OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-Preserving Data Publishing in Process Mining
Venue: arXiv cs.CR 2021
Year: 2021

Process mining aims to provide insights into the actual processes based on event data. These data are often recorded by information systems and are widely available. However, they often contain sensitive private information that should be analyzed responsibly. Therefore, privacy issues in process mining are recently receiving more attention. Privacy preservation techniques obviously need to modify the original data, yet, at the same time, they are supposed to preserve the data utility. Privacy-preserving transformations of the data may lead to incorrect or misleading analysis results. Hence, new infrastructures need to be designed for publishing the privacy-aware event data whose aim is to provide metadata regarding the privacy-related transformations on event data without revealing details of privacy preservation techniques or the protected information. In this paper, we provide formal definitions for the main anonymization operations, used by privacy models in process mining. These are used to create an infrastructure for recording the privacy metadata. We advocate the proposed privacy metadata in practice by designing a privacy extension for the XES standard and a general data structure for event data which are not in the form of standard event logs.
Review of Specific Features and Challenges in the Current Internet of Things Systems Impacting their Security and Reliability
Venue: arXiv cs.CR 2021
Year: 2021

The current development of the Internet of Things (IoT) technology poses significant challenges to researchers and industry practitioners. Among these challenges, security and reliability particularly deserve attention. In this paper, we provide a consolidated analysis of the root causes of these challenges, their relations, and their possible impacts on IoT systems' general quality characteristics. Further understanding of these challenges is useful for IoT quality engineers when defining testing strategies for their systems and researchers to consider when discussing possible research directions. In this study, twenty specific features of current IoT systems are discussed, divided into five main categories: (1) Economic, managerial and organisational aspects, (2) Infrastructural challenges, (3) Security and privacy challenges, (4) Complexity challenges and (5) Interoperability problems.
Adversarial Machine Learning for 5G Communications Security
Venue: arXiv cs.NI 2021
Year: 2021

Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.
Does Crowdfunding Really Foster Innovation? Evidence from the Board Game Industry
Venue: arXiv cs.SI 2021
Year: 2021

Crowdfunding offers inventors and entrepreneurs alternative access to resources with which they can develop and realize their ideas. Besides helping to secure capital, crowdfunding also connects creators with engaged early supporters who provide public feedback. But does this process foster truly innovative outcomes? Does the proliferation of crowdfunding in an industry make it more innovative overall? Prior studies investigating the link between crowdfunding and innovation do not compare traditional and crowdfunded products and so while claims that crowdfunding supports innovation are theoretically sound, they lack empirical backing. We address this gap using a unique dataset of board games, an industry with significant crowdfunding activity in recent years. Each game is described by how it combines fundamental mechanisms such as dice-rolling, negotiation, and resource-management, from which we develop quantitative measures of innovation in game design. Using these measures to compare games, we find that crowdfunded games tend to be more distinctive from previous games than their traditionally published counterparts. They are also significantly more likely to implement novel combinations of mechanisms. Crowdfunded games are not just transient experiments: subsequent games imitate their novel ideas. These results hold in regression models controlling for game and designer-level confounders. Our findings demonstrate that the innovative potential of crowdfunding goes beyond individual products to entire industries, as new ideas spill over to traditionally funded products.
Theorem Proving and Algebra
Venue: arXiv cs.LO 2021
Year: 2021

This book can be seen either as a text on theorem proving that uses techniques from general algebra, or else as a text on general algebra illustrated and made concrete by practical exercises in theorem proving. The book considers several different logical systems, including first-order logic, Horn clause logic, equational logic, and first-order logic with equality. Similarly, several different proof paradigms are considered. However, we do emphasize equational logic, and for simplicity we use only the OBJ3 software system, though it is used in a rather flexible manner. We do not pursue the lofty goal of mechanizing proofs like those of which mathematicians are justly so proud; instead, we seek to take steps towards providing mechanical assistance for proofs that are useful for computer scientists in developing software and hardware. This more modest goal has the advantage of both being achievable and having practical benefits. The following topics are covered: many-sorted signature, algebra and homomorphism; term algebra and substitution; equation and satisfaction; conditional equations; equational deduction and its completeness; deduction for conditional equations; the theorem of constants; interpretation and equivalence of theories; term rewriting, termination, confluence and normal form; abstract rewrite systems; standard models, abstract data types, initiality, and induction; rewriting and deduction modulo equations; first-order logic, models, and proof planning; second-order algebra; order-sorted algebra and rewriting; modules; unification and completion; and hidden algebra. In parallel with these are a gradual introduction to OBJ3, applications to group theory, various abstract data types (such as number systems, lists, and stacks), propositional calculus, hardware verification, the λ-calculus, correctness of functional programs, and other topics.
PVA: Pixel-aligned Volumetric Avatars
Venue: arXiv cs.CV 2021
Year: 2021

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus sidestepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision.We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting.
Toward Inclusion of Children as Software Engineering Stakeholders
Venue: arXiv cs.SE 2021
Year: 2021

Background: A growing amount of software is available to children today. Children use both software that has been explicitly developed for them and software for general users. While they obtain clear benefits from software, such as access to creativity tools and learning resources, children are also exposed to several risks and disadvantages, such as privacy violation, inactivity, or safety risks that can even lead to death. The research and development community is addressing and investigating positive and negative impacts of software for children one by one, but no comprehensive model exists that relates software engineering and children as stakeholders in their own right. Aims: The final objective of this line of research is to propose effective ways in which children can be involved in Software Engineering activities as stakeholders. Specifically, in this paper, we investigate the quality aspects that are of interest for children, as quality is a crucial aspect in the development of any kind of software, especially for stakeholders like children. Method: Our contribution is based mainly on an analysis of studies at the intersection between Software Engineering (especially software quality) and Child Computer Interaction. Results: We identify a set of qualities and a preliminary set of guidelines that can be used by researchers and practitioners in understanding the complex interrelations between Software Engineering and children. Based on the qualities and the guidelines, researchers can design empirical investigations to obtain deeper insights into the phenomenon and propose new Software Engineering knowledge specific for this type of stakeholders. Conclusions: This conceptualization is a first step towards a framework to support children as stakeholders in software engineering.
Neural Storage: A New Paradigm of Elastic Memory
Venue: arXiv cs.AI 2021
Year: 2021

Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static - i.e., they do not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for dramatic performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data - as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/retention algorithms in NS, which integrate a formalized learning process. Using a full-blown operational model, we demonstrate that NS achieves an order of magnitude improvement in memory access performance for two representative applications when compared to traditional content-based memory.
Layer reconstruction and missing link prediction of multilayer network with a Maximum A Posteriori estimation
Venue: arXiv cs.SI 2021
Year: 2021

A multilayer network is composed of multiple layers, where different layers have the same set of vertices but represent different types of interactions. Nevertheless, some layers are interdependent or structurally similar in the multilayer network. In this paper, we present a maximum a posteriori estimation based model to reconstruct a specific layer in the multilayer network. The SimHash algorithm is used to compute the similarities between various layers. And the layers with similar structures are used to determine the parameters of the conjugate prior. With this model, we can also predict missing links and direct experiments for finding potential links. We test the method through two real multilayer networks, and the results show that the maximum a posteriori estimation is promising in reconstructing the layer of interest even with a large number of missing links.
Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?
Venue: arXiv cs.RO 2021
Year: 2021

Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present bottom up solutions using various control methods for different types of movements. Health research on the other hand focuses on clinical assessment and rehabilitation leaving arguably important differences between the two domains. In particular, little is known quantitatively on what ADLs humans perform in their everyday environment - at home, work etc. This information can help guide development and prioritization of robotic technology for in-home assistive robotic deployment. This study targets several lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care communities have different terms and taxonomies for representing tasks and motions. We derive and discuss a robotics-relevant taxonomy from this quantitative ADL task and ICF motion data in attempt to ameliorate these taxonomic differences. Our statistics quantify that humans reach, open drawers, doors, and retrieve and use objects hundreds of times a day. Commercial wheelchair mounted robot arms can help 150,000 upper body disabled in the USA alone, but only a few hundred robots are deployed. Better user interfaces, and more capable robots can increase the potential user base and number of ADL tasks solved significantly.
Design of Full-Duplex Millimeter-Wave Integrated Access and Backhaul Networks
Venue: arXiv cs.IT 2021
Year: 2021

One of the key technologies for the future cellular networks is full duplex (FD)-enabled integrated access and backhaul (IAB) networks operating in the millimeter-wave (mmWave) frequencies. The main challenge in realizing FD-IAB networks is mitigating the impact of self-interference (SI) in the wideband mmWave frequencies. In this article, we first introduce the 3GPP IAB network architectures and wideband mmWave channel models. By utilizing the subarray-based hybrid precoding scheme at the FD-IAB node, multiuser interference is mitigated using zero-forcing at the transmitter, whereas the residual SI after successfully deploying antenna and analog cancellation is canceled by a minimum mean square error baseband combiner at the receiver. The spectral efficiency (SE) is evaluated for the RF insertion loss (RFIL) with different kinds of phase shifters and channel uncertainty. Simulation results show that, in the presence of the RFIL, the almost double SE, which is close to that obtained from fully connected hybrid precoding, can be achieved as compared to half duplex systems when the uncertainties are of low strength.
Unsupervised Domain Adaptation of Black-Box Source Models
Venue: arXiv cs.LG 2021
Year: 2021

Unsupervised domain adaptation (UDA) aims to learn models for a target domain of unlabeled data by transferring knowledge from a labeled source domain. In the traditional UDA setting, labeled source data are assumed to be available for adaptation. Due to increasing concerns for data privacy, source-free UDA is highly appreciated as a new UDA setting, where only a trained source model is assumed to be available, while labeled source data remain private. However, trained source models may also be unavailable in practice since source models may have commercial values and exposing source models brings risks to the source domain, e.g., problems of model misuse and white-box attacks. In this work, we study a subtly different setting, named Black-Box Unsupervised Domain Adaptation (B$^2$UDA), where only the application programming interface of source model is accessible to the target domain; in other words, the source model itself is kept as a black-box one. To tackle B$^2$UDA, we propose a simple yet effective method, termed Iterative Learning with Noisy Labels (IterLNL). With black-box models as tools of noisy labeling, IterLNL conducts noisy labeling and learning with noisy labels (LNL), iteratively. To facilitate the implementation of LNL in B$^2$UDA, we estimate the noise rate from model predictions of unlabeled target data and propose category-wise sampling to tackle the unbalanced label noise among categories. Experiments on benchmark datasets show the efficacy of IterLNL. Given neither source data nor source models, IterLNL performs comparably with traditional UDA methods that make full use of labeled source data.
Monte Carlo Methods for Calculating Shapley-Shubik Power Index in Weighted Majority Games
Venue: arXiv cs.GT 2021
Year: 2021

This paper addresses Monte Carlo algorithms for calculating the Shapley-Shubik power index in weighted majority games. First, we analyze a naive Monte Carlo algorithm and discuss the required number of samples. We then propose an efficient Monte Carlo algorithm and show that our algorithm reduces the required number of samples as compared to the naive algorithm.
Almost Optimal Inapproximability of Multidimensional Packing Problems
Venue: arXiv cs.DS 2021
Year: 2021

Multidimensional packing problems generalize the classical packing problems such as Bin Packing, Multiprocessor Scheduling by allowing the jobs to be $d$-dimensional vectors. While the approximability of the scalar problems is well understood, there has been a significant gap between the approximation algorithms and the hardness results for the multidimensional variants. In this paper, we close this gap by giving almost tight hardness results for these problems. 1. We show that Vector Bin Packing has no polynomial time $Ω( \log d)$ factor asymptotic approximation algorithm when $d$ is a large constant, assuming $\textsf{P}\neq \textsf{NP}$. This matches the $\ln d + O(1)$ factor approximation algorithms (Chekuri, Khanna SICOMP 2004, Bansal, Caprara, Sviridenko SICOMP 2009, Bansal, Eliás, Khan SODA 2016) upto constants. 2. We show that Vector Scheduling has no polynomial time algorithm with an approximation ratio of $Ω\left( (\log d)^{1-ε}\right)$ when $d$ is part of the input, assuming $\textsf{NP}\nsubseteq \textsf{ZPTIME}\left( n^{(\log n)^{O(1)}}\right)$. This almost matches the $O\left( \frac{\log d}{\log \log d}\right)$ factor algorithms(Harris, Srinivasan JACM 2019, Im, Kell, Kulkarni, Panigrahi SICOMP 2019). We also show that the problem is NP-hard to approximate within $(\log \log d)^{ω(1)}$. 3. We show that Vector Bin Covering is NP-hard to approximate within $Ω\left( \frac{\log d}{\log \log d}\right)$ when $d$ is part of the input, almost matching the $O(\log d)$ factor algorithm (Alon et al., Algorithmica 1998). Previously, no hardness results that grow with $d$ were known for Vector Scheduling and Vector Bin Covering when $d$ is part of the input and for Vector Bin Packing when $d$ is a fixed constant.
Sequential Naive Learning
Venue: arXiv cs.LG 2021
Year: 2021

We analyze boundedly rational updating from aggregate statistics in a model with binary actions and binary states. Agents each take an irreversible action in sequence after observing the unordered set of previous actions. Each agent first forms her prior based on the aggregate statistic, then incorporates her signal with the prior based on Bayes rule, and finally applies a decision rule that assigns a (mixed) action to each belief. If priors are formed according to a discretized DeGroot rule, then actions converge to the state (in probability), i.e., \emph{asymptotic learning}, in any informative information structure if and only if the decision rule satisfies probability matching. This result generalizes to unspecified information settings where information structures differ across agents and agents know only the information structure generating their own signal. Also, the main result extends to the case of $n$ states and $n$ actions.
Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance
Venue: arXiv cs.CL 2021
Year: 2021

Social media such as Twitter, Facebook, etc. has led to a generated growing number of comments that contains users opinions. Sentiment analysis research deals with these comments to extract opinions which are positive or negative. Arabic language is a rich morphological language; thus, classical techniques of English sentiment analysis cannot be used for Arabic. Word embedding technique can be considered as one of successful methods to gaping the morphological problem of Arabic. Many works have been done for Arabic sentiment analysis based on word embedding, but there is no study focused on variable parameters. This study will discuss three parameters (Window size, Dimension of vector and Negative Sample) for Arabic sentiment analysis using DBOW and DMPV architectures. A large corpus of previous works generated to learn word representations and extract features. Four binary classifiers (Logistic Regression, Decision Tree, Support Vector Machine and Naive Bayes) are used to detect sentiment. The performance of classifiers evaluated based on; Precision, Recall and F1-score.
Group Fairness: Independence Revisited
Venue: arXiv cs.CY 2021
Year: 2021

This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that independence is not a suitable measure of group fairness. This position is at least partially based on two influential papers (Dwork et al., 2012, Hardt et al., 2016) that provide arguments against independence. We revisit these arguments, and we find that the case against independence is rather weak. We also give arguments in favor of independence, showing that it plays a distinctive role in considerations of fairness. Finally, we discuss how to balance different fairness considerations.