OpenRaiser/Intern-Atlas huggingface dataset:
One-Shot Recognition of Manufacturing Defects in Steel Surfaces
Venue: arXiv cs.CV 2020
Year: 2020

Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.
Demonstrating 100 Gbps in and out of the public Clouds
Venue: arXiv cs.PF 2020
Year: 2020

There is increased awareness and recognition that public Cloud providers do provide capabilities not found elsewhere, with elasticity being a major driver. The value of elastic scaling is however tightly coupled to the capabilities of the networks that connect all involved resources, both in the public Clouds and at the various research institutions. This paper presents results of measurements involving file transfers inside public Cloud providers, fetching data from on-prem resources into public Cloud instances and fetching data from public Cloud storage into on-prem nodes. The networking of the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and the Google Cloud Platform, has been benchmarked. The on-prem nodes were managed by either the Pacific Research Platform or located at the University of Wisconsin - Madison. The observed sustained throughput was of the order of 100 Gbps in all the tests moving data in and out of the public Clouds and throughput reaching into the Tbps range for data movements inside the public Cloud providers themselves. All the tests used HTTP as the transfer protocol.
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
Venue: arXiv cs.LG 2020
Year: 2020

Combinatorial optimization plays an important role in real-world problem solving. In the big data era, the dimensionality of a combinatorial optimization problem is usually very large, which poses a significant challenge to existing solution methods. In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic travelling salesman problems (TSP). We demonstrate that our method can greedily remove decision variables from an optimization problem that are predicted not to be part of an optimal solution. More specifically, we investigate our model's capability to generalize on test instances that have not been seen during the training phase. We consider three scenarios where training and test instances are different in terms of: 1) problem characteristics; 2) problem sizes; and 3) problem types. Our experiments show that this machine learning based technique can generalize reasonably well over a wide range of TSP test instances with different characteristics or sizes. While the accuracy of predicting unused variables naturally deteriorates as a test instance is further away from the training set, we observe that even when tested on a different TSP problem variant, the machine learning model still makes useful predictions about which variables can be eliminated without significantly impacting solution quality.
Prta: A System to Support the Analysis of Propaganda Techniques in the News
Venue: arXiv cs.CL 2020
Year: 2020

Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 "infodemic", have brought into the spotlight the dangers of online disinformation. There has been a lot of research focusing on fact-checking and disinformation detection. However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages. Revealing the use of such techniques can help promote media literacy and critical thinking, and eventually contribute to limiting the impact of "fake news" and disinformation campaigns. Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user based on time interval, keywords, and/or political orientation of the media. Moreover, it allows users to analyze any text or URL through a dedicated interface or via an API. The system is available online: https://www.tanbih.org/prta
A Priority-Based Cross-Layer Design for Future VANETs through Full-Duplex Technology
Venue: arXiv cs.NI 2020
Year: 2020

Among all requirements for vehicle-to-everything (V2X) communications, successful delivery of packets with small delay is of the highest significance. Especially, the delivery of a message before a potential accident (i.e. emergency message) should be guaranteed. In this work, we propose a novel cross-layer design to enhance the delivery of emergency messages so that accidents can be further avoided. Particularly, in the PHY layer, imperfect full-duplex (FD) simultaneous transmitting and sensing is analysed and dynamic thresholds for determining the channel status before and during transmissions are mathematically formulated. Then a novel FD MAC protocol, named priority-based multiple access (PBMA), based on prioritised messaging between different vehicles is proposed. Average collision probability, collision duration, waiting time as well as successful delivery rate of the system are formulated too. The delivery performance of emergency messages is also mathematically derived. In addition, comparisons have been made among three different mechanisms. Benchmark is the DSRC standard which uses half-duplex (HD) technology with enhanced distributed channel access (EDCA) protocol. We also compare our proposed protocol with FD EDCA. Simulations have verified the accuracy of our analysis. They have also illustrated the delivery of emergency messages has been enhanced by deploying our proposed design.
Compact Distributed Certification of Planar Graphs
Venue: arXiv cs.DC 2020
Year: 2020

Naor, Parter, and Yogev (SODA 2020) have recently demonstrated the existence of a \emph{distributed interactive proof} for planarity (i.e., for certifying that a network is planar), using a sophisticated generic technique for constructing distributed IP protocols based on sequential IP protocols. The interactive proof for planarity is based on a distributed certification of the correct execution of any given sequential linear-time algorithm for planarity testing. It involves three interactions between the prover and the randomized distributed verifier (i.e., it is a \dMAM\/ protocol), and uses small certificates, on $O(\log n)$ bits in $n$-node networks. We show that a single interaction from the prover suffices, and randomization is unecessary, by providing an explicit description of a \emph{proof-labeling scheme} for planarity, still using certificates on just $O(\log n)$ bits. We also show that there are no proof-labeling schemes -- in fact, even no \emph{locally checkable proofs} -- for planarity using certificates on $o(\log n)$ bits.
Networks in a World Unknown: Public WhatsApp Groups in the Venezuelan Refugee Crisis
Venue: arXiv cs.SI 2020
Year: 2020

By early March 2020, five million Venezuelans had fled their home country after its complete economic and institutional collapse, and over 1.6 million have migrated to Colombia. Migrants struggle to start their lives over in Colombia, having arrived with few economic resources, and often no legal documentation, in cities with little to offer them. Venezuelan migrants, however, rely heavily on mobile phones and social media networks as lifelines for information, opportunities, and resources -- making WhatsApp both a critical tool for migrants' settlement and integration, as well as an invaluable source of data through which we can better understand migrant experiences. This thesis explores the dynamics of public WhatsApp groups used by Venezuelan migrants to Colombia, and what they can tell us about how migrants use and share information. We center our research on information spread and trust, especially as they intersect with concentration and geographic heterogeneity within groups. We analyze messages and memberships broadly, then explore interaction within groups, fake news and economic scams, and effects of the coronavirus pandemic. Our results have a range of policy implications, from reflections on Colombia's decision to shut its borders amidst the coronavirus pandemic, to understandings of how aid organizations can effectively share information over social media channels.
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Venue: arXiv cs.CV 2020
Year: 2020

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.
Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation
Venue: arXiv cs.HC 2020
Year: 2020

Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post-flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of DATA COMETS, an open-source and web-based interactive visualization tool for post-flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in-the-wild usage. A free copy of this paper and source code are available at osf.io/h4p7g
Two Dimensions for Organizing Immersive Analytics: Toward a Taxonomy for Facet and Position
Venue: arXiv cs.HC 2020
Year: 2020

As immersive analytics continues to grow as a discipline, so too should its underlying methodological support. Taxonomies play an important role for information visualization and human computer interaction. They provide an organization of the techniques used in a particular domain that better enable researchers to describe their work, discover existing methods, and identify gaps in the literature. Existing taxonomies in related fields do not capture or describe the unique paradigms employed in immersive analytics. We conceptualize a taxonomy that organizes immersive analytics according to two dimensions: spatial and visual presentation. Each intersection of this taxonomy represents a unique design paradigm which, when thoroughly explored, can aid in the design and research of new immersive analytic applications.
Red-Blue Point Separation for Points on a Circle
Venue: arXiv cs.DS 2020
Year: 2020

Given a set R of red points and a set B of blue points in the plane, the Red-Blue point separation problem asks if there are at most k lines that separate R from B, that is, each cell induced by the lines of the solution is either empty or monochromatic (containing points of only one color). A common variant of the problem is when the lines are required to be axis-parallel. The problem is known to be NP-complete for both scenarios, and W[1]-hard parameterized by k in the former setting and FPT in the latter. We demonstrate a polynomial-time algorithm for the special case when the points lie on a circle. Further, we also demonstrate the W-hardness of a related problem in the axis-parallel setting, where the question is if there are p horizontal and q vertical lines that separate R from B. The hardness here is shown in the parameter p.
Android Malware Clustering using Community Detection on Android Packages Similarity Network
Venue: arXiv cs.CR 2020
Year: 2020

The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building a scalable and obfuscation resilient similarity network infrastructure of malicious apps. Our approach is based on our proposed concept, namely malicious community, in which we consider malicious instances that share common features are the most likely part of the same malware family. Using this concept, we presumably assume that multiple similar Android apps with different authors are most likely to be malicious. Specifically, Cypider leverages this assumption for the detection of variants of known malware families and zero-day malicious apps. Cypider applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and possibly malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a one-class machine learning model for each malicious community. Besides, we proposed an enhanced Cypider framework, which requires less memory, x650, and less time to build the similarity network, x700, compared to the original version, without affecting the fingerprinting performance of the framework. We introduce a systematic approach to locate the best threshold on different feature content vectors, which simplifies the overall detection process.
Toward Enabling Reproducibility for Data-Intensive Research using the Whole Tale Platform
Venue: arXiv cs.DC 2020
Year: 2020

Whole Tale http://wholetale.org is a web-based, open-source platform for reproducible research supporting the creation, sharing, execution, and verification of "Tales" for the scientific research community. Tales are executable research objects that capture the code, data, and environment along with narrative and workflow information needed to re-create computational results from scientific studies. Creating reproducible research objects that enable reproducibility, transparency, and re-execution for computational experiments requiring significant compute resources or utilizing massive data is an especially challenging open problem. We describe opportunities, challenges, and solutions to facilitating reproducibility for data- and compute-intensive research, that we call "Tales at Scale," using the Whole Tale computing platform. We highlight challenges and solutions in frontend responsiveness needs, gaps in current middleware design and implementation, network restrictions, containerization, and data access. Finally, we discuss challenges in packaging computational experiment implementations for portable data-intensive Tales and outline future work.
Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
Venue: arXiv cs.CL 2020
Year: 2020

Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed. (2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis.
The effect of Target Normalization and Momentum on Dying ReLU
Venue: arXiv cs.LG 2020
Year: 2020

Optimizing parameters with momentum, normalizing data values, and using rectified linear units (ReLUs) are popular choices in neural network (NN) regression. Although ReLUs are popular, they can collapse to a constant function and "die", effectively removing their contribution from the model. While some mitigations are known, the underlying reasons of ReLUs dying during optimization are currently poorly understood. In this paper, we consider the effects of target normalization and momentum on dying ReLUs. We find empirically that unit variance targets are well motivated and that ReLUs die more easily, when target variance approaches zero. To further investigate this matter, we analyze a discrete-time linear autonomous system, and show theoretically how this relates to a model with a single ReLU and how common properties can result in dying ReLU. We also analyze the gradients of a single-ReLU model to identify saddle points and regions corresponding to dying ReLU and how parameters evolve into these regions when momentum is used. Finally, we show empirically that this problem persist, and is aggravated, for deeper models including residual networks.
Mean Oriented Riesz Features for Micro Expression Classification
Venue: arXiv cs.CV 2020
Year: 2020

Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human's real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact, an image sequence is transformed with this tool, then the image phase variations are extracted and filtered as proxies for motion. Furthermore, the dominant orientation constancy from the Riesz transform is exploited to average the micro-expression sequence into an image pair. Based on that, the Mean Oriented Riesz Feature description is introduced. Finally the performance of our methods are tested in two spontaneous micro-expressions databases and compared to state-of-the-art methods.
Novelty Search makes Evolvability Inevitable
Venue: arXiv cs.NE 2020
Year: 2020

Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straightforward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reach-able behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability.
Digital Social Contracts: A Foundation for an Egalitarian and Just Digital Society
Venue: arXiv cs.CY 2020
Year: 2020

Almost two centuries ago Pierre-Joseph Proudhon proposed social contracts -- voluntary agreements among free people -- as a foundation from which an egalitarian and just society can emerge. A \emph{digital social contract} is the novel incarnation of this concept for the digital age: a voluntary agreement between people that is specified, undertaken, and fulfilled in the digital realm. It embodies the notion of "code-is-law" in its purest form, in that a digital social contract is in fact a program -- code in a social contracts programming language, which specifies the digital actions parties to the social contract may take; and the parties to the contract are entrusted, equally, with the task of ensuring that each party abides by the contract. Parties to a social contract are identified via their public keys, and the one and only type of action a party to a digital social contract may take is a "digital speech act" -- signing an utterance with her private key and sending it to the other parties to the contract. Here, we present a formal definition of a digital social contract as agents that communicate asynchronously via crypto-speech acts, where the output of each agent is the input of all the other agents. We outline an abstract design for a social contracts programming language and show, via programming examples, that key application areas, including social community; simple sharing-economy applications; egalitarian currency networks; and democratic community governance, can all be expressed elegantly and efficiently as digital social contracts.