OpenRaiser/Intern-Atlas huggingface dataset:
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL). Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames. We begin with an analysis of requirements that such an AI system should satisfy in order to be practically applicable in video game development, and identify the elements of the DRL model used in the DeepCrawl prototype. The successes and limitations of DeepCrawl are documented through a series of playability tests performed on the final game. We believe that the techniques we propose offer insight into innovative new avenues for the development of behaviors for non-player characters in video games, as they offer the potential to overcome critical issues with
Learning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in India
Venue: arXiv cs.LG 2020
Year: 2020

Female sex workers(FSWs) are one of the most vulnerable and stigmatized groups in society. As a result, they often suffer from a lack of quality access to care. Grassroot organizations engaged in improving health services are often faced with the challenge of improving the effectiveness of interventions due to complex influences. This work combines structure learning, discriminative modeling, and grass-root level expertise of designing interventions across five different Indian states to discover the influence of non-obvious factors for improving safe-sex practices in FSWs. A bootstrapped, ensemble-averaged Bayesian Network structure was learned to quantify the factors that could maximize condom usage as revealed from the model. A discriminative model was then constructed using XgBoost and random forest in order to predict condom use behavior The best model achieved 83% sensitivity, 99% specificity, and 99% area under the precision-recall curve for the prediction. Both generative and discriminative modeling approaches revealed that financial literacy training was the primary influence and predictor of condom use in FSWs. These insights have led to a currently ongoing field trial for assessing the real-world utility of this approach. Our work highlights the potential of explainable models for transparent discovery and prioritization of anti-HIV interventions in female sex workers in a resource-limited setting.
Teaching the Machine to Explain Itself using Domain Knowledge
Venue: arXiv cs.LG 2020
Year: 2020

Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions. However, non-technical humans-in-the-loop struggle to comprehend the rationale behind model predictions, hindering trust in algorithmic decision-making systems. Considerable research work on AI explainability attempts to win back trust in AI systems by developing explanation methods but there is still no major breakthrough. At the same time, popular explanation methods (e.g., LIME, and SHAP) produce explanations that are very hard to understand for non-data scientist persona. To address this, we present JOEL, a neural network-based framework to jointly learn a decision-making task and associated explanations that convey domain knowledge. JOEL is tailored to human-in-the-loop domain experts that lack deep technical ML knowledge, providing high-level insights about the model's predictions that very much resemble the experts' own reasoning. Moreover, we collect the domain feedback from a pool of certified experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations. Lastly, we resort to semantic mappings between legacy expert systems and domain taxonomies to automatically annotate a bootstrap training set, overcoming the absence of concept-based human annotations. We validate JOEL empirically on a real-world fraud detection dataset. We show that JOEL can generalize the explanations from the bootstrap dataset. Furthermore, obtained results indicate that human teaching can further improve the explanations prediction quality by approximately $13.57\%$.
Classifying Malware Using Function Representations in a Static Call Graph
Venue: arXiv cs.CR 2020
Year: 2020

We propose a deep learning approach for identifying malware families using the function call graphs of x86 assembly instructions. Though prior work on static call graph analysis exists, very little involves the application of modern, principled feature learning techniques to the problem. In this paper, we introduce a system utilizing an executable's function call graph where function representations are obtained by way of a recurrent neural network (RNN) autoencoder which maps sequences of x86 instructions into dense, latent vectors. These function embeddings are then modeled as vertices in a graph with edges indicating call dependencies. Capturing rich, node-level representations as well as global, topological properties of an executable file greatly improves malware family detection rates and contributes to a more principled approach to the problem in a way that deliberately avoids tedious feature engineering and domain expertise. We test our approach by performing several experiments on a Microsoft malware classification data set and achieve excellent separation between malware families with a classification accuracy of 99.41%.
Can I Take Your Subdomain? Exploring Related-Domain Attacks in the Modern Web
Venue: arXiv cs.CR 2020
Year: 2020

Related-domain attackers control a sibling domain of their target web application, e.g., as the result of a subdomain takeover. Despite their additional power over traditional web attackers, related-domain attackers received only limited attention by the research community. In this paper we define and quantify for the first time the threats that related-domain attackers pose to web application security. In particular, we first clarify the capabilities that related-domain attackers can acquire through different attack vectors, showing that different instances of the related-domain attacker concept are worth attention. We then study how these capabilities can be abused to compromise web application security by focusing on different angles, including: cookies, CSP, CORS, postMessage and domain relaxation. By building on this framework, we report on a large-scale security measurement on the top 50k domains from the Tranco list that led to the discovery of vulnerabilities in 887 sites, where we quantified the threats posed by related-domain attackers to popular web applications.
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs
Venue: arXiv cs.CR 2020
Year: 2020

Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages. Number Theoretic Transform (NTT), a specialized form of Discrete Fourier Transform (DFT) in the finite field of integers, is the key algorithm that enables fast computation on encrypted ciphertexts in HE. Prior works have accelerated NTT and its inverse transformation on a popular parallel processing platform, GPU, by leveraging DFT optimization techniques. However, these GPU-based studies lack a comprehensive analysis of the primary differences between NTT and DFT or only consider small HE parameters that have tight constraints in the number of arithmetic operations that can be performed without decryption. In this paper, we analyze the algorithmic characteristics of NTT and DFT and assess the performance of NTT when we apply the optimizations that are commonly applicable to both DFT and NTT on modern GPUs. From the analysis, we identify that NTT suffers from severe main-memory bandwidth bottleneck on large HE parameter sets. To tackle the main-memory bandwidth issue, we propose a novel NTT-specific on-the-fly root generation scheme dubbed on-the-fly twiddling (OT). Compared to the baseline radix-2 NTT implementation, after applying all the optimizations, including OT, we achieve 4.2x speedup on a modern GPU.
Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
Venue: arXiv cs.CR 2020
Year: 2020

Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world. This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case. Leveraging previously processed digital forensic cases and their component artefact relevancy classifications can facilitate an opportunity for training automated artificial intelligence based evidence processing systems. These can significantly aid investigators in the discovery and prioritisation of evidence. This paper presents one approach for file artefact relevancy determination building on the growing trend towards a centralised, Digital Forensics as a Service (DFaaS) paradigm. This approach enables the use of previously encountered pertinent files to classify newly discovered files in an investigation. Trained models can aid in the detection of these files during the acquisition stage, i.e., during their upload to a DFaaS system. The technique generates a relevancy score for file similarity using each artefact's filesystem metadata and associated timeline events. The approach presented is validated against three experimental usage scenarios.
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
Venue: arXiv cs.CY 2020
Year: 2020

This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as "real world." Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges.
Intervention Design for Effective Sim2Real Transfer
Venue: arXiv cs.RO 2020
Year: 2020

The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain this success through the lens of causal inference, positioning domain randomization and data augmentation as interventions on the environment which encourage invariance to irrelevant features. Such interventions include visual perturbations that have no effect on reward and dynamics. This encourages the learning algorithm to be robust to these types of variations and learn to attend to the true causal mechanisms for solving the task. This connection leads to two key findings: (1) perturbations to the environment do not have to be realistic, but merely show variation along dimensions that also vary in the real world, and (2) use of an explicit invariance-inducing objective improves generalization in sim2sim and sim2real transfer settings over just data augmentation or domain randomization alone. We demonstrate the capability of our method by performing zero-shot transfer of a robot arm reach task on a 7DoF Jaco arm learning from pixel observations.
Numerical computation of stress-permeability relationships of fracture networks in a shale rock
Venue: arXiv cs.CE 2020
Year: 2020

We present stress-sensitive permeability relationships for two-dimensional fracture networks in the Opalinus Clay from the Mont Terri underground rock laboratory. These relationships may be used as a proxy for fracture network permeability in numerical models that resolve large spatial scales and are used in a variety of GeoEnergy applications involving flow in shaly rocks. To obtain these relationships we present a numerical procedure that uses experimentally determined stress-permeability relationships to numerically compute the effective permeability of the network. The material discontinuities stemming from the fractures are treated by a simple contact-interaction algorithm that accounts for normal interaction between fracture walls, allowing us to calculate the permeability of a fracture network under different stress conditions. We apply the procedure to four fracture networks digitized from two galleries of the Mont Terri rock laboratory. These fracture networks are mapped from the damage zone of the Main Fault that intersects the Opalinus Clay. The networks show a maximum variation of four orders of magnitude when stress ranges from 1MPa to 20 MPa. Our numerical procedure not only establishes representative stress-permeability relationships for a fractured rock mass under stress, but also provides a proxy for fracture network permeability for simulation in fractured formations.
Barriers and Challenges of Computing Students in an Online Learning Environment: Insights from One Private University in the Philippines
Venue: arXiv cs.CY 2020
Year: 2020

While the literature presents various advantages of using blended learning, policymakers must identify the barriers and challenges faced by students that may cripple their online learning experience. Understanding these barriers can help academic institutions craft policies to advance and improve the students' online learning experience. This study was conducted to determine the challenges of computing students in one private University in the Philippines during the period where the entire Luzon region was placed under the Enhanced Community Quarantine (ECQ) as a response to the COVID-19 pandemic. A survey through MS Forms Pro was performed to identify the experiences of students in online learning. The survey ran from March 16 to March 18, 2020, which yielded a total of 300 responses. Descriptive statistics revealed that the top three barriers and challenges encountered by students were 1. the difficulty of clarifying topics or discussions with the professors, 2.the lack of study or working area for doing online activities, and 3. the lack of a good Internet connection for participating in online activities. It can be concluded that both students and faculty members were not fully prepared to undergo full online learning. More so, some faculty members may have failed to adapt to the needs of the students in an online learning environment. While the primary data of the study mainly came from the students, it would also be an excellent addition to understand the perspective of the faculty members in terms of their experiences with their students. Their insights could help validate the responses in the survey and provide other barriers that may not have been included in the study.
Approximate kNN Classification for Biomedical Data
Venue: arXiv cs.LG 2020
Year: 2020

We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNA-seq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the "Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability.
Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh
Venue: arXiv cs.CV 2020
Year: 2020

Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate of the vehicles of Bangladesh and recognizing characters using tesseract from the detected license plates. Here we also present a Graphical User Interface (GUI) based on Tkinter, a python package. The license plate detection model is trained with mean average precision (mAP) of 90.50% and performed in a single TESLA T4 GPU with an average of 14 frames per second (fps) on real time video footage.
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Venue: arXiv cs.LG 2020
Year: 2020

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.
Domain Adaptation on Semantic Segmentation for Aerial Images
Venue: arXiv cs.CV 2020
Year: 2020

Semantic segmentation has achieved significant advances in recent years. While deep neural networks perform semantic segmentation well, their success rely on pixel level supervision which is expensive and time-consuming. Further, training using data from one domain may not generalize well to data from a new domain due to a domain gap between data distributions in the different domains. This domain gap is particularly evident in aerial images where visual appearance depends on the type of environment imaged, season, weather, and time of day when the environment is imaged. Subsequently, this distribution gap leads to severe accuracy loss when using a pretrained segmentation model to analyze new data with different characteristics. In this paper, we propose a novel unsupervised domain adaptation framework to address domain shift in the context of aerial semantic image segmentation. To this end, we solve the problem of domain shift by learn the soft label distribution difference between the source and target domains. Further, we also apply entropy minimization on the target domain to produce high-confident prediction rather than using high-confident prediction by pseudo-labeling. We demonstrate the effectiveness of our domain adaptation framework using the challenge image segmentation dataset of ISPRS, and show improvement over state-of-the-art methods in terms of various metrics.
A Laptop Ensemble Performance System using Recurrent Neural Networks
Venue: arXiv cs.HC 2020
Year: 2020

The popularity of applying machine learning techniques in musical domains has created an inherent availability of freely accessible pre-trained neural network (NN) models ready for use in creative applications. This work outlines the implementation of one such application in the form of an assistance tool designed for live improvisational performances by laptop ensembles. The primary intention was to leverage off-the-shelf pre-trained NN models as a basis for assisting individual performers either as musical novices looking to engage with more experienced performers or as a tool to expand musical possibilities through new forms of creative expression. The system expands upon a variety of ideas found in different research areas including new interfaces for musical expression, generative music and group performance to produce a networked performance solution served via a web-browser interface. The final implementation of the system offers performers a mixture of high and low-level controls to influence the shape of sequences of notes output by locally run NN models in real time, also allowing performers to define their level of engagement with the assisting generative models. Two test performances were played, with the system shown to feasibly support four performers over a four minute piece while producing musically cohesive and engaging music. Iterations on the design of the system exposed technical constraints on the use of a JavaScript environment for generative models in a live music context, largely derived from inescapable processing overheads.
Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study
Venue: arXiv cs.LG 2020
Year: 2020

Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications towards the early detection and intervention of mental health disorders. One promising data source to help monitor human behavior is from daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected attributes (e.g., race, gender). In this paper, we study behavioral markers or daily mood using a recent dataset of mobile behaviors from high-risk adolescent populations. Using computational models, we find that multimodal modeling of both text and app usage features is highly predictive of daily mood over each modality alone. Furthermore, we evaluate approaches that reliably obfuscate user identity while remaining predictive of daily mood. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier as compared to unimodal approaches.