OpenRaiser/Intern-Atlas huggingface dataset:
"How Was Your Weekend?" Software Development Teams Working From Home During COVID-19
Venue: arXiv cs.SE 2021
Year: 2021

The mass shift to working at home during the COVID-19 pandemic radically changed the way many software development teams collaborate and communicate. To investigate how team culture and team productivity may also have been affected, we conducted two surveys at a large software company. The first, an exploratory survey during the early months of the pandemic with 2,265 developer responses, revealed that many developers faced challenges reaching milestones and that their team productivity had changed. We also found through qualitative analysis that important team culture factors such as communication and social connection had been affected. For example, the simple phrase "How was your weekend?" had become a subtle way to show peer support. In our second survey, we conducted a quantitative analysis of the team cultural factors that emerged from our first survey to understand the prevalence of the reported changes. From 608 developer responses, we found that 74% of these respondents missed social interactions with colleagues and 51% reported a decrease in their communication ease with colleagues. We used data from the second survey to build a regression model to identify important team culture factors for modeling team productivity. We found that the ability to brainstorm with colleagues, difficulty communicating with colleagues, and satisfaction with interactions from social activities are important factors that are associated with how developers report their software development team's productivity. Our findings inform how managers and leaders in large software companies can support sustained team productivity during times of crisis and beyond.
A Ramsey Theorem for Finite Monoids
Venue: arXiv cs.FL 2021
Year: 2021

Repeated idempotent elements are commonly used to characterise iterable behaviours in abstract models of computation. Therefore, given a monoid $M$, it is natural to ask how long a sequence of elements of $M$ needs to be to ensure the presence of consecutive idempotent factors. This question is formalised through the notion of the Ramsey function $R_M$ associated to M, obtained by mapping every positive integer $k$ to the minimal integer $R_M(k)$ such that every word $u$ in $M^*$ of length $R_M(k)$ contains $k$ consecutive non-empty factors that correspond to the same idempotent element of $M$. In this work, we study the behaviour of the Ramsey function $R_M$ by investigating the regular $D$-length of $M$, defined as the largest size $L(M)$ of a submonoid of $M$ isomorphic to the set of natural numbers $\{1,2, ..., L(M)\}$ equipped with the Max operation. We show that the regular $D$-length of $M$ determines the degree of $R_M$, by proving that $k^{L(M)} \leq R_M(k) \leq (k|M|^4)^{L(M)}$. To allow applications of this result, we provide the value of the regular $D$-length of diverse monoids. In particular, we prove that the full monoid of $n \times n$ Boolean matrices, which is used to express transition monoids of non-deterministic automata, has a regular $D$-length of $\frac{n^2+n+2}{2}$.
Differentiable Nonparametric Belief Propagation
Venue: arXiv cs.RO 2021
Year: 2021

We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model. In this work, we replace each crafted factor with a differentiable neural network enabling the factors to be learned using an efficient optimization routine from labeled data. By combining differentiable neural networks with an efficient belief propagation algorithm, our method learns to maintain a set of marginal posterior samples using end-to-end training. We evaluate our differentiable nonparametric belief propagation (DNBP) method on a set of articulated pose tracking tasks and compare performance with a recurrent neural network. Results from this comparison demonstrate the effectiveness of using learned factors for tracking and suggest the practical advantage over hand-crafted approaches. The project webpage is available at: progress.eecs.umich.edu/projects/dnbp.
SoftNER: Mining Knowledge Graphs From Cloud Incidents
Venue: arXiv cs.SE 2021
Year: 2021

The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Prior work on incident management has heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for mining Knowledge Graphs from incident reports. First, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for extracting factual information in the form of named entities. Next, we present an approach to mine relations between the named entities for automatically constructing knowledge graphs. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that the unsupervised machine learning pipeline has a high precision of 0.96. Our multi-task learning based deep learning model also outperforms the state-of-the-art NER models. Lastly, using the knowledge extracted by SoftNER, we are able to build accurate models for applications such as incident triaging and recommending entities based on their relevance to incident titles.
Affordance-based Reinforcement Learning for Urban Driving
Venue: arXiv cs.LG 2021
Year: 2021

Traditional autonomous vehicle pipelines that follow a modular approach have been very successful in the past both in academia and industry, which has led to autonomy deployed on road. Though this approach provides ease of interpretation, its generalizability to unseen environments is limited and hand-engineering of numerous parameters is required, especially in the prediction and planning systems. Recently, deep reinforcement learning has been shown to learn complex strategic games and perform challenging robotic tasks, which provides an appealing framework for learning to drive. In this work, we propose a deep reinforcement learning framework to learn optimal control policy using waypoints and low-dimensional visual representations, also known as affordances. We demonstrate that our agents when trained from scratch learn the tasks of lane-following, driving around inter-sections as well as stopping in front of other actors or traffic lights even in the dense traffic setting. We note that our method achieves comparable or better performance than the baseline methods on the original and NoCrash benchmarks on the CARLA simulator.
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Venue: arXiv cs.CL 2021
Year: 2021

Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
Venue: arXiv cs.AI 2021
Year: 2021

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity perspectives.
Deadlock in packet switching networks
Venue: arXiv cs.NI 2021
Year: 2021

A deadlock in a packet switching network is a state in which one or more messages have not yet reached their target, yet cannot progress any further. We formalize three different notions of deadlock in the context of packet switching networks, to which we refer as global, local and weak deadlock. We establish the precise relations between these notions, and prove they characterize different sets of deadlocks. Moreover, we implement checking of deadlock freedom of packet switching networks using the symbolic model checker nuXmv. We show experimentally that the implementation is effective at finding subtle deadlock situations in packet switching networks.
Preprocessing Imprecise Points for the Pareto Front
Venue: arXiv cs.CG 2021
Year: 2021

In the preprocessing model for uncertain data we are given a set of regions R which model the uncertainty associated with an unknown set of points P. In this model there are two phases: a preprocessing phase, in which we have access only to R, followed by a reconstruction phase, in which we have access to points in P at a certain retrieval cost C per point. We study the following algorithmic question: how fast can we construct the pareto front of P in the preprocessing model? We show that if R is a set of pairwise-disjoint axis-aligned rectangles, then we can preprocess R to reconstruct the Pareto front of P efficiently. To refine our algorithmic analysis, we introduce a new notion of algorithmic optimality which relates to the entropy of the uncertainty regions. Our proposed uncertainty-region optimality falls on the spectrum between worst-case optimality and instance optimality. We prove that instance optimality is unobtainable in the preprocessing model, whenever the classic algorithmic problem reduces to sorting. Our results are worst-case optimal in the preprocessing phase; in the reconstruction phase, our results are uncertainty-region optimal with respect to real RAM instructions, and instance optimal with respect to point retrievals.
Impact of Autonomous Vehicle Technology on Long Distance Travel Behavior
Venue: arXiv cs.CY 2021
Year: 2021

Although rapid progress in-vehicle automated technology has sped up the possibility of using fully automated technology for public use, little research has been done on the possible influences of autonomous vehicles (AVs) technology on long-distance travel. This technology has the potential to have a significant effect on intercity trips. This study analyzed a travel survey to anticipate the impact of this technology on long-distance trips. We have divided trips into two different categories including trips for pleasure and trips for business. Different hypotheses based on the authors' knowledge and assisted by existing literature have been defined for each type of trip. By using the Pearson method these hypotheses have been tested and the positive or negative responses from respondents have been evaluated. The findings show that using AVs for pleasure trips can increase the number of travelers and stimulate people to choose longer distances for their trips. In addition, people enjoy more and will be interested to travel more frequently. For business trips, AV technology can reduce travel costs and job-related stress. Unlike pleasure trips for which people are not interested in traveling at night, business travelers prefer to travel at night.
GSM-GPRS Based Smart Street Light
Venue: arXiv cs.CY 2021
Year: 2021

Street lighting system has always been the traditional manual system of illuminating the streets in Bangladesh, where a dedicated person is posted only to control the street lights of a zone, who roams around the zonal area to switch on and switch off the lights two times a day, which brings about the exhibition of bright lights in street even after sunrise and in some cases maybe the whole day. This results in insertion to the budget. In addition to this, faulty lights may not come to the heed of the concerned authority for a long time which leads to the technical downside. This paper demonstrates a process of controlling the street lights in country like Bangladesh employing SIM900 GSM-GPRS Shield which comes up with the provision of manual control, semi-automated control as well as full-automated control.
TrustSECO: An Interview Survey into Software Trust
Venue: arXiv cs.SE 2021
Year: 2021

The software ecosystem is a trust-rich part of the world. Collaboratively, software engineers trust major hubs in the ecosystem, such as package managers, repository services, and programming language ecosystems. This trust, however, is often broken by vulnerabilities, ransomware, and abuse from malignant actors. But what is trust? In this paper we explore, through twelve in-depth interviews with software engineers, how they perceive trust in their daily work. From the interviews we conclude three things. First, software engineers make a distinction between an adoption factor and a trust factor when selecting a package. Secondly, while in literature mostly technical factors are considered as the main trust factors, the software engineers in this study conclude that organizational factors are more important. Finally, we find that different kinds of software engineers require different views on trust, and that it is impossible to create one unified perception of trust. Keywords: software ecosystem trust, empirical software engineering, TrustSECO, external software adoption, cross-sectional exploratory interview analysis, trust perception.
Needmining: Designing Digital Support to Elicit Needs from Social Media
Venue: arXiv cs.CY 2021
Year: 2021

Today's businesses face a high pressure to innovate in order to succeed in highly competitive markets. Successful innovations, though, typically require the identification and analysis of customer needs. While traditional, established need elicitation methods are time-proven and have demonstrated their capabilities to deliver valuable insights, they lack automation and scalability and, thus, are expensive and time-consuming. In this article, we propose an approach to automatically identify and quantify customer needs by utilizing a novel data source: Users voluntarily and publicly expose information about themselves via social media, as for instance Facebook or Twitter. These posts may contain valuable information about the needs, wants, and demands of their authors. We apply a Design Science Research (DSR) methodology to add design knowledge and artifacts for the digitalization of innovation processes, in particular to provide digital support for the elicitation of customer needs. We want to investigate whether automated, speedy, and scalable need elicitation from social media is feasible. We concentrate on Twitter as a data source and on e-mobility as an application domain. In a first design cycle we conceive, implement and evaluate a method to demonstrate the feasibility of identifying those social media posts that actually express customer needs. In a second cycle, we build on this artifact to additionally quantify the need information elicited, and prove its feasibility. Third, we integrate both developed methods into an end-user software artifact and test usability in an industrial use case. Thus, we add new methods for need elicitation to the body of knowledge, and introduce concrete tooling for innovation management in practice.
SRACARE: Secure Remote Attestation with Code Authentication and Resilience Engine
Venue: arXiv cs.CR 2021
Year: 2021

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data. This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities. Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems. Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state. Previous researchers have demonstrated independent methods for attack detection and safeguard. However, the majority of them lack in providing onboard system recovery and secure communication techniques. To bridge this gap, this manuscript proposes SRACARE- a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks. The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array(FPGA) board. This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery. The framework achieves these with only 8 % performance overhead and a very small increase in hardware-software footprint.
A General Framework for Hypercomplex-valued Extreme Learning Machines
Venue: arXiv cs.LG 2021
Year: 2021

This paper aims to establish a framework for extreme learning machines (ELMs) on general hypercomplex algebras. Hypercomplex neural networks are machine learning models that feature higher-dimension numbers as parameters, inputs, and outputs. Firstly, we review broad hypercomplex algebras and show a framework to operate in these algebras through real-valued linear algebra operations in a robust manner. We proceed to explore a handful of well-known four-dimensional examples. Then, we propose the hypercomplex-valued ELMs and derive their learning using a hypercomplex-valued least-squares problem. Finally, we compare real and hypercomplex-valued ELM models' performance in an experiment on time-series prediction and another on color image auto-encoding. The computational experiments highlight the excellent performance of hypercomplex-valued ELMs to treat high-dimensional data, including models based on unusual hypercomplex algebras.
Switched Systems as Hybrid Programs
Venue: arXiv cs.LO 2021
Year: 2021

Real world systems of interest often feature interactions between discrete and continuous dynamics. Various hybrid system formalisms have been used to model and analyze this combination of dynamics, ranging from mathematical descriptions, e.g., using impulsive differential equations and switching, to automata-theoretic and language-based approaches. This paper bridges two such formalisms by showing how various classes of switched systems can be modeled using the language of hybrid programs from differential dynamic logic (dL). The resulting models enable the formal specification and verification of switched systems using dL and its existing deductive verification tools such as KeYmaera X. Switched systems also provide a natural avenue for the generalization of dL's deductive proof theory for differential equations. The completeness results for switched system invariants proved in this paper enable effective safety verification of those systems in dL.
Player-AI Interaction: What Neural Network Games Reveal About AI as Play
Venue: arXiv cs.HC 2021
Year: 2021

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.
Blind Image Deblurring based on Kernel Mixture
Venue: arXiv cs.CV 2021
Year: 2021

Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. A data-driven decision for the number of base kernels to combine makes the structure even more flexible. We apply this approach to a remote sensing problem to recover images from blurry images of satellite. This case study shows the superiority of the proposed method regulating the blur kernel in comparison with state-of-the-art methods that regulates the latent image.
Manifestly Phased Communication via Shared Session Types
Venue: arXiv cs.PL 2021
Year: 2021

Session types denote message protocols between concurrent processes, allowing a type-safe expression of inter-process communication. Although previous work demonstrate a well-defined notion of subtyping where processes have different perceptions of the protocol, these formulations were limited to linear session types where each channel of communication has a unique provider and client. In this paper, we extend subtyping to shared session types where channels can now have multiple clients instead of a single client. We demonstrate that this generalization can statically capture protocol requirements that span multiple phases of interactions of a client with a shared service provider, something not possible in prior proposals. Moreover, the phases are manifest in the type of the client.