OpenRaiser/Intern-Atlas huggingface dataset:
Compressing Vision Transformers for Low-Resource Visual Learning
Venue: arXiv cs.CV 2023
Year: 2023

Vision transformer (ViT) and its variants have swept through visual learning leaderboards and offer state-of-the-art accuracy in tasks such as image classification, object detection, and semantic segmentation by attending to different parts of the visual input and capturing long-range spatial dependencies. However, these models are large and computation-heavy. For instance, the recently proposed ViT-B model has 86M parameters making it impractical for deployment on resource-constrained devices. As a result, their deployment on mobile and edge scenarios is limited. In our work, we aim to take a step toward bringing vision transformers to the edge by utilizing popular model compression techniques such as distillation, pruning, and quantization. Our chosen application environment is an unmanned aerial vehicle (UAV) that is battery-powered and memory-constrained, carrying a single-board computer on the scale of an NVIDIA Jetson Nano with 4GB of RAM. On the other hand, the UAV requires high accuracy close to that of state-of-the-art ViTs to ensure safe object avoidance in autonomous navigation, or correct localization of humans in search-and-rescue. Inference latency should also be minimized given the application requirements. Hence, our target is to enable rapid inference of a vision transformer on an NVIDIA Jetson Nano (4GB) with minimal accuracy loss. This allows us to deploy ViTs on resource-constrained devices, opening up new possibilities in surveillance, environmental monitoring, etc. Our implementation is made available at https://github.com/chensy7/efficient-vit.
Hy-DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks
Venue: arXiv cs.SI 2023
Year: 2023

Nowadays social media is the primary platform for people to obtain news and share information. Combating online fake news has become an urgent task to reduce the damage it causes to society. Existing methods typically improve their fake news detection performances by utilizing textual auxiliary information (such as relevant retweets and comments) or simple structural information (i.e., graph construction). However, these methods face two challenges. First, an increasing number of users tend to directly forward the source news without adding comments, resulting in a lack of textual auxiliary information. Second, simple graphs are unable to extract complex relations beyond pairwise association in a social context. Given that real-world social networks are intricate and involve high-order relations, we argue that exploring beyond pairwise relations between news and users is crucial for fake news detection. Therefore, we propose constructing an attributed hypergraph to represent non-textual and high-order relations for user participation in news spreading. We also introduce a hypergraph neural network-based method called Hy-DeFake to tackle the challenges. Our proposed method captures semantic information from news content, credibility information from involved users, and high-order correlations between news and users to learn distinctive embeddings for fake news detection. The superiority of Hy-DeFake is demonstrated through experiments conducted on four widely-used datasets, and it is compared against eight baselines using four evaluation metrics.
Learning Vehicle Dynamics from Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains
Venue: arXiv cs.RO 2023
Year: 2023

In the realm of autonomous mobile robots, safe navigation through unpaved outdoor environments remains a challenging task. Due to the high-dimensional nature of sensor data, extracting relevant information becomes a complex problem, which hinders adequate perception and path planning. Previous works have shown promising performances in extracting global features from full-sized images. However, they often face challenges in capturing essential local information. In this paper, we propose Crop-LSTM, which iteratively takes cropped image patches around the current robot's position and predicts the future position, orientation, and bumpiness. Our method performs local feature extraction by paying attention to corresponding image patches along the predicted robot trajectory in the 2D image plane. This enables more accurate predictions of the robot's future trajectory. With our wheeled mobile robot platform Raicart, we demonstrated the effectiveness of Crop-LSTM for point-goal navigation in an unpaved outdoor environment. Our method enabled safe and robust navigation using RGBD images in challenging unpaved outdoor terrains. The summary video is available at https://youtu.be/iIGNZ8ignk0.
Reversible Two-Party Computations
Venue: arXiv cs.FL 2023
Year: 2023

Deterministic synchronous systems consisting of two finite automata running in opposite directions on a shared read-only input are studied with respect to their ability to perform reversible computations, which means that the automata are also backward deterministic and, thus, are able to uniquely step the computation back and forth. We study the computational capacity of such devices and obtain on the one hand that there are regular languages that cannot be accepted by such systems. On the other hand, such systems can accept even non-semilinear languages. Since the systems communicate by sending messages, we consider also systems where the number of messages sent during a computation is restricted. We obtain a finite hierarchy with respect to the allowed amount of communication inside the reversible classes and separations to general, not necessarily reversible, classes. Finally, we study closure properties and decidability questions and obtain that the questions of emptiness, finiteness, inclusion, and equivalence are not semidecidable if a superlogarithmic amount of communication is allowed.
Operations on Boolean and Alternating Finite Automata
Venue: arXiv cs.FL 2023
Year: 2023

We examine the complexity of basic regular operations on languages represented by Boolean and alternating finite automata. We get tight upper bounds m+n and m+n+1 for union, intersection, and difference, 2^m+n and 2^m+n+1 for concatenation, 2^n+n and 2^n+n+1 for square, m and m+1 for left quotient, 2^m and 2^m+1 for right quotient. We also show that in both models, the complexity of complementation and symmetric difference is n and m+n, respectively, while the complexity of star and reversal is 2^n. All our witnesses are described over a unary or binary alphabets, and whenever we use a binary alphabet, it is always optimal.
On the Degree of Extension of Some Models Defining Non-Regular Languages
Venue: arXiv cs.FL 2023
Year: 2023

This work is a survey of the main results reported for the degree of extension of two models defining non-regular languages, namely the context-free grammar and the extended automaton over groups. More precisely, we recall the main results regarding the degree on non-regularity of a context-free grammar as well as the degree of extension of finite automata over groups. Finally, we consider a similar measure for the finite automata with translucent letters and present some preliminary results. This measure could be considered for many mechanisms that extend a less expressive one.
Weighted Automata over Vector Spaces
Venue: arXiv cs.FL 2023
Year: 2023

In this paper we deal with three models of weighted automata that take weights in the field of real numbers. The first of these models are classical weighted finite automata, the second one are crisp-deterministic weighted automata, and the third one are weighted automata over a vector space. We explore the interrelationships between weighted automata over a vector space and other two models.
Comparative Transition System Semantics for Cause-Respecting Reversible Prime Event Structures
Venue: arXiv cs.FL 2023
Year: 2023

Reversible computing is a new paradigm that has emerged recently and extends the traditional forwards-only computing mode with the ability to execute in backwards, so that computation can run in reverse as easily as in forward. Two approaches to developing transition system (automaton-like) semantics for event structure models are distinguished in the literature. In the first case, states are considered as configurations (sets of already executed events), and transitions between states are built by starting from the initial configuration and repeatedly adding executable events. In the second approach, states are understood as residuals (model fragments that have not yet been executed), and transitions are constructed by starting from the given event structure as the initial state and deleting already executed (and conflicting) parts thereof during execution. The present paper focuses on an investigation of how the two approaches are interrelated for the model of prime event structures extended with cause-respecting reversibility. The bisimilarity of the resulting transition systems is proved, taking into account step semantics of the model under consideration.
Simultaneous Measurement of Multiple Acoustic Attributes Using Structured Periodic Test Signals Including Music and Other Sound Materials
Venue: arXiv cs.SD 2023
Year: 2023

We introduce a general framework for measuring acoustic properties such as liner time-invariant (LTI) response, signal-dependent time-invariant (SDTI) component, and random and time-varying (RTV) component simultaneously using structured periodic test signals. The framework also enables music pieces and other sound materials as test signals by "safeguarding" them by adding slight deterministic "noise." Measurement using swept-sin, MLS (Maxim Length Sequence), and their variants are special cases of the proposed framework. We implemented interactive and real-time measuring tools based on this framework and made them open-source. Furthermore, we applied this framework to assess pitch extractors objectively.
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
Venue: arXiv cs.LG 2023
Year: 2023

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challenges such as over-smoothing, over-squashing, and limited expressive power continue to impact the performance of GNNs. In this study, inspired from the time-reversal principle commonly utilized in classical and quantum physics, we reverse the time direction of the graph heat equation. The resulted reversing process yields a class of high pass filtering functions that enhance the sharpness of graph node features. Leveraging this concept, we introduce the Multi-Scaled Heat Kernel based GNN (MHKG) by amalgamating diverse filtering functions' effects on node features. To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power. Notably, we illustrate that all aforementioned issues can be characterized and analyzed via the properties of the filtering functions, and uncover a trade-off between over-smoothing and over-squashing: enhancing node feature sharpness will make model suffer more from over-squashing, and vice versa. Furthermore, we manipulate the time again to show how G-MHKG can handle both two issues under mild conditions. Our conclusive experiments highlight the effectiveness of proposed models. It surpasses several GNN baseline models in performance across graph datasets characterized by both homophily and heterophily.
A Study on Indoor Noise Levels in a Set of School Buildings in Greece utilizing an IoT infrastructure
Venue: arXiv cs.CY 2023
Year: 2023

Monitoring noise pollution in urban areas in a more systematic manner has been gaining traction as a theme among the research community, especially with the rise of smart cities and the IoT. However, although it affects our everyday life in a profound way, monitoring indoor noise levels inside workplaces and public buildings has so far grabbed less of our attention. In this work, we report on noise levels data produced by an IoT infrastructure installed inside 5 school buildings in Greece. Our results indicate that such data can help to produce a more accurate picture of the conditions that students and educators experience every day, and also provide useful insights in terms of health risks and aural comfort.
On Minimizing the Energy of a Spherical Graph Representation
Venue: arXiv cs.CG 2023
Year: 2023

Graph representations are the generalization of geometric graph drawings from the plane to higher dimensions. A method introduced by Tutte to optimize properties of graph drawings is to minimize their energy. We explore this minimization for spherical graph representations, where the vertices lie on a unit sphere such that the origin is their barycentre. We present a primal and dual semidefinite program which can be used to find such a spherical graph representation minimizing the energy. We denote the optimal value of this program by $ρ(G)$ for a given graph $G$. The value turns out to be related to the second largest eigenvalue of the adjacency matrix of $G$, which we denote by $λ_2$. We show that for $G$ regular, $ρ(G) \leq \frac{λ_{2}}{2} \cdot v(G)$, and that equality holds if and only if the $λ_{2}$ eigenspace contains a spherical 1-design. Moreover, if $G$ is a random $d$-regular graph, $ρ(G)=\left(\sqrt{(d-1)} +o(1)\right)\cdot v(G)$, asymptotically almost surely.
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques
Venue: arXiv cs.LG 2023
Year: 2023

Log data store event execution patterns that correspond to underlying workflows of systems or applications. While most logs are informative, log data also include artifacts that indicate failures or incidents. Accordingly, log data are often used to evaluate anomaly detection techniques that aim to automatically disclose unexpected or otherwise relevant system behavior patterns. Recently, detection approaches leveraging deep learning have increasingly focused on anomalies that manifest as changes of sequential patterns within otherwise normal event traces. Several publicly available data sets, such as HDFS, BGL, Thunderbird, OpenStack, and Hadoop, have since become standards for evaluating these anomaly detection techniques, however, the appropriateness of these data sets has not been closely investigated in the past. In this paper we therefore analyze six publicly available log data sets with focus on the manifestations of anomalies and simple techniques for their detection. Our findings suggest that most anomalies are not directly related to sequential manifestations and that advanced detection techniques are not required to achieve high detection rates on these data sets.
Aligning Large Language Models for Clinical Tasks
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs) have demonstrated remarkable adaptability, showcasing their capacity to excel in tasks for which they were not explicitly trained. However, despite their impressive natural language processing (NLP) capabilities, effective alignment of LLMs remains a crucial challenge when deploying them for specific clinical applications. The ability to generate responses with factually accurate content and to engage in non-trivial reasoning steps are crucial for the LLMs to be eligible for applications in clinical medicine. Employing a combination of techniques including instruction-tuning and in-prompt strategies like few-shot and chain-of-thought prompting has significantly enhanced the performance of LLMs. Our proposed alignment strategy for medical question-answering, known as 'expand-guess-refine', offers a parameter and data-efficient solution. A preliminary analysis of this method demonstrated outstanding performance, achieving a score of 70.63% on a subset of questions sourced from the USMLE dataset.
A Large-Scale Empirical Study on Semantic Versioning in Golang Ecosystem
Venue: arXiv cs.SE 2023
Year: 2023

Third-party libraries (TPLs) have become an essential component of software, accelerating development and reducing maintenance costs. However, breaking changes often occur during the upgrades of TPLs and prevent client programs from moving forward. Semantic versioning (SemVer) has been applied to standardize the versions of releases according to compatibility, but not all releases follow SemVer compliance. Lots of work focuses on SemVer compliance in ecosystems such as Java and JavaScript beyond Golang (Go for short). Due to the lack of tools to detect breaking changes and dataset for Go, developers of TPLs do not know if breaking changes occur and affect client programs, and developers of client programs may hesitate to upgrade dependencies in terms of breaking changes. To bridge this gap, we conduct the first large-scale empirical study in the Go ecosystem to study SemVer compliance in terms of breaking changes and their impact. In detail, we purpose GoSVI (Go Semantic Versioning Insight) to detect breaking changes and analyze their impact by resolving identifiers in client programs and comparing their types with breaking changes. Moreover, we collect the first large-scale Go dataset with a dependency graph from GitHub, including 124K TPLs and 532K client programs. Based on the dataset, our results show that 86.3% of library upgrades follow SemVer compliance and 28.6% of no-major upgrades introduce breaking changes. Furthermore, the tendency to comply with SemVer has improved over time from 63.7% in 2018/09 to 92.2% in 2023/03. Finally, we find 33.3% of downstream client programs may be affected by breaking changes. These findings provide developers and users of TPLs with valuable insights to help make decisions related to SemVer.
Autonomous and Collaborative Smart Home Security System (ACSHSS)
Venue: arXiv cs.CR 2023
Year: 2023

Firstly, the proposed solution provides remotely accessible integrated IoT resources for the safety and security of the building. By using Sha ort Messaging System (SMS), the age is sent to the user by the Global System for Mobile (GSM) system. An SMS alert is sent to the user in case any sensor detects an abnormality in their operation. Secondly, an authentication mechanism is deployed to enable only authorized users to access resources. Thirdly, in case of a malicious approach in accessing IoT resources, a timely alert should be received by the owner. A Network Intrusion Detection System (NIDS) is deployed to detect and real-time information in case of any suspicious activity while accessing the Internet of Things network.
GroupEnc: encoder with group loss for global structure preservation
Venue: arXiv cs.LG 2023
Year: 2023

Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion of structure preservation at both local and global levels to create a deep learning model, based on a variational autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm. Our encoder model, called GroupEnc, uses a 'group loss' function to create embeddings with less global structure distortion than VAEs do, while keeping the model parametric and the architecture flexible. We validate our approach using publicly available biological single-cell transcriptomic datasets, employing RNX curves for evaluation.
Designing Situated Dashboards: Challenges and Opportunities
Venue: arXiv cs.HC 2023
Year: 2023

Situated Visualization is an emerging field that unites several areas - visualization, augmented reality, human-computer interaction, and internet-of-things, to support human data activities within the ubiquitous world. Likewise, dashboards are broadly used to simplify complex data through multiple views. However, dashboards are only adapted for desktop settings, and requires visual strategies to support situatedness. We propose the concept of AR-based situated dashboards and present design considerations and challenges developed over interviews with experts. These challenges aim to propose directions and opportunities for facilitating the effective designing and authoring of situated dashboards.
Prompt-based Effective Input Reformulation for Legal Case Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models directly encode the whole legal text of a case to generate a case representation, which is then utilised to conduct a nearest neighbour search for retrieval. Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information. In this paper, a novel legal case retrieval framework, PromptCase, is proposed to tackle these challenges. Firstly, legal facts and legal issues are identified and formally defined as the key features facilitating legal case retrieval based on a thorough study of the definition of relevant cases from a legal perspective. Secondly, with the determining legal features, a prompt-based encoding scheme is designed to conduct an effective encoding with language models. Extensive zero-shot experiments have been conducted on two benchmark datasets in legal case retrieval, which demonstrate the superior retrieval effectiveness of the proposed PromptCase. The code has been released on https://github.com/yanran-tang/PromptCase.
Helper Recommendation with seniority control in Online Health Community
Venue: arXiv cs.SI 2023
Year: 2023

Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support. Social support in OHCs plays a crucial role in easing and rehabilitating patients. However, many time-sensitive questions from patients often remain unanswered due to the multitude of threads and the random nature of patient visits in OHCs. To address this issue, it is imperative to propose a recommender system that assists solution seekers in finding appropriate problem helpers. Nevertheless, developing a recommendation algorithm to enhance social support in OHCs remains an under-explored area. Traditional recommender systems cannot be directly adapted due to the following obstacles. First, unlike user-item links in traditional recommender systems, it is hard to model the social support behind helper-seeker links in OHCs since they are formed based on various heterogeneous reasons. Second, it is difficult to distinguish the impact of historical activities in characterizing patients. Third, it is significantly challenging to ensure that the recommended helpers possess sufficient expertise to assist the seekers. To tackle the aforementioned challenges, we develop a Monotonically regularIzed diseNTangled Variational Autoencoders (MINT) model to strengthen social support in OHCs.