OpenRaiser/Intern-Atlas huggingface dataset:
Citation Analysis of Computer Systems Papers
Venue: arXiv cs.DL 2023
Year: 2023

Citation analysis is used extensively in the bibliometrics literature to assess the impact of individual works, researchers, institutions, and even entire fields of study. In this paper, we analyze citations in one large and influential field within computer science, namely computer systems. Using citation data from a cross-sectional sample of 2,088 papers in 50 systems conferences from 2017, we examine four research questions: overall distribution of systems citations; their evolution over time; the differences between databases (Google Scholar and Scopus) for systems papers, and; the characteristics of self-citations in the field. We find that only 1.5% of papers remain uncited after five years, while 12.8% accrued at least 100 citations, both statistics comparing favorably to many other scientific fields. The most cited subfields and conference areas within systems were security, databases, and computer architecture. Most papers achieved their first citation within a year from publication, and the median citation count continued to grow at an almost linear rate over five years, with only a few papers peaking before that. We also find that early citations could be linked to papers with a freely available preprint, or may be primarily composed of self-citations. The ratio of self-citations to total citations starts relatively high for most papers but appears to stabilize by 12--18 months, at which point highly cited papers revert to predominately external citations. Past self-citation count (taken from each paper's reference list) appears to bear little if any relationship with the future self-citation count of each paper. The choice of citation database also makes little difference in relative citation comparisons, despite marked differences in absolute counts.
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Venue: arXiv cs.RO 2023
Year: 2023

Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple vehicles. Most existing approaches to automatic intersection management, however, only consider fully automated traffic. In practice, mixed traffic, i.e., the simultaneous road usage by automated and human-driven vehicles, will be prevalent. The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning. We build upon our previous works that showed the applicability of such machine learning methods to fully automated traffic. The scene representation is extended for mixed traffic and considers uncertainty in the human drivers' intentions. In the simulation-based evaluation, we model measurement uncertainties through noise processes that are tuned using real-world data. The paper evaluates the proposed method against an enhanced first in - first out scheme, our baseline for mixed traffic management. With increasing share of automated vehicles, the learned planner significantly increases the vehicle throughput and reduces the delay due to interaction. Non-automated vehicles benefit virtually alike.
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Venue: arXiv cs.CV 2023
Year: 2023

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.
Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP
Venue: arXiv cs.CV 2023
Year: 2023

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation and manipulation. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF that shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color images from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate the algorithm on well-established datasets and observe that our method improves tracking performance and increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces. Our novel formulation of combined ICP with frame-to-keyframe photometric error minimization further improves tracking results. Lastly, we introduce Sim3 point-to-plane ICP for refining pose priors in a multi-sensor scenario with different scale factors.
Behavioural Reports of Multi-Stage Malware
Venue: arXiv cs.CR 2023
Year: 2023

The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats. The current trend in malware detection is to employ machine learning models to aid in the classification process. We propose a new dataset with the objective of improving current anti-malware systems. The focus of this dataset is to improve host based intrusion detection systems by providing API call sequences for thousands of malware samples executed in Windows 10 virtual machines. A tutorial on how to create and expand this dataset is provided along with a benchmark demonstrating how to use this dataset to classify malware. The data contains long sequences of API calls for each sample, and in order to create models that can be deployed in resource constrained devices, three feature selection methods were tested. The principal innovation, however, lies in the multi-label classification system in which one sequence of APIs can be tagged with multiple labels describing its malicious behaviours.
Crawling the Internal Knowledge-Base of Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desirable to have means for representing this body of knowledge in an interpretable way. However, there is currently no mechanism for such a representation. Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model. We describe a procedure for ``crawling'' the internal knowledge-base of a language model. Specifically, given a seed entity, we expand a knowledge-graph around it. The crawling procedure is decomposed into sub-tasks, realized through specially designed prompts that control for both precision (i.e., that no wrong facts are generated) and recall (i.e., the number of facts generated). We evaluate our approach on graphs crawled starting from dozens of seed entities, and show it yields high precision graphs (82-92%), while emitting a reasonable number of facts per entity.
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Venue: arXiv cs.AI 2023
Year: 2023

Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor may be installed at hundreds of customers. Because these tools perform distinct but highly similar tasks, it is interesting to be able to quickly produce a high-quality controller for machine $N+1$ given the controllers already produced for machines $1..N$. This is even more important when the controllers are learned through Reinforcement Learning, as training takes time, energy and other resources. In this paper, we apply Policy Intersection, a Policy Shaping method, to help a Reinforcement Learning agent learn to solve a new variant of a compressors control problem faster, by transferring knowledge from several previously learned controllers. We show that our approach outperforms loading an old controller, and significantly improves performance in the long run.
YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment
Venue: arXiv cs.CV 2023
Year: 2023

In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.
Oscilloscope: Detecting BGP Hijacks in the Data Plane
Venue: arXiv cs.NI 2023
Year: 2023

The lack of security of the Internet routing protocol (BGP) has allowed attackers to divert Internet traffic and consequently perpetrate service disruptions, monetary frauds, and even citizen surveillance for decades. State-of-the-art defenses rely on geo-distributed BGP monitors to detect rogue BGP announcements. As we show, though, attackers can easily evade detection by engineering their announcements. This paper presents Oscilloscope, an approach to accurately detect BGP hijacks by relying on real-time traffic analysis. As hijacks inevitably change the characteristics of the diverted traffic, the key idea is to track these changes in real time and flag them. The main challenge is that "normal" Internet events (e.g., network reconfigurations, link failures, load balancing) also change the underlying traffic characteristics - and they are way more frequent than hijacks. Naive traffic analyses would hence lead to too many false positives. We observe that hijacks typically target a subset of the prefixes announced by Internet service providers and only divert a subset of their traffic. In contrast, normal events lead to more uniform changes across prefixes and traffic. Oscilloscope uses this observation to filter out non-hijack events by checking whether they affect multiple related prefixes or not. Our experimental evaluation demonstrates that Oscilloscope quickly and accurately detects hijacks in realistic traffic traces containing hundreds of events.
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
Venue: arXiv cs.CL 2023
Year: 2023

To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation techniques, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.
Federated Learning for Water Consumption Forecasting in Smart Cities
Venue: arXiv cs.LG 2023
Year: 2023

Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly. This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption. The proposed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility. In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity. Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.
Monadic Expressions and their Derivatives [extended version]
Venue: arXiv cs.FL 2023
Year: 2023

We propose another interpretation of well-known derivatives computations from regular expressions, due to Brzozowski, Antimirov or Lombardy and Sakarovitch, in order to abstract the underlying data structures (e.g. sets or linear combinations) using the notion of monad. As an example of this generalization advantage, we first introduce a new derivation technique based on the graded module monad and then show an application of this technique to generalize the parsing of expression with capture groups and back references. We also extend operators defining expressions to any n-ary functions over value sets, such as classical operations (like negation or intersection for Boolean weights) or more exotic ones (like algebraic mean for rational weights). Moreover, we present how to compute a (non-necessarily finite) automaton from such an extended expression, using the Colcombet and Petrisan categorical definition of automata. These category theory concepts allow us to perform this construction in a unified way, whatever the underlying monad. Finally, to illustrate our work, we present a Haskell implementation of these notions using advanced techniques of functional programming, and we provide a web interface to manipulate concrete examples.
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy
Venue: arXiv cs.CV 2023
Year: 2023

AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility in exploring new types of art that would be impossible in the past. Inspired by the pictogram subset of Chinese characters, we proposed PaCaNet, a CycleGAN-based pipeline for producing novel artworks that fuse two different art types, traditional Chinese painting and calligraphy. In an effort to produce stable and diversified output, we adopted three main technical innovations: 1. Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images. 2. Controlling the preference over generated Chinese calligraphy by freezing randomly sampled parameters in pre-trained models. 3. Using a regularization method to encourage the models to produce images similar to Chinese paintings. Furthermore, we conducted a systematic study to explore the performance of PaCaNet in diversifying fused Chinese painting and calligraphy, which showed satisfying results. In conclusion, we provide a new direction of creating arts by fusing the visual information in paintings and the stroke features in Chinese calligraphy. Our approach creates a unique aesthetic experience rooted in the origination of Chinese hieroglyph characters. It is also a unique opportunity to delve deeper into traditional artwork and, in doing so, to create a meaningful impact on preserving and revitalizing traditional heritage.
Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education
Venue: arXiv cs.CL 2023
Year: 2023

There is a lack of enough qualified teachers across Africa which hampers efforts to provide adequate learning support such as educational question answering (EQA) to students. An AI system that can enable students to ask questions via text or voice and get instant answers will make high-quality education accessible. Despite advances in the field of AI, there exists no robust benchmark or challenge to enable building such an (EQA) AI within the African context. Ghana's National Science and Maths Quiz competition (NSMQ) is the perfect competition to evaluate the potential of such an AI due to its wide coverage of scientific fields, variety of question types, highly competitive nature, and live, real-world format. The NSMQ is a Jeopardy-style annual live quiz competition in which 3 teams of 2 students compete by answering questions across biology, chemistry, physics, and math in 5 rounds over 5 progressive stages until a winning team is crowned for that year. In this position paper, we propose the NSMQ AI Grand Challenge, an AI Grand Challenge for Education using Ghana's National Science and Maths Quiz competition (NSMQ) as a case study. Our proposed grand challenge is to "Build an AI to compete live in Ghana's National Science and Maths Quiz (NSMQ) competition and win - performing better than the best contestants in all rounds and stages of the competition." We describe the competition, and key technical challenges to address along with ideas from recent advances in machine learning that could be leveraged to solve this challenge. This position paper is a first step towards conquering such a challenge and importantly, making advances in AI for education in the African context towards democratizing high-quality education across Africa.
Thought Bubbles: A Proxy into Players' Mental Model Development
Venue: arXiv cs.HC 2023
Year: 2023

Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players' mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players' mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people's mental model development and influence their decisions.
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
Venue: arXiv cs.CV 2023
Year: 2023

Differentially Private Stochastic Gradient Descent (DP-SGD) limits the amount of private information deep learning models can memorize during training. This is achieved by clipping and adding noise to the model's gradients, and thus networks with more parameters require proportionally stronger perturbation. As a result, large models have difficulties learning useful information, rendering training with DP-SGD exceedingly difficult on more challenging training tasks. Recent research has focused on combating this challenge through training adaptations such as heavy data augmentation and large batch sizes. However, these techniques further increase the computational overhead of DP-SGD and reduce its practical applicability. In this work, we propose using the principle of sparse model design to solve precisely such complex tasks with fewer parameters, higher accuracy, and in less time, thus serving as a promising direction for DP-SGD. We achieve such sparsity by design by introducing equivariant convolutional networks for model training with Differential Privacy. Using equivariant networks, we show that small and efficient architecture design can outperform current state-of-the-art models with substantially lower computational requirements. On CIFAR-10, we achieve an increase of up to $9\%$ in accuracy while reducing the computation time by more than $85\%$. Our results are a step towards efficient model architectures that make optimal use of their parameters and bridge the privacy-utility gap between private and non-private deep learning for computer vision.
MED1stMR: Mixed Reality to Enhance Training of Medical First Responder]{MED1stMR: Mixed Reality to Enhance the Training of Medical First Responders for Challenging Contexts
Venue: arXiv cs.CY 2023
Year: 2023

Mass-casualty incidents with a large number of injured persons caused by human-made or by natural disasters are increasing globally. In such situations, medical first responders (MFRs) need to perform diagnosis, basic life support, or other first aid to help stabilize victims and keep them alive to wait for the arrival of further support. Situational awareness and effective coping with acute stressors is essential to enable first responders to take appropriate action that saves lives. Virtual Reality (VR) has been demonstrated in several domains to be a serious alternative, and in some areas also a significant improvement to conventional learning and training. Especially for the challenges in the training of MFRs, it can be highly useful for practicing and learning domains where the context of the training is not easily available. VR training offers controlled, easy-to-create environments that can be created and trained repeatedly under the same conditions. As an advanced alternative to VR, Mixed Reality (MR) environments have the potential to augment current VR training by providing a dynamic simulation of an environment and hands-on practice on injured victims. Building on this interpretation of MR, the main aim of MED1stMR is to develop a new generation of MR training with haptic feedback for enhanced realism. in this workshop paper, we will present the vision of the project and suggest questions for discussion.