OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing programming languages by community characteristics on Github and StackOverflow
Venue: arXiv cs.SE 2020
Year: 2020

The choice of programming language is a very important decision as it not only affects the performance and maintainability of the software but also dictates the talent pool and community support available. To better understand the trade-offs involved in making such a decision, we define and compute popularity, demand, availability and community engagement of programming languages through online collaboration platforms. We perform our analysis using data from Github and StackOverflow, two of the most popular programming communities. We get data related projects, languages and developer engagement from Github and programming questions with answers along with language tags from StackOverflow. We compute metrics separately for the two data sources and then combine the metrics to provide a holistic and robust picture of the communities for the most popular programming languages.
Descriptions of issues and comments for predicting issue success in software projects
Venue: arXiv cs.SE 2020
Year: 2020

Software development tasks must be performed successfully to achieve software quality and customer satisfaction. Knowing whether software tasks are likely to fail is essential to ensure the success of software projects. Issue Tracking Systems store information of software tasks (issues) and comments, which can be useful to predict issue success; however; almost no research on this topic exists. This work studies the usefulness of textual descriptions of issues and comments for predicting whether issues will be resolved successfully or not. Issues and comments of 588 software projects were extracted from four popular Issue Tracking Systems. Seven machine learning classifiers were trained on 30k issues and more than 120k comments, and more than 6000 experiments were performed to predict the success of three types of issues: bugs, improvements and new features. The results provided evidence that descriptions of issues and comments are useful for predicting issue success with more than 85% of accuracy and precision, and that the predictions of issue success vary over time. Words related to software development were particularly relevant for predicting issue success. Other communication aspects and their relationship to the success of software projects must be researched in detail using data from software tools.
Time-Dilation Attacks on the Lightning Network
Venue: arXiv cs.CR 2020
Year: 2020

Lightning Network (LN) is a widely-used network of payment channels enabling faster and cheaper Bitcoin transactions. In this paper, we outline three ways an attacker can steal funds from honest LN users. The attacks require dilating the time for victims to become aware of new blocks by eclipsing (isolating) victims from the network and delaying block delivery. While our focus is on the LN, time-dilation attacks may be relevant to any second-layer protocol that relies on a timely reaction. According to our measurements, it is currently possible to steal the total channel capacity by keeping a node eclipsed for as little as 2 hours. Since trust-minimized Bitcoin light clients currently connect to a very limited number of random nodes, running just 500 Sybil nodes allows an attacker to Eclipse 47\% of newly deployed light clients (and hence prime them for an attack). As for the victims running a full node, since they are often used by large hubs or service providers, an attacker may justify the higher Eclipse attack cost by stealing all their available liquidity. In addition, time-dilation attacks neither require access to hashrate nor purchasing from a victim. Thus, this class of attacks is a more practical way of stealing funds via Eclipse attacks than previously anticipated double-spending. We argue that simple detection techniques based on the slow block arrival alone are not effective, and implementing more sophisticated detection is not trivial. We suggest that a combination of anti-Eclipse/anti-Sybil measures are crucial for mitigating time-dilation attacks.
Recapture as You Want
Venue: arXiv cs.CV 2020
Year: 2020

With the increasing prevalence and more powerful camera systems of mobile devices, people can conveniently take photos in their daily life, which naturally brings the demand for more intelligent photo post-processing techniques, especially on those portrait photos. In this paper, we present a portrait recapture method enabling users to easily edit their portrait to desired posture/view, body figure and clothing style, which are very challenging to achieve since it requires to simultaneously perform non-rigid deformation of human body, invisible body-parts reasoning and semantic-aware editing. We decompose the editing procedure into semantic-aware geometric and appearance transformation. In geometric transformation, a semantic layout map is generated that meets user demands to represent part-level spatial constraints and further guides the semantic-aware appearance transformation. In appearance transformation, we design two novel modules, Semantic-aware Attentive Transfer (SAT) and Layout Graph Reasoning (LGR), to conduct intra-part transfer and inter-part reasoning, respectively. SAT module produces each human part by paying attention to the semantically consistent regions in the source portrait. It effectively addresses the non-rigid deformation issue and well preserves the intrinsic structure/appearance with rich texture details. LGR module utilizes body skeleton knowledge to construct a layout graph that connects all relevant part features, where graph reasoning mechanism is used to propagate information among part nodes to mine their relations. In this way, LGR module infers invisible body parts and guarantees global coherence among all the parts. Extensive experiments on DeepFashion, Market-1501 and in-the-wild photos demonstrate the effectiveness and superiority of our approach. Video demo is at: \url{https://youtu.be/vTyq9HL6jgw}.
Descriptor Revision for Conditionals: Literal Descriptors and Conditional Preservation
Venue: arXiv cs.AI 2020
Year: 2020

Descriptor revision by Hansson is a framework for addressing the problem of belief change. In descriptor revision, different kinds of change processes are dealt with in a joint framework. Individual change requirements are qualified by specific success conditions expressed by a belief descriptor, and belief descriptors can be combined by logical connectives. This is in contrast to the currently dominating AGM paradigm shaped by Alchourrón, Gärdenfors, and Makinson, where different kinds of changes, like a revision or a contraction, are dealt with separately. In this article, we investigate the realisation of descriptor revision for a conditional logic while restricting descriptors to the conjunction of literal descriptors. We apply the principle of conditional preservation developed by Kern-Isberner to descriptor revision for conditionals, show how descriptor revision for conditionals under these restrictions can be characterised by a constraint satisfaction problem, and implement it using constraint logic programming. Since our conditional logic subsumes propositional logic, our approach also realises descriptor revision for propositional logic.
Kaya: A Testing Framework for Blockchain-based Decentralized Applications
Venue: arXiv cs.SE 2020
Year: 2020

In recent years, many decentralized applications based on blockchain (DApp) have been developed. However, due to inadequate testing, DApps are easily exposed to serious vulnerabilities. We find three main challenges for DApp testing, i.e., the inherent complexity of DApp, inconvenient pre-state setting, and not-so-readable logs. In this paper, we propose a testing framework named Kaya to bridge these gaps. Kaya has three main functions. Firstly, Kaya proposes DApp behavior description language (DBDL) to make writing test cases easier. Test cases written in DBDL can also be automatically executed by Kaya. Secondly, Kaya supports a flexible and convenient way for test engineers to set the blockchain pre-states easily. Thirdly, Kaya transforms incomprehensible addresses into readable variables for easy comprehension. With these functions, Kaya can help test engineers test DApps more easily. Besides, to fit the various application environments, we provide two ways for test engineers to use Kaya, i.e., UI and command-line. Our experimental case demonstrates the potential of Kaya in helping test engineers to test DApps more easily.
Monitoring Data Distribution and Exploitation in a Global-Scale Microservice Artefact Observatory
Venue: arXiv cs.SE 2020
Year: 2020

Reusable microservice artefacts are often deployed as black or grey boxes, with little concern for their properties and quality, beyond a syntactical interface description. This leads application developers to chaotic and opportunistic assumptions about how a composite application will behave in the real world. Systematically analyzing and tracking these publicly available artefacts will grant much needed predictability to microservice-based deployments. By establishing a distributed observatory and knowledge base, it is possible to track microservice repositories and analyze the artefacts reliably, and provide insights on their properties and quality to developers and researchers alike. This position paper argues for a federated research infrastructure with consensus voting among participants to establish and preserve ground truth about the insights.
LogDos: A Novel Logging-based DDoS Prevention Mechanism in Path Identifier-Based Information Centric Networks
Venue: arXiv cs.NI 2020
Year: 2020

Information Centric Networks (ICNs) have emerged in recent years as a new networking paradigm for the next-generation Internet. The primary goal of these networks is to provide effective mechanisms for content distribution and retrieval based on in-network content caching. The design of different ICN architectures addressed many of the security issues found in the traditional Internet. Therefore, allowing for a secure, reliable, and scalable communication over the Internet. However, recent research studies showed that these architectures are vulnerable to different types of DDoS attacks. In this paper, we propose a defense mechanism against distributed denial of service attacks (DDoS) in path-identifier based information centric networks. The proposed mechanism, called LogDos, performs GET Message logging based filtering and employs Bloom filter based logging to store incoming GET messages such that corresponding content messages are verified, while filtering packets originating from malicious hosts. We develop three versions of LogDos with varying levels of storage overhead at LogDos-enabled router. Extensive simulation experiments show that LogDos is very effective against DDoS attacks as it can filter more than 99.98 % of attack traffic in different attack scenarios while incurring acceptable storage overhead.
Fast Algorithms for Join Operations on Tree Decompositions
Venue: arXiv cs.DS 2020
Year: 2020

Treewidth is a measure of how tree-like a graph is. It has many important algorithmic applications because many NP-hard problems on general graphs become tractable when restricted to graphs of bounded treewidth. Algorithms for problems on graphs of bounded treewidth mostly are dynamic programming algorithms using the structure of a tree decomposition of the graph. The bottleneck in the worst-case run time of these algorithms often is the computations for the so called join nodes in the associated nice tree decomposition. In this paper, we review two different approaches that have appeared in the literature about computations for the join nodes: one using fast zeta and Möbius transforms and one using fast Fourier transforms. We combine these approaches to obtain new, faster algorithms for a broad class of vertex subset problems known as the [σ,ρ]-domination problems. Our main result is that we show how to solve [σ,ρ]-domination problems in $O(s^{t+2} t n^2 (t\log(s)+\log(n)))$ arithmetic operations. Here, t is the treewidth, s is the (fixed) number of states required to represent partial solutions of the specific [σ,ρ]-domination problem, and n is the number of vertices in the graph. This reduces the polynomial factors involved compared to the previously best time bound (van Rooij, Bodlaender, Rossmanith, ESA 2009) of $O( s^{t+2} (st)^{2(s-2)} n^3 )$ arithmetic operations. In particular, this removes the dependence of the degree of the polynomial on the fixed number of states~$s$.
A Novel Approach to Solve K-Center Problems with Geographical Placement
Venue: arXiv cs.DC 2020
Year: 2020

The facility location problem is a well-known challenge in logistics that is proven to be NP-hard. In this paper we specifically simulate the geographical placement of facilities to provide adequate service to customers. Determining reasonable center locations is an important challenge for a management since it directly effects future service costs. Generally, the objective is to place the central nodes such that all customers have convenient access to them. We analyze the problem and compare different placement strategies and evaluate the number of required centers. We use several existing approaches and propose a new heuristic for the problem. For our experiments we consider various scenarios and employ simulation to evaluate the performance of the optimization algorithms. Our new optimization approach shows a significant improvement. The presented results are generally applicable to many domains, e.g., the placement of military bases, the planning of content delivery networks, or the placement of warehouses.
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding
Venue: arXiv cs.AI 2020
Year: 2020

Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
Venue: arXiv cs.IT 2020
Year: 2020

Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems. Considering that labeled training samples are hard to obtain, unsupervised deep learning has been proposed to solve functional optimization problems with statistical constraints recently. However, most existing problems in wireless communications are variable optimizations, and many problems are with instantaneous constraints. In this paper, we establish a unified framework of using unsupervised deep learning to solve both kinds of problems with both instantaneous and statistic constraints. For a constrained variable optimization, we first convert it into an equivalent functional optimization problem with instantaneous constraints. Then, to ensure the instantaneous constraints in the functional optimization problems, we use DNN to approximate the Lagrange multiplier functions, which is trained together with a DNN to approximate the policy. We take two resource allocation problems in ultra-reliable and low-latency communications as examples to illustrate how to guarantee the complex and stringent quality-of-service (QoS) constraints with the framework. Simulation results show that unsupervised learning outperforms supervised learning in terms of QoS violation probability and approximation accuracy of the optimal policy, and can converge rapidly with pre-training.
Model-Based Compensation of Moving Tissue for State Recognition in Robotic-Assisted Pedicle Drilling
Venue: arXiv cs.RO 2020
Year: 2020

Drilling is one of the hardest parts of pedicle screw fixation, and it is one of the most dangerous operations because inaccurate screw placement would injury vital tissues, particularly when the vertebra is not stationary. Here we demonstrate the drilling state recognition method for moving tissue by compensating the displacement based on a simplified motion predication model of a vertebra with respect to the tidal volume. To adapt it to different patients, the prediction model was built based on the physiological data recorded from subjects themselves. In addition, the spindle speed of the drilling tool was investigated to find a suitable speed for the robotic-assisted system. To ensure patient safety, a monitoring system was built based on the thrusting force and tracked position information. Finally, experiments were carried out on a fresh porcine lamellar bone fixed on a 3-PRS parallel robot used to simulate the vertebra displacement. The success rate of the robotic-assisted drilling procedure reached 95% when the moving bone was compensated.
Compressive Subspace Learning with Antenna Cross-correlations for Wideband Spectrum Sensing
Venue: arXiv cs.IT 2020
Year: 2020

Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or adopting a multiple-input multiple-output (MIMO) channel without considering the spatial correlations, which will degrade their performances. In this paper, we consider a spatially correlated MIMO channel and propose two CSL algorithms (i.e., mCSLSACC and vCSLACC) which exploit antenna cross-correlations, where the mCSLSACC utilizes an antenna averaging temporal decomposition, and the vCSLACC uses a spatial-temporal joint decomposition. For both algorithms, the conditions of statistical covariance matrices (SCMs) without noise corruption are derived. Through establishing the singular value relation of SCMs in statistical sense between the proposed and traditional CSL algorithms, we show the superiority of the proposed CSL algorithms. By further depicting the receiving correlation matrix of MIMO channel with the exponential correlation model, we give important closed-form expressions for the proposed CSL algorithms in terms of the amplification of singular values over traditional CSL algorithms. Such expressions provide a possibility to determine optimal algorithm parameters for high system performances in an analytical way. Simulations validate the correctness of this work and its performance improvement over existing works in terms of WBSS performance.
Coordinating Multiagent Industrial Symbiosis
Venue: arXiv cs.MA 2020
Year: 2020

We present a formal multiagent framework for coordinating a class of collaborative industrial practices called Industrial Symbiotic Networks (ISNs) as cooperative games. The game-theoretic formulation of ISNs enables systematic reasoning about what we call the ISN implementation problem. Specifically, the characteristics of ISNs may lead to the inapplicability of standard fair and stable benefit allocation methods. Inspired by realistic ISN scenarios and following the literature on normative multiagent systems, we consider regulations and normative socio-economic policies as coordination instruments that in combination with ISN games resolve the situation. In this multiagent system, employing Marginal Contribution Nets (MC-Nets) as rule-based cooperative game representations foster the combination of regulations and ISN games with no loss in expressiveness. We develop algorithmic methods for generating regulations that ensure the implementability of ISNs and as a policy support, present the policy requirements that guarantee the implementability of all the desired ISNs in a balanced-budget way.
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
Venue: arXiv cs.CV 2020
Year: 2020

The geometry of three-dimensional (3D) graphs, consisting of nodes and edges, plays a crucial role in many important applications. An excellent example is molecular graphs, whose geometry influences important properties of a molecule including its reactivity and biological activity. To facilitate the incorporation of geometry in deep learning on 3D graphs, we define three types of geometric graph representations: positional, angle-geometric and distance-geometric. For proof of concept, we use the distance-geometric graph representation for geometric graph convolutions. Further, to utilize standard graph convolution networks, we employ a simple edge weight / edge distance correlation scheme, whose parameters can be fixed using reference values or determined through Bayesian hyperparameter optimization. The results of geometric graph convolutions, for the ESOL and Freesol datasets, show significant improvement over those of standard graph convolutions. Our work demonstrates the feasibility and promise of incorporating geometry, using the distance-geometric graph representation, in deep learning on 3D graphs.
Shapley Value as Principled Metric for Structured Network Pruning
Venue: arXiv cs.LG 2020
Year: 2020

Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks. The usual pruning pipeline consists of ranking the network internal filters and activations with respect to their contributions to the network performance, removing the units with the lowest contribution, and fine-tuning the network to reduce the harm induced by pruning. Recent results showed that random pruning performs on par with other metrics, given enough fine-tuning resources. In this work, we show that this is not true on a low-data regime when fine-tuning is either not possible or not effective. In this case, reducing the harm caused by pruning becomes crucial to retain the performance of the network. First, we analyze the problem of estimating the contribution of hidden units with tools suggested by cooperative game theory and propose Shapley values as a principled ranking metric for this task. We compare with several alternatives proposed in the literature and discuss how Shapley values are theoretically preferable. Finally, we compare all ranking metrics on the challenging scenario of low-data pruning, where we demonstrate how Shapley values outperform other heuristics.