OpenRaiser/Intern-Atlas huggingface dataset:
On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification
Venue: arXiv cs.SD 2019
Year: 2019

Residual learning is a recently proposed learning framework to facilitate the training of very deep neural networks. Residual blocks or units are made of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or residual connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers that make up a residual block. While ResNet architectures for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, few works have adopted ResNet architectures so far for 1D audio classification tasks. Thus, the suitability of different residual block designs for raw audio classification is partly unknown. The purpose of this paper is to analyze and discuss the performance of several residual block implementations within a state-of-the-art CNN-based architecture for end-to-end audio classification using raw audio waveforms. For comparison purposes, we analyze as well the performance of the residual blocks under a similar 2D architecture using a conventional time-frequency audio represen-tation as input. The results show that the achieved accuracy is considerably dependent, not only on the specific residual block implementation, but also on the selected input normalization.
Challenges for Security Assessment of Enterprises in the IoT Era
Venue: arXiv cs.CR 2019
Year: 2019

For years, attack graphs have been an important tool for security assessment of enterprise networks, but IoT devices, a new player in the IT world, might threat the reliability of this tool. In this paper, we review the challenges that must be addressed when using attack graphs to model and analyze enterprise networks that include IoT devices. In addition, we propose novel ideas and countermeasures aimed at addressing these challenges.
Co-Designing in Social VR. Process awareness and suitable representations to empower user participation
Venue: arXiv cs.HC 2019
Year: 2019

To allow non-designers' involvement in design projects new methods are needed. Co-design gives the same opportunity to all the multidisciplinary participants to co-create ideas simultaneously. Nevertheless, current co-design processes involving such users tend to limit their contribution to the proposal of basic design ideas only through brainstorming. The co-design approach needs to be enhanced by a properly suited representational ecosystem supporting active participation and by conscious use of structured verbal exchanges giving awareness of the creative process. In this respect, we developed two social virtual reality co-design systems, and a co-design verbal exchange methodology to favour participants' awareness of the co-creative process. By using such representations and verbal exchanges, participants could co-create with more ease by benefiting from being informed of the process and from the collective immersion, empowering their participation. This paper presents the rationale behind this approach of using Social VR in co-design and the feedback of three co-design workshops.
Cryptocurrency Smart Contracts for Distributed Consensus of Public Randomness
Venue: arXiv cs.CR 2019
Year: 2019

Most modern electronic devices can produce a random number. However, it is difficult to see how a group of mutually distrusting entities can have confidence in any such hardware-produced stream of random numbers, since the producer could control the output to their gain. In this work, we use public and immutable cryptocurrency smart contracts, along with a set of potentially malicious randomness providers, to produce a trustworthy stream of timestamped public random numbers. Our contract eliminates the ability of a producer to predict or control the generated random numbers, including the stored history of random numbers. We consider and mitigate the threat of collusion between the randomness providers and miners in a second, more complex contract.
Toward Maximum Grip Process Modeling in Software Engineering
Venue: arXiv cs.SE 2019
Year: 2019

Process modeling (PM) in software engineering involves a specific way of understanding the world. In this context, philosophical work is not merely intrinsically important; it can also stand up to some of the more established software engineering research metrics. The object-oriented methodology takes an object as the central concept of modeling. This paper follows from a series of papers that focus on the notion of thinging in the context of the analysis phase of software system modeling. We use an abstract machine named the Thinging Machine (TM) as the mechanism by which things reveal themselves. We introduce a more in-depth investigation of a grand TM that Signifies the totality of entities in the modeled system. We also present new notions, such as maximum grip, which refers to the level of granularity of the significance where optimum visibility of the model s meaning is given. The outcomes of this research indicate a positive improvement in the field of PM that may lead to enhance understanding of the object-oriented approach. TM also presents the possibility of developing a new method in PM.
A Generative Model for Punctuation in Dependency Trees
Venue: arXiv cs.CL 2019
Year: 2019

Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree's "true" punctuation marks are not observed (Nunberg, 1990). These latent "underlying" marks serve to delimit or separate constituents in the syntax tree. When the tree's yield is rendered as a written sentence, a string rewriting mechanism transduces the underlying marks into "surface" marks, which are part of the observed (surface) string but should not be regarded as part of the tree. We formalize this idea in a generative model of punctuation that admits efficient dynamic programming. We train it without observing the underlying marks, by locally maximizing the incomplete data likelihood (similarly to EM). When we use the trained model to reconstruct the tree's underlying punctuation, the results appear plausible across 5 languages, and in particular, are consistent with Nunberg's analysis of English. We show that our generative model can be used to beat baselines on punctuation restoration. Also, our reconstruction of a sentence's underlying punctuation lets us appropriately render the surface punctuation (via our trained underlying-to-surface mechanism) when we syntactically transform the sentence.
Exploring the Role of Prior Beliefs for Argument Persuasion
Venue: arXiv cs.CL 2019
Year: 2019

Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one's stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader level factors: political and religious ideology. We find that prior beliefs affected by these reader level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers
Venue: arXiv cs.LG 2019
Year: 2019

Today's cloud service architectures follow a "one size fits all" deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiveness requirements, which as we demonstrate renders the "one size fits all" approach inefficient in practice. We use a production-grade speech recognition engine, which serves several thousands of users, and an open source computer vision based system, to explain our point. To overcome the limitations of the "one size fits all" approach, we recommend Tolerance Tiers where each MLaaS tier exposes an accuracy/responsiveness characteristic, and consumers can programmatically select a tier. We evaluate our proposal on the CPU-based automatic speech recognition (ASR) engine and cutting-edge neural networks for image classification deployed on both CPUs and GPUs. The results show that our proposed approach provides an MLaaS cloud service architecture that can be tuned by the end API user or consumer to outperform the conventional "one size fits all" approach.
Software Engineering Research Community Viewpoints on Rapid Reviews
Venue: arXiv cs.SE 2019
Year: 2019

Background: One of the most important current challenges of Software Engineering (SE) research is to provide relevant evidence to practice. In health related fields, Rapid Reviews (RRs) have shown to be an effective method to achieve that goal. However, little is known about how the SE research community perceives the potential applicability of RRs. Aims: The goal of this study is to understand the SE research community viewpoints towards the use of RRs as a means to provide evidence to practitioners. Method: To understand their viewpoints, we invited 37 researchers to analyze 50 opinion statements about RRs, and rate them according to what extent they agree with each statement. Q-Methodology was employed to identify the most salient viewpoints, represented by the so called factors. Results: Four factors were identified: Factor A groups undecided researchers that need more evidence before using RRs; Researchers grouped in Factor B are generally positive about RRs, but highlight the need to define minimum standards; Factor C researchers are more skeptical and reinforce the importance of high quality evidence; Researchers aligned to Factor D have a pragmatic point of view, considering RRs can be applied based on the context and constraints faced by practitioners. Conclusions: In conclusion, although there are opposing viewpoints, there are also some common grounds. For example, all viewpoints agree that both RRs and Systematic Reviews can be poorly or well conducted.
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree
Venue: arXiv cs.DS 2019
Year: 2019

Decision Tree is a classic formulation of active learning: given $n$ hypotheses with nonnegative weights summing to 1 and a set of tests that each partition the hypotheses, output a decision tree using the provided tests that uniquely identifies each hypothesis and has minimum (weighted) average depth. Previous works showed that the greedy algorithm achieves a $O(\log n)$ approximation ratio for this problem and it is NP-hard beat a $O(\log n)$ approximation, settling the complexity of the problem. However, for Uniform Decision Tree, i.e. Decision Tree with uniform weights, the story is more subtle. The greedy algorithm's $O(\log n)$ approximation ratio was the best known, but the largest approximation ratio known to be NP-hard is $4-\varepsilon$. We prove that the greedy algorithm gives a $O(\frac{\log n}{\log C_{OPT}})$ approximation for Uniform Decision Tree, where $C_{OPT}$ is the cost of the optimal tree and show this is best possible for the greedy algorithm. As a corollary, we resolve a conjecture of Kosaraju, Przytycka, and Borgstrom. Leveraging this result, for all $α\in(0,1)$, we exhibit a $\frac{9.01}α$ approximation algorithm to Uniform Decision Tree running in subexponential time $2^{\tilde O(n^α)}$. As a corollary, achieving any super-constant approximation ratio on Uniform Decision Tree is not NP-hard, assuming the Exponential Time Hypothesis. This work therefore adds approximating Uniform Decision Tree to a small list of natural problems that have subexponential time algorithms but no known polynomial time algorithms. All our results hold for Decision Tree with weights not too far from uniform. A key technical contribution of our work is showing a connection between greedy algorithms for Uniform Decision Tree and for Min Sum Set Cover.
Emergence of Exploratory Look-Around Behaviors through Active Observation Completion
Venue: arXiv cs.CV 2019
Year: 2019

Standard computer vision systems assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is a major challenge in itself. We address the problem of learning to look around: how can an agent learn to acquire informative visual observations? We propose a reinforcement learning solution, where the agent is rewarded for reducing its uncertainty about the unobserved portions of its environment. Specifically, the agent is trained to select a short sequence of glimpses after which it must infer the appearance of its full environment. To address the challenge of sparse rewards, we further introduce sidekick policy learning, which exploits the asymmetry in observability between training and test time. The proposed methods learn observation policies that not only perform the completion task for which they are trained, but also generalize to exhibit useful "look-around" behavior for a range of active perception tasks.
Generalization of Dempster-Shafer theory: A complex belief function
Venue: arXiv cs.AI 2019
Year: 2019

Dempster-Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. However, the existing evidence theory is insufficient to consider the situations where it has no capability to express the fluctuations of data at a given phase of time during their execution, and the uncertainty and imprecision which are inevitably involved in the data occur concurrently with changes to the phase or periodicity of the data. In this paper, therefore, a generalized Dempster-Shafer evidence theory is proposed. To be specific, a mass function in the generalized Dempster-Shafer evidence theory is modeled by a complex number, called as a complex basic belief assignment, which has more powerful ability to express uncertain information. Based on that, a generalized Dempster's combination rule is exploited. In contrast to the classical Dempster's combination rule, the condition in terms of the conflict coefficient between the evidences K<1 is released in the generalized Dempster's combination rule. Hence, it is more general and applicable than the classical Dempster's combination rule. When the complex mass function is degenerated from complex numbers to real numbers, the generalized Dempster's combination rule degenerates to the classical evidence theory under the condition that the conflict coefficient between the evidences K is less than 1. In a word, this generalized Dempster-Shafer evidence theory provides a promising way to model and handle more uncertain information.
FSM Error Messages
Venue: arXiv cs.FL 2019
Year: 2019

Computer Science students, in general, find Automata Theory difficult and mostly unrelated to their area of study. To mitigate these perceptions, FSM, a library to program state machines and grammars, was developed to bring programming to the Automata Theory classroom. The results of the library's maiden voyage at Seton Hall University had a positive impact on students, but the students found the library difficult to use due to the error messages generated. These messages were generated by the host language meaning that students needed to be familiar with the library's implementation to make sense of them. This article presents the design of and results obtained from using an error-messaging system tailor-made for FSM. The effectiveness of the library was measured by both a control group study and a survey. The results strongly suggest that the error-messaging system has had a positive impact on students' attitude towards automata theory, towards programming in FSM, and towards FSM error messages. The consequence has been a marked improvement on students' ability to implement algorithms developed as part of constructive proofs by making the debugging of FSM programs easier.
An Approach for Reviewing Security-Related Aspects in Agile Requirements Specifications of Web Applications
Venue: arXiv cs.SE 2019
Year: 2019

Defects in requirements specifications can have severe consequences during the software development lifecycle. Some of them result in overall project failure due to incorrect or missing quality characteristics such as security. There are several concerns that make security difficult to deal with; for instance, (1) when stakeholders discuss general requirements in (review) meetings, they are often not aware that they should also discuss security-related topics, and (2) they typically do not have enough security expertise. These concerns become even more challenging in agile development contexts, where lightweight documentation is typically involved. The goal of this paper is to design and evaluate an approach to support reviewing security-related aspects in agile requirements specifications of web applications. The designed approach considers user stories and security specifications as input and relates those user stories to security properties via Natural Language Processing (NLP) techniques. Based on the related security properties, our approach then identifies high-level security requirements from the Open Web Application Security Project (OWASP) to be verified and generates a focused reading techniques to support reviewers in detecting detects. We evaluate our approach via two controlled experiment trials, comparing the effectiveness and efficiency of novice inspectors verifying security aspects in agile requirements using our reading technique against using the complete list of OWASP high-level security requirements. The (statistically significant) results indicate that using the reading technique has a positive impact (with very large effect size) on the performance of inspectors in terms of effectiveness and efficiency.
Investigating Compilation Errors of Students Learning Haskell
Venue: arXiv cs.PL 2019
Year: 2019

While functional programming is an efficient way to express complex software, functional programming languages have a steep learning curve. Haskell can be challenging to learn for students who were only introduced to imperative programming. It is important to look for methods and tools that may reduce the difficulty of learning functional programming. Finding methods to help students requires understanding the errors that students make while learning Haskell. There are several previous studies revealing data about Haskell compiler errors, but they do not focus on the analysis of the compiler errors or they only study a certain kind of compiler errors. This study investigates compilation errors of novice Haskell students and make suggestions on how their learning efficiency can be improved. Unlike previous studies we focus on uncovering the root problems with the student solutions by analysing samples of their submissions.
Enhancing Python Compiler Error Messages via Stack Overflow
Venue: arXiv cs.SE 2019
Year: 2019

Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered. Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy. Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the Python programming language. Pycee automatically queries Stack Overflow to provide customised and summarised information within the IDE. We evaluated two Pycee variants through a think-aloud user study during which 16 programmers completed Python programming tasks while using Pycee. Results: The majority of participants agreed that Pycee was helpful while completing the study tasks. When compared to a baseline relying on the official Python documentation to enhance compiler error messages, participants generally preferred Pycee in terms of helpfulness, citing concrete suggestions for fixes and example code as major benefits. Conclusions: Our results confirm that data from online sources such as Stack Overflow can be successfully used to automatically enhance compiler error messages. Our work opens up venues for future work to further enhance compiler error messages as well as to automatically reuse content from Stack Overflow for other aspects of programming.
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
Venue: arXiv cs.CV 2019
Year: 2019

Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks to generate 3D shapes, none of them reach the level of quality that deep learning synthesis approaches for images provide. In this work we present a method for a convolutional point cloud decoder/generator that makes use of recent advances in the domain of image synthesis. Namely, we use Adaptive Instance Normalization and offer an intuition on why it can improve training. Furthermore, we propose extensions to the minimization of the commonly used Chamfer distance for auto-encoding point clouds. In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results. The results are evaluated in an auto-encoding setup to offer both qualitative and quantitative analysis. The proposed decoder is validated by an extensive ablation study and is able to outperform current state of the art results in a number of experiments. We show the applicability of our method in the fields of point cloud upsampling, single view reconstruction, and shape synthesis.
Pooled Steganalysis in JPEG: how to deal with the spreading strategy?
Venue: arXiv cs.CR 2019
Year: 2019

In image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonably assess that the steganalyst does not know the strategy used to spread the payload across images. To the best of our knowledge, in this case, the most appropriate solution for pooled steganalysis is to use a Single-Image Detector (SID) to estimate/quantify if an image is cover or stego, and to average the scores obtained on the set of images. In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known spreading strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach. Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy. Another interesting observation is that DeLS spreading strategy behaves really better than all the other spreading strategies. Those observations results in the experimentation with six different spreading strategies made on Jpeg images with J-UNIWARD, a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies.