading to represent a visual cue or gesture which corresponds to a subgroup of phonemes where the phonemes are visually indistinguishable. A phoneme is the smallest sound one can utter, because there are more phonemes per viseme, maps between units show a many-to-one relationship. Many maps have been presented, we compare these and our results show Lee's is best. We propose a new method of speaker-dependent phoneme-to-viseme maps and compare these to Lee's. Our results show the sensitivity of phoneme clustering and we use our new knowledge to augment a conventional MLR system. It has been observed in MLR, that classifiers need training on test subjects to achieve accuracy. Thus machine lipreading is highly speaker-dependent. Conversely speaker independence is robust classification of non-training speakers. We investigate the dependence of phoneme-to-viseme maps between speakers and show there is not a high variability of visemes, but there is high variability in trajectory between visemes of individual speakers with the same ground truth. This implies a dependency upon the number of visemes within each set for each individual. We show that prior phoneme-to-viseme maps rarely have enough visemes and the optimal size, which varies by speaker, ranges from 11-35. Finally we decode from visemes back to phonemes and into words. Our novel approach uses the optimum range visemes within hierarchical training of phoneme classifiers and demonstrates a significant increase in classification accuracy.
Relationship Profiling over Social Networks: Reverse Smoothness from Similarity to Closeness
Venue: arXiv cs.SI 2017
Year: 2017

On social networks, while nodes bear rich attributes, we often lack the `semantics' of why each link is formed-- and thus we are missing the `road signs' to navigate and organize the complex social universe. How to identify relationship semantics without labels? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., education) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship-- our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning-- only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture `high-order' smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of ARP.
DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction
Venue: arXiv cs.LG 2017
Year: 2017

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affected by many complex factors such as inter-region traffic, vehicles' relations, and sudden events. However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data. This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city. This algorithm uses three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow. Each residual neural network consists of a branch of residual convolutional units. DeepTFP aggregates the outputs of the three residual neural networks to optimize the parameters of a time series prediction model. Contrast experiments on mobile time series data from the transportation system of England demonstrate that the proposed DeepTFP outperforms the Long Short-Term Memory (LSTM) architecture based method in prediction accuracy.
Accelerating CS in Parallel Imaging Reconstructions Using an Efficient and Effective Circulant Preconditioner
Venue: arXiv cs.CV 2017
Year: 2017

Purpose: Design of a preconditioner for fast and efficient parallel imaging and compressed sensing reconstructions. Theory: Parallel imaging and compressed sensing reconstructions become time consuming when the problem size or the number of coils is large, due to the large linear system of equations that has to be solved in l_1 and l_2-norm based reconstruction algorithms. Such linear systems can be solved efficiently using effective preconditioning techniques. Methods: In this paper we construct such a preconditioner by approximating the system matrix of the linear system, which comprises the data fidelity and includes total variation and wavelet regularization, by a matrix with the assumption that is a block circulant matrix with circulant blocks. Due to its circulant structure, the preconditioner can be constructed quickly and its inverse can be evaluated fast using only two fast Fourier transformations. We test the performance of the preconditioner for the conjugate gradient method as the linear solver, integrated into the Split Bregman algorithm. Results: The designed circulant preconditioner reduces the number of iterations required in the conjugate gradient method by almost a factor of~5. The speed up results in a total acceleration factor of approximately 2.5 for the entire reconstruction algorithm when implemented in MATLAB, while the initialization time of the preconditioner is negligible. Conclusion: The proposed preconditioner reduces the reconstruction time for parallel imaging and compressed sensing in a Split Bregman implementation and can easily handle large systems since it is Fourier-based, allowing for efficient computations. Key words: preconditioning; compressed sensing; Split Bregman; parallel imaging
CELIO: An application development framework for interactive spaces
Venue: arXiv cs.HC 2017
Year: 2017

Developing applications for interactive space is different from developing cross-platform applications for personal computing. Input, output, and architectural variations in each interactive space introduce big overhead in terms of cost and time for developing, deploying and maintaining applications for interactive spaces. Often, these applications become on-off experience tied to the deployed spaces. To alleviate this problem and enable rapid responsive space design applications similar to responsive web design, we present CELIO application development framework for interactive spaces. The framework is micro services based and neatly decouples application and design specifications from hardware and architecture specifications of an interactive space. In this paper, we describe this framework and its implementation details. Also, we briefly discuss the use cases developed using this framework.
Combining absolute and relative pointing for fast and accurate distant interaction
Venue: arXiv cs.HC 2017
Year: 2017

Traditional relative pointing devices such as mice and trackpads are unsuitable for pointing at distant displays, because they encumber the users by requiring either a flat surface to operate on or being held by two hands. Past research has examined many new pointing methods, but few could surpass the speed and accuracy of mice and trackpads. This paper introduces a new pointing system that is developed based on HTC Vive, a relatively low-cost virtual reality system, and proposes two methods of combining absolute and relative pointing. The proposed methods were compared against single-mode pointing methods (i.e., pure absolute pointing and pure relative pointing) in a Fitts' law study. The results show that with only a short period of practice, one hybrid pointing technique enabled faster and more accurate pointing than both single-mode pointing techniques, which included a trackpad.
A Comparative Analysis of Materialized Views Selection and Concurrency Control Mechanisms in NoSQL Databases
Venue: arXiv cs.DB 2017
Year: 2017

Increasing resource demands require relational databases to scale. While relational databases are well suited for vertical scaling, specialized hardware can be expensive. Conversely, emerging NewSQL and NoSQL data stores are designed to scale horizontally. NewSQL databases provide ACID transaction support; however, joins are limited to the partition keys, resulting in restricted query expressiveness. On the other hand, NoSQL databases are designed to scale out linearly on commodity hardware; however, they are limited by slow join performance. Hence, we consider if the NoSQL join performance can be improved while ensuring ACID semantics and without drastically sacrificing write performance, disk utilization and query expressiveness. This paper presents the Synergy system that leverages schema and workload driven mechanism to identify materialized views and a specialized concurrency control system on top of a NoSQL database to enable scalable data management with familiar relational conventions. Synergy trades slight write performance degradation and increased disk utilization for faster join performance (compared to standard NoSQL databases) and improved query expressiveness (compared to NewSQL databases). Experimental results using the TPC-W benchmark show that, for a database populated with 1M customers, the Synergy system exhibits a maximum performance improvement of 80.5% as compared to other evaluated systems.
Counterfactual Language Model Adaptation for Suggesting Phrases
Venue: arXiv cs.CL 2017
Year: 2017

Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.
Automatic Structural Scene Digitalization
Venue: arXiv cs.GR 2017
Year: 2017

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.
Eugene Garfield's Scholarly Impact: A Scientometric Review
Venue: arXiv cs.DL 2017
Year: 2017

The concept of citation indexing has become deeply involved in many parts of research itself and the broad environment in which research plays an integral role, ranging from research evaluation, numerous indicators, to an increasingly wider range of scientific disciplines. In this article, we pay tribute to Eugene Garfield and present a scientometric review of the intellectual assets that he brought to us. In addition, we explore the intellectual landscape that has subsequently evolved in connection to many of his ideas. We illustrate what systematic reviews of the scientific literature may reveal and what we may learn from the rich information conveyed through citation-induced patterns. The study is conducted with CiteSpace, one of many science mapping tools based on data from the Web of Science and Scopus. Without Garfield's inventions, none of these would be possible.
Fast Sketch-based Recovery of Correlation Outliers
Venue: arXiv cs.DS 2017
Year: 2017

Many data sources can be interpreted as time-series, and a key problem is to identify which pairs out of a large collection of signals are highly correlated. We expect that there will be few, large, interesting correlations, while most signal pairs do not have any strong correlation. We abstract this as the problem of identifying the highly correlated pairs in a collection of n mostly pairwise uncorrelated random variables, where observations of the variables arrives as a stream. Dimensionality reduction can remove dependence on the number of observations, but further techniques are required to tame the quadratic (in n) cost of a search through all possible pairs. We develop a new algorithm for rapidly finding large correlations based on sketch techniques with an added twist: we quickly generate sketches of random combinations of signals, and use these in concert with ideas from coding theory to decode the identity of correlated pairs. We prove correctness and compare performance and effectiveness with the best LSH (locality sensitive hashing) based approach.
Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques
Venue: arXiv cs.CR 2017
Year: 2017

Split manufacturing is a promising technique to defend against fab-based malicious activities such as IP piracy, overbuilding, and insertion of hardware Trojans. However, a network flow-based proximity attack, proposed by Wang et al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing is highly vulnerable. Here in this work, we present two practical layout techniques towards secure split manufacturing: (i) gate-level graph coloring and (ii) clustering of same-type gates. Our approach shows promising results against the advanced proximity attack, lowering its success rate by 5.27x, 3.19x, and 1.73x on average compared to the unprotected layouts when splitting at metal layers M1, M2, and M3, respectively. Also, it largely outperforms previous defense efforts; we observe on average 8x higher resilience when compared to representative prior art. At the same time, extensive simulations on ISCAS'85 and MCNC benchmarks reveal that our techniques incur an acceptable layout overhead. Apart from this empirical study, we provide---for the first time---a theoretical framework for quantifying the layout-level resilience against any proximity-induced information leakage. Towards this end, we leverage the notion of mutual information and provide extensive results to validate our model.
Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation
Venue: arXiv cs.CV 2017
Year: 2017

The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.
On the Effective Use of Pretraining for Natural Language Inference
Venue: arXiv cs.CL 2017
Year: 2017

Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-to-sequence models for natural language inference (NLI). Specifically, we compare three types of embeddings: random, pretrained (GloVe, word2vec), and retrofitted (pretrained plus WordNet information). We show that pretrained embeddings outperform both random and retrofitted ones in a large NLI corpus. Further experiments on more controlled data sets shed light on the contexts for which retrofitted embeddings can be useful. We also explore two principled approaches to initializing the rest of the model parameters, Gaussian and orthogonal, showing that the latter yields gains of up to 2.9% in the NLI task.
Machine Translation Evaluation with Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

We present a framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is embedded into compact distributed vector representations, and fed into a multi-layer neural network that models nonlinear interactions between each of the hypotheses and the reference, as well as between the two hypotheses. We experiment with the benchmark datasets from the WMT Metrics shared task, on which we obtain the best results published so far, with the basic network configuration. We also perform a series of experiments to analyze and understand the contribution of the different components of the network. We evaluate variants and extensions, including fine-tuning of the semantic embeddings, and sentence-based representations modeled with convolutional and recurrent neural networks. In summary, the proposed framework is flexible and generalizable, allows for efficient learning and scoring, and provides an MT evaluation metric that correlates with human judgments, and is on par with the state of the art.
Tiered Sampling: An Efficient Method for Approximate Counting Sparse Motifs in Massive Graph Streams
Venue: arXiv cs.DS 2017
Year: 2017

We introduce Tiered Sampling, a novel technique for approximate counting sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size $M$, which can be magnitudes smaller than the number of edges. Our methods addresses the challenging task of counting sparse motifs - sub-graph patterns that have low probability to appear in a sample of $M$ edges in the graph, which is the maximum amount of data available to the algorithms in each step. To obtain an unbiased and low variance estimate of the count we partition the available memory to tiers (layers) of reservoir samples. While the base layer is a standard reservoir sample of edges, other layers are reservoir samples of sub-structures of the desired motif. By storing more frequent sub-structures of the motif, we increase the probability of detecting an occurrence of the sparse motif we are counting, thus decreasing the variance and error of the estimate. We demonstrate the advantage of our method in the specific applications of counting sparse 4 and 5-cliques in massive graphs. We present a complete analytical analysis and extensive experimental results using both synthetic and real-world data. Our results demonstrate the advantage of our method in obtaining high-quality approximations for the number of 4 and 5-cliques for large graphs using a very limited amount of memory, significantly outperforming the single edge sample approach for counting sparse motifs in large scale graphs.
QFUN: Towards Machine Learning in QBF
Venue: arXiv cs.LO 2017
Year: 2017

This paper reports on the QBF solver QFUN that has won the non-CNF track in the recent QBF evaluation. The solver is motivated by the fact that it is easy to construct Quantified Boolean Formulas (QBFs) with short winning strategies (Skolem/Herbrand functions) but are hard to solve by nowadays solvers. This paper argues that a solver benefits from generalizing a set of individual wins into a strategy. This idea is realized on top of the competitive RAReQS algorithm by utilizing machine learning. The results of the implemented prototype are highly encouraging.
Solving differential equations with unknown constitutive relations as recurrent neural networks
Venue: arXiv cs.LG 2017
Year: 2017

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and we use recurrent neural network to "learn" the reaction rate from this data. This is achieved by including a discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow's recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. Use of techniques from recent deep learning literature enables training of functions with behavior manifesting over thousands of time steps. Our networks are structurally similar to recurrent neural networks, but differences in design and function require modifications to the conventional wisdom about training such networks.