OpenRaiser/Intern-Atlas huggingface dataset:
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
Venue: arXiv cs.CR 2020
Year: 2020

In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metadata, high-quality labels derived from multiple sources, information about vendor detections of the malware samples at the time of collection, and additional ``tags'' related to each malware sample to serve as additional targets. In addition to features and metadata, we also provide approximately 10 million ``disarmed'' malware samples -- samples with both the optional\_headers.subsystem and file\_header.machine flags set to zero -- that may be used for further exploration of features and detection strategies. We also provide Python code to interact with the data and features, as well as baseline neural network and gradient boosted decision tree models and their results, with full training and evaluation code, to serve as a starting point for further experimentation.
The Complexity of Comparative Text Analysis -- "The Gardener is always the Murderer" says the Fourth Machine
Venue: arXiv cs.CL 2020
Year: 2020

There is a heated debate about how far computers can map the complexity of text analysis compared to the abilities of the whole team of human researchers. A "deep" analysis of a given text is still beyond the possibilities of modern computers. In the heart of the existing computational text analysis algorithms there are operations with real numbers, such as additions and multiplications according to the rules of algebraic fields. However, the process of "comparing" has a very precise mathematical structure, which is different from the structure of an algebraic field. The mathematical structure of "comparing" can be expressed by using Boolean rings. We build on this structure and define the corresponding algebraic equations lifting algorithms of comparative text analysis onto the "correct" algebraic basis. From this point of view, we can investigate the question of {\em computational} complexity of comparative text analysis.
Secondary Use of Employee COVID-19 Symptom Reporting as Syndromic Surveillance as an Early Warning Signal of Future Hospitalizations
Venue: arXiv cs.CY 2020
Year: 2020

Importance: Alternative methods for hospital utilization forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited. Objective: Determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to forecast COVID-19 hospitalizations in the communities where employees live. Design: Retrospective cohort study. Setting: Large academic hospital network of 10 hospitals accounting for a total of 2,384 beds and 136,000 discharges in New England. Participants: 6,841 employees working on-site of Hospital 1 from April 2, 2020 to November 4, 2020, who live in the 10 hospitals' service areas. Interventions: Mandatory, daily employee self-reported symptoms were collected using an automated text messaging system. Main Outcomes: Mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) of 7 day forecasts of daily COVID-19 hospital census at each hospital. Results: 6,841 employees, with a mean age of 40.8 (SD = 13.6), 8.8 years of service (SD = 10.4), and 74.8% were female (n = 5,120), living in the 10 hospitals' service areas. Our model has an MAE of 6.9 COVID-19 patients and a WMAPE of 1.5% for hospitalizations for the entire hospital network. The individual hospitals had an MAE that ranged from 0.9 to 4.5 patients (WMAPE ranged from 2.1% to 16.1%). At Hospital 1, a doubling of the number of employees reporting symptoms (which corresponds to 4 additional employees reporting symptoms at the mean for Hospital 1) is associated with a 5% increase in COVID-19 hospitalizations at Hospital 1 in 7 days (95% CI: (0.02, 0.07)). Conclusions: We found that a real-time employee health attestation tool used at a single hospital could be used to predict subsequent hospitalizations in 7 days at hospitals throughout a larger hospital network in New England.
Investigation of the Impacts of COVID-19 on the Electricity Consumption of a University Dormitory Using Weather Normalization
Venue: arXiv cs.CY 2020
Year: 2020

This study investigated the impacts of the COVID-19 pandemic on the electricity consumption of a university dormitory building in the southern U.S. The historical electricity consumption data of this university dormitory building and weather data of an on-campus weather station, which were collected from January 1st, 2017 to July 31st, 2020, were used for analysis. Four inverse data-driven prediction models, i.e., Artificial Neural Network, Long Short-Term Memory Recurrent Neural Network, eXtreme Gradient Boosting, and Light Gradient Boosting Machine, were exploited to account for the influence of the weather conditions. The results suggested that the total electricity consumption of the objective building decreased by nearly 41% (about 276,000 kWh (942 MMBtu)) compared with the prediction value during the campus shutdown due to the COVID-19. Besides, the daily load ratio (DLR) varied significantly as well. In general, the DLR decreased gradually from 80% to nearly 40% in the second half of March 2020, maintained on a relatively stable level between 30% to 60% in April, May, and June 2020, and then slowly recovered to 80% of the normal capacity in July 2020.
FinTech E-Commerce Payment Application User Experience Analysis during COVID-19 Pandemic
Venue: arXiv cs.CY 2020
Year: 2020

The application of information technology in the era of big data and cloud computing has led to the trend of electronic payments through financial technology, or FinTech. One of the most popular FinTech applications in Indonesia is Go-Pay in the Gojek start-up application. This research will analyze how the FinTech Go-Pay user experience both for transactions on Gojek and at merchants that collaborate with Gojek. User Experience (UX) is analyzed using the User Experience Questionnaire which consists of 6 (six) variables (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty). Total data collected amounted to 258. After analyzing the calculation results, the mean scores are obtained in the following order: Efficiency, Perspicuity, Stimulation, Attractiveness, Dependability, and Novelty. Then when compared with benchmark data the following sequence is obtained: Efficiency, Perspicuity, Stimulation, Attractiveness, Dependability, and Novelty. Overall the Go-Pay service is efficient and perspicuity, but the Go-Pay service needs to improve its novelty. This article provides additional knowledge or novelty contributions, especially for user experience analysis using FinTech applications.
Robust One Shot Audio to Video Generation
Venue: arXiv cs.CV 2020
Year: 2020

Audio to Video generation is an interesting problem that has numerous applications across industry verticals including film making, multi-media, marketing, education and others. High-quality video generation with expressive facial movements is a challenging problem that involves complex learning steps for generative adversarial networks. Further, enabling one-shot learning for an unseen single image increases the complexity of the problem while simultaneously making it more applicable to practical scenarios. In the paper, we propose a novel approach OneShotA2V to synthesize a talking person video of arbitrary length using as input: an audio signal and a single unseen image of a person. OneShotA2V leverages curriculum learning to learn movements of expressive facial components and hence generates a high-quality talking-head video of the given person. Further, it feeds the features generated from the audio input directly into a generative adversarial network and it adapts to any given unseen selfie by applying fewshot learning with only a few output updation epochs. OneShotA2V leverages spatially adaptive normalization based multi-level generator and multiple multi-level discriminators based architecture. The input audio clip is not restricted to any specific language, which gives the method multilingual applicability. Experimental evaluation demonstrates superior performance of OneShotA2V as compared to Realistic Speech-Driven Facial Animation with GANs(RSDGAN) [43], Speech2Vid [8], and other approaches, on multiple quantitative metrics including: SSIM (structural similarity index), PSNR (peak signal to noise ratio) and CPBD (image sharpness). Further, qualitative evaluation and Online Turing tests demonstrate the efficacy of our approach.
Adaptive Verifiable Training Using Pairwise Class Similarity
Venue: arXiv cs.LG 2020
Year: 2020

Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, a non-robust LeNet model has a 21.63% error rate, while a model created using verifiable training and a L-infinity robustness criterion of 8/255, has an error rate of 57.10%. Upon examination, we find that when labeling visually similar classes, the model's error rate is as high as 61.65%. We attribute the loss in performance to inter-class similarity. Similar classes (i.e., close in the feature space) increase the difficulty of learning a robust model. While it's desirable to train a robust model for a large robustness region, pairwise class similarities limit the potential gains. Also, consideration must be made regarding the relative cost of mistaking similar classes. In security or safety critical tasks, similar classes are likely to belong to the same group, and thus are equally sensitive. In this work, we propose a new approach that utilizes inter-class similarity to improve the performance of verifiable training and create robust models with respect to multiple adversarial criteria. First, we use agglomerate clustering to group similar classes and assign robustness criteria based on the similarity between clusters. Next, we propose two methods to apply our approach: (1) Inter-Group Robustness Prioritization, which uses a custom loss term to create a single model with multiple robustness guarantees and (2) neural decision trees, which trains multiple sub-classifiers with different robustness guarantees and combines them in a decision tree architecture. On Fashion-MNIST and CIFAR10, our approach improves clean performance by 9.63% and 30.89% respectively. On CIFAR100, our approach improves clean performance by 26.32%.
GAN Ensemble for Anomaly Detection
Venue: arXiv cs.LG 2020
Year: 2020

When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial Networks (GANs) to anomaly detection tasks and show good performances from these models. Motivated by the observation that GAN ensembles often outperform single GANs in generation tasks, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators and a group of discriminators are trained together, so every generator gets feedback from multiple discriminators, and vice versa. Compared to a single GAN, a GAN ensemble can better model the distribution of normal data and thus better detect anomalies. Our theoretical analysis of GANs and GAN ensembles explains the role of a GAN discriminator in anomaly detection. In the empirical study, we evaluate ensembles constructed from four types of base models, and the results show that these ensembles clearly outperform single models in a series of tasks of anomaly detection.
The Emerging Threats of Deepfake Attacks and Countermeasures
Venue: arXiv cs.CR 2020
Year: 2020

Deepfake technology (DT) has taken a new level of sophistication. Cybercriminals now can manipulate sounds, images, and videos to defraud and misinform individuals and businesses. This represents a growing threat to international institutions and individuals which needs to be addressed. This paper provides an overview of deepfakes, their benefits to society, and how DT works. Highlights the threats that are presented by deepfakes to businesses, politics, and judicial systems worldwide. Additionally, the paper will explore potential solutions to deepfakes and conclude with future research direction.
Towards a Two-Tier Hierarchical Infrastructure: An Offline Payment System for Central Bank Digital Currencies
Venue: arXiv cs.CR 2020
Year: 2020

Digital payments traditionally rely on online communications with several intermediaries such as banks, payment networks, and payment processors in order to authorize and process payment transactions. While these communication networks are designed to be highly available with continuous uptime, there may be times when an end-user experiences little or no access to network connectivity. The growing interest in digital forms of payments has led central banks around the world to explore the possibility of issuing a new type of central-bank money, known as central bank digital currency (CBDC). To facilitate the secure issuance and transfer of CBDC, we envision a CBDC design under a two-tier hierarchical trust infrastructure, which is implemented using public-key cryptography with the central bank as the root certificate authority for generating digital signatures, and other financial institutions as intermediate certificate authorities. One important design feature for CBDC that can be developed under this hierarchical trust infrastructure is an offline capability to create secure point-to-point offline payments through the use of authorized hardware. An offline capability for CBDC as digital cash can create a resilient payment system for consumers and businesses to transact in any situation. We propose an offline payment system (OPS) protocol for CBDC that allows a user to make digital payments to another user while both users are temporarily offline and unable to connect to payment intermediaries (or even the Internet). OPS can be used to instantly complete a transaction involving any form of digital currency over a point-to-point channel without communicating with any payment intermediary, achieving virtually unbounded throughput and real-time transaction latency.
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Venue: arXiv cs.CL 2020
Year: 2020

The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains. In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales. We investigate multiple ways to automatically generate rationales using pre-trained language models, neural knowledge models, and distant supervision from related tasks, and train generative models capable of composing explanatory rationales for unseen instances. We demonstrate our approach on the defeasible inference task, a nonmonotonic reasoning task in which an inference may be strengthened or weakened when new information (an update) is introduced. Our model shows promises at generating post-hoc rationales explaining why an inference is more or less likely given the additional information, however, it mostly generates trivial rationales reflecting the fundamental limitations of neural language models. Conversely, the more realistic setup of jointly predicting the update or its type and generating rationale is more challenging, suggesting an important future direction.
General Policies, Serializations, and Planning Width
Venue: arXiv cs.AI 2020
Year: 2020

It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of problem variables and is often no greater than two. Yet, while the notion of width has become part of the state-of-the-art planning algorithms like BFWS, there is still no good explanation for why so many benchmark domains have bounded width. In this work, we address this question by relating bounded width and serialized width to ideas of generalized planning, where general policies aim to solve multiple instances of a planning problem all at once. We show that bounded width is a property of planning domains that admit optimal general policies in terms of features that are explicitly or implicitly represented in the domain encoding. The results are extended to much larger class of domains with bounded serialized width where the general policies do not have to be optimal. The study leads also to a new simple, meaningful, and expressive language for specifying domain serializations in the form of policy sketches which can be used for encoding domain control knowledge by hand or for learning it from traces. The use of sketches and the meaning of the theoretical results are all illustrated through a number of examples.
Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach
Venue: arXiv cs.CR 2020
Year: 2020

This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile- Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCC environment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.
Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach.
Optimization Techniques to Improve Inference Performance of a Forward Propagating Neural Network on an FPGA
Venue: arXiv cs.AR 2020
Year: 2020

This paper describes an optimized implementation of a Forward Propagating Classification Neural Network which has been previously trained. The implementation described highlights a novel means of using Python scripts to generate a Verilog hardware implementation. The characteristics of this implementation include optimizations to scale input data, use selected addends instead of multiplication functions, hardware friendly activation functions and simplified output selection. Inference performance comparison of a 28x28 pixel 'hand-written' recognition NN between a software implementation on an Intel i7 vs a Xilinx FPGA will be detailed.
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks (DNNs) significantly improved the accuracy of optical character recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the Fast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
Towards Improving Spatiotemporal Action Recognition in Videos
Venue: arXiv cs.CV 2020
Year: 2020

Spatiotemporal action recognition deals with locating and classifying actions in videos. Motivated by the latest state-of-the-art real-time object detector You Only Watch Once (YOWO), we aim to modify its structure to increase action detection precision and reduce computational time. Specifically, we propose four novel approaches in attempts to improve YOWO and address the imbalanced class issue in videos by modifying the loss function. We consider two moderate-sized datasets to apply our modification of YOWO - the popular Joint-annotated Human Motion Data Base (J-HMDB-21) and a private dataset of restaurant video footage provided by a Carnegie Mellon University-based startup, Agot.AI. The latter involves fast-moving actions with small objects as well as unbalanced data classes, making the task of action localization more challenging. We implement our proposed methods in the GitHub repository https://github.com/stoneMo/YOWOv2.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Venue: arXiv cs.AI 2020
Year: 2020

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.