OpenRaiser/Intern-Atlas huggingface dataset:
A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues
Venue: arXiv cs.CR 2021
Year: 2021

Blockchain technology has taken on a leading role in today's industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. With the integration of Blockchain technology into different industrial applications, many application designs, security and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific security and privacy needs of targeted applications with functional parameters, there is still a need for a research study on the application, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on crucial application and security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of different security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues in industrial applications that help to design secure Blockchain-based applications as future directions.
Hybrid Movie Recommender System based on Resource Allocation
Venue: arXiv cs.IR 2021
Year: 2021

Recommender Systems are inevitable to personalize user's experiences on the Internet. They are using different approaches to recommend the Top-K items to users according to their preferences. Nowadays recommender systems have become one of the most important parts of largescale data mining techniques. In this paper, we propose a Hybrid Movie Recommender System (HMRS) based on Resource Allocation to improve the accuracy of recommendation and solve the cold start problem for a new movie. HMRS-RA uses a self-organizing mapping neural network to clustering the users into N clusters. The users' preferences are different according to their age and gender, therefore HMRS-RA is a combination of a Content-Based Method for solving the cold start problem for a new movie and a Collaborative Filtering model besides the demographic information of users. The experimental results based on the MovieLens dataset show that the HMRS-RA increases the accuracy of recommendation compared to the state-of-art and similar works.
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep learning models are driven entirely by data without consideration of any prior knowledge, which dramatically increases the complexity of neural networks and limits the application scope and model generalizability. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises to provide substantially improved imaging tools for various clinical imaging and image-guided interventions.
There is no data like more data -- current status of machine learning datasets in remote sensing
Venue: arXiv cs.LG 2021
Year: 2021

Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation of remote sensing data. In this paper, we review the historic development of such datasets, discuss their features based on a few selected examples, and address open issues for future developments.
Affine Transport for Sim-to-Real Domain Adaptation
Venue: arXiv cs.RO 2021
Year: 2021

Sample-efficient domain adaptation is an open problem in robotics. In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation. First, we derive the affine transport framework; then, we extend the basic framework with Procrustes alignment to model arbitrary affine transformations. We evaluate the method in a number of OpenAI Gym sim-to-sim experiments with simulation environments, as well as on a sim-to-real domain adaptation task of a robot hitting a hockeypuck such that it slides and stops at a target position. In each experiment, we evaluate the results when transferring between each pair of dynamics domains. The results show that affine transport can significantly reduce the model adaptation error in comparison to using the original, non-adapted dynamics model.
Empirical Investigation of Factors that Influence Human Presence and Agency in Telepresence Robot
Venue: arXiv cs.RO 2021
Year: 2021

Nowadays, a community starts to find the need for human presence in an alternative way, there has been tremendous research and development in advancing telepresence robots. People tend to feel closer and more comfortable with telepresence robots as many senses a human presence in robots. In general, many people feel the sense of agency from the face of a robot, but some telepresence robots without arm and body motions tend to give a sense of human presence. It is important to identify and configure how the telepresence robots affect a sense of presence and agency to people by including human face and slight face and arm motions. Therefore, we carried out extensive research via web-based experiment to determine the prototype that can result in soothing human interaction with the robot. The experiments featured videos of a telepresence robot n = 128, 2 x 2 between-participant study robot face factor: video-conference, robot-like face; arm motion factor: moving vs. static) to investigate the factors significantly affecting human presence and agency with the robot. We used two telepresence robots: an affordable robot platform and a modified version for human interaction enhancements. The findings suggest that participants feel agency that is closer to human-likeness when the robot's face was replaced with a human's face and without a motion. The robot's motion invokes a feeling of human presence whether the face is human or robot-like.
Effects of interactivity and presentation on review-based explanations for recommendations
Venue: arXiv cs.HC 2021
Year: 2021

User reviews have become an important source for recommending and explaining products or services. Particularly, providing explanations based on user reviews may improve users' perception of a recommender system (RS). However, little is known about how review-based explanations can be effectively and efficiently presented to users of RS. We investigate the potential of interactive explanations in review-based RS in the domain of hotels, and propose an explanation scheme inspired by dialog models and formal argument structures. Additionally, we also address the combined effect of interactivity and different presentation styles (i.e. using only text, a bar chart or a table), as well as the influence that different user characteristics might have on users' perception of the system and its explanations. To such effect, we implemented a review-based RS using a matrix factorization explanatory method, and conducted a user study. Our results show that providing more interactive explanations in review-based RS has a significant positive influence on the perception of explanation quality, effectiveness and trust in the system by users, and that user characteristics such as rational decision-making style and social awareness also have a significant influence on this perception.
The Cynicism of Modern Cybercrime: Automating the Analysis of Surface Web Marketplaces
Venue: arXiv cs.CR 2021
Year: 2021

Cybercrime is continuously growing in numbers and becoming more sophisticated. Currently, there are various monetisation and money laundering methods, creating a huge, underground economy worldwide. A clear indicator of these activities is online marketplaces which allow cybercriminals to trade their stolen assets and services. While traditionally these marketplaces are available through the dark web, several of them have emerged in the surface web. In this work, we perform a longitudinal analysis of a surface web marketplace. The information was collected through targeted web scrapping that allowed us to identify hundreds of merchants' profiles for the most widely used surface web marketplaces. In this regard, we discuss the products traded in these markets, their prices, their availability, and the exchange currency. This analysis is performed in an automated way through a machine learning-based pipeline, allowing us to quickly and accurately extract the needed information. The outcomes of our analysis evince that illegal practices are leveraged in surface marketplaces and that there are not effective mechanisms towards their takedown at the time of writing.
RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features
Venue: arXiv cs.SD 2021
Year: 2021

Recent interest in exploiting Deep Learning techniques for Noise Suppression, has led to the creation of Hybrid Denoising Systems that combine classic Signal Processing with Deep Learning. In this paper, we concentrated our efforts on extending the RNNoise denoising system (arXiv:1709.08243) with the inclusion of complementary features during the training phase. We present a comprehensive explanation of the set-up process of a modified system and present the comparative results derived from a performance evaluation analysis, using a reference version of RNNoise as control.
Exploring the relevance of ORCID as a source of study of data sharing activities at the individual-level: a methodological discussion
Venue: arXiv cs.DL 2021
Year: 2021

ORCID is a scientific infrastructure created to solve the problem of author name ambiguity. Over the years ORCID has also become a useful source for studying academic activities reported by researchers. Our objective in this research was to use ORCID to analyze one of these research activities: the publication of datasets. We illustrate how the identification of datasets that shared in researchers' ORCID profiles enables the study of the characteristics of the researchers who have produced them. To explore the relevance of ORCID to study data sharing practices we obtained all ORCID profiles reporting at least one dataset in their "works" list, together with information related to the individual researchers producing the datasets. The retrieved data was organized and analyzed in a SQL database hosted at CWTS. Our results indicate that DataCite is by far the most important data source for providing information about datasets recorded in ORCID. There is also a substantial overlap between DataCite records with other repositories (Figshare, Dryad, and Zenodo). The analysis of the distribution of researchers producing datasets shows that the top six countries with more data producers, also have a relatively higher percentage of people who have produced datasets out of total researchers with datasets than researchers in the total ORCID. By disciplines, researchers that belong to the areas of Natural Sciences and Medicine and Life Sciences are those with the largest amount of reported datasets. Finally, we observed that researchers who have started their PhD around 2015 published their first dataset earlier that those researchers that started their PhD before. The work concludes with some reflections of the possibilities of ORCID as a relevant source for research on data sharing practices.
Bi-objective Search with Bi-directional A*
Venue: arXiv cs.AI 2021
Year: 2021

Bi-objective search is a well-known algorithmic problem, concerned with finding a set of optimal solutions in a two-dimensional domain. This problem has a wide variety of applications such as planning in transport systems or optimal control in energy systems. Recently, bi-objective A*-based search (BOA*) has shown state-of-the-art performance in large networks. This paper develops a bi-directional and parallel variant of BOA*, enriched with several speed-up heuristics. Our experimental results on 1,000 benchmark cases show that our bi-directional A* algorithm for bi-objective search (BOBA*) can optimally solve all of the benchmark cases within the time limit, outperforming the state of the art BOA*, bi-objective Dijkstra and bi-directional bi-objective Dijkstra by an average runtime improvement of a factor of five over all of the benchmark instances.
Tracking Captured Variables in Types
Venue: arXiv cs.PL 2021
Year: 2021

Type systems usually characterize the shape of values but not their free variables. However, there are many desirable safety properties one could guarantee if one could track how references can escape. For example, one may implement algebraic effect handlers using capabilities -- a value which permits one to perform the effect -- safely if one can guarantee that the capability itself does not escape the scope bound by the effect handler. To this end, we study the $\textrm{CF}_{<:}$ calculus, a conservative and lightweight extension of $\textrm{System F}_{<:}$, to track how values and their references can be captured and escape. We show that existing terms in $\textrm{System F}_{<:}$ embed naturally in our calculus, and that many natural problems can be expressed in a system that tracks variable references like we do in $\textrm{CF}_{<:}$. We also give mechanized proofs of the soundness properties of $\textrm{CF}_{<:}$ in Coq. The type system presented in $\textrm{CF}_{<:}$ is powerful enough to reason about safety in the context of many natural extensions of $\textrm{CF}_{<:}$ such as region-based memory-management, non-local returns, and effect handlers.
ConQuer-92 -- The revised report on the conceptual query language LISA-D
Venue: arXiv cs.DB 2021
Year: 2021

In this report the conceptual query language ConQuer-92 is introduced. This query language serves as the backbone of InfoAssistant's query facilities. Furthermore, this language can also be used for the specification of derivation rules (e.g. subtype defining rules) and textual constraints in InfoModeler. This report is solely concerned with a formal definition, and the explanation thereof, of ConQuer-92. The implementation of ConQuer-92 in SQL-92 will be treated in a separate report.
The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community
Venue: arXiv cs.SI 2021
Year: 2021

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of `incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity.
Matching Targets Across Domains with RADON, the Re-Identification Across Domain Network
Venue: arXiv cs.LG 2021
Year: 2021

We present a novel convolutional neural network that learns to match images of an object taken from different viewpoints or by different optical sensors. Our Re-Identification Across Domain Network (RADON) scores pairs of input images from different domains on similarity. Our approach extends previous work on Siamese networks and modifies them to more challenging use cases, including low- and no-shot learning, in which few images of a specific target are available for training. RADON shows strong performance on cross-view vehicle matching and cross-domain person identification in a no-shot learning environment.
Unilateral Ground Contact Force Regulations in Thruster-Assisted Legged Locomotion
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we study the regulation of the Ground Contact Forces (GRF) in thruster-assisted legged locomotion. We will employ Reference Governors (RGs) for enforcing GRF constraints in Harpy model which is a bipedal robot that is being developed at Northeastern University. Optimization-based methods and whole body control are widely used for enforcing the no-slip constraints in legged locomotion which can be very computationally expensive. In contrast, RGs can enforce these constraints by manipulating joint reference trajectories using Lyapunov stability arguments which can be computed much faster. The addition of the thrusters in our model allows to manipulate the gait parameters and the GRF without sacrificing the locomotion stability.
Efficient Assignment of Identities in Anonymous Populations
Venue: arXiv cs.DC 2021
Year: 2021

We consider the fundamental problem of assigning distinct labels to agents in the probabilistic model of population protocols. Our protocols operate under the assumption that the size $n$ of the population is embedded in the transition function. Our labeling protocols are silent w.h.p., i.e., eventually each agent reaches its final state and remains in it forever w.h.p., as well as safe, i.e., never update the label assigned to any single agent. We first present a fast, silent w.h.p.and safe labeling protocol for which the required number of interactions is asymptotically optimal, i.e., $O(n \log n/ε)$ w.h.p. It uses $(2+ε)n+O(n^c)$ states, for any $c<1,$ and the label range $1,\dots,(1+ε)n.$ Furthermore, we consider the so-called pool labeling protocols that include our fast protocol. We show that the expected number of interactions required by any pool protocol is $\ge \frac{n^2}{r+1}$, when the labels range is $1,\dots, n+r<2n.$ Next, we provide a protocol which is silent and safe once a unique leader is provided, and uses only $n+5\sqrt n +O(n^c)$ states, for any $c<1,$ and draws labels from the range $1,\dots,n.$ The expected number of interactions required by the protocol is $O(n^3).$ On the other hand, we show that (even if a unique leader is given in advance) any silent protocol that produces a valid labeling and is safe with probability $>1-\frac 1n$, uses $\ge n+\sqrt {n-1} -1$ states. Hence, our protocol is almost state-optimal. We also present a generalization of the protocol to include a trade-off between the number of states and the expected number of interactions. Furthermore, we show that for any silent and safe labeling protocol utilizing $n+t<2n$ states the expected number of interactions required to achieve a valid labeling is $\ge \frac{n^2}{t+1}$.
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Venue: arXiv cs.CV 2021
Year: 2021

Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal modeling and then average multiple snippet-level predictions to yield the final video-level prediction. Thus, their video-level prediction does not consider spatio-temporal features of how video evolves along the temporal dimension. In this paper, we introduce a novel Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. To be more specific, we attempt to generate a dynamic kernel for a convolutional operation to aggregate long-range temporal information among adjacent snippets adaptively. The DSA module is an efficient plug-and-play module and can be combined with the off-the-shelf clip-based models (i.e., TSM, I3D) to perform powerful long-range modeling with minimal overhead. The final video architecture, coined as DSANet. We conduct extensive experiments on several video recognition benchmarks (i.e., Mini-Kinetics-200, Kinetics-400, Something-Something V1 and ActivityNet) to show its superiority. Our proposed DSA module is shown to benefit various video recognition models significantly. For example, equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved from 74.9% to 78.2% on Kinetics-400. Codes are available at https://github.com/whwu95/DSANet.
Solving the One-dimensional Distance Geometry Problem by Optical Computing
Venue: arXiv cs.ET 2021
Year: 2021

Distance geometry problem belongs to a class of hard problems in classical computation that can be understood in terms of a set of inputs processed according to a given transformation, and for which the number of possible outcomes grows exponentially with the number of inputs. It is conjectured that quantum computing schemes can solve problems belonging to this class in a time that grows only at a polynomial rate with the number of inputs. While quantum computers are still being developed, there are some classical optics computation approaches that can perform very well for specific tasks. Here, we present an optical computing approach for the distance geometry problem in one dimension and show that it is very promising in the classical computing regime.
Climate Action During COVID-19 Recovery and Beyond: A Twitter Text Mining Study
Venue: arXiv cs.SI 2021
Year: 2021

The Coronavirus pandemic created a global crisis that prompted immediate large-scale action, including economic shutdowns and mobility restrictions. These actions have had devastating effects on the economy, but some positive effects on the environment. As the world recovers from the pandemic, we ask the following question: What is the public attitude towards climate action during COVID-19 recovery and beyond? We answer this question by analyzing discussions on the Twitter social media platform. We find that most discussions support climate action and point out lessons learned during pandemic response that can shape future climate policy, although skeptics continue to have a presence. Additionally, concerns arise in the context of climate action during the pandemic, such as mitigating the risk of COVID-19 transmission on public transit.
Bias in Machine Learning Software: Why? How? What to do?
Venue: arXiv cs.LG 2021
Year: 2021

Increasingly, software is making autonomous decisions in case of criminal sentencing, approving credit cards, hiring employees, and so on. Some of these decisions show bias and adversely affect certain social groups (e.g. those defined by sex, race, age, marital status). Many prior works on bias mitigation take the following form: change the data or learners in multiple ways, then see if any of that improves fairness. Perhaps a better approach is to postulate root causes of bias and then applying some resolution strategy. This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples. Our Fair-SMOTE algorithm removes biased labels; and rebalances internal distributions such that based on sensitive attribute, examples are equal in both positive and negative classes. On testing, it was seen that this method was just as effective at reducing bias as prior approaches. Further, models generated via Fair-SMOTE achieve higher performance (measured in terms of recall and F1) than other state-of-the-art fairness improvement algorithms. To the best of our knowledge, measured in terms of number of analyzed learners and datasets, this study is one of the largest studies on bias mitigation yet presented in the literature.