OpenRaiser/Intern-Atlas huggingface dataset:
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Venue: arXiv cs.DC 2017
Year: 2017

We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed.
Time-Space Tradeoffs for Learning from Small Test Spaces: Learning Low Degree Polynomial Functions
Venue: arXiv cs.LG 2017
Year: 2017

We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior work. This extension is based on a measure of how matrices amplify the 2-norms of probability distributions that is more refined than the 2-norms of these matrices. As applications that follow from our new technique, we show that any algorithm that learns $m$-variate homogeneous polynomial functions of degree at most $d$ over $\mathbb{F}_2$ from evaluations on randomly chosen inputs either requires space $Ω(mn)$ or $2^{Ω(m)}$ time where $n=m^{Θ(d)}$ is the dimension of the space of such functions. These bounds are asymptotically optimal since they match the tradeoffs achieved by natural learning algorithms for the problems.
Embracing a new era of highly efficient and productive quantum Monte Carlo simulations
Venue: arXiv cs.DC 2017
Year: 2017

QMCPACK has enabled cutting-edge materials research on supercomputers for over a decade. It scales nearly ideally but has low single-node efficiency due to the physics-based abstractions using array-of-structures objects, causing inefficient vectorization. We present a systematic approach to transform QMCPACK to better exploit the new hardware features of modern CPUs in portable and maintainable ways. We develop miniapps for fast prototyping and optimizations. We implement new containers in structure-of-arrays data layout to facilitate vectorizations by the compilers. Further speedup and smaller memory-footprints are obtained by computing data on the fly with the vectorized routines and expanding single-precision use. All these are seamlessly incorporated in production QMCPACK. We demonstrate upto 4.5x speedups on recent Intel processors and IBM Blue Gene/Q for representative workloads. Energy consumption is reduced significantly commensurate to the speedup factor. Memory-footprints are reduced by up-to 3.8x, opening the possibility to solve much larger problems of future.
Internet of Tangible Things (IoTT): Challenges and Opportunities for Tangible Interaction with IoT
Venue: arXiv cs.HC 2017
Year: 2017

In the Internet of Things era, an increasing number of household devices and everyday objects are able to send to and retrieve information from the Internet, offering innovative services to the user. However, most of these devices provide only smartphone or web interfaces to control the IoT object properties and functions. As a result, generally, the interaction is disconnected from the physical world, decreasing the user experience and increasing the risk of isolating the user in digital bubbles. We argue that tangible interaction can counteract this trend and this paper discusses the potential benefits and the still open challenges of tangible interaction applied to the Internet of Things. To underline this need, we introduce the term Internet of Tangible Things. In the article, after an analysis of current open challenges for Human-Computer Interaction in IoT, we summarize current trends in tangible interaction and extrapolate eight tangible interaction properties that could be exploited for designing novel interactions with IoT objects. Through a systematic literature review of tangible interaction applied to IoT, we show what has been already explored in the systems that pioneered the field and the future explorations that still have to be conducted.
Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance
Venue: arXiv cs.CR 2017
Year: 2017

As the size and complexity of software systems increase, the number and sophistication of software security flaws increase as well. The analysis of these flaws began as a manual approach, but it soon became apparent that tools were necessary to assist human experts in this task, resulting in a number of techniques and approaches that automated aspects of the vulnerability analysis process. Recently, DARPA carried out the Cyber Grand Challenge, a competition among autonomous vulnerability analysis systems designed to push the tool-assisted human-centered paradigm into the territory of complete automation. However, when the autonomous systems were pitted against human experts it became clear that certain tasks, albeit simple, could not be carried out by an autonomous system, as they require an understanding of the logic of the application under analysis. Based on this observation, we propose a shift in the vulnerability analysis paradigm, from tool-assisted human-centered to human-assisted tool-centered. In this paradigm, the automated system orchestrates the vulnerability analysis process, and leverages humans (with different levels of expertise) to perform well-defined sub-tasks, whose results are integrated in the analysis. As a result, it is possible to scale the analysis to a larger number of programs, and, at the same time, optimize the use of expensive human resources. In this paper, we detail our design for a human-assisted automated vulnerability analysis system, describe its implementation atop an open-sourced autonomous vulnerability analysis system that participated in the Cyber Grand Challenge, and evaluate and discuss the significant improvements that non-expert human assistance can offer to automated analysis approaches.
Space Subdivision to Speed-up Convex Hull Construction in E3
Venue: arXiv cs.DS 2017
Year: 2017

Convex hulls are fundamental geometric tools used in a number of algorithms. This paper presents a fast, simple to implement and robust Smart Convex Hull (S-CH) algorithm for computing the convex hull of a set of points in E3. This algorithm is based on "spherical" space subdivision. The main idea of the S-CH algorithm is to eliminate as many input points as possible before the convex hull construction. The experimental results show that only a very small number of points are used for the final convex hull calculation. Experiments made also proved that the proposed S-CH algorithm achieves a better time complexity in comparison with other algorithms in E3.
Measuring Inconsistency in Argument Graphs
Venue: arXiv cs.AI 2017
Year: 2017

There have been a number of developments in measuring inconsistency in logic-based representations of knowledge. In contrast, the development of inconsistency measures for computational models of argument has been limited. To address this shortcoming, this paper provides a general framework for measuring inconsistency in abstract argumentation, together with some proposals for specific measures, and a consideration of measuring inconsistency in logic-based instantiations of argument graphs, including a review of some existing proposals and a consideration of how existing logic-based measures of inconsistency can be applied.
Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining
Venue: arXiv cs.CY 2017
Year: 2017

Data Mining is best-known for its analytical and prediction capabilities. It is used in several areas such as fraud detection, predicting client behavior, money market behavior, bankruptcy prediction. It can also help in establishing an educational ecosystem, which discovers useful knowledge, and assist educators to take proactive decisions to boost student performance and employability. This paper presents an empirical study that compares varied classification algorithms on two datasets of MCA (Masters in Computer Applications) students collected from various affiliated colleges of a reputed state university in India. One dataset includes only primary attributes, whereas other dataset is feeded with secondary psychometric attributes in it. The results showcase that solely primary academic attributes do not lead to smart prediction accuracy of students employability, once they square measure within the initial year of their education. The study analyzes and stresses the role of secondary psychometric attributes for better prediction accuracy and analysis of students performance. Timely prediction and analysis of students performance can help Management, Teachers and Students to work on their gray areas for better results and employment opportunities.
Identifying Reference Spans: Topic Modeling and Word Embeddings help IR
Venue: arXiv cs.CL 2017
Year: 2017

The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system's performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.
Communication-Free Parallel Supervised Topic Models
Venue: arXiv cs.LG 2017
Year: 2017

Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC) methods are commonly used in learning graphical models. However, MCMC cannot be directly applied in learning topic models because of the quasi-ergodicity problem caused by multimodal distribution of topics. In this paper, we develop an embarrassingly parallel MCMC algorithm for sLDA. Our algorithm works by switching the order of sampled topics combination and labeling variable prediction in sLDA, in which it overcomes the quasi-ergodicity problem because high-dimension topics that follow a multimodal distribution are projected into one-dimension document labels that follow a unimodal distribution. Our empirical experiments confirm that the out-of-sample prediction performance using our embarrassingly parallel algorithm is comparable to non-parallel sLDA while the computation time is significantly reduced.
Design and Optimization of VoD schemes with Client Caching in Wireless Multicast Networks
Venue: arXiv cs.IT 2017
Year: 2017

Due to the explosive growth in multimedia traffic, the scalability of video-on-demand (VoD) services becomes increasingly important. By exploiting the potential cache ability at the client side, the performance of VoD multicast delivery can be improved through video segment pre-caching. In this paper, we address the performance limits of client caching enabled VoD schemes in wireless multicast networks with asynchronous requests. Both reactive and proactive systems are investigated. Specifically, for the reactive system where videos are transmitted on demand, we propose a joint cache allocation and multicast delivery scheme to minimize the average bandwidth consumption under the zero-delay constraint. For the proactive system where videos are periodically broadcasted, a joint design of the cache-bandwidth allocation algorithm and the delivery mechanism is developed to minimize the average waiting time under the total bandwidth constraint. In addition to the full access pattern where clients view videos in their entirety, we further consider the access patterns with random endpoints, fixed-size intervals and downloading demand, respectively. The impacts of different access patterns on the resource-allocation algorithm and the delivery mechanism are elaborated. Simulation results validate the accuracy of the analytical results and also provide useful insights in designing VoD networks with client caching.
More Accurate Recommendations for Method-Level Changes
Venue: arXiv cs.SE 2017
Year: 2017

During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments. We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.
Local Cyber-physical Attack with Leveraging Detection in Smart Grid
Venue: arXiv cs.SY 2017
Year: 2017

A well-designed attack in the power system can cause an initial failure and then results in large-scale cascade failure. Several works have discussed power system attack through false data injection, line-maintaining attack, and line-removing attack. However, the existing methods need to continuously attack the system for a long time, and, unfortunately, the performance cannot be guaranteed if the system states vary. To overcome this issue, we consider a new type of attack strategy called combinational attack which masks a line-outage at one position but misleads the control center on line outage at another position. Therefore, the topology information in the control center is interfered by our attack. We also offer a procedure of selecting the vulnerable lines of its kind. The proposed method can effectively and continuously deceive the control center in identifying the actual position of line-outage. The system under attack will be exposed to increasing risks as the attack continuously. Simulation results validate the efficiency of the proposed attack strategy.
Identification of Dynamic Systems with Interval Arithmetic
Venue: arXiv cs.SY 2017
Year: 2017

This paper aims to identify three electrical systems: a series RLC circuit, a motor/generator coupled system, and the Duffing-Ueda oscillator. In order to obtain the system's models was used the error reduction ratio and the Akaike information criterion. Our approach to handle the numerical errors was the interval arithmetic by means of the resolution of the least squares estimation. The routines was implemented in Intlab, a Matlab toolbox devoted to arithmetic interval. Finally, the interval RMSE was calculated to verify the quality of the obtained models. The applied methodology was satisfactory, since the obtained intervals encompass the system's data and allow to demonstrate how the numerical errors affect the answers.
Automatic Selection of t-SNE Perplexity
Venue: arXiv cs.AI 2017
Year: 2017

t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection. In practice, proper tuning of t-SNE perplexity requires users to understand the inner working of the method as well as to have hands-on experience. We propose a model selection objective for t-SNE perplexity that requires negligible extra computation beyond that of the t-SNE itself. We empirically validate that the perplexity settings found by our approach are consistent with preferences elicited from human experts across a number of datasets. The similarities of our approach to Bayesian information criteria (BIC) and minimum description length (MDL) are also analyzed.
A Hint-Based Technique for System Level Model-Based Test Case Prioritization
Venue: arXiv cs.SE 2017
Year: 2017

Test Case Prioritization (TCP) techniques aim at proposing new test case execution orders to favor the achievement of certain testing goal, such as fault detection. Current TCP research focus mainly on code-based regression testing; however in the Model-Based Testing (MBT) context, we still need more investigation. General TCP techniques do not use historical information, since this information is often unavailable. Therefore, techniques use different sources of information to guide prioritization. We propose a novel technique that guides its operation using provided hints, the Hint-Based Adaptive Random Prioritization - HARP. Hints are indications or suggestions provided by developers about error-prone functionalities. As hints may be hard to collect automatically, we also propose an approach of collecting them. To validate our findings, we performed an experiment measuring the effect of introducing hints to HARP. It shows that hints can improve HARP's performance comparing to its baseline. Then, we investigated the ability of developers/managers to provide good hints and used them in a case study. This analysis showed that developers and managers are able to provide useful hints, which improves HARP's fault detection comparing to its baseline. Nonetheless, the provided hints should be consensual among the development team members.
Test Case Prioritization Techniques for Model-Based Testing: A Replicated Study
Venue: arXiv cs.SE 2017
Year: 2017

Recently, several Test Case Prioritization (TCP) techniques have been proposed to order test cases for achieving a goal during test execution, particularly, revealing faults sooner. In the Model-Based Testing (MBT) context, such techniques are usually based on heuristics related to structural elements of the model and derived test cases. In this sense, techniques' performance may vary due to a number of factors. While empirical studies comparing the performance of TCP techniques have already been presented in literature, there is still little knowledge, particularly in the MBT context, about which factors may influence the outcomes suggested by a TCP technique. In a previous family of empirical studies focusing on labeled transition systems, we identified that the model layout, i.e. amount of branches, joins, and loops in the model, alone may have little influence on the performance of TCP techniques investigated, whereas characteristics of test cases that actually fail definitely influences their performance. However, we considered only synthetic artifacts in the study, which reduced the ability of representing properly the reality. In this paper, we present a replication of one of these studies, now with a larger and more representative selection of techniques and considering test suites from industrial applications as experimental objects. Our objective is to find out whether the results remain while increasing the validity in comparison to the original study. Results reinforce that there is no best performer among the investigated techniques and characteristics of test cases that fail represent an important factor, although adaptive random based techniques are less affected by it.
SESA: Supervised Explicit Semantic Analysis
Venue: arXiv cs.CL 2017
Year: 2017

In recent years supervised representation learning has provided state of the art or close to the state of the art results in semantic analysis tasks including ranking and information retrieval. The core idea is to learn how to embed items into a latent space such that they optimize a supervised objective in that latent space. The dimensions of the latent space have no clear semantics, and this reduces the interpretability of the system. For example, in personalization models, it is hard to explain why a particular item is ranked high for a given user profile. We propose a novel model of representation learning called Supervised Explicit Semantic Analysis (SESA) that is trained in a supervised fashion to embed items to a set of dimensions with explicit semantics. The model learns to compare two objects by representing them in this explicit space, where each dimension corresponds to a concept from a knowledge base. This work extends Explicit Semantic Analysis (ESA) with a supervised model for ranking problems. We apply this model to the task of Job-Profile relevance in LinkedIn in which a set of skills defines our explicit dimensions of the space. Every profile and job are encoded to this set of skills their similarity is calculated in this space. We use RNNs to embed text input into this space. In addition to interpretability, our model makes use of the web-scale collaborative skills data that is provided by users for each LinkedIn profile. Our model provides state of the art result while it remains interpretable.
Contextuality from missing and versioned data
Venue: arXiv cs.DB 2017
Year: 2017

Traditionally categorical data analysis (e.g. generalized linear models) works with simple, flat datasets akin to a single table in a database with no notion of missing data or conflicting versions. In contrast, modern data analysis must deal with distributed databases with many partial local tables that need not always agree. The computational agents tabulating these tables are spatially separated, with binding speed-of-light constraints and data arriving too rapidly for these distributed views ever to be fully informed and globally consistent. Contextuality is a mathematical property which describes a kind of inconsistency arising in quantum mechanics (e.g. in Bell's theorem). In this paper we show how contextuality can arise in common data collection scenarios, including missing data and versioning (as in low-latency distributed databases employing snapshot isolation). In the companion paper, we develop statistical models adapted to this regime.