OpenRaiser/Intern-Atlas huggingface dataset:
Socio-technical constraints and affordances of virtual collaboration -- A study of four online hackathons
Venue: arXiv cs.HC 2022
Year: 2022

Hackathons and similar time-bounded events have become a popular form of collaboration. They are commonly organized as in-person events during which teams engage in intense collaboration over a short period of time to complete a project that is of interest to them. Most research to date has focused on studying how teams collaborate in a co-located setting, pointing towards the advantages of radical co-location. The global pandemic of 2020, however, has led to many hackathons moving online, which challenges our current understanding of how they function. In this paper, we address this gap by presenting findings from a multiple-case study of 10 hackathon teams that participated in 4 hackathons across two continents. By analyzing the collected data, we found that teams merged synchronous and asynchronous means of communication to maintain a common understanding of work progress as well as to maintain awareness of each other's tasks. Task division was self-assigned based on individual skills or interests, while leaders emerged from different strategies (e.g., participant experience, the responsibility of registering the team in an event). Some of the affordances of in-person hackathons, such as the radical co-location of team members, could be partially reproduced in teams that kept synchronous communication channels while working (i.e., shared audio territories), in a sort of "radical virtual co-location". However, others, such as interactions with other teams, easy access to mentors, and networking with other participants, decreased. In addition, the technical constraints of the different communication tools and platforms brought technical problems and were overwhelming to participants. Our work contributes to understanding the virtual collaboration of small teams in the context of online hackathons and how technologies and event structures proposed by organizers imply this collaboration.
On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations
Venue: arXiv cs.SD 2022
Year: 2022

Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model's design space and informed decision-making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four Machine Learning (ML) approaches in the modelling of surrogates of Sound Transmission Loss (STL). Feature importance and feature engineering are used to improve the models' accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed.
Designing Perceptual Puzzles by Differentiating Probabilistic Programs
Venue: arXiv cs.GR 2022
Year: 2022

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.
Generating Topological Structure of Floorplans from Room Attributes
Venue: arXiv cs.CV 2022
Year: 2022

Analysis of indoor spaces requires topological information. In this paper, we propose to extract topological information from room attributes using what we call Iterative and adaptive graph Topology Learning (ITL). ITL progressively predicts multiple relations between rooms; at each iteration, it improves node embeddings, which in turn facilitates generation of a better topological graph structure. This notion of iterative improvement of node embeddings and topological graph structure is in the same spirit as \cite{chen2020iterative}. However, while \cite{chen2020iterative} computes the adjacency matrix based on node similarity, we learn the graph metric using a relational decoder to extract room correlations. Experiments using a new challenging indoor dataset validate our proposed method. Qualitative and quantitative evaluation for layout topology prediction and floorplan generation applications also demonstrate the effectiveness of ITL.
Direct 3D Printing of Soft Fluidic Actuators with Graded Porosity
Venue: arXiv cs.RO 2022
Year: 2022

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. Within this work, the Infill-Foam (InFoam) is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relation between nozzle height and coil radius and the extruded length were necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46\% to 89\%. In compression tests, the cubes showed large changes in modulus (more than 200 times), density (-89\% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.
Polylogarithmic Sketches for Clustering
Venue: arXiv cs.DS 2022
Year: 2022

Given $n$ points in $\ell_p^d$, we consider the problem of partitioning points into $k$ clusters with associated centers. The cost of a clustering is the sum of $p^{\text{th}}$ powers of distances of points to their cluster centers. For $p \in [1,2]$, we design sketches of size poly$(\log(nd),k,1/ε)$ such that the cost of the optimal clustering can be estimated to within factor $1+ε$, despite the fact that the compressed representation does not contain enough information to recover the cluster centers or the partition into clusters. This leads to a streaming algorithm for estimating the clustering cost with space poly$(\log(nd),k,1/ε)$. We also obtain a distributed memory algorithm, where the $n$ points are arbitrarily partitioned amongst $m$ machines, each of which sends information to a central party who then computes an approximation of the clustering cost. Prior to this work, no such streaming or distributed-memory algorithm was known with sublinear dependence on $d$ for $p \in [1,2)$.
RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy
Venue: arXiv cs.LG 2022
Year: 2022

The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics and pathomics, i.e. the extraction of quantitative features from radiology and histopathology images routinely collected to predict clinical outcomes or to guide clinical decisions using artificial intelligence algorithms. Nevertheless, how to combine them into a single multimodal framework is still an open issue. In this work we therefore develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiation therapy treatment outcomes for non-small-cell lung cancer patients. Within this context, we investigate eight different late fusion rules (i.e. product, maximum, minimum, mean, decision template, Dempster-Shafer, majority voting, and confidence rule) and two patient-wise aggregation rules leveraging the richness of information given by computer tomography images and whole-slide scans. The experiments in leave-one-patient-out cross-validation on an in-house cohort of 33 patients show that the proposed multimodal paradigm with an AUC equal to $90.9\%$ outperforms each unimodal approach, suggesting that data integration can advance precision medicine. As a further contribution, we also compare the hand-crafted representations with features automatically computed by deep networks, and the late fusion paradigm with early fusion, another popular multimodal approach. In both cases, the experiments show that the proposed multimodal approach provides the best results.
A review of Federated Learning in Intrusion Detection Systems for IoT
Venue: arXiv cs.CR 2022
Year: 2022

Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Internet of Things devices. Current approaches rely on powerful centralized servers that receive data from all their parties -- violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach where different agents collaboratively train a shared model, neither exposing training data to others nor requiring a compute-intensive centralized infrastructure. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection. Both technologies are described in detail and current scientific progress is reviewed and categorized. Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.
Measurement uncertainty and unicity of single number quantities describing the spatial decay of speech level in open-plan offices
Venue: arXiv cs.SD 2022
Year: 2022

The ISO 3382-3 standard (2012) defines single number quantities (SNQs) which evaluate the acoustic quality of open-plan offices, but does not address the issue of measurement uncertainties. This study focusses on the SNQs present in this standard related to spatial decay of speech, i.e. D 2S , L pAS4m and r c. The aim is to provide additional information to the limited literature on the measurement uncertainties of these SNQs by use of both analytical developments and a stochastic approach based on simulations. The accuracy of the analytical developments was studied thanks to simulations of the sound propagation within a series of offices (1 layout, 16 acoustic configurations with different screen heights and different acoustic qualities of screens and ceiling). The SNQs obtained in the simulations cover a wide range: D 2S between 3.4 and 7.5 dB(A), L pAS4m between 40.6 and 51.9 dB(A) and r c between 2.5 and 14.7 m. Therefore, the simulations are representative of a broad set of acoustic qualities. Estimated uncertainties have a magnitude of 0.4 dB(A) for D 2S and vary between 0.4 and 0.7 dB(A) for L pAS4m and between 0.2 and 1.5 m for r c over a measurement path comprising 7 measurement positions. The simulations also raise the question of describing the acoustic quality of an office using a single value for the indicators. The results of the simulations show that in some cases, D 2S values significantly depend on the measurement path, leading to a strong increase of its measurement uncertainty if a unique value is to be considered.
Building Change Detection using Multi-Temporal Airborne LiDAR Data
Venue: arXiv cs.CV 2022
Year: 2022

Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing the necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: 'newly built', 'demolished', 'taller' and 'shorter'. The detected changes are visualized in one map for better interpretation.
Design, Implementation, and Evaluation of Blockchain-Based Trusted Achievement Record System for Students in Higher Education
Venue: arXiv cs.CR 2022
Year: 2022

With a growing number of institutions involved in the global education market, it has become increasingly challenging to verify the authenticity of academic achievements such as CVs and diplomas. Blockchain is an enabling technology that can play a key role in solving this problem. This study introduces a blockchain-based achievement record system that produces a verifiable record of achievements. The proposed system aims to facilitate the process of authentication and validation of certificates reliably, easily, and quickly, leveraging the unique capabilities offered through Blockchain technology (public Ethereum Blockchain) and smart contracts. We present the design and implementation of the system and its components and tools. We then evaluate the system through a number of studies to measure the system's usability, effectiveness, performance, and cost. A System Usability Scale (SUS) test gave a scale of 77.1. Through a literature survey, we demonstrate that this system is a significant improvement on legacy systems, being both more user-friendly and more efficient. We also conduct a detailed cost analysis and discuss the positives and limitations of alternative blockchain solutions.
These Deals Won't Last! Longevity, Uniformity and Bias in Product Badge Assignment in E-Commerce Platforms
Venue: arXiv cs.SI 2022
Year: 2022

Product badges are ubiquitous in e-commerce platforms, acting as effective psychological triggers to nudge customers to buy specific products, boosting revenues. However, to the best of our knowledge, there has been no attempt to systematically study these badges and their several idiosyncrasies - we intend to close this gap in our current work. Specifically, we try to answer questions such as: How long does a product retain a badge on a given platform? If a product is sold on different platforms, then does it receive similar badges? How do the products that receive badges differ from those which do not, in terms of price, customer rating, etc. We collect longitudinal data from several e-commerce platforms over 45 days, and find that although most of the badges are short-lived, there are several permanent badge assignments and that too for badges meant to denote urgency or scarcity. Furthermore, it is unclear how the badge assignments are done, and we find evidence that highly-rated products are missing out on badges compared to lower quality ones. Our work calls for greater transparency in the badge assignment process to inform customers, as well as to reduce dissatisfaction among the sellers dependent on the platforms for their revenues.
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory
Venue: arXiv cs.CR 2022
Year: 2022

The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance or defense, demand security and privacy which today's solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today's solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion - the ciphertext is much bigger than the native aggregate of native data types. In this paper, we present MemFHE which is the first accelerator of both client and server for the latest Ring-GSW (Gentry, Sahai, and Waters) based homomorphic encryption schemes using Processing In Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data, while providing in-situ execution and extensive parallelism needed for FHE's polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE's server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security-levels based on the application requirements. We evaluate MemFHE for various security-levels and compare it with state-of-the-art CPU implementations for Ring-GSW based FHE. MemFHE is up to 20kx (265x) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2007x higher throughput than the state-of-the-art while implementing neural networks with FHE.
Defining the role of open source software in research reproducibility
Venue: arXiv cs.CY 2022
Year: 2022

Reproducibility is inseparable from transparency, as sharing data, code and computational environment is a pre-requisite for being able to retrace the steps of producing the research results. Others have made the case that this artifact sharing should adopt appropriate licensing schemes that permit reuse, modification and redistribution. I make a new proposal for the role of open source software, stemming from the lessons it teaches about distributed collaboration and a commitment-based culture. Reviewing the defining features of open source software (licensing, development, communities), I look for explanation of its success from the perspectives of connectivism -- a learning theory for the digital age -- and the language-action framework of Winograd and Flores. I contend that reproducibility engenders trust, which we routinely build in community via conversations, and the practices of open source software help us to learn how to be more effective learning (discovering) together, contributing to the same goal.
Wasmati: An Efficient Static Vulnerability Scanner for WebAssembly
Venue: arXiv cs.CR 2022
Year: 2022

WebAssembly is a new binary instruction format that allows targeted compiled code written in high-level languages to be executed with near-native speed by the browser's JavaScript engine. However, given that WebAssembly binaries can be compiled from unsafe languages like C/C++, classical code vulnerabilities such as buffer overflows or format strings can be transferred over from the original programs down to the cross-compiled binaries. As a result, this possibility of incorporating vulnerabilities in WebAssembly modules has widened the attack surface of modern web applications. This paper presents Wasmati, a static analysis tool for finding security vulnerabilities in WebAssembly binaries. It is based on the generation of a code property graph (CPG), a program representation previously adopted for detecting vulnerabilities in various languages but hitherto unapplied to WebAssembly. We formalize the definition of CPG for WebAssembly, introduce techniques to generate CPG for complex WebAssembly, and present four different query specification languages for finding vulnerabilities by traversing a program's CPG. We implemented ten queries capturing different vulnerability types and extensively tested Wasmati on four heterogeneous datasets. We show that Wasmati can scale the generation of CPGs for large real-world applications and can efficiently find vulnerabilities for all our query types. We have also tested our tool on WebAssembly binaries collected in the wild and identified several potential vulnerabilities, some of which we have manually confirmed to exist unless the enclosing application properly sanitizes the interaction with such affected binaries.
Novel Applications for VAE-based Anomaly Detection Systems
Venue: arXiv cs.LG 2022
Year: 2022

The recent rise in deep learning technologies fueled innovation and boosted scientific research. Their achievements enabled new research directions for deep generative modeling (DGM), an increasingly popular approach that can create novel and unseen data, starting from a given data set. As the technology shows promising applications, many ethical issues also arise. For example, their misuse can enable disinformation campaigns and powerful phishing attempts. Research also indicates different biases affect deep learning models, leading to social issues such as misrepresentation. In this work, we formulate a novel setting to deal with similar problems, showing that a repurposed anomaly detection system effectively generates novel data, avoiding generating specified unwanted data. We propose Variational Auto-encoding Binary Classifiers (V-ABC): a novel model that repurposes and extends the Auto-encoding Binary Classifier (ABC) anomaly detector, using the Variational Auto-encoder (VAE). We survey the limitations of existing approaches and explore many tools to show the model's inner workings in an interpretable way. This proposal has excellent potential for generative applications: models that rely on user-generated data could automatically filter out unwanted content, such as offensive language, obscene images, and misleading information.
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Venue: arXiv cs.DC 2022
Year: 2022

Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.