OpenRaiser/Intern-Atlas huggingface dataset:
An Experimental mmWave Channel Model for UAV-to-UAV Communications
Venue: arXiv cs.NI 2020
Year: 2020

Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high-bandwidth wireless technologies for both radio access and backhaul. With this respect, the millimeter wave (mmWave) spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra-high data-rates for aerial platforms. Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro-mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments. To fill this gap, we propose an empirical propagation loss model for UAV-to-UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.
Towards a Systems Engineering based Automotive Product Engineering Process
Venue: arXiv cs.SE 2020
Year: 2020

Deficit and redundancies in existing automotive product development hinder a systems engineering based development. In this paper we discuss a methodical procedure to eliminate deficits in the current product development and in turn to enable the introduction of a new systems engineering based development methodology. As the core part of our approach, we discuss how to transform an opaque heterogeneous product development to a homogenous consistent product development taking into account existing disciplines. Our approach paves the way to achieve a process structure that is more amenable to verification and validation. We show the effectiveness of our proposed solution approach on an automotive use case.
Efficient Evaluation of the Partition Function of RBMs with Annealed Importance Sampling
Venue: arXiv cs.LG 2020
Year: 2020

Probabilistic models based on Restricted Boltzmann Machines (RBMs) imply the evaluation of normalized Boltzmann factors, which in turn require from the evaluation of the partition function Z. The exact evaluation of Z, though, becomes a forbiddingly expensive task as the system size increases. This even worsens when one considers most usual learning algorithms for RBMs, where the exact evaluation of the gradient of the log-likelihood of the empirical distribution of the data includes the computation of Z at each iteration. The Annealed Importance Sampling (AIS) method provides a tool to stochastically estimate the partition function of the system. So far, the standard use of the AIS algorithm in the Machine Learning context has been done using a large number of Monte Carlo steps. In this work we show that this may not be required if a proper starting probability distribution is employed as the initialization of the AIS algorithm. We analyze the performance of AIS in both small- and large-sized problems, and show that in both cases a good estimation of Z can be obtained with little computational cost.
PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites
Venue: arXiv cs.CR 2020
Year: 2020

Phishing has grown significantly in the past few years and is predicted to further increase in the future. The dynamics of phishing introduce challenges in implementing a robust phishing detection system and selecting features which can represent phishing despite the change of attack. In this paper, we propose PhishZip which is a novel phishing detection approach using a compression algorithm to perform website classification and demonstrate a systematic way to construct the word dictionaries for the compression models using word occurrence likelihood analysis. PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. We also propose the use of compression ratio as a novel machine learning feature which significantly improves machine learning based phishing detection over previous studies. Using compression ratios as additional features, the true positive rate significantly improves by 30.3% (from 51.47% to 81.77%), while the accuracy increases by 11.84% (from 71.20% to 83.04%).
Comparative Analysis of Polynomial and Rational Approximations of Hyperbolic Tangent Function for VLSI Implementation
Venue: arXiv cs.AR 2020
Year: 2020

Deep neural networks yield the state-of-the-art results in many computer vision and human machine interface applications such as object detection, speech recognition etc. Since, these networks are computationally expensive, customized accelerators are designed for achieving the required performance at lower cost and power. One of the key building blocks of these neural networks is non-linear activation function such as sigmoid, hyperbolic tangent (tanh), and ReLU. A low complexity accurate hardware implementation of the activation function is required to meet the performance and area targets of the neural network accelerators. Even though, various methods and implementations of tanh activation function have been published, a comparative study is missing. This paper presents comparative analysis of polynomial and rational methods and their hardware implementation.
Survey of Human Factors in Crisis Responsive Software Development
Venue: arXiv cs.SE 2020
Year: 2020

Software development, despite all the significant improvements it contributes to society, is a very expensive high-risk venture. Every software project commences with the intention to deliver a software product on time and within budget, but a great percentage of these software projects either fail to achieve their full potential or fail entirely. A lot of factors come to play in this regard and the dominant amongst them, which is often overlooked and oftentimes the biggest or strongest factor, is the human factor, as software development is a basic human endeavor. Knowledge and understanding of human factors can greatly influence the success of software development. The purpose of this survey is to find out and throw more light on the human factors that have influences in crisis responsive software development, spell out some recommendations, and suggest future research directions. In this paper, we use a case study to investigate the human factors.
Reinforcement Learning Assisted Load Test Generation for E-Commerce Applications
Venue: arXiv cs.PF 2020
Year: 2020

Background: End-user satisfaction is not only dependent on the correct functioning of the software systems but is also heavily dependent on how well those functions are performed. Therefore, performance testing plays a critical role in making sure that the system responsively performs the indented functionality. Load test generation is a crucial activity in performance testing. Existing approaches for load test generation require expertise in performance modeling, or they are dependent on the system model or the source code. Aim: This thesis aims to propose and evaluate a model-free learning-based approach for load test generation, which doesn't require access to the system models or source code. Method: In this thesis, we treated the problem of optimal load test generation as a reinforcement learning (RL) problem. We proposed two RL-based approaches using q-learning and deep q-network for load test generation. In addition, we demonstrated the applicability of our tester agents on a real-world software system. Finally, we conducted an experiment to compare the efficiency of our proposed approaches to a random load test generation approach and a baseline approach. Results: Results from the experiment show that the RL-based approaches learned to generate effective workloads with smaller sizes and in fewer steps. The proposed approaches led to higher efficiency than the random and baseline approaches. Conclusion: Based on our findings, we conclude that RL-based agents can be used for load test generation, and they act more efficiently than the random and baseline approaches.
MiniConf -- A Virtual Conference Framework
Venue: arXiv cs.HC 2020
Year: 2020

MiniConf is a framework for hosting virtual academic conferences motivated by the sudden inability for these events to be hosted globally. The framework is designed to be global and asynchronous, interactive, and to promote browsing and discovery. We developed the system to be sustainable and maintainable, in particular ensuring that it is open-source, easy to setup, and scalable on minimal hardware. In this technical report, we discuss design decisions, provide technical detail, and show examples of a case study deployment.
Micro SIDs: a solution for Efficient Representation of Segment IDs in SRv6 Networks
Venue: arXiv cs.NI 2020
Year: 2020

The Segment Routing (SR) architecture is based on loose source routing. A list of instructions, called segments can be added to the packet headers, to influence the forwarding and the processing of the packets in an SR enabled network. In SRv6 (Segment Routing over IPv6 data plane) the segments are represented with IPv6 addresses, which are 16 bytes long. There are some SRv6 service scenarios that may require to carry a large number of segments in the IPv6 packet headers. This can be inefficient due to the overheads of the segment list, or can even cause processing issues in the crossed nodes due to the very large header to be processed. In this paper we present the Micro SID solution for the efficient representation of segment identifiers. With this solution, the length of the segment list can be drastically reduced.
Computing nearest neighbour interchange distances between ranked phylogenetic trees
Venue: arXiv cs.DS 2020
Year: 2020

Many popular algorithms for searching the space of leaf-labelled trees are based on tree rearrangement operations. Under any such operation, the problem is reduced to searching a graph where vertices are trees and (undirected) edges are given by pairs of trees connected by one rearrangement operation (sometimes called a move). Most popular are the classical nearest neighbour interchange, subtree prune and regraft, and tree bisection and reconnection moves. The problem of computing distances, however, is NP-hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although ranked phylogenetic trees are one of the central objects of interest in applications such as cancer research, immunology, and epidemiology, the computational complexity of the shortest path problem for these trees remained unsolved for decades. In this paper, we settle this problem for the ranked nearest neighbour interchange operation by establishing that the complexity depends on the weight difference between the two types of tree rearrangements (rank moves and edge moves), and varies from quadratic, which is the lowest possible complexity for this problem, to NP-hard, which is the highest. In particular, our result provides the first example of a phylogenetic tree rearrangement operation for which shortest paths, and hence the distance, can be computed efficiently. Specifically, our algorithm scales to trees with thousands of leaves (and likely hundreds of thousands if implemented efficiently). We also connect the problem of computing distances in our graph of ranked trees with the well-known version of this problem on unranked trees by introducing a parameter for the weight difference between move types. We propose to study a family of shortest path problems indexed by this parameter with computational complexity varying from quadratic to NP-hard.
Context-Aware Attentive Knowledge Tracing
Venue: arXiv cs.LG 2020
Year: 2020

Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner's future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to $6\%$ in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.
User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights
Venue: arXiv cs.HC 2020
Year: 2020

A goal of clinical researchers is to understand the progression of a disease through a set of biomarkers. Researchers often conduct observational studies, where they collect numerous samples from selected subjects throughout multiple years. Hidden Markov Models (HMMs) can be applied to discover latent states and their transition probabilities over time. However, it is challenging for clinical researchers to interpret the outcomes and to gain insights about the disease. Thus, this demo introduces an interactive visualization system called DPVis, which was designed to help researchers to interactively explore HMM outcomes. The demo provides guidelines of how to implement the clinician-in-the-loop approach for analyzing longitudinal, observational health data with visual analytics.
Exploring the Impact of COVID-19 Lockdown on Social Roles and Emotions while Working from Home
Venue: arXiv cs.HC 2020
Year: 2020

In the opening months of 2020, COVID-19 changed the way for which people work, forcing more people to work from home. This research investigates the impact of COVID-19 on five researchers' work and private roles, happiness, and mobile and desktop activity patterns. Desktop and smartphone application usage were gathered before and during COVID-19. Individuals' roles and happiness were captured through experience sampling. Our analysis show that researchers tend to work more during COVID-19 resulting an imbalance of work and private roles. We also found that as working styles and patterns as well as individual behaviour changed, reported valence distribution was less varied in the later weeks of the pandemic when compared to the start. This shows a resilient adaptation to the disruption caused by the pandemic.
A Case Study on Software Vulnerability Coordination
Venue: arXiv cs.SE 2020
Year: 2020

Context: Coordination is a fundamental tenet of software engineering. Coordination is required also for identifying discovered and disclosed software vulnerabilities with Common Vulnerabilities and Exposures (CVEs). Motivated by recent practical challenges, this paper examines the coordination of CVEs for open source projects through a public mailing list. Objective: The paper observes the historical time delays between the assignment of CVEs on a mailing list and the later appearance of these in the National Vulnerability Database (NVD). Drawing from research on software engineering coordination, software vulnerabilities, and bug tracking, the delays are modeled through three dimensions: social networks and communication practices, tracking infrastructures, and the technical characteristics of the CVEs coordinated. Method: Given a period between 2008 and 2016, a sample of over five thousand CVEs is used to model the delays with nearly fifty explanatory metrics. Regression analysis is used for the modeling. Results: The results show that the CVE coordination delays are affected by different abstractions for noise and prerequisite constraints. These abstractions convey effects from the social network and infrastructure dimensions. Particularly strong effect sizes are observed for annual and monthly control metrics, a control metric for weekends, the degrees of the nodes in the CVE coordination networks, and the number of references given in NVD for the CVEs archived. Smaller but visible effects are present for metrics measuring the entropy of the emails exchanged, traces to bug tracking systems, and other related aspects. The empirical signals are weaker for the technical characteristics. Conclusion: [...]
Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion
Venue: arXiv cs.CV 2020
Year: 2020

In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. The latest LRMA methods - Robust Principal Component Analysis (RPCA) resort to using the nuclear norm minimization (NNM) as a convex relaxation of the non-convex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We use a more flexible model, namely the Weighted Schatten p-Norm Minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives a better approximation to the original low-rank assumption but also considers the importance of different rank components. In this paper, a comparison of the low-rank recovery performance of two LRMA algorithms- RPCA and WSNM is brought out on occluded human facial images. The analysis is performed on facial images from the Yale database and over own database , where different facial expressions, spectacles, varying illumination account for the facial occlusions. The paper also discusses the prominent trends observed from the experimental results performed through the application of these algorithms. As low-rank images sometimes might fail to capture the details of a face adequately, we further propose a novel method to use the image-histogram of the sparse images thus obtained to identify the individual in any given image. Extensive experimental results show, both qualitatively and quantitatively, that WSNM surpasses RPCA in its performance more effectively by removing facial occlusions, thus giving recovered low-rank images of higher PSNR and SSIM.
Improved approximation schemes for early work scheduling on identical parallel machines with common due date
Venue: arXiv cs.DS 2020
Year: 2020

We study the early work scheduling problem on identical parallel machines in order to maximize the total early work, i.e., the parts of non-preemptive jobs executed before a common due date. By preprocessing and constructing an auxiliary instance which has several good properties, we propose an efficient polynomial time approximation scheme with running time $O(n)$, which improves the result in [Györgyi, P., Kis, T. (2020). A common approximation framework for early work, late work, and resource leveling problems. {\it European Journal of Operational Research}, 286(1), 129-137], and a fully polynomial time approximation scheme with running time $O(n)$ when the number of machines is a fixed number, which improves the result in [Chen, X., Liang, Y., Sterna, M., Wang, W., Błażewicz, J. (2020b). Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date. {\it European Journal of Operational Research}, 284(1), 67-74], where $n$ is the number of jobs, and the hidden constant depends on the desired accuracy.
MQT-TZ: Hardening IoT Brokers Using ARM TrustZone
Venue: arXiv cs.CR 2020
Year: 2020

The publish-subscribe paradigm is an efficient communication scheme with strong decoupling between the nodes, that is especially fit for large-scale deployments. It adapts natively to very dynamic settings and it is used in a diversity of real-world scenarios, including finance, smart cities, medical environments, or IoT sensors. Several of the mentioned application scenarios require increasingly stringent security guarantees due to the sensitive nature of the exchanged messages as well as the privacy demands of the clients/stakeholders/receivers. MQTT is a lightweight topic-based publish-subscribe protocol popular in edge and IoT settings, a de-facto standard widely adopted nowadays by the industry and researchers. However, MQTT brokers must process data in clear, hence exposing a large attack surface. This paper presents MQT-TZ, a secure MQTT broker leveraging Arm TrustZone, a trusted execution environment (TEE) commonly found even on inexpensive devices largely available on the market (such as Raspberry Pi units). We define a mutual TLS-based handshake and a two-layer encryption for end-to-end security using the TEE as a trusted proxy. The experimental evaluation of our fully implemented prototype with micro-, macro-benchmarks, as well as with real-world industrial workloads from a MedTech use-case, highlights several trade-offs using TrustZone TEE. We report several lessons learned while building and evaluating our system. We release MQT-TZ as open-source.
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability
Venue: arXiv cs.SE 2020
Year: 2020

Background: Developers spend a lot of their time on understanding source code. Static code analysis tools can draw attention to code that is difficult for developers to understand. However, most of the findings are based on non-validated metrics, which can lead to confusion and code, that is hard to understand, not being identified. Aims: In this work, we validate a metric called Cognitive Complexity which was explicitly designed to measure code understandability and which is already widely used due to its integration in well-known static code analysis tools. Method: We conducted a systematic literature search to obtain data sets from studies which measured code understandability. This way we obtained about 24,000 understandability evaluations of 427 code snippets. We calculated the correlations of these measurements with the corresponding metric values and statistically summarized the correlation coefficients through a meta-analysis. Results: Cognitive Complexity positively correlates with comprehension time and subjective ratings of understandability. The metric showed mixed results for the correlation with the correctness of comprehension tasks and with physiological measures. Conclusions: It is the first validated and solely code-based metric which is able to reflect at least some aspects of code understandability. Moreover, due to its methodology, this work shows that code understanding is currently measured in many different ways, which we also do not know how they are related. This makes it difficult to compare the results of individual studies as well as to develop a metric that measures code understanding in all its facets.