OpenRaiser/Intern-Atlas huggingface dataset:
Relational Graph Neural Networks for Fraud Detection in a Super-App environment
Venue: arXiv cs.LG 2021
Year: 2021

Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems. In this paper we propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. To this end, we apply the framework on different heterogeneous graphs of users, devices, and credit cards; and finally use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity, further proofing how they can leverage that into better decisions and fraud detection strategies.
Malware Classification Using Transfer Learning
Venue: arXiv cs.CR 2021
Year: 2021

With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malware classification is an important tools to combat that threat. One of the successful approaches to classification is based on malware images and deep learning. While many deep learning architectures are very accurate they usually take a long time to train. In this work we perform experiments on multiple well known, pre-trained, deep network architectures in the context of transfer learning. We show that almost all them classify malware accurately with a very short training period.
The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
Venue: arXiv cs.IR 2021
Year: 2021

Despite advances in neural machine translation, cross-lingual retrieval tasks in which queries and documents live in different natural language spaces remain challenging. Although neural translation models may provide an intuitive approach to tackle the cross-lingual problem, their resource-consuming training and advanced model structures may complicate the overall retrieval pipeline and reduce users engagement. In this paper, we build our end-to-end Cross-Lingual Arabic Information REtrieval (CLAIRE) system based on the cross-lingual word embedding where searchers are assumed to have a passable passive understanding of Arabic and various supporting information in English is provided to aid retrieval experience. The proposed system has three major advantages: (1) The usage of English-Arabic word embedding simplifies the overall pipeline and avoids the potential mistakes caused by machine translation. (2) Our CLAIRE system can incorporate arbitrary word embedding-based neural retrieval models without structural modification. (3) Early empirical results on an Arabic news collection show promising performance.
Empirical Security and Privacy Analysis of Mobile Symptom Checking Applications on Google Play
Venue: arXiv cs.CR 2021
Year: 2021

Smartphone technology has drastically improved over the past decade. These improvements have seen the creation of specialized health applications, which offer consumers a range of health-related activities such as tracking and checking symptoms of health conditions or diseases through their smartphones. We term these applications as Symptom Checking apps or simply SymptomCheckers. Due to the sensitive nature of the private data they collect, store and manage, leakage of user information could result in significant consequences. In this paper, we use a combination of techniques from both static and dynamic analysis to detect, trace and categorize security and privacy issues in 36 popular SymptomCheckers on Google Play. Our analyses reveal that SymptomCheckers request a significantly higher number of sensitive permissions and embed a higher number of third-party tracking libraries for targeted advertisements and analytics exploiting the privileged access of the SymptomCheckers in which they exist, as a mean of collecting and sharing critically sensitive data about the user and their device. We find that these are sharing the data that they collect through unencrypted plain text to the third-party advertisers and, in some cases, to malicious domains. The results reveal that the exploitation of SymptomCheckers is present in popular apps, still readily available on Google Play.
Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance
Venue: arXiv cs.RO 2021
Year: 2021

Supernumerary Robotics Device (SRD) is an ideal solution to provide robotic assistance in overhead manual manipulation. Since two arms are occupied for the overhead task, it is desired to have additional arms to assist us in achieving other subtasks such as supporting the far end of a long plate and pushing it upward to fit in the ceiling. In this study, a method that maps human muscle force to SRD for overhead task assistance is proposed. Our methodology is to utilize redundant DoFs such as the idle muscles in the leg to control the supporting force of the SRD. A sEMG device is worn on the operator's shank where muscle signals are measured, parsed, and transmitted to SRD for control. In the control aspect, we adopted stiffness control in the task space based on torque control at the joint level. We are motivated by the fact that humans can achieve daily manipulation merely through simple inherent compliance property in joint driven by muscles. We explore to estimate the force of some particular muscles in humans and control the SRD to imitate the behaviors of muscle and output supporting forces to accomplish the subtasks such as overhead supporting. The sEMG signals detected from human muscles are extracted, filtered, rectified, and parsed to estimate the muscle force. We use this force information as the intent of the operator for proper overhead supporting force. As one of the well-known compliance control methods, stiffness control is easy to achieve using a few of straightforward parameters such as stiffness and equilibrium point. Through tuning the stiffness and equilibrium point, the supporting force of SRD in task space can be easily controlled. The muscle force estimated by sEMG is mapped to the desired force in the task space of the SRD. The desired force is transferred into stiffness or equilibrium point to output the corresponding supporting force.
DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving
Venue: arXiv cs.DC 2021
Year: 2021

Distributed algorithms to solve linear equations in multi-agent networks have attracted great research attention and many iteration-based distributed algorithms have been developed. The convergence speed is a key factor to be considered for distributed algorithms, and it is shown dependent on the spectral radius of the iteration matrix. However, the iteration matrix is determined by the network structure and is hardly pre-tuned, making the iterative-based distributed algorithms may converge very slowly when the spectral radius is close to 1. In contrast, in centralized optimization, the Conjugate Gradient (CG) is a widely adopted idea to speed up the convergence of the centralized solvers, which can guarantee convergence in fixed steps. In this paper, we propose a general distributed implementation of CG, called DCG. DCG only needs local communication and local computation, while inheriting the characteristic of fast convergence. DCG guarantees to converge in $4Hn$ rounds, where $H$ is the maximum hop number of the network and $n$ is the number of nodes. We present the applications of DCG in solving the least square problem and network localization problem. The results show the convergence speed of DCG is three orders of magnitude faster than the widely used Richardson iteration method.
Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications
Venue: arXiv cs.LG 2021
Year: 2021

With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data scientists in their tasks. In this work we describe our technological concept for such a model management system. This concept includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment. We describe this concept with a close focus on model lifecycle requirements stemming from our industry application cases, but generalize key features that are relevant for all applications of machine learning.
Kinetic surface friction rendering for interactive sonification: an initial exploration
Venue: arXiv cs.HC 2021
Year: 2021

Inspired by the role sound and friction play in interactions with everyday objects, this work aims to identify some of the ways in which kinetic surface friction rendering can complement interactive sonification controlled by movable objects. In order to do this, a tactile system is presented which implements a movable physical object with programmable friction. Important aspects of this system include the capacity to display high-resolution kinetic friction patterns, the ability to algorithmically define interactions directly in terms of physical units, and the complete integration of audio and tactile synthesis. A prototype interaction spatially mapping arbitrary 1D signal data on a surface and directly converting these to sound and friction during movements across the surface is described. The results of a pilot evaluation of this interaction indicate how kinetic surface friction rendering can be a means for giving dynamically created virtual objects for sonification a tangible presence. Some specific possible roles for movement input and friction output are identified, as well as issues to be considered when applying and further developing this type of haptic feedback in the context of interactive sonification.
10 Years Later: The Mathematics Subject Classification and Linked Open Data
Venue: arXiv cs.DL 2021
Year: 2021

Ten years ago, the Mathematics Subject Classification MSC 2010 was released, and a corresponding machine-readable Linked Open Data collection was published using the Simple Knowledge Organization System (SKOS). Now, the new MSC 2020 is out. This paper recaps the last ten years of working on machine-readable MSC data and presents the new machine-readable MSC 2020. We describe the processing required to convert the version of record, as agreed by the editors of zbMATH and Mathematical Reviews, into the Linked Open Data form we call MSC2020-SKOS. The new form includes explicit marking of the changes from 2010 to 2020, some translations of English code descriptions into Chinese, Italian, and Russian, and extra material relating MSC to other mathematics classification efforts. We also outline future potential uses for MSC2020-SKOS in semantic indexing and sketch its embedding in a larger vision of scientific research data.
Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
Venue: arXiv cs.DC 2021
Year: 2021

Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of a concrete job are helpful in such situations, yet they are usually bound to a specific job execution context (e.g. node type, software versions, job parameters) due to the few considered input parameters. Even in case of slight context changes, such supportive models need to be retrained and cannot benefit from historical execution data from related contexts. This paper presents Bellamy, a novel modeling approach that combines scale-outs, dataset sizes, and runtimes with additional descriptive properties of a dataflow job. It is thereby able to capture the context of a job execution. Moreover, Bellamy is realizing a two-step modeling approach. First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts. Subsequently, the general model is optimized for the specific situation at hand, based on the available data for the concrete context. We evaluate our approach on two publicly available datasets consisting of execution data from various dataflow jobs carried out in different environments, showing that Bellamy outperforms state-of-the-art methods.
Separator logic and star-free expressions for graphs
Venue: arXiv cs.LO 2021
Year: 2021

We describe two formalisms for defining graph languages, and prove that they are equivalent: 1. Separator logic. This is first-order logic on graphs which is allowed to use the edge relation, and for every $n \in \{0,1,\ldots \}$ a relation of arity $n+2$ which says that "vertex $s$ can be connected to vertex $t$ by a path that avoids vertices $v_1,\ldots,v_n$". 2. Star-free graph expressions. These are expressions that describe graphs with distinguished vertices called ports, and which are built from finite languages via Boolean combinations and the operations on graphs with ports used to construct tree decompositions. Furthermore, we prove a variant of Schützenberger's theorem (about star-free languages being those recognized by a periodic monoids) for graphs of bounded pathwidth. A corollary is that, given $k$ and a graph language represented by an \mso formula, one can decide if the language can be defined in either of two equivalent formalisms on graphs of pathwidth at most $k$.
Towards Semantic Interoperability in Historical Research: Documenting Research Data and Knowledge with Synthesis
Venue: arXiv cs.DL 2021
Year: 2021

A vast area of research in historical science concerns the documentation and study of artefacts and related evidence. Current practice mostly uses spreadsheets or simple relational databases to organise the information as rows with multiple columns of related attributes. This form offers itself for data analysis and scholarly interpretation, however it also poses problems including i) the difficulty for collaborative but controlled documentation by a large number of users, ii) the lack of representation of the details from which the documented relations are inferred, iii) the difficulty to extend the underlying data structures as well as to combine and integrate data from multiple and diverse information sources, and iv) the limitation to reuse the data beyond the context of a particular research activity. To support historians to cope with these problems, in this paper we describe the Synthesis documentation system and its use by a large number of historians in the context of an ongoing research project in the field of History of Art. The system is Web-based and collaborative, and makes use of existing standards for information documentation and publication (CIDOC-CRM, RDF), focusing on semantic interoperability and the production of data of high value and long-term validity.
Self-Supervised Learning for Fine-Grained Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Fine-grained image classification involves identifying different subcategories of a class which possess very subtle discriminatory features. Fine-grained datasets usually provide bounding box annotations along with class labels to aid the process of classification. However, building large scale datasets with such annotations is a mammoth task. Moreover, this extensive annotation is time-consuming and often requires expertise, which is a huge bottleneck in building large datasets. On the other hand, self-supervised learning (SSL) exploits the freely available data to generate supervisory signals which act as labels. The features learnt by performing some pretext tasks on huge unlabelled data proves to be very helpful for multiple downstream tasks. Our idea is to leverage self-supervision such that the model learns useful representations of fine-grained image classes. We experimented with 3 kinds of models: Jigsaw solving as pretext task, adversarial learning (SRGAN) and contrastive learning based (SimCLR) model. The learned features are used for downstream tasks such as fine-grained image classification. Our code is available at http://github.com/rush2406/Self-Supervised-Learning-for-Fine-grained-Image-Classification
Closing the B-tree vs. LSM-tree Write Amplification Gap on Modern Storage Hardware with Built-in Transparent Compression
Venue: arXiv cs.DB 2021
Year: 2021

This paper studies the design of B-tree that can take full advantage of modern storage hardware with built-in transparent compression. Recent years have witnessed significant interest in applying log-structured merge tree (LSM-tree) as an alternative to B-tree. The current consensus is that, compared with B-tree, LSM-tree has distinct advantages in terms of storage space efficiency and write amplification. This paper argues that one should revisit this belief upon the arrival of storage hardware with built-in transparent compression. Advanced storage appliances~(e.g., all-flash array) and emerging computational storage drives perform hardware-based lossless data compression, transparent to OS and user applications. Beyond straightforwardly reducing the physical storage cost difference between B-tree and LSM-tree, such modern storage hardware brings new opportunities to innovate B-tree implementation in order to largely reduce its write amplification. As the first step to explore the potential, this paper presents three simple design techniques (i.e., deterministic page shadowing, localized page modification logging, and sparse redo logging) that can leverage such modern storage hardware to significantly reduce the B-tree write amplification. We implemented these design techniques and carried out experiments on a commercial storage drive with built-in transparent compression. The results show that the proposed design techniques can reduce the B-tree write amplification by over 10x. Compared with RocksDB (a popular key-value store built upon LSM-tree), the implemented B-tree can achieve similar or even smaller write amplification and physical storage space usage.
Preventing Spoliation of Evidence with Blockchain: A Perspective from South Asia
Venue: arXiv cs.CY 2021
Year: 2021

Evidence destruction and tempering is a time-tested tactic to protect the powerful perpetrators, criminals, and corrupt officials. Countries where law enforcing institutions and judicial system can be comprised, and evidence destroyed or tampered, ordinary citizens feel disengaged with the investigation or prosecution process, and in some instances, intimidated due to the vulnerability to exposure and retribution. Using Distributed Ledger Technologies (DLT), such as blockchain, as the underpinning technology, here we propose a conceptual model - 'EvidenceChain', through which citizens can anonymously upload digital evidence, having assurance that the integrity of the evidence will be preserved in an immutable and indestructible manner. Person uploading the evidence can anonymously share it with investigating authorities or openly with public, if coerced by the perpetrators or authorities. Transferring the ownership of evidence from authority to ordinary citizen, and custodianship of evidence from susceptible centralized repository to an immutable and indestructible distributed repository, can cause a paradigm shift of power that not only can minimize spoliation of evidence but human rights abuse too. Here the conceptual model was theoretically tested against some high-profile spoliation of evidence cases from four South Asian developing countries that often rank high in global corruption index and low in human rights index.
The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI
Venue: arXiv cs.CY 2021
Year: 2021

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Exploring the Scope and Potential of Local Newspaper-based Dengue Surveillance in Bangladesh
Venue: arXiv cs.CY 2021
Year: 2021

Dengue fever has been considered to be one of the global public health problems of the twenty-first century, especially in tropical and subtropical countries of the global south. The high morbidity and mortality rates of Dengue fever impose a huge economic and health burden for middle and low-income countries. It is so prevalent in such regions that enforcing a granular level of surveillance is quite impossible. Therefore, it is crucial to explore an alternative cost-effective solution that can provide updates of the ongoing situation in a timely manner. In this paper, we explore the scope and potential of a local newspaper-based dengue surveillance system, using well-known data-mining techniques, in Bangladesh from the analysis of the news contents written in the native language. In addition, we explain the working procedure of developing a novel database, using human-in-the-loop technique, for further analysis, and classification of dengue and its intervention-related news. Our classification method has an f-score of 91.45%, and matches the ground truth of reported cases quite closely. Based on the dengue and intervention-related news, we identified the regions where more intervention efforts are needed to reduce the rate of dengue infection. A demo of this project can be accessed at: http://erdos.dsm.fordham.edu:3009/
Strategic Voting in the Context of Negotiating Teams
Venue: arXiv cs.GT 2021
Year: 2021

A negotiating team is a group of two or more agents who join together as a single negotiating party because they share a common goal related to the negotiation. Since a negotiating team is composed of several stakeholders, represented as a single negotiating party, there is need for a voting rule for the team to reach decisions. In this paper, we investigate the problem of strategic voting in the context of negotiating teams. Specifically, we present a polynomial-time algorithm that finds a manipulation for a single voter when using a positional scoring rule. We show that the problem is still tractable when there is a coalition of manipulators that uses a x-approval rule. The coalitional manipulation problem becomes computationally hard when using Borda, but we provide a polynomial-time algorithm with the following guarantee: given a manipulable instance with k manipulators, the algorithm finds a successful manipulation with at most one additional manipulator. Our results hold for both constructive and destructive manipulations.