OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Training for Deep Learning-based Intrusion Detection Systems
Venue: arXiv cs.CR 2021
Year: 2021

Nowadays, Deep Neural Networks (DNNs) report state-of-the-art results in many machine learning areas, including intrusion detection. Nevertheless, recent studies in computer vision have shown that DNNs can be vulnerable to adversarial attacks that are capable of deceiving them into misclassification by injecting specially crafted data. In security-critical areas, such attacks can cause serious damage; therefore, in this paper, we examine the effect of adversarial attacks on deep learning-based intrusion detection. In addition, we investigate the effectiveness of adversarial training as a defense against such attacks. Experimental results show that with sufficient distortion, adversarial examples are able to mislead the detector and that the use of adversarial training can improve the robustness of intrusion detection.
Boosting Masked Face Recognition with Multi-Task ArcFace
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we address the problem of face recognition with masks. Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This sanitary measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. In addition, the need has arisen for applications capable of detecting whether the subjects are wearing masks to control the spread of the virus. To overcome these problems a full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function. From the original face-recognition dataset, a masked version is generated using data augmentation, and both datasets are combined during the training process. The selected network, based on ResNet-50, is modified to also output the probability of mask usage without adding any computational cost. Furthermore, the ArcFace loss is combined with the mask-usage classification loss, resulting in a new function named Multi-Task ArcFace (MTArcFace). Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets. Furthermore, it achieves an average accuracy of 99.78% in mask-usage classification.
Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement.
CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

We propose a novel framework for cross-modal zero-shot learning (ZSL) in the context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema mainly considers simultaneous mappings among the two image views and the semantic side information. Therefore, it is desirable to consider fine-grained classes mainly in the sketch domain using highly discriminative and semantically rich feature space. However, the existing deep generative modeling-based SBIR approaches majorly focus on bridging the gaps between the seen and unseen classes by generating pseudo-unseen-class samples. Besides, violating the ZSL protocol by not utilizing any unseen-class information during training, such techniques do not pay explicit attention to modeling the discriminative nature of the shared space. Also, we note that learning a unified feature space for both the multi-view visual data is a tedious task considering the significant domain difference between sketches and color images. In this respect, as a remedy, we introduce a novel framework for zero-shot SBIR. While we define a cross-modal triplet loss to ensure the discriminative nature of the shared space, an innovative cross-modal attention learning strategy is also proposed to guide feature extraction from the image domain exploiting information from the respective sketch counterpart. In order to preserve the semantic consistency of the shared space, we consider a graph CNN-based module that propagates the semantic class topology to the shared space. To ensure an improved response time during inference, we further explore the possibility of representing the shared space in terms of hash codes. Experimental results obtained on the benchmark TU-Berlin and the Sketchy datasets confirm the superiority of CrossATNet in yielding state-of-the-art results.
A Generative Approach for User-Centered, Collaborative, Domain-Specific Modeling Environments
Venue: arXiv cs.PL 2021
Year: 2021

The use of low- and no-code modeling tools is today an established way in practice to give non-programmers an opportunity to master their digital challenges independently, using the means of model-driven software development. However, the existing tools are limited to a very small number of different domains such as mobile app development, which can be attributed to the enormous demands that a user has on such a tool today. These demands exceed the mere use of a modeling environment as such and require cross-cutting concerns such as: easy access, direct usability and simultaneous collaboration, which result in additional effort in the realization of such tools. Our solution is based on the idea to support and simplify the creation of new domain-specific holistic tools by generating it entirely based on a declarative specification with a domain-specific meta-tool. The meta-tool Pyro demonstrated and analyzed here focuses on graph-based graphical languages to fully generate a complete, directly executable tool starting from a meta-model in order to meet all cross-cutting requirements.
Multi-target prediction for dummies using two-branch neural networks
Venue: arXiv cs.LG 2021
Year: 2021

Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations are multi-label classification, multivariate regression, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. Despite the significant similarities, all these domains have evolved separately into distinct research areas over the last two decades. This led to the development of a plethora of highly-engineered methods, and created a substantially-high entrance barrier for machine learning practitioners that are not experts in the field. In this work we present a generic deep learning methodology that can be used for a wide range of multi-target prediction problems. We introduce a flexible multi-branch neural network architecture, partially configured via a questionnaire that helps end-users to select a suitable MTP problem setting for their needs. Experimental results for a wide range of domains illustrate that the proposed methodology manifests a competitive performance compared to methods from specific MTP domains.
GDPR-Compliant Use of Blockchain for Secure Usage Logs
Venue: arXiv cs.CR 2021
Year: 2021

The unique properties of blockchain enable central requirements of distributed secure logging: Immutability, integrity, and availability. Especially when providing transparency about data usages, a blockchain-based secure log can be beneficial, as no trusted third party is required. Yet, with data governed by privacy legislation such as the GDPR or CCPA, the core advantage of immutability becomes a liability. After a rightful request, an individual's personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve this issue, we exploit a legal property of pseudonymized data: They are only regarded personal data if they can be associated with an individual's identity. We make use of this fact by presenting P3, a pseudonym provisioning system for secure usage logs including a protocol for recording new usages. For each new block, a one-time transaction pseudonym is generated. The pseudonym generation algorithm guarantees unlinkability and enables proof of ownership. These properties enable GDPR-compliant use of blockchain, as data subjects can exercise their legal rights with regards to their personal data. The new-usage protocol ensures non-repudiation, and therefore accountability and liability. Most importantly, our approach does not require a trusted third party and is independent of the utilized blockchain software.
A Separation of $γ$ and $b$ via Thue--Morse Words
Venue: arXiv cs.DM 2021
Year: 2021

We prove that for $n\geq 2$, the size $b(t_n)$ of the smallest bidirectional scheme for the $n$th Thue--Morse word $t_n$ is $n+2$. Since Kutsukake et al. [SPIRE 2020] show that the size $γ(t_n)$ of the smallest string attractor for $t_n$ is $4$ for $n \geq 4$, this shows for the first time that there is a separation between the size of the smallest string attractor $γ$ and the size of the smallest bidirectional scheme $b$, i.e., there exist string families such that $γ= o(b)$.
Perceptual Loss for Robust Unsupervised Homography Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimizes the distance in the feature space between the warped image from the source viewpoint and the corresponding image from the target viewpoint. Since we use a fixed pre-trained feature extractor and the only learnable component of our framework is the homography network, we effectively decouple the homography estimation from representation learning. We use an additional photometric distortion step in the synthetic COCO dataset generation to better represent the illumination variation of the real-world scenarios. We show that biHomE achieves state-of-the-art performance on synthetic COCO dataset, which is also comparable or better compared to supervised approaches. Furthermore, the empirical results demonstrate the robustness of our approach to illumination variation compared to existing methods.
The Emperor's New Autofill Framework: A Security Analysis of Autofill on iOS and Android
Venue: arXiv cs.CR 2021
Year: 2021

Password managers help users more effectively manage their passwords, encouraging them to adopt stronger passwords across their many accounts. In contrast to desktop systems where password managers receive no system-level support, mobile operating systems provide autofill frameworks designed to integrate with password managers to provide secure and usable autofill for browsers and other apps installed on mobile devices. In this paper, we evaluate mobile autofill frameworks on iOS and Android, examining whether they achieve substantive benefits over the ad-hoc desktop environment or become a problematic single point of failure. Our results find that while the frameworks address several common issues, they also enforce insecure behavior and fail to provide password managers sufficient information to override the frameworks' insecure behavior, resulting in mobile managers being less secure than their desktop counterparts overall. We also demonstrate how these frameworks act as a confused deputy in manager-assisted credential phishing attacks. Our results demonstrate the need for significant improvements to mobile autofill frameworks. We conclude the paper with recommendations for the design and implementation of secure autofill frameworks.
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Venue: arXiv cs.LG 2021
Year: 2021

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20m resolution, matching topography and mesoscale (1.28km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (>x50) over the spatial resolution found in numerical models. This allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech
Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization within the given map. A planning algorithm searches for both an approach pose and a path in a single path planning problem. We design a path following controller leveraging the legged harvester's capabilities for negotiating rough terrain. Upon reaching the approach pose, the machine grabs a tree with a general-purpose gripper. This process repeats for all the trees selected by the operator. Our system has been tested on a testing field with tree trunks and in a natural forest. To the best of our knowledge, this is the first time this level of autonomy has been shown on a full-size hydraulic machine operating in a realistic environment.
Developing a New Tool to Implement Computer-Supported Active Learning Strategies in the Engineering Classroom
Venue: arXiv cs.CE 2021
Year: 2021

Successful implementation of active learning strategies in the engineering classroom -- and in particular in certain subjects which are highly technological in nature such as, for instance, rocket engines and space propulsion -- means overcoming certain challenges that arise from the fact that these are extremely complex systems to analyze. In this paper, we address the specific means to overcome one of such challenges: the lack of readily available software tools that are suitable for implementing this sort of teaching strategies within the engineering training. In particular, we develop a new tool for the modeling and simulation of liquid-propellant rocket engines specially tailored for the classroom, taking a systematic approach to the development of such tool based on the needs of modern teaching practices. After a thorough review of the available literature on the topic, the few most critical features that our tool should have in order to serve its purported goal are identified. Subsequently, a pilot experience to assess the impact of the usage of said tool on the learners' performance was carried out, showcasing excellent results, both in terms of the students' perceived quality of their training as well as in terms of their grade of retention and understanding of the matter. The conclusions of this study, especially the guidelines for the development of software tools aimed at the classroom, nevertheless, should be applicable to any other highly technological discipline, extending the scope of this paper beyond merely the subject of rocket science in engineering.
Efficient Retrieval Optimized Multi-task Learning
Venue: arXiv cs.CL 2021
Year: 2021

Recently, there have been significant advances in neural methods for tackling knowledge-intensive tasks such as open domain question answering (QA). These advances are fueled by combining large pre-trained language models with learnable retrieval of documents. Majority of these models use separate encoders for learning query representation, passage representation for the retriever and an additional encoder for the downstream task. Using separate encoders for each stage/task occupies a lot of memory and makes it difficult to scale to a large number of tasks. In this paper, we propose a novel Retrieval Optimized Multi-task (ROM) framework for jointly training self-supervised tasks, knowledge retrieval, and extractive question answering. Our ROM approach presents a unified and generalizable framework that enables scaling efficiently to multiple tasks, varying levels of supervision, and optimization choices such as different learning schedules without changing the model architecture. It also provides the flexibility of changing the encoders without changing the architecture of the system. Using our framework, we achieve comparable or better performance than recent methods on QA, while drastically reducing the number of parameters.
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks
Venue: arXiv cs.CL 2021
Year: 2021

Integrating external knowledge into commonsense reasoning tasks has shown progress in resolving some, but not all, knowledge gaps in these tasks. For knowledge integration to yield peak performance, it is critical to select a knowledge graph (KG) that is well-aligned with the given task's objective. We present an approach to assess how well a candidate KG can correctly identify and accurately fill in gaps of reasoning for a task, which we call KG-to-task match. We show this KG-to-task match in 3 phases: knowledge-task identification, knowledge-task alignment, and knowledge-task integration. We also analyze our transformer-based KG-to-task models via commonsense probes to measure how much knowledge is captured in these models before and after KG integration. Empirically, we investigate KG matches for the SocialIQA (SIQA) (Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0 (Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al., 2019a), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Koupaee and Wang, 2018). With our methods we are able to demonstrate that ATOMIC, an event-inference focused KG, is the best match for SIQA and MCScript2.0, and that the taxonomic ConceptNet and WikiHow-based KGs are the best matches for PIQA across all 3 analysis phases. We verify our methods and findings with human evaluation.
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importance of evaluating derivative-free optimizers for tuning the hyperparameters of machine learning models. This was the first black-box optimization challenge with a machine learning emphasis. It was based on tuning (validation set) performance of standard machine learning models on real datasets. This competition has widespread impact as black-box optimization (e.g., Bayesian optimization) is relevant for hyperparameter tuning in almost every machine learning project as well as many applications outside of machine learning. The final leaderboard was determined using the optimization performance on held-out (hidden) objective functions, where the optimizers ran without human intervention. Baselines were set using the default settings of several open-source black-box optimization packages as well as random search.
Predicting Human Trajectories by Learning and Matching Patterns
Venue: arXiv cs.AI 2021
Year: 2021

As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others' trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout.
FPGA Synthesis of Ternary Memristor-CMOS Decoders
Venue: arXiv cs.ET 2021
Year: 2021

The search for a compatible application of memristor-CMOS logic gates has remained elusive, as the data density benefits are offset by slow switching speeds and resistive dissipation. Active microdisplays typically prioritize pixel density (and therefore resolution) over that of speed, where the most widely used refresh rates fall between 25-240 Hz. Therefore, memristor-CMOS logic is a promising fit for peripheral IO logic in active matrix displays. In this paper, we design and implement a ternary 1-3 line decoder and a ternary 2-9 line decoder which are used to program a seven segment LED display. SPICE simulations are conducted in a 50-nm process, and the decoders are synthesized on an Altera Cyclone IV field-programmable gate array (FPGA) development board which implements a ternary memristor model designed in Quartus II. We compare our hardware results to a binary coded decimal (BCD)-to-seven segment display decoder, and show our memristor-CMOS approach reduces the total IO power consumption by a factor of approximately 6 times at a maximum synthesizable frequency of 293.77MHz. Although the speed is approximately half of the native built-in BCD-to-seven decoder, the comparatively slow refresh rates of typical microdisplays indicate this to be a tolerable trade-off, which promotes data density over speed.