OpenRaiser/Intern-Atlas huggingface dataset:
ESBMC v7.3: Model Checking C++ Programs using Clang AST
Venue: arXiv cs.LO 2023
Year: 2023

This paper introduces ESBMC v7.3, the latest Efficient SMT-Based Context-Bounded Model Checker version, which now incorporates a new clang-based C++ front-end. While the previous CPROVER-based front-end served well for handling C++03 programs, it encountered challenges keeping up with the evolving C++ language. As new language and library features were added in each C++ version, the limitations of the old front-end became apparent, leading to difficult-to-maintain code. Consequently, modern C++ programs were challenging to verify. To overcome this obstacle, we redeveloped the front-end, opting for a more robust approach using clang. The new front-end efficiently traverses the Abstract Syntax Tree (AST) in-memory using clang APIs and transforms each AST node into ESBMC's Intermediate Representation. Through extensive experimentation, our results demonstrate that ESBMC v7.3 with the new front-end significantly reduces parse and conversion errors, enabling successful verification of a wide range of C++ programs, thereby outperforming previous ESBMC versions.
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
Venue: arXiv cs.LG 2023
Year: 2023

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.
Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach
Venue: arXiv cs.LG 2023
Year: 2023

Chemical and biomass processing systems release volatile matter compounds into the environment daily. Catalytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine learning can handle complex relationships in big data and optimize reaction conditions, making it an effective solution for addressing the mentioned issues. This study is the first to develop a machine-learning-based research framework for modeling, understanding, and optimizing the catalytic steam reforming of volatile matter compounds. Toluene catalytic steam reforming is used as a case study to show how chemical/textural analyses (e.g., X-ray diffraction analysis) can be used to obtain input features for machine learning models. Literature is used to compile a database covering a variety of catalyst characteristics and reaction conditions. The process is thoroughly analyzed, mechanistically discussed, modeled by six machine learning models, and optimized using the particle swarm optimization algorithm. Ensemble machine learning provides the best prediction performance (R2 > 0.976) for toluene conversion and product distribution. The optimal tar conversion (higher than 77.2%) is obtained at temperatures between 637.44 and 725.62 °C, with a steam-to-carbon molar ratio of 5.81-7.15 and a catalyst BET surface area 476.03-638.55 m2/g. The feature importance analysis satisfactorily reveals the effects of input descriptors on model prediction. Operating conditions (50.9%) and catalyst properties (49.1%) are equally important in modeling. The developed framework can expedite the search for optimal catalyst characteristics and reaction conditions, not only for catalytic chemical processing but also for related research areas.
Optical Script Identification for multi-lingual Indic-script
Venue: arXiv cs.AI 2023
Year: 2023

Script identification and text recognition are some of the major domains in the application of Artificial Intelligence. In this era of digitalization, the use of digital note-taking has become a common practice. Still, conventional methods of using pen and paper is a prominent way of writing. This leads to the classification of scripts based on the method they are obtained. A survey on the current methodologies and state-of-art methods used for processing and identification would prove beneficial for researchers. The aim of this article is to discuss the advancement in the techniques for script pre-processing and text recognition. In India there are twelve prominent Indic scripts, unlike the English language, these scripts have layers of characteristics. Complex characteristics such as similarity in text shape make them difficult to recognize and analyze, thus this requires advance preprocessing methods for their accurate recognition. A sincere attempt is made in this survey to provide a comparison between all algorithms. We hope that this survey would provide insight to a researcher working not only on Indic scripts but also other languages.
Temporally-Adaptive Models for Efficient Video Understanding
Venue: arXiv cs.CV 2023
Year: 2023

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://github.com/alibaba-mmai-research/TAdaConv.
Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention
Venue: arXiv cs.CV 2023
Year: 2023

Convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved remarkable success in various vision tasks. However, many architectures do not consider interactions between feature maps from different stages and scales, which may limit their performance. In this work, we propose a simple add-on attention module to overcome these limitations via multi-stage and cross-scale interactions. Specifically, the proposed Multi-Stage Cross-Scale Attention (MSCSA) module takes feature maps from different stages to enable multi-stage interactions and achieves cross-scale interactions by computing self-attention at different scales based on the multi-stage feature maps. Our experiments on several downstream tasks show that MSCSA provides a significant performance boost with modest additional FLOPs and runtime.
Deepsea: A Meta-ocean Prototype for Undersea Exploration
Venue: arXiv cs.HC 2023
Year: 2023

Metaverse has attracted great attention from industry and academia in recent years. Metaverse for the ocean (Meta-ocean) is the implementation of the Metaverse technologies in virtual emersion of the ocean which is beneficial for people yearning for the ocean. It has demonstrated great potential for tourism and education with its strong immersion and appealing interactive user experience. However, quite limited endeavors have been spent on exploring the full possibility of Meta-ocean, especially in modeling the movements of marine creatures. In this paper, we first investigate the technology status of Metaverse and virtual reality (VR) and develop a prototype that builds the Meta-ocean in VR devices with strong immersive visual effects. Then, we demonstrate a method to model the undersea scene and marine creatures and propose an optimized path algorithm based on the Catmull-Rom spline to model the movements of marine life. Finally, we conduct a user study to analyze our Meta-ocean prototype. This user study illustrates that our new prototype can give us strong immersion and an appealing interactive user experience.
BATINet: Background-Aware Text to Image Synthesis and Manipulation Network
Venue: arXiv cs.CV 2023
Year: 2023

Background-Induced Text2Image (BIT2I) aims to generate foreground content according to the text on the given background image. Most studies focus on generating high-quality foreground content, although they ignore the relationship between the two contents. In this study, we analyzed a novel Background-Aware Text2Image (BAT2I) task in which the generated content matches the input background. We proposed a Background-Aware Text to Image synthesis and manipulation Network (BATINet), which contains two key components: Position Detect Network (PDN) and Harmonize Network (HN). The PDN detects the most plausible position of the text-relevant object in the background image. The HN harmonizes the generated content referring to background style information. Finally, we reconstructed the generation network, which consists of the multi-GAN and attention module to match more user preferences. Moreover, we can apply BATINet to text-guided image manipulation. It solves the most challenging task of manipulating the shape of an object. We demonstrated through qualitative and quantitative evaluations on the CUB dataset that the proposed model outperforms other state-of-the-art methods.
FoodSAM: Any Food Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we explore the zero-shot capability of the Segment Anything Model (SAM) for food image segmentation. To address the lack of class-specific information in SAM-generated masks, we propose a novel framework, called FoodSAM. This innovative approach integrates the coarse semantic mask with SAM-generated masks to enhance semantic segmentation quality. Besides, we recognize that the ingredients in food can be supposed as independent individuals, which motivated us to perform instance segmentation on food images. Furthermore, FoodSAM extends its zero-shot capability to encompass panoptic segmentation by incorporating an object detector, which renders FoodSAM to effectively capture non-food object information. Drawing inspiration from the recent success of promptable segmentation, we also extend FoodSAM to promptable segmentation, supporting various prompt variants. Consequently, FoodSAM emerges as an all-encompassing solution capable of segmenting food items at multiple levels of granularity. Remarkably, this pioneering framework stands as the first-ever work to achieve instance, panoptic, and promptable segmentation on food images. Extensive experiments demonstrate the feasibility and impressing performance of FoodSAM, validating SAM's potential as a prominent and influential tool within the domain of food image segmentation. We release our code at https://github.com/jamesjg/FoodSAM.
Blockchain-Based Transferable Digital Rights of Land
Venue: arXiv cs.DC 2023
Year: 2023

Land, being a scarce and valuable resource, is in high demand, especially in densely populated areas of older cities. Development authorities require land for infrastructure projects and other amenities, while landowners hold onto their land for both its usage and its financial value. Transferable Development Rights (TDRs) serve as a mechanism to separate the development rights associated with the land from the physical land itself. Development authorities acquire the land by offering compensation in the form of TDRs, which hold monetary value. In this paper, we present the tokenization of development rights, focusing on the implementation in collaboration with a development authority. While there have been previous implementations of land tokenization, we believe our approach is the first to tokenize development rights specifically. Our implementation addresses practical challenges related to record-keeping, ground verification of land, and the unique identification of stakeholders. We ensure the accurate evaluation of development rights by incorporating publicly available circle rates, which consider the ground development of the land and its surrounding areas.
Breaking a Barrier in Constructing Compact Indexes for Parameterized Pattern Matching
Venue: arXiv cs.DS 2023
Year: 2023

A parameterized string (p-string) is a string over an alphabet $(Σ_{s} \cup Σ_{p})$, where $Σ_{s}$ and $Σ_{p}$ are disjoint alphabets for static symbols (s-symbols) and for parameter symbols (p-symbols), respectively. Two p-strings $x$ and $y$ are said to parameterized match (p-match) if and only if $x$ can be transformed into $y$ by applying a bijection on $Σ_{p}$ to every occurrence of p-symbols in $x$. The indexing problem for p-matching is to preprocess a p-string $T$ of length $n$ so that we can efficiently find the occurrences of substrings of $T$ that p-match with a given pattern. Extending the Burrows-Wheeler Transform (BWT) based index for exact string pattern matching, Ganguly et al. [SODA 2017] proposed the first compact index (named pBWT) for p-matching, and posed an open problem on how to construct it in compact space, i.e., in $O(n \lg |Σ_{s} \cup Σ_{p}|)$ bits of space. Hashimoto et al. [SPIRE 2022] partially solved this problem by showing how to construct some components of pBWTs for $T$ in $O(n \frac{|Σ_{p}| \lg n}{\lg \lg n})$ time in an online manner while reading the symbols of $T$ from right to left. In this paper, we improve the time complexity to $O(n \frac{\lg |Σ_{p}| \lg n}{\lg \lg n})$. We remark that removing the multiplicative factor of $|Σ_{p}|$ from the complexity is of great interest because it has not been achieved for over a decade in the construction of related data structures like parameterized suffix arrays even in the offline setting. We also show that our data structure can support backward search, a core procedure of BWT-based indexes, at any stage of the online construction, making it the first compact index for p-matching that can be constructed in compact space and even in an online manner.
Large-Scale Learning on Overlapped Speech Detection: New Benchmark and New General System
Venue: arXiv cs.SD 2023
Year: 2023

Overlapped Speech Detection (OSD) is an important part of speech applications involving analysis of multi-party conversations. However, most of existing OSD systems are trained and evaluated on small datasets with limited application domains, which led to the robustness of them lacks benchmark for evaluation and the accuracy of them remains inadequate in realistic acoustic environments. To solve these problem, we conduct a study of large-scale learning (LSL) in OSD tasks and propose a new general OSD system named CF-OSD with LSL based on Conformer network and LSL. In our study, a large-scale test set consisting of 151h labeled speech of different styles, languages and sound-source distances is produced and used as a new benchmark for evaluating the generality of OSD systems. Rigorous comparative experiments are designed and used to evaluate the effectiveness of LSL in OSD tasks and define the OSD model of our general OSD system. The experiment results show that LSL can significantly improve the accuracy and robustness of OSD systems, and the CF-OSD with LSL system significantly outperforms other OSD systems on our proposed benchmark. Moreover, our system has also achieved state-of-the-art performance on existing small dataset benchmarks, reaching 81.6\% and 53.8\% in the Alimeeting testset and DIHARD II evaluation set, respectively.
Validation-Driven Development
Venue: arXiv cs.SE 2023
Year: 2023

Formal methods play a fundamental role in asserting the correctness of requirements specifications. However, historically, formal method experts have primarily focused on verifying those specifications. Although equally important, validation of requirements specifications often takes the back seat. This paper introduces a validation-driven development (VDD) process that prioritizes validating requirements in formal development. The VDD process is built upon problem frames - a requirements analysis approach - and validation obligations (VOs) - the concept of breaking down the overall validation of a specification and linking it to refinement steps. The effectiveness of the VDD process is demonstrated through a case study in the aviation industry.
Learning to Guide Human Experts via Personalized Large Language Models
Venue: arXiv cs.AI 2023
Year: 2023

In learning to defer, a predictor identifies risky decisions and defers them to a human expert. One key issue with this setup is that the expert may end up over-relying on the machine's decisions, due to anchoring bias. At the same time, whenever the machine chooses the deferral option the expert has to take decisions entirely unassisted. As a remedy, we propose learning to guide (LTG), an alternative framework in which -- rather than suggesting ready-made decisions -- the machine provides guidance useful to guide decision-making, and the human is entirely responsible for coming up with a decision. We also introduce SLOG, an LTG implementation that leverages (a small amount of) human supervision to convert a generic large language model into a module capable of generating textual guidance, and present preliminary but promising results on a medical diagnosis task.
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
Venue: arXiv cs.LG 2023
Year: 2023

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hierarchy and replay them when new tasks arrive. Edge devices that adopt CL to preserve data privacy are typically energy-sensitive and thus require high model accuracy while not compromising energy efficiency, i.e., cost-effectiveness. Our work is the first to explore the design space of hierarchical memory replay-based CL to gain insights into achieving cost-effectiveness on edge devices. We present Miro, a novel system runtime that carefully integrates our insights into the CL framework by enabling it to dynamically configure the CL system based on resource states for the best cost-effectiveness. To reach this goal, Miro also performs online profiling on parameters with clear accuracy-energy trade-offs and adapts to optimal values with low overhead. Extensive evaluations show that Miro significantly outperforms baseline systems we build for comparison, consistently achieving higher cost-effectiveness.
A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation
Venue: arXiv cs.CL 2023
Year: 2023

Recent studies have shown that the multi-encoder models are agnostic to the choice of context, and the context encoder generates noise which helps improve the models in terms of BLEU score. In this paper, we further explore this idea by evaluating with context-aware pronoun translation test set by training multi-encoder models trained on three different context settings viz, previous two sentences, random two sentences, and a mix of both as context. Specifically, we evaluate the models on the ContraPro test set to study how different contexts affect pronoun translation accuracy. The results show that the model can perform well on the ContraPro test set even when the context is random. We also analyze the source representations to study whether the context encoder generates noise. Our analysis shows that the context encoder provides sufficient information to learn discourse-level information. Additionally, we observe that mixing the selected context (the previous two sentences in this case) and the random context is generally better than the other settings.
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling
Venue: arXiv cs.CL 2023
Year: 2023

Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently been increasing with model size - but so has the monetary cost of querying the ever larger models. Importantly, however, not all inputs are equally hard: some require larger LMs for obtaining a satisfactory solution, whereas for others smaller LMs suffice. Based on this fact, we design a framework for cost-effective language model choice, called "Fly-swat or cannon" (FORC). Given a set of inputs and a set of candidate LMs, FORC judiciously assigns each input to an LM predicted to do well on the input according to a so-called meta-model, aiming to achieve high overall performance at low cost. The cost-performance tradeoff can be flexibly tuned by the user. Options include, among others, maximizing total expected performance (or the number of processed inputs) while staying within a given cost budget, or minimizing total cost while processing all inputs. We evaluate FORC on 14 datasets covering five natural language tasks, using four candidate LMs of vastly different size and cost. With FORC, we match the performance of the largest available LM while achieving a cost reduction of 63%. Via our publicly available library, researchers as well as practitioners can thus save large amounts of money without sacrificing performance.
Security of XCB and HCTR
Venue: arXiv cs.CR 2023
Year: 2023

Tweakable Enciphering Scheme (TES) is a length preserving scheme which provides confidentiality and admissible integrity. XCB (Extended Code Book) is a TES which was introduced in 2004. In 2007, it was modified and security bound was provided. Later, these two versions were referred to as XCBv1 and XCBv2 respectively. XCBv2 was proposed as the IEEE-std 1619.2 2010 for encryption of sector oriented storage media. In 2013, first time Security bound of XCBv1 was given and XCBv2's security bound was enhanced. A constant of $2^{22}$ appears in the security bounds of the XCBv1 and XCBv2. We showed that this constant of $2^{22}$ can be reduced to $2^{5}$. Further, we modified the XCB (MXCB) scheme such that it gives better security bound compared to the present XCB scheme. We also analyzed some weak keys attack on XCB and a type of TES known as HCTR (proposed in 2005). We performed distinguishing attack and the hash key recovery attack on HCTR. Next, we analyzed the dependency of the two different keys in HCTR.
Audio-Visual Spatial Integration and Recursive Attention for Robust Sound Source Localization
Venue: arXiv cs.MM 2023
Year: 2023

The objective of the sound source localization task is to enable machines to detect the location of sound-making objects within a visual scene. While the audio modality provides spatial cues to locate the sound source, existing approaches only use audio as an auxiliary role to compare spatial regions of the visual modality. Humans, on the other hand, utilize both audio and visual modalities as spatial cues to locate sound sources. In this paper, we propose an audio-visual spatial integration network that integrates spatial cues from both modalities to mimic human behavior when detecting sound-making objects. Additionally, we introduce a recursive attention network to mimic human behavior of iterative focusing on objects, resulting in more accurate attention regions. To effectively encode spatial information from both modalities, we propose audio-visual pair matching loss and spatial region alignment loss. By utilizing the spatial cues of audio-visual modalities and recursively focusing objects, our method can perform more robust sound source localization. Comprehensive experimental results on the Flickr SoundNet and VGG-Sound Source datasets demonstrate the superiority of our proposed method over existing approaches. Our code is available at: https://github.com/VisualAIKHU/SIRA-SSL