OpenRaiser/Intern-Atlas huggingface dataset:
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Venue: arXiv cs.SE 2024
Year: 2024

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based Software Testing (SBST) methods often struggle with complex software units, achieving suboptimal test coverage. Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance. As a result LLM-generated testsuites still suffer from low coverage. In this paper, we present SymPrompt, a code-aware prompting strategy for LLMs in test generation. SymPrompt's approach is based on recent work that demonstrates LLMs can solve more complex logical problems when prompted to reason about the problem in a multi-step fashion. We apply this methodology to test generation by deconstructing the testsuite generation process into a multi-stage sequence, each of which is driven by a specific prompt aligned with the execution paths of the method under test, and exposing relevant type and dependency focal context to the model. Our approach enables pretrained LLMs to generate more complete test cases without any additional training. We implement SymPrompt using the TreeSitter parsing framework and evaluate on a benchmark challenging methods from open source Python projects. SymPrompt enhances correct test generations by a factor of 5 and bolsters relative coverage by 26% for CodeGen2. Notably, when applied to GPT-4, SymPrompt improves coverage by over 2x compared to baseline prompting strategies.
Comparing Template-based and Template-free Language Model Probing
Venue: arXiv cs.CL 2024
Year: 2024

The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 different LMs on 10 probing English datasets -- 4 template-based and 6 template-free -- in general and biomedical domains to answer the following research questions: (RQ1) Do model rankings differ between the two approaches? (RQ2) Do models' absolute scores differ between the two approaches? (RQ3) Do the answers to RQ1 and RQ2 differ between general and domain-specific models? Our findings are: 1) Template-free and template-based approaches often rank models differently, except for the top domain-specific models. 2) Scores decrease by up to 42% Acc@1 when comparing parallel template-free and template-based prompts. 3) Perplexity is negatively correlated with accuracy in the template-free approach, but, counter-intuitively, they are positively correlated for template-based probing. 4) Models tend to predict the same answers frequently across prompts for template-based probing, which is less common when employing template-free techniques.
Common Sense Reasoning for Deepfake Detection
Venue: arXiv cs.CV 2024
Year: 2024

State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake features, they may fall short in representing unnatural `non-physical' semantic facial attributes -- blurry hairlines, double eyebrows, rigid eye pupils, or unnatural skin shading. However, such facial attributes are easily perceived by humans and used to discern the authenticity of an image based on human common sense. Furthermore, image-based feature extraction methods that provide visual explanations via saliency maps can be hard to interpret for humans. To address these challenges, we frame deepfake detection as a Deepfake Detection VQA (DD-VQA) task and model human intuition by providing textual explanations that describe common sense reasons for labeling an image as real or fake. We introduce a new annotated dataset and propose a Vision and Language Transformer-based framework for the DD-VQA task. We also incorporate text and image-aware feature alignment formulation to enhance multi-modal representation learning. As a result, we improve upon existing deepfake detection models by integrating our learned vision representations, which reason over common sense knowledge from the DD-VQA task. We provide extensive empirical results demonstrating that our method enhances detection performance, generalization ability, and language-based interpretability in the deepfake detection task.
A Reinforcement Learning Based Controller to Minimize Forces on the Crutches of a Lower-Limb Exoskeleton
Venue: arXiv cs.RO 2024
Year: 2024

Metabolic energy consumption of a powered lower-limb exoskeleton user mainly comes from the upper body effort since the lower body is considered to be passive. However, the upper body effort of the users is largely ignored in the literature when designing motion controllers. In this work, we use deep reinforcement learning to develop a locomotion controller that minimizes ground reaction forces (GRF) on crutches. The rationale for minimizing GRF is to reduce the upper body effort of the user. Accordingly, we design a model and a learning framework for a human-exoskeleton system with crutches. We formulate a reward function to encourage the forward displacement of a human-exoskeleton system while satisfying the predetermined constraints of a physical robot. We evaluate our new framework using Proximal Policy Optimization, a state-of-the-art deep reinforcement learning (RL) method, on the MuJoCo physics simulator with different hyperparameters and network architectures over multiple trials. We empirically show that our learning model can generate joint torques based on the joint angle, velocities, and the GRF on the feet and crutch tips. The resulting exoskeleton model can directly generate joint torques from states in line with the RL framework. Finally, we empirically show that policy trained using our method can generate a gait with a 35% reduction in GRF with respect to the baseline.
Large Language Models for Mathematical Reasoning: Progresses and Challenges
Venue: arXiv cs.CL 2024
Year: 2024

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated resolution of mathematical problems. However, the landscape of mathematical problem types is vast and varied, with LLM-oriented techniques undergoing evaluation across diverse datasets and settings. This diversity makes it challenging to discern the true advancements and obstacles within this burgeoning field. This survey endeavors to address four pivotal dimensions: i) a comprehensive exploration of the various mathematical problems and their corresponding datasets that have been investigated; ii) an examination of the spectrum of LLM-oriented techniques that have been proposed for mathematical problem-solving; iii) an overview of factors and concerns affecting LLMs in solving math; and iv) an elucidation of the persisting challenges within this domain. To the best of our knowledge, this survey stands as one of the first extensive examinations of the landscape of LLMs in the realm of mathematics, providing a holistic perspective on the current state, accomplishments, and future challenges in this rapidly evolving field.
CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins
Venue: arXiv cs.LG 2024
Year: 2024

Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in terms of requiring advanced Internet of Things (IoT) infrastructure and computing approaches to achieve enhanced accuracy and scalability. In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges. Within our framework, IoBNT devices are deployed to gather image-based biological data across various physical environments, leveraging the strong capabilities of CNNs for robust machine vision and pattern recognition. Subsequently, FL is utilized to aggregate insights from these disparate data sources, creating a refined global model that continually enhances accuracy and predictive reliability, which is crucial for the effective deployment of DTs in biotechnology. The primary contribution is the development of a novel framework that synergistically combines CNN and FL, augmented by the capabilities of the IoBNT. This novel approach is specifically tailored to enhancing DTs in the biotechnology industry. The results showcase enhancements in the reliability and safety of microorganism DTs, while preserving their accuracy. Furthermore, the proposed framework excels in energy efficiency and security, offering a user-friendly and adaptable solution. This broadens its applicability across diverse sectors, including biotechnology and pharmaceutical industries, as well as clinical and hospital settings.
A Crucial Parameter for Rank-Frequency Relation in Natural Languages
Venue: arXiv cs.CL 2024
Year: 2024

$f \propto r^{-α} \cdot (r+γ)^{-β}$ has been empirically shown more precise than a naïve power law $f\propto r^{-α}$ to model the rank-frequency ($r$-$f$) relation of words in natural languages. This work shows that the only crucial parameter in the formulation is $γ$, which depicts the resistance to vocabulary growth on a corpus. A method of parameter estimation by searching an optimal $γ$ is proposed, where a ``zeroth word'' is introduced technically for the calculation. The formulation and parameters are further discussed with several case studies.
The Maude strategy language
Venue: arXiv cs.LO 2024
Year: 2024

Rewriting logic is a natural and expressive framework for the specification of concurrent systems and logics. The Maude specification language provides an implementation of this formalism that allows executing, verifying, and analyzing the represented systems. These specifications declare their objects by means of terms and equations, and provide rewriting rules to represent potentially non-deterministic local transformations on the state. Sometimes a controlled application of these rules is required to reduce non-determinism, to capture global, goal-oriented or efficiency concerns, or to select specific executions for their analysis. That is what we call a strategy. In order to express them, respecting the separation of concerns principle, a Maude strategy language was proposed and developed. The first implementation of the strategy language was done in Maude itself using its reflective features. After ample experimentation, some more features have been added and, for greater efficiency, the strategy language has been implemented in C++ as an integral part of the Maude system. This paper describes the Maude strategy language along with its semantics, its implementation decisions, and several application examples from various fields.
Effective Bug Detection in Graph Database Engines: An LLM-based Approach
Venue: arXiv cs.DB 2024
Year: 2024

Graph database engines play a pivotal role in efficiently storing and managing graph data across various domains, including bioinformatics, knowledge graphs, and recommender systems. Ensuring data accuracy within graph database engines is paramount, as inaccuracies can yield unreliable analytical outcomes. Current bug-detection approaches are confined to specific graph query languages, limiting their applicabilities when handling graph database engines that use various graph query languages across various domains. Moreover, they require extensive prior knowledge to generate queries for detecting bugs. To address these challenges, we introduces DGDB, a novel paradigm harnessing large language models(LLM), such as ChatGPT, for comprehensive bug detection in graph database engines. DGDB leverages ChatGPT to generate high-quality queries for different graph query languages. It subsequently employs differential testing to identify bugs in graph database engines. We applied this paradigm to graph database engines using the Gremlin query language and those using the Cypher query language, generating approximately 4,000 queries each. In the latest versions of Neo4j, Agensgraph, and JanusGraph databases, we detected 2, 5, and 3 wrong-result bugs, respectively.
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling
Venue: arXiv cs.CV 2024
Year: 2024

Visual storytelling aims to automatically generate a coherent story based on a given image sequence. Unlike tasks like image captioning, visual stories should contain factual descriptions, worldviews, and human social commonsense to put disjointed elements together to form a coherent and engaging human-writeable story. However, most models mainly focus on applying factual information and using taxonomic/lexical external knowledge when attempting to create stories. This paper introduces SCO-VIST, a framework representing the image sequence as a graph with objects and relations that includes human action motivation and its social interaction commonsense knowledge. SCO-VIST then takes this graph representing plot points and creates bridges between plot points with semantic and occurrence-based edge weights. This weighted story graph produces the storyline in a sequence of events using Floyd-Warshall's algorithm. Our proposed framework produces stories superior across multiple metrics in terms of visual grounding, coherence, diversity, and humanness, per both automatic and human evaluations.
On the Minimum Depth of Circuits with Linear Number of Wires Encoding Good Codes
Venue: arXiv cs.CC 2024
Year: 2024

Let $S_d(n)$ denote the minimum number of wires of a depth-$d$ (unbounded fan-in) circuit encoding an error-correcting code $C:\{0, 1\}^n \to \{0, 1\}^{32n}$ with distance at least $4n$. Gál, Hansen, Koucký, Pudlák, and Viola [IEEE Trans. Inform. Theory 59(10), 2013] proved that $S_d(n) = Θ_d(λ_d(n)\cdot n)$ for any fixed $d \ge 3$. By improving their construction and analysis, we prove $S_d(n)= O(λ_d(n)\cdot n)$. Letting $d = α(n)$, a version of the inverse Ackermann function, we obtain circuits of linear size. This depth $α(n)$ is the minimum possible to within an additive constant 2; we credit the nearly-matching depth lower bound to Gál et al., since it directly follows their method (although not explicitly claimed or fully verified in that work), and is obtained by making some constants explicit in a graph-theoretic lemma of Pudlák [Combinatorica, 14(2), 1994], extending it to super-constant depths. We also study a subclass of MDS codes $C: \mathbb{F}^n \to \mathbb{F}^m$ characterized by the Hamming-distance relation $\mathrm{dist}(C(x), C(y)) \ge m - \mathrm{dist}(x, y) + 1$ for any distinct $x, y \in \mathbb{F}^n$. (For linear codes this is equivalent to the generator matrix being totally invertible.) We call these superconcentrator-induced codes, and we show their tight connection with superconcentrators. Specifically, we observe that any linear or nonlinear circuit encoding a superconcentrator-induced code must be a superconcentrator graph, and any superconcentrator graph can be converted to a linear circuit, over a sufficiently large field (exponential in the size of the graph), encoding a superconcentrator-induced code.
Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks
Venue: arXiv cs.DC 2024
Year: 2024

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.
Reproducibility of Build Environments through Space and Time
Venue: arXiv cs.SE 2024
Year: 2024

Modern software engineering builds up on the composability of software components, that rely on more and more direct and transitive dependencies to build their functionalities. This principle of reusability however makes it harder to reproduce projects' build environments, even though reproducibility of build environments is essential for collaboration, maintenance and component lifetime. In this work, we argue that functional package managers provide the tooling to make build environments reproducible in space and time, and we produce a preliminary evaluation to justify this claim. Using historical data, we show that we are able to reproduce build environments of about 7 million Nix packages, and to rebuild 99.94% of the 14 thousand packages from a 6-year-old Nixpkgs revision.
Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous methods that rely on geometric approximations, our presented approach provides more realistic coverage estimates by utilizing accurate and detailed 3D simulation environments. Our method leverages point clouds from LiDAR sensors or camera depth images from high-fidelity simulations of target scenarios to provide accurate and actionable visibility estimates. A Monte Carlo-based reference sensor simulation enables us to accurately estimate blind spot size as a metric of coverage, as well as detection probabilities of objects at arbitrary positions.
HERITRACE: Tracing Evolution and Bridging Data for Streamlined Curatorial Work in the GLAM Domain
Venue: arXiv cs.DL 2024
Year: 2024

HERITRACE is a semantic data management system tailored for the GLAM sector. It is engineered to streamline data curation for non-technical users while also offering an efficient administrative interface for technical staff. The paper compares HERITRACE with other established platforms such as OmekaS, Semantic MediaWiki, Research Space, and CLEF, emphasizing its advantages in user friendliness, provenance management, change tracking, customization capabilities, and data integration. The system leverages SHACL for data modeling and employs the OpenCitations Data Model (OCDM) for provenance and change tracking, ensuring a harmonious blend of advanced technical features and user accessibility. Future developments include the integration of a robust authentication system and the expansion of data compatibility via the RDF Mapping Language (RML), enhancing HERITRACE's utility in digital heritage management.
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
Venue: arXiv cs.AI 2024
Year: 2024

In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications. Recommender systems are prominent examples of these machine learning (ML) systems that aid users in making decisions. The majority of past literature research on RS fairness treats user and item fairness concerns independently, ignoring the fact that recommender systems function in a two-sided marketplace. In this paper, we propose CP-FairRank, an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side in a joint objective framework. The framework is generalizable and may take into account varied fairness settings based on group segmentation, recommendation model selection, and domain, which is one of its key characteristics. For instance, we demonstrate that the system may jointly increase consumer and producer fairness when (un)protected consumer groups are defined on the basis of their activity level and main-streamness, while producer groups are defined according to their popularity level. For empirical validation, through large-scale on eight datasets and four mainstream collaborative filtering (CF) recommendation models, we demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising or very little overall recommendation quality, demonstrating the role algorithms may play in avoiding data biases.
Preconditioning for Physics-Informed Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications. In this paper, we propose to use condition number as a metric to diagnose and mitigate the pathologies in PINNs. Inspired by classical numerical analysis, where the condition number measures sensitivity and stability, we highlight its pivotal role in the training dynamics of PINNs. We prove theorems to reveal how condition number is related to both the error control and convergence of PINNs. Subsequently, we present an algorithm that leverages preconditioning to improve the condition number. Evaluations of 18 PDE problems showcase the superior performance of our method. Significantly, in 7 of these problems, our method reduces errors by an order of magnitude. These empirical findings verify the critical role of the condition number in PINNs' training.
A Manifold Representation of the Key in Vision Transformers
Venue: arXiv cs.CV 2024
Year: 2024

Vision Transformers implement multi-head self-attention via stacking multiple attention blocks. The query, key, and value are often intertwined and generated within those blocks via a single, shared linear transformation. This paper explores the concept of disentangling the key from the query and value, and adopting a manifold representation for the key. Our experiments reveal that decoupling and endowing the key with a manifold structure can enhance the model's performance. Specifically, ViT-B exhibits a 0.87% increase in top-1 accuracy, while Swin-T sees a boost of 0.52% in top-1 accuracy on the ImageNet-1K dataset, with eight charts in the manifold key. Our approach also yields positive results in object detection and instance segmentation tasks on the COCO dataset. We establish that these performance gains are not merely due to the simplicity of adding more parameters and computations. Future research may investigate strategies for cutting the budget of such representations and aim for further performance improvements based on our findings.