OpenRaiser/Intern-Atlas huggingface dataset:
Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation
Venue: arXiv cs.PL 2023
Year: 2023

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques.
Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation
Venue: arXiv cs.RO 2023
Year: 2023

Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, \emph{e.g.}, household robots and rescue robots. In this task, an embodied agent must search for and navigate to the sound source with egocentric visual and audio observations. However, the existing methods are limited in two aspects: 1) poor generalization to unheard sound categories; 2) sample inefficient in training. Focusing on these two problems, we propose a brain-inspired plug-and-play method to learn a semantic-agnostic and spatial-aware representation for generalizable visual-audio navigation. We meticulously design two auxiliary tasks for respectively accelerating learning representations with the above-desired characteristics. With these two auxiliary tasks, the agent learns a spatially-correlated representation of visual and audio inputs that can be applied to work on environments with novel sounds and maps. Experiment results on realistic 3D scenes (Replica and Matterport3D) demonstrate that our method achieves better generalization performance when zero-shot transferred to scenes with unseen maps and unheard sound categories.
Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT
Venue: arXiv cs.SE 2023
Year: 2023

Context: AI-assisted code generation tools have become increasingly prevalent in software engineering, offering the ability to generate code from natural language prompts or partial code inputs. Notable examples of these tools include GitHub Copilot, Amazon CodeWhisperer, and OpenAI's ChatGPT. Objective: This study aims to compare the performance of these prominent code generation tools in terms of code quality metrics, such as Code Validity, Code Correctness, Code Security, Code Reliability, and Code Maintainability, to identify their strengths and shortcomings. Method: We assess the code generation capabilities of GitHub Copilot, Amazon CodeWhisperer, and ChatGPT using the benchmark HumanEval Dataset. The generated code is then evaluated based on the proposed code quality metrics. Results: Our analysis reveals that the latest versions of ChatGPT, GitHub Copilot, and Amazon CodeWhisperer generate correct code 65.2%, 46.3%, and 31.1% of the time, respectively. In comparison, the newer versions of GitHub CoPilot and Amazon CodeWhisperer showed improvement rates of 18% for GitHub Copilot and 7% for Amazon CodeWhisperer. The average technical debt, considering code smells, was found to be 8.9 minutes for ChatGPT, 9.1 minutes for GitHub Copilot, and 5.6 minutes for Amazon CodeWhisperer. Conclusions: This study highlights the strengths and weaknesses of some of the most popular code generation tools, providing valuable insights for practitioners. By comparing these generators, our results may assist practitioners in selecting the optimal tool for specific tasks, enhancing their decision-making process.
Can SAM Count Anything? An Empirical Study on SAM Counting
Venue: arXiv cs.CV 2023
Year: 2023

Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at \url{https://github.com/Vision-Intelligence-and-Robots-Group/count-anything}.
Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection
Venue: arXiv cs.CR 2023
Year: 2023

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses.
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing
Venue: arXiv cs.CR 2023
Year: 2023

With the onset of the Information Era and the rapid growth of information technology, ample space for processing and extracting data has opened up. However, privacy concerns may stifle expansion throughout this area. The challenge of reliable mining techniques when transactions disperse across sources is addressed in this study. This work looks at the prospect of creating a new set of three algorithms that can obtain maximum privacy, data utility, and time savings while doing so. This paper proposes a unique double encryption and Transaction Splitter approach to alter the database to optimize the data utility and confidentiality tradeoff in the preparation phase. This paper presents a customized apriori approach for the mining process, which does not examine the entire database to estimate the support for each attribute. Existing distributed data solutions have a high encryption complexity and an insufficient specification of many participants' properties. Proposed solutions provide increased privacy protection against a variety of attack models. Furthermore, in terms of communication cycles and processing complexity, it is much simpler and quicker. Proposed work tests on top of a realworld transaction database demonstrate that the aim of the proposed method is realistic.
Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET
Venue: arXiv cs.NI 2023
Year: 2023

Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput.
SequeL: A Continual Learning Library in PyTorch and JAX
Venue: arXiv cs.LG 2023
Year: 2023

Continual Learning is an important and challenging problem in machine learning, where models must adapt to a continuous stream of new data without forgetting previously acquired knowledge. While existing frameworks are built on PyTorch, the rising popularity of JAX might lead to divergent codebases, ultimately hindering reproducibility and progress. To address this problem, we introduce SequeL, a flexible and extensible library for Continual Learning that supports both PyTorch and JAX frameworks. SequeL provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. The library is designed towards modularity and simplicity, making the API suitable for both researchers and practitioners. We release SequeL\footnote{\url{https://github.com/nik-dim/sequel}} as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense Network
Venue: arXiv cs.CV 2023
Year: 2023

For years, Single Image Super Resolution (SISR) has been an interesting and ill-posed problem in computer vision. The traditional super-resolution (SR) imaging approaches involve interpolation, reconstruction, and learning-based methods. Interpolation methods are fast and uncomplicated to compute, but they are not so accurate and reliable. Reconstruction-based methods are better compared with interpolation methods, but they are time-consuming and the quality degrades as the scaling increases. Even though learning-based methods like Markov random chains are far better than all the previous ones, they are unable to match the performance of deep learning models for SISR. This study examines the Residual Dense Networks architecture proposed by Yhang et al. [17] and analyzes the importance of its components. By leveraging hierarchical features from original low-resolution (LR) images, this architecture achieves superior performance, with a network structure comprising four main blocks, including the residual dense block (RDB) as the core. Through investigations of each block and analyses using various loss metrics, the study evaluates the effectiveness of the architecture and compares it to other state-of-the-art models that differ in both architecture and components.
Timing the Transient Execution: A New Side-Channel Attack on Intel CPUs
Venue: arXiv cs.CR 2023
Year: 2023

The transient execution attack is a type of attack leveraging the vulnerability of modern CPU optimization technologies. New attacks surface rapidly. The side-channel is a key part of transient execution attacks to leak data. In this work, we discover a vulnerability that the change of the EFLAGS register in transient execution may have a side effect on the Jcc (jump on condition code) instruction after it in Intel CPUs. Based on our discovery, we propose a new side-channel attack that leverages the timing of both transient execution and Jcc instructions to deliver data. This attack encodes secret data to the change of register which makes the execution time of context slightly slower, which can be measured by the attacker to decode data. This attack doesn't rely on the cache system and doesn't need to reset the EFLAGS register manually to its initial state before the attack, which may make it more difficult to detect or mitigate. We implemented this side-channel on machines with Intel Core i7-6700, i7-7700, and i9-10980XE CPUs. In the first two processors, we combined it as the side-channel of the Meltdown attack, which could achieve 100\% success leaking rate. We evaluate and discuss potential defenses against the attack. Our contributions include discovering security vulnerabilities in the implementation of Jcc instructions and EFLAGS register and proposing a new side-channel attack that does not rely on the cache system.
AI Design, Design AI, Human-Centred AI and the Theatre of the Absurd the language, life and times of a UX designer
Venue: arXiv cs.HC 2023
Year: 2023

This article connects the concepts and phenomena of Design AI, AI in creative industries and AIs capacity for creativity. It links Design AI to UX design and UX designer discourse. Its vagueness and the prominence of UX designers as speakers and writers in the spectacle of cultural AI discourse. The article then, draws comparisons between the Theatre of the Absurd and the UX designer performances of design AI. It additionally sheds light on ToA and the human condition in terms of existentialism, present within the practice of engaging in design that intends to link human experience to technological system logic. This is a theoretical article that utilises examples from UX events published on Youtube, as well as UX designer blogs, in order to illustrate the mechanics of the ToA present within contemporary AI and UX designer discourse.
Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments
Venue: arXiv cs.IR 2023
Year: 2023

The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections. Prior works have investigated assessor quality & behaviour, though into the impact of a document's presentation modality on assessor efficiency and effectiveness. Given the rise of voice-based interfaces, we investigate whether it is feasible for assessors to judge the relevance of text documents via a voice-based interface. We ran a user study (n = 49) on a crowdsourcing platform where participants judged the relevance of short and long documents sampled from the TREC Deep Learning corpus-presented to them either in the text or voice modality. We found that: (i) participants are equally accurate in their judgements across both the text and voice modality; (ii) with increased document length it takes participants significantly longer (for documents of length > 120 words it takes almost twice as much time) to make relevance judgements in the voice condition; and (iii) the ability of assessors to ignore stimuli that are not relevant (i.e., inhibition) impacts the assessment quality in the voice modality-assessors with higher inhibition are significantly more accurate than those with lower inhibition. Our results indicate that we can reliably leverage the voice modality as a means to effectively collect relevance labels from crowdworkers.
Electromechanical memcapacitive neurons for energy-efficient spiking neural networks
Venue: arXiv cs.ET 2023
Year: 2023

In this article, we introduce a new nanoscale electromechanical device -- a leaky memcapacitor -- and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movable-plate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, the resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several spiking types observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive and leaking resistive functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.
Self-Supervised Adversarial Imitation Learning
Venue: arXiv cs.LG 2023
Year: 2023

Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unlabelled snapshots of the states to decode state pairs into actions. However, the iterative learning scheme employed by these techniques is prone to get trapped into bad local minima. Previous work uses goal-aware strategies to solve this issue. However, this requires manual intervention to verify whether an agent has reached its goal. We address this limitation by incorporating a discriminator into the original framework, offering two key advantages and directly solving a learning problem previous work had. First, it disposes of the manual intervention requirement. Second, it helps in learning by guiding function approximation based on the state transition of the expert's trajectories. Third, the discriminator solves a learning issue commonly present in the policy model, which is to sometimes perform a `no action' within the environment until the agent finally halts.
RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments
Venue: arXiv cs.RO 2023
Year: 2023

Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.git
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups
Venue: arXiv cs.CL 2023
Year: 2023

Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in both natural language processing (NLP) and computer vision helped building document-focused pre-training methods, leveraging a multimodal understanding of the document text, layout and image modalities. However, these breakthroughs also led to the emergence of a new DocKIE subtask of extractive document Question Answering (DocQA), as part of the Machine Reading Comprehension (MRC) research field. In this work, we compare the Question Answering approach with the classical token classification approach for document key information extraction. We designed experiments to benchmark five different experimental setups : raw performances, robustness to noisy environment, capacity to extract long entities, fine-tuning speed on Few-Shot Learning and finally Zero-Shot Learning. Our research showed that when dealing with clean and relatively short entities, it is still best to use token classification-based approach, while the QA approach could be a good alternative for noisy environment or long entities use-cases.
Simulating Stellar Merger using HPX/Kokkos on A64FX on Supercomputer Fugaku
Venue: arXiv cs.DC 2023
Year: 2023

The increasing availability of machines relying on non-GPU architectures, such as ARM A64FX in high-performance computing, provides a set of interesting challenges to application developers. In addition to requiring code portability across different parallelization schemes, programs targeting these architectures have to be highly adaptable in terms of compute kernel sizes to accommodate different execution characteristics for various heterogeneous workloads. In this paper, we demonstrate an approach to code and performance portability that is based entirely on established standards in the industry. In addition to applying Kokkos as an abstraction over the execution of compute kernels on different heterogeneous execution environments, we show that the use of standard C++ constructs as exposed by the HPX runtime system enables superb portability in terms of code and performance based on the real-world Octo-Tiger astrophysics application. We report our experience with porting Octo-Tiger to the ARM A64FX architecture provided by Stony Brook's Ookami and Riken's Supercomputer Fugaku and compare the resulting performance with that achieved on well established GPU-oriented HPC machines such as ORNL's Summit, NERSC's Perlmutter and CSCS's Piz Daint systems. Octo-Tiger scaled well on Supercomputer Fugaku without any major code changes due to the abstraction levels provided by HPX and Kokkos. Adding vectorization support for ARM's SVE to Octo-Tiger was trivial thanks to using standard C++