OpenRaiser/Intern-Atlas huggingface dataset:
CAT-LM: Training Language Models on Aligned Code And Tests
Venue: arXiv cs.SE 2023
Year: 2023

Testing is an integral part of the software development process. Yet, writing tests is time-consuming and therefore often neglected. Classical test generation tools such as EvoSuite generate behavioral test suites by optimizing for coverage, but tend to produce tests that are hard to understand. Language models trained on code can generate code that is highly similar to that written by humans, but current models are trained to generate each file separately, as is standard practice in natural language processing, and thus fail to consider the code-under-test context when producing a test file. In this work, we propose the Aligned Code And Tests Language Model (CAT-LM), a GPT-style language model with 2.7 Billion parameters, trained on a corpus of Python and Java projects. We utilize a novel pretraining signal that explicitly considers the mapping between code and test files when available. We also drastically increase the maximum sequence length of inputs to 8,192 tokens, 4x more than typical code generation models, to ensure that the code context is available to the model when generating test code. We analyze its usefulness for realistic applications, showing that sampling with filtering (e.g., by compilability, coverage) allows it to efficiently produce tests that achieve coverage similar to ones written by developers while resembling their writing style. By utilizing the code context, CAT-LM generates more valid tests than even much larger language models trained with more data (CodeGen 16B and StarCoder) and substantially outperforms a recent test-specific model (TeCo) at test completion. Overall, our work highlights the importance of incorporating software-specific insights when training language models for code and paves the way to more powerful automated test generation.
Dynamic Spatio-Temporal Summarization using Information Based Fusion
Venue: arXiv cs.CV 2023
Year: 2023

In the era of burgeoning data generation, managing and storing large-scale time-varying datasets poses significant challenges. With the rise of supercomputing capabilities, the volume of data produced has soared, intensifying storage and I/O overheads. To address this issue, we propose a dynamic spatio-temporal data summarization technique that identifies informative features in key timesteps and fuses less informative ones. This approach minimizes storage requirements while preserving data dynamics. Unlike existing methods, our method retains both raw and summarized timesteps, ensuring a comprehensive view of information changes over time. We utilize information-theoretic measures to guide the fusion process, resulting in a visual representation that captures essential data patterns. We demonstrate the versatility of our technique across diverse datasets, encompassing particle-based flow simulations, security and surveillance applications, and biological cell interactions within the immune system. Our research significantly contributes to the realm of data management, introducing enhanced efficiency and deeper insights across diverse multidisciplinary domains. We provide a streamlined approach for handling massive datasets that can be applied to in situ analysis as well as post hoc analysis. This not only addresses the escalating challenges of data storage and I/O overheads but also unlocks the potential for informed decision-making. Our method empowers researchers and experts to explore essential temporal dynamics while minimizing storage requirements, thereby fostering a more effective and intuitive understanding of complex data behaviors.
Model Explanation via Support Graphs
Venue: arXiv cs.LO 2023
Year: 2023

In this note, we introduce the notion of support graph to define explanations for any model of a logic program. An explanation is an acyclic support graph that, for each true atom in the model, induces a proof in terms of program rules represented by labels. A classical model may have zero, one or several explanations: when it has at least one, it is called a justified model. We prove that all stable models are justified whereas, in general, the opposite does not hold, at least for disjunctive programs. We also provide a meta-programming encoding in Answer Set Programming that generates the explanations for a given stable model of some program. We prove that the encoding is sound and complete, that is, there is a one-to-one correspondence between each answer set of the encoding and each explanation for the original stable model.
It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera
Venue: arXiv cs.CV 2023
Year: 2023

Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature. In this work, we propose a model-adaptation approach to personalize the training of a CNNLSTM model and improve recognition accuracy while reducing data requirements. Our approach contributes to the field of dynamic hand gesture recognition while driving by providing a more efficient and accurate method that can be customized for individual users, ultimately enhancing the safety and convenience of in-vehicle interactions, as well as driver's experience and system trust. We incorporate hardware enhancement using a time-of-flight camera and algorithmic enhancement through data augmentation, personalized adaptation, and incremental learning techniques. We evaluate the performance of our approach in terms of recognition accuracy, achieving up to 90\%, and show the effectiveness of personalized adaptation and incremental learning for a user-centered design.
Learning Class-Specific Spectral Patterns to Improve Deep Learning Based Scene-Level Fire Smoke Detection from Multi-Spectral Satellite Imagery
Venue: arXiv cs.CY 2023
Year: 2023

Detecting fire smoke is crucial for the timely identification of early wildfires using satellite imagery. However, the spatial and spectral similarity of fire smoke to other confounding aerosols, such as clouds and haze, often confuse even the most advanced deep-learning (DL) models. Nonetheless, these aerosols also present distinct spectral characteristics in some specific bands, and such spectral patterns are useful for distinguishing the aerosols more accurately. Early research tried to derive various threshold values from the reflectance and brightness temperature in specific spectral bands to differentiate smoke and cloud pixels. However, such threshold values were determined based on domain knowledge and are hard to generalise. In addition, such threshold values were manually derived from specific combinations of bands to infer spectral patterns, making them difficult to employ in deep-learning models. In this paper, we introduce a DL module called input amplification (InAmp) which is designed to enable DL models to learn class-specific spectral patterns automatically from multi-spectral satellite imagery and improve the fire smoke detection accuracy. InAmp can be conveniently integrated with different DL architectures. We evaluate the effectiveness of the InAmp module on different Convolutional neural network (CNN) architectures using two satellite imagery datasets: USTC_SmokeRS, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) with three spectral bands; and Landsat_Smk, derived from Landsat 5/8 with six spectral bands. Our experimental results demonstrate that the InAmp module improves the fire smoke detection accuracy of the CNN models. Additionally, we visualise the spectral patterns extracted by the InAmp module using test imagery and demonstrate that the InAmp module can effectively extract class-specific spectral patterns.
Control of Soft Pneumatic Actuators with Approximated Dynamical Modeling
Venue: arXiv cs.RO 2023
Year: 2023

This paper introduces a full system modeling strategy for a syringe pump and soft pneumatic actuators(SPAs). The soft actuator is conceptualized as a beam structure, utilizing a second-order bending model. The equation of natural frequency is derived from Euler's bending theory, while the damping ratio is estimated by fitting step responses of soft pneumatic actuators. Evaluation of model uncertainty underscores the robustness of our modeling methodology. To validate our approach, we deploy it across four prototypes varying in dimensional parameters. Furthermore, a syringe pump is designed to drive the actuator, and a pressure model is proposed to construct a full system model. By employing this full system model, the Linear-Quadratic Regulator (LQR) controller is implemented to control the soft actuator, achieving high-speed responses and high accuracy in both step response and square wave function response tests. Both the modeling method and the LQR controller are thoroughly evaluated through experiments. Lastly, a gripper, consisting of two actuators with a feedback controller, demonstrates stable grasping of delicate objects with a significantly higher success rate.
5G Network Slicing: Analysis of Multiple Machine Learning Classifiers
Venue: arXiv cs.CR 2023
Year: 2023

The division of one physical 5G communications infrastructure into several virtual network slices with distinct characteristics such as bandwidth, latency, reliability, security, and service quality is known as 5G network slicing. Each slice is a separate logical network that meets the requirements of specific services or use cases, such as virtual reality, gaming, autonomous vehicles, or industrial automation. The network slice can be adjusted dynamically to meet the changing demands of the service, resulting in a more cost-effective and efficient approach to delivering diverse services and applications over a shared infrastructure. This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.
SnB Collaborative Visualization
Venue: arXiv cs.GR 2023
Year: 2023

We describe a system for visualization and editing of data in a computational chemistry environment. The system is a collaborative tool allowing researchers using virtual reality and/or desktop computer displays to work together on results of the Shake-and-Bake structure determination application.
Improvement and Enhancement of YOLOv5 Small Target Recognition Based on Multi-module Optimization
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, the limitations of YOLOv5s model on small target detection task are deeply studied and improved. The performance of the model is successfully enhanced by introducing GhostNet-based convolutional module, RepGFPN-based Neck module optimization, CA and Transformer's attention mechanism, and loss function improvement using NWD. The experimental results validate the positive impact of these improvement strategies on model precision, recall and mAP. In particular, the improved model shows significant superiority in dealing with complex backgrounds and tiny targets in real-world application tests. This study provides an effective optimization strategy for the YOLOv5s model on small target detection, and lays a solid foundation for future related research and applications.
Empirical Study of PEFT techniques for Winter Wheat Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Parameter Efficient Fine Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with minimal computational needs. Despite these advances, more research has yet to delve into potential PEFT applications in real-life scenarios, particularly in the critical domains of remote sensing and crop monitoring. The diversity of climates across different regions and the need for comprehensive large-scale datasets have posed significant obstacles to accurately identify crop types across varying geographic locations and changing growing seasons. This study seeks to bridge this gap by comprehensively exploring the feasibility of cross-area and cross-year out-of-distribution generalization using the State-of-the-Art (SOTA) wheat crop monitoring model. The aim of this work is to explore PEFT approaches for crop monitoring. Specifically, we focus on adapting the SOTA TSViT model to address winter wheat field segmentation, a critical task for crop monitoring and food security. This adaptation process involves integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and prompt tuning. Using PEFT techniques, we achieved notable results comparable to those achieved using full fine-tuning methods while training only a mere 0.7% parameters of the whole TSViT architecture. The in-house labeled data-set, referred to as the Beqaa-Lebanon dataset, comprises high-quality annotated polygons for wheat and non-wheat classes with a total surface of 170 kmsq, over five consecutive years. Using Sentinel-2 images, our model achieved a 84% F1-score. We intend to publicly release the Lebanese winter wheat data set, code repository, and model weights.
AI-Generated Images as Data Source: The Dawn of Synthetic Era
Venue: arXiv cs.CV 2023
Year: 2023

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .
A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detection
Venue: arXiv cs.CV 2023
Year: 2023

Remote sensing change detection between bi-temporal images receives growing concentration from researchers. However, comparing two bi-temporal images for detecting changes is challenging, as they demonstrate different appearances. In this paper, we propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks, which regards the detection model as a generator and attains the optimal weights of the detection model without increasing the parameters of the detection model through generative-adversarial strategy, boosting the spatial contiguity of predictions. Moreover, We design a multi-level feature extractor for effectively fusing multi-level features, which adopts the pre-trained model to extract multi-level features from bi-temporal images and introduces aggregate connections to fuse them. To strengthen the identification of multi-scale objects, we propose a multi-scale adaptive fusion module to adaptively fuse multi-scale features through various receptive fields and design a context refinement module to explore contextual dependencies. Moreover, the DAGAN framework utilizes the 4-layer convolution network as a discriminator to identify whether the synthetic image is fake or real. Extensive experiments represent that the DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.
Reduction and efficient solution of MILP models of mixed Hamming packings yielding improved upper bounds
Venue: arXiv cs.DM 2023
Year: 2023

Mixed Hamming packings are considered: the maximal cardinality given a minimum codeword Hamming distance of mixed codes is addressed via mixed integer programming models. Adopting the concept of contact graph from classical continuous sphere packing problems, a reduction technique for the models is introduced, which enables their efficient solution. Several best known upper bounds are improved and some of them are found to be sharp.
OOD Aware Supervised Contrastive Learning
Venue: arXiv cs.LG 2023
Year: 2023

Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e. in-distribution data (ID). Most OOD works focus on the classification models trained with Cross Entropy (CE) and attempt to fix its inherent issues. In this work we leverage powerful representation learned with Supervised Contrastive (SupCon) training and propose a holistic approach to learn a classifier robust to OOD data. We extend SupCon loss with two additional contrast terms. The first term pushes auxiliary OOD representations away from ID representations without imposing any constraints on similarities among auxiliary data. The second term pushes OOD features far from the existing class prototypes, while pushing ID representations closer to their corresponding class prototype. When auxiliary OOD data is not available, we propose feature mixing techniques to efficiently generate pseudo-OOD features. Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning. We compare against different OOD detection methods on the common benchmarks and show state-of-the-art results.
Language Models as Knowledge Bases for Visual Word Sense Disambiguation
Venue: arXiv cs.CL 2023
Year: 2023

Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies between linguistic sense disambiguation and fine-grained multimodal retrieval. The recent advancements in the development of visiolinguistic (VL) transformers suggest some off-the-self implementations with encouraging results, which however we argue that can be further improved. To this end, we propose some knowledge-enhancement techniques towards improving the retrieval performance of VL transformers via the usage of Large Language Models (LLMs) as Knowledge Bases. More specifically, knowledge stored in LLMs is retrieved with the help of appropriate prompts in a zero-shot manner, achieving performance advancements. Moreover, we convert VWSD to a purely textual question-answering (QA) problem by considering generated image captions as multiple-choice candidate answers. Zero-shot and few-shot prompting strategies are leveraged to explore the potential of such a transformation, while Chain-of-Thought (CoT) prompting in the zero-shot setting is able to reveal the internal reasoning steps an LLM follows to select the appropriate candidate. In total, our presented approach is the first one to analyze the merits of exploiting knowledge stored in LLMs in different ways to solve WVSD.
Some New Results With k-set agreement
Venue: arXiv cs.DC 2023
Year: 2023

In this article, we investigate the solvability of $k$-set agreement among $n$ processes in distributed systems prone to different types of process failures. Specifically, we explore two scenarios: synchronous message-passing systems prone to up to $t$ Byzantine failures of processes. And asynchronous shared memory systems prone to up to $t$ crash failures of processes. Our goal is to address the gaps left by previous works\cite{SSS,AsynchKset} in these areas. For Byzantine failures case we consider systems with authentication where processes have unforgeable signatures. For synchronous message-passing systems, we present an authenticated algorithm that achieves $k$-set agreement in only two rounds, with no constraints on the number of faults $t$, with $k$ determined as $k \geq \lfloor \frac{n}{n-t} \rfloor + 1$. In fact the lower bound for $k$ is $k \geq \lfloor \frac{n}{n-t} \rfloor $ that is obtained by an algorithm based on traditional consensus with $t+1$ rounds. In asynchronous shared memory systems, we introduce an algorithm that accomplishes $k$-set agreement for values of $k$ greater than $ \lfloor \frac{n-t}{n-2t} \rfloor +1$. This algorithm uses a snapshot primitive to handle crash failures and enable effective set agreement.
Acyclic Petri and Workflow Nets with Resets
Venue: arXiv cs.FL 2023
Year: 2023

In this paper we propose two new subclasses of Petri nets with resets, for which the reachability and coverability problems become tractable. Namely, we add an acyclicity condition that only applies to the consumptions and productions, not the resets. The first class is acyclic Petri nets with resets, and we show that coverability is PSPACE-complete for them. This contrasts the known Ackermann-hardness for coverability in (not necessarily acyclic) Petri nets with resets. We prove that the reachability problem remains undecidable for acyclic Petri nets with resets. The second class concerns workflow nets, a practically motivated and natural subclass of Petri nets. Here, we show that both coverability and reachability in acyclic workflow nets with resets are PSPACE-complete. Without the acyclicity condition, reachability and coverability in workflow nets with resets are known to be equally hard as for Petri nets with resets, that being Ackermann-hard and undecidable, respectively.
Throughput and Coverage Trade-Off in Integrated Terrestrial and Non-Terrestrial Networks: an Optimization Framework
Venue: arXiv cs.NI 2023
Year: 2023

In past years, non-terrestrial networks (NTNs) have emerged as a viable solution for providing ubiquitous connectivity for future wireless networks due to their ability to reach large geographical areas. However, the efficient integration and operation of an NTN with a classic terrestrial network (TN) is challenging due the large amount of parameters to tune. In this paper, we consider the downlink scenario of an integrated TN-NTN transmitting over the S band, comprised of low-earth orbit (LEO) satellites overlapping a large-scale ground cellular network. We propose a new resource management framework to optimize the user equipment (UE) performance by properly controlling the spectrum allocation, the UE association and the transmit power of ground base stations (BSs) and satellites. Our study reveals that, in rural scenarios, NTNs, combined with the proposed radio resource management framework, reduce the number of UEs that are out of coverage, highlighting the important role of NTNs in providing ubiquitous connectivity, and greatly improve the overall capacity of the network. Specifically, our solution leads to more than 200% gain in terms of mean data rate with respect to a network without satellites and a standard integrated TN-NTN when the resource allocation setting follows 3GPP recommendation.