OpenRaiser/Intern-Atlas huggingface dataset:
Towards a generalizable simulation framework to study collisions between spacecraft and debris
Venue: arXiv cs.RO 2023
Year: 2023

In recent years, computer simulators of rigid-body systems have been successfully used to improve and expand the field of developing new space robots, becoming a leading tool for the preliminary investigation and evaluation of space robotic missions. However, the impressive progress in performance has not been matched yet by an improvement in modelling capabilities, which remain limited to very basic representations of real systems. We present a new approach to modelling and simulation of collision-inclusive multibody dynamics by leveraging symbolic models generated by a computer algebra system (CAS). While similar investigations into contact dynamics on other domains exploit pre-existing models of common multibody systems (e.g., industrial robot arms, humanoids, and wheeled robots), our focus is on allowing researchers to develop models of novel designs of systems that are not as common or yet to be fabricated: e.g., small spacecraft manipulators. In this paper, we demonstrate the usefulness of our approach to investigate spacecraft-debris collision dynamics.
NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we propose a methodology for task 10 of SemEval23, focusing on detecting and classifying online sexism in social media posts. The task is tackling a serious issue, as detecting harmful content on social media platforms is crucial for mitigating the harm of these posts on users. Our solution for this task is based on an ensemble of fine-tuned transformer-based models (BERTweet, RoBERTa, and DeBERTa). To alleviate problems related to class imbalance, and to improve the generalization capability of our model, we also experiment with data augmentation and semi-supervised learning. In particular, for data augmentation, we use back-translation, either on all classes, or on the underrepresented classes only. We analyze the impact of these strategies on the overall performance of the pipeline through extensive experiments. while for semi-supervised learning, we found that with a substantial amount of unlabelled, in-domain data available, semi-supervised learning can enhance the performance of certain models. Our proposed method (for which the source code is available on Github attains an F1-score of 0.8613 for sub-taskA, which ranked us 10th in the competition
Towards Smart Education through the Internet of Things: A Review
Venue: arXiv cs.CY 2023
Year: 2023

IoT is a fundamental enabling technology for creating smart spaces, which can assist the effective face-to-face and online education systems. The transition to smart education (integrating IoT and AI into the education system) is appealing, which has a concrete impact on learners' engagement, motivation, attendance, and deep learning. Traditional education faces many challenges, including administration, pedagogy, assessment, and classroom supervision. Recent developments in ICT (e.g., IoT, AI and 5G, etc.) have yielded lots of smart solutions for various aspects of life; however, smart solutions are not well integrated into the education system. In particular, the COVID-19 pandemic situation had further emphasized the adoption of new smart solutions in education. This study reviews the related studies and addresses the (i) problems in the traditional education system with possible solutions, (ii) the transition towards smart education, and (iii) research challenges in the transition to smart education (i.e, computational and social resistance). Considering these studies, smart solutions (e.g., smart pedagogy, smart assessment, smart classroom, smart administration, etc.) are introduced to the problems of the traditional system. This exploratory study opens new trends for scholars and the market to integrate ICT, IoT, and AI into smart education.
The tale of two MS MARCO -- and their unfair comparisons
Venue: arXiv cs.IR 2023
Year: 2023

The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over the years. But, it turns out that two different corpora of MS MARCO are used in the literature, the official one and a second one where passages were augmented with titles, mostly due to the introduction of the Tevatron code base. However, the addition of titles actually leaks relevance information, while breaking the original guidelines of the MS MARCO-passage dataset. In this work, we investigate the differences between the two corpora and demonstrate empirically that they make a significant difference when evaluating a new method. In other words, we show that if a paper does not properly report which version is used, reproducing fairly its results is basically impossible. Furthermore, given the current status of reviewing, where monitoring state-of-the-art results is of great importance, having two different versions of a dataset is a large problem. This is why this paper aims to report the importance of this issue so that researchers can be made aware of this problem and appropriately report their results.
Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language
Venue: arXiv cs.CV 2023
Year: 2023

Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility. However, even with computational approaches that have promising result in making concrete 3D art, computationally addressing abstract 3D art with high-quality and controllability remains an open question. To fill this gap, we propose to explore computational creativity in making abstract 3D art by bridging evolution strategies (ES) and 3D rendering through customizable parameterization of scenes. We demonstrate that our approach is capable of placing semi-transparent triangles in 3D scenes that, when viewed from specified angles, render into films that look like artists' specification expressed in natural language. This provides a new way for the artist to easily express creativity ideas for abstract 3D art. The supplementary material, which contains code, animation for all figures, and more examples, is here: https://es3dart.github.io/
Topological properties and organizing principles of semantic networks
Venue: arXiv cs.CL 2023
Year: 2023

Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be taken into account when designing NLP algorithms, yet they remain to be structurally investigated. We study the properties of semantic networks from ConceptNet, defined by 7 semantic relations from 11 different languages. We find that semantic networks have universal basic properties: they are sparse, highly clustered, and many exhibit power-law degree distributions. Our findings show that the majority of the considered networks are scale-free. Some networks exhibit language-specific properties determined by grammatical rules, for example networks from highly inflected languages, such as e.g. Latin, German, French and Spanish, show peaks in the degree distribution that deviate from a power law. We find that depending on the semantic relation type and the language, the link formation in semantic networks is guided by different principles. In some networks the connections are similarity-based, while in others the connections are more complementarity-based. Finally, we demonstrate how knowledge of similarity and complementarity in semantic networks can improve NLP algorithms in missing link inference.
Faster High Accuracy Multi-Commodity Flow from Single-Commodity Techniques
Venue: arXiv cs.DS 2023
Year: 2023

Since the development of efficient linear program solvers in the 80s, all major improvements for solving multi-commodity flows to high accuracy came from improvements to general linear program solvers. This differs from the single commodity problem (e.g.~maximum flow) where all recent improvements also rely on graph specific techniques such as graph decompositions or the Laplacian paradigm (see e.g.~[CMSV17,KLS20,BLL+21,CKL+22]). This phenomenon sparked research to understand why these graph techniques are unlikely to help for multi-commodity flow. [Kyng, Zhang'20] reduced solving multi-commodity Laplacians to general linear systems and [Ding, Kyng, Zhang'22] showed that general linear programs can be reduced to 2-commodity flow. However, the reductions create sparse graph instances, so improvement to multi-commodity flows on denser graphs might exist. We show that one can indeed speed up multi-commodity flow algorithms on non-sparse graphs using graph techniques from single-commodity flow algorithms. This is the first improvement to high accuracy multi-commodity flow algorithms that does not just stem from improvements to general linear program solvers. In particular, using graph data structures from recent min-cost flow algorithm by [BLL+21] based on the celebrated expander decomposition framework, we show that 2-commodity flow on an $n$-vertex $m$-edge graph can be solved in $\tilde{O}(\sqrt{m}n^{ω-1/2})$ time for current bounds on fast matrix multiplication $ω\approx 2.373$, improving upon the previous fastest algorithms with $\tilde{O}(m^ω)$ [CLS19] and $\tilde{O}(\sqrt{m}n^2)$ [KV96] time complexity. For general $k$ commodities, our algorithm runs in $\tilde{O}(k^{2.5}\sqrt{m}n^{ω-1/2})$ time.
Room dimensions and absorption inference from room transfer function via machine learning
Venue: arXiv cs.SD 2023
Year: 2023

The inference of the absorption configuration of an existing room solely using acoustic signals can be challenging. This research presents two methods for estimating the room dimensions and frequency-dependent absorption coefficients using room transfer functions. The first method, a knowledge-based approach, calculates the room dimensions through damped resonant frequencies of the room. The second method, a machine learning approach, employs multi-task convolutional neural networks for inferring the room dimensions and frequency-dependent absorption coefficients of each surface. The study shows that accurate wave-based simulation data can be used to train neural networks for real-world measurements and demonstrates a potential for this algorithm to be used to estimate the boundary input data for room acoustic simulations. The proposed methods can be a valuable tool for room acoustic simulations during acoustic renovation or intervention projects, as they enable to infer the room geometry and absorption conditions with reasonably small data requirements.
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
Venue: arXiv cs.CL 2023
Year: 2023

Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, intents and slots with translated unstructured utterance. To address this, we propose an Inter-bilingual Seq2seq Semantic parsing dataset IE-SEMPARSE for 11 distinct Indian languages. We highlight the proposed task's practicality, and evaluate existing multilingual seq2seq models across several train-test strategies. Our experiment reveals a high correlation across performance of original multilingual semantic parsing datasets (such as mTOP, multilingual TOP and multiATIS++) and our proposed IE-SEMPARSE suite.
Bibliometric Data Fusion for Biomedical Information Retrieval
Venue: arXiv cs.DL 2023
Year: 2023

Digital libraries in the scientific domain provide users access to a wide range of information to satisfy their diverse information needs. Here, ranking results play a crucial role in users' satisfaction. Exploiting bibliometric metadata, e.g., publications' citation counts or bibliometric indicators in general, for automatically identifying the most relevant results can boost retrieval performance. This work proposes bibliometric data fusion, which enriches existing systems' results by incorporating bibliometric metadata such as citations or altmetrics. Our results on three biomedical retrieval benchmarks from TREC Precision Medicine (TREC-PM) show that bibliometric data fusion is a promising approach to improve retrieval performance in terms of normalized Discounted Cumulated Gain (nDCG) and Average Precision (AP), at the cost of the Precision at 10 (P@10) rate. Patient users especially profit from this lightweight, data-sparse technique that applies to any digital library.
GULP: Solar-Powered Smart Garbage Segregation Bins with SMS Notification and Machine Learning Image Processing
Venue: arXiv cs.LG 2023
Year: 2023

This study intends to build a smartbin that segregates solid waste into its respective bins. To make the waste management process more interesting for the end-users; to notify the utility staff when the smart bin needs to be unloaded; to encourage an environment-friendly smart bin by utilizing renewable solar energy source. The researchers employed an Agile Development approach because it enables teams to manage their workloads successfully and create the highest-quality product while staying within their allocated budget. The six fundamental phases are planning, design, development, test, release, and feedback. The Overall quality testing result that was provided through the ISO/IEC 25010 evaluation which concludes a positive outcome. The overall average was 4.55, which is verbally interpreted as excellent. Additionally, the application can also independently run with its solar energy source. Users were able to enjoy the whole process of waste disposal through its interesting mechanisms. Based on the findings, a compressor is recommended to compress the trash when the trash level reaches its maximum point to create more rooms for more garbage. An algorithm to determine multiple garbage at a time is also recommended. Adding a solar tracker coupled with solar panel will help produce more renewable energy for the smart bin.
Simply Realising an Imprecise Polyline is NP-hard
Venue: arXiv cs.CG 2023
Year: 2023

We consider the problem of deciding, given a sequence of regions, if there is a choice of points, one for each region, such that the induced polyline is simple or weakly simple, meaning that it can touch but not cross itself. Specifically, we consider the case where each region is a translate of the same shape. We show that the problem is NP-hard when the shape is a unit-disk or unit-square. We argue that the problem is NP-complete when the shape is a vertical unit-segment.
Hypernymization of named entity-rich captions for grounding-based multi-modal pretraining
Venue: arXiv cs.CV 2023
Year: 2023

Named entities are ubiquitous in text that naturally accompanies images, especially in domains such as news or Wikipedia articles. In previous work, named entities have been identified as a likely reason for low performance of image-text retrieval models pretrained on Wikipedia and evaluated on named entities-free benchmark datasets. Because they are rarely mentioned, named entities could be challenging to model. They also represent missed learning opportunities for self-supervised models: the link between named entity and object in the image may be missed by the model, but it would not be if the object were mentioned using a more common term. In this work, we investigate hypernymization as a way to deal with named entities for pretraining grounding-based multi-modal models and for fine-tuning on open-vocabulary detection. We propose two ways to perform hypernymization: (1) a ``manual'' pipeline relying on a comprehensive ontology of concepts, and (2) a ``learned'' approach where we train a language model to learn to perform hypernymization. We run experiments on data from Wikipedia and from The New York Times. We report improved pretraining performance on objects of interest following hypernymization, and we show the promise of hypernymization on open-vocabulary detection, specifically on classes not seen during training.
How to design, and tune, a computed torque controller: An introduction and a Matlab example
Venue: arXiv cs.RO 2023
Year: 2023

This note briefly introduces the computed torque control method for trajectory tracking. The method is applicable to fully actuated robots, i.e, those whose inverse dynamics can be solved for any feasible acceleration. This includes many systems, like robot arms or hands, or any tree-like mechanism with all its joints actuated. Using simple explanations, we see how such a controller can be obtained using feedback linearization, and how its gains can be tuned to satisfy a desired settling time for the error signal. We end up discussing the advantages and shortcomings of the controller. A companion Matlab script can be downloaded from https://bit.ly/3QShxYi that implements and tests the controller on a simple actuated pendulum.
TABLET: Learning From Instructions For Tabular Data
Venue: arXiv cs.LG 2023
Year: 2023

Acquiring high-quality data is often a significant challenge in training machine learning (ML) models for tabular prediction, particularly in privacy-sensitive and costly domains like medicine and finance. Providing natural language instructions to large language models (LLMs) offers an alternative solution. However, it is unclear how effectively instructions leverage the knowledge in LLMs for solving tabular prediction problems. To address this gap, we introduce TABLET, a benchmark of 20 diverse tabular datasets annotated with instructions that vary in their phrasing, granularity, and technicality. Additionally, TABLET includes the instructions' logic and structured modifications to the instructions. We find in-context instructions increase zero-shot F1 performance for Flan-T5 11b by 44% on average and 13% for ChatGPT on TABLET. Also, we explore the limitations of using LLMs for tabular prediction in our benchmark by evaluating instruction faithfulness. We find LLMs often ignore instructions and fail to predict specific instances correctly, even with examples. Our analysis on TABLET shows that, while instructions help LLM performance, learning from instructions for tabular data requires new capabilities.
Exploiting CNNs for Semantic Segmentation with Pascal VOC
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we present a comprehensive study on semantic segmentation with the Pascal VOC dataset. Here, we have to label each pixel with a class which in turn segments the entire image based on the objects/entities present. To tackle this, we firstly use a Fully Convolution Network (FCN) baseline which gave 71.31% pixel accuracy and 0.0527 mean IoU. We analyze its performance and working and subsequently address the issues in the baseline with three improvements: a) cosine annealing learning rate scheduler(pixel accuracy: 72.86%, IoU: 0.0529), b) data augmentation(pixel accuracy: 69.88%, IoU: 0.0585) c) class imbalance weights(pixel accuracy: 68.98%, IoU: 0.0596). Apart from these changes in training pipeline, we also explore three different architectures: a) Our proposed model -- Advanced FCN (pixel accuracy: 67.20%, IoU: 0.0602) b) Transfer Learning with ResNet (Best performance) (pixel accuracy: 71.33%, IoU: 0.0926 ) c) U-Net(pixel accuracy: 72.15%, IoU: 0.0649). We observe that the improvements help in greatly improving the performance, as reflected both, in metrics and segmentation maps. Interestingly, we observe that among the improvements, dataset augmentation has the greatest contribution. Also, note that transfer learning model performs the best on the pascal dataset. We analyse the performance of these using loss, accuracy and IoU plots along with segmentation maps, which help us draw valuable insights about the working of the models.
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach
Venue: arXiv cs.AR 2023
Year: 2023

Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a single criterion, i.e., nets switching activity. This paper proposes a multi-criteria reinforcement learning (RL) HT detection tool that features a tunable reward function for different HT detection scenarios. The tool allows for exploring existing detection strategies and can adapt new detection scenarios with minimal effort. We also propose a generic methodology for comparing HT detection methods fairly. Our preliminary results show an average of 84.2% successful HT detection in ISCAS-85 benchmark
ESCM: An Efficient and Secure Communication Mechanism for UAV Networks
Venue: arXiv cs.NI 2023
Year: 2023

UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. Therefore, an efficient and secure communication method designed for UAV networks is necessary. We propose a communication mechanism ESCM. For high efficiency, ESCM provides a routing protocol based on the artificial bee colony (ABC) algorithm to accelerate communications between UAVs. Meanwhile, we use blockchain to guarantee the security of UAV networks. However, blockchain has unstable links in high-mobility networks resulting in low consensus efficiency and high communication overhead. Consequently, ESCM introduces digital twin (DT), which transforms the UAV network into a static network by mapping UAVs from the physical world into Cyberspace. This virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a blockchain consensus based on network coding, named Proof of Network Coding (PoNC). Analysis and simulation show that the above modules in ESCM have advantages over existing schemes. Through ablation studies, we demonstrate that these modules are indispensable for efficient and secure communication of UAV networks.
A Security Verification Framework of Cryptographic Protocols Using Machine Learning
Venue: arXiv cs.CR 2023
Year: 2023

We propose a security verification framework for cryptographic protocols using machine learning. In recent years, as cryptographic protocols have become more complex, research on automatic verification techniques has been focused on. The main technique is formal verification. However, the formal verification has two problems: it requires a large amount of computational time and does not guarantee decidability. We propose a method that allows security verification with computational time on the order of linear with respect to the size of the protocol using machine learning. In training machine learning models for security verification of cryptographic protocols, a sufficient amount of data, i.e., a set of protocol data with security labels, is difficult to collect from academic papers and other sources. To overcome this issue, we propose a way to create arbitrarily large datasets by automatically generating random protocols and assigning security labels to them using formal verification tools. Furthermore, to exploit structural features of protocols, we construct a neural network that processes a protocol along its series and tree structures. We evaluate the proposed method by applying it to verification of practical cryptographic protocols.