OpenRaiser/Intern-Atlas huggingface dataset:
IoT Security: On-Chip Secure Deletion Scheme using ECC Modulation in IoT Appliances
Venue: arXiv cs.CR 2023
Year: 2023

NAND flash memory-based IoT devices inherently suffer from data retention issues. In IoT security, these retention issues are significant and require a robust solution for secure deletion. Secure deletion methods can be categorized into off-chip and on-chip schemes. Off-chip secure deletion schemes, based on block-level erasure operations, are unable to perform real-time trim operations. Consequently, they are vulnerable to hacking threats. On the other hand, on-chip secure deletion schemes enable real-time trim operations by performing deletion on a page-by-page basis. However, the on-chip scheme introduces a challenge of program disturbance for neighboring page data. The proposed on-chip deletion scheme tackles this problem by utilizing ECC code modulation through a partial program operation. This approach significantly reduces the program disturbance issue associated with neighboring page data. Moreover, the proposed code modulation secure deletion scheme allows for real-time verification of the deletion of original data.
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving
Venue: arXiv cs.CV 2023
Year: 2023

Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.
AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
Venue: arXiv cs.AI 2023
Year: 2023

The AI4GCC competition presents a bold step forward in the direction of integrating machine learning with traditional economic policy analysis. Below, we highlight two potential areas for improvement that could enhance the competition's ability to identify and evaluate proposed negotiation protocols. Firstly, we suggest the inclusion of an additional index that accounts for consumption/utility as part of the evaluation criteria. Secondly, we recommend further investigation into the learning dynamics of agents in the simulator and the game theoretic properties of outcomes from proposed negotiation protocols. We hope that these suggestions can be of use for future iterations of the competition/simulation.
Generating Transferable and Stealthy Adversarial Patch via Attention-guided Adversarial Inpainting
Venue: arXiv cs.CV 2023
Year: 2023

Adversarial patch attacks can fool the face recognition (FR) models via small patches. However, previous adversarial patch attacks often result in unnatural patterns that are easily noticeable. Generating transferable and stealthy adversarial patches that can efficiently deceive the black-box FR models while having good camouflage is challenging because of the huge stylistic difference between the source and target images. To generate transferable, natural-looking, and stealthy adversarial patches, we propose an innovative two-stage attack called Adv-Inpainting, which extracts style features and identity features from the attacker and target faces, respectively and then fills the patches with misleading and inconspicuous content guided by attention maps. In the first stage, we extract multi-scale style embeddings by a pyramid-like network and identity embeddings by a pretrained FR model and propose a novel Attention-guided Adaptive Instance Normalization layer (AAIN) to merge them via background-patch cross-attention maps. The proposed layer can adaptively fuse identity and style embeddings by fully exploiting priority contextual information. In the second stage, we design an Adversarial Patch Refinement Network (APR-Net) with a novel boundary variance loss, a spatial discounted reconstruction loss, and a perceptual loss to boost the stealthiness further. Experiments demonstrate that our attack can generate adversarial patches with improved visual quality, better stealthiness, and stronger transferability than state-of-the-art adversarial patch attacks and semantic attacks.
Developing an Informal-Formal Persian Corpus
Venue: arXiv cs.CL 2023
Year: 2023

Informal language is a style of spoken or written language frequently used in casual conversations, social media, weblogs, emails and text messages. In informal writing, the language faces some lexical and/or syntactic changes varying among different languages. Persian is one of the languages with many differences between its formal and informal styles of writing, thus developing informal language processing tools for this language seems necessary. Such a converter needs a large aligned parallel corpus of colloquial-formal sentences which can be useful for linguists to extract a regulated grammar and orthography for colloquial Persian as is done for the formal language. In this paper we explain our methodology in building a parallel corpus of 50,000 sentence pairs with alignments in the word/phrase level. The sentences were attempted to cover almost all kinds of lexical and syntactic changes between informal and formal Persian, therefore both methods of exploring and collecting from the different resources of informal scripts and following the phonological and morphological patterns of changes were applied to find as much instances as possible. The resulting corpus has about 530,000 alignments and a dictionary containing 49,397 word and phrase pairs.
Towards General and Fast Video Derain via Knowledge Distillation
Venue: arXiv cs.CV 2023
Year: 2023

As a common natural weather condition, rain can obscure video frames and thus affect the performance of the visual system, so video derain receives a lot of attention. In natural environments, rain has a wide variety of streak types, which increases the difficulty of the rain removal task. In this paper, we propose a Rain Review-based General video derain Network via knowledge distillation (named RRGNet) that handles different rain streak types with one pre-training weight. Specifically, we design a frame grouping-based encoder-decoder network that makes full use of the temporal information of the video. Further, we use the old task model to guide the current model in learning new rain streak types while avoiding forgetting. To consolidate the network's ability to derain, we design a rain review module to play back data from old tasks for the current model. The experimental results show that our developed general method achieves the best results in terms of running speed and derain effect.
Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers
Venue: arXiv cs.SI 2023
Year: 2023

In this paper, we describe a new algorithm called Preferential Attachment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others--the very things we seek to prevent. PreAttacK represents a significant departure from these approaches. We provide some of the first detailed distributional analyses of how new fake (and real) accounts first attempt to request friends after joining a major network (Facebook). We show that even before a new account has made friends or shared content, these initial friend request behaviors evoke a natural multi-class extension of the canonical Preferential Attachment model of social network growth. We use this model to derive a new algorithm, PreAttacK. We prove that in relevant problem instances, PreAttacK near-optimally approximates the posterior probability that a new account is fake under this multi-class Preferential Attachment model of new accounts' (not-yet-answered) friend requests. These are the first provable guarantees for fake account detection that apply to new users, and that do not require strong homophily assumptions. This principled approach also makes PreAttacK the only algorithm with provable guarantees that obtains state-of-the-art performance on new users on the global Facebook network, where it converges to AUC=0.9 after new users send + receive a total of just 20 not-yet-answered friend requests. For comparison, state-of-the-art benchmarks do not obtain this AUC even after observing additional data on new users' first 100 friend requests. Thus, unlike mainstream algorithms, PreAttacK converges before the median new fake account has made a single friendship (accepted friend request) with a human.
Machine Learning aided Computer Architecture Design for CNN Inferencing Systems
Venue: arXiv cs.AR 2023
Year: 2023

Efficient and timely calculations of Machine Learning (ML) algorithms are essential for emerging technologies like autonomous driving, the Internet of Things (IoT), and edge computing. One of the primary ML algorithms used in such systems is Convolutional Neural Networks (CNNs), which demand high computational resources. This requirement has led to the use of ML accelerators like GPGPUs to meet design constraints. However, selecting the most suitable accelerator involves Design Space Exploration (DSE), a process that is usually time-consuming and requires significant manual effort. Our work presents approaches to expedite the DSE process by identifying the most appropriate GPGPU for CNN inferencing systems. We have developed a quick and precise technique for forecasting the power and performance of CNNs during inference, with a MAPE of 5.03% and 5.94%, respectively. Our approach empowers computer architects to estimate power and performance in the early stages of development, reducing the necessity for numerous prototypes. This saves time and money while also improving the time-to-market period.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Venue: arXiv cs.CV 2023
Year: 2023

With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced - fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion for the selection of fusion methods.
Explainable AI applications in the Medical Domain: a systematic review
Venue: arXiv cs.AI 2023
Year: 2023

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.
Scheduling for Periodic Multi-Source Systems with Peak-Age Violation Guarantees
Venue: arXiv cs.IT 2023
Year: 2023

Age of information (AoI) is an effective performance metric measuring the freshness of information and is particularly suitable for applications involving status update. In this paper, using the age violation probability as the metric, scheduling for heterogeneous multi-source systems is studied. Two queueing disciplines, namely the infinite packet queueing discipline and the single packet queueing discipline, are considered for scheduling packets within each source. A generalized round-robin (GRR) scheduling policy is then proposed to schedule the sources. Bounds on the exponential decay rate of the age violation probability for the proposed GRR scheduling policy under each queueing discipline are rigorously analyzed. Simulation results are provided, which show that the proposed GRR scheduling policy can efficiently serve many sources with heterogeneous arrivals and that our bounds can capture the true decay rate quite accurately. When specialized to the homogeneous source setting, the analysis concretizes the common belief that the single packet queueing discipline has a better AoI performance than the infinite packet queueing discipline. Moreover, simulations on this special case reveals that under the proposed scheduling policy, the two disciplines would have similar asymptotic performance when the inter-arrival time is much larger than the total transmission time.
Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis
Venue: arXiv cs.CL 2023
Year: 2023

Deceptive text classification is a critical task in natural language processing that aims to identify deceptive o fraudulent content. This study presents a comparative analysis of machine learning and transformer-based approaches for deceptive text classification. We investigate the effectiveness of traditional machine learning algorithms and state-of-the-art transformer models, such as BERT, XLNET, DistilBERT, and RoBERTa, in detecting deceptive text. A labeled dataset consisting of deceptive and non-deceptive texts is used for training and evaluation purposes. Through extensive experimentation, we compare the performance metrics, including accuracy, precision, recall, and F1 score, of the different approaches. The results of this study shed light on the strengths and limitations of machine learning and transformer-based methods for deceptive text classification, enabling researchers and practitioners to make informed decisions when dealing with deceptive content.
Exploring XAI for the Arts: Explaining Latent Space in Generative Music
Venue: arXiv cs.AI 2023
Year: 2023

Explainable AI has the potential to support more interactive and fluid co-creative AI systems which can creatively collaborate with people. To do this, creative AI models need to be amenable to debugging by offering eXplainable AI (XAI) features which are inspectable, understandable, and modifiable. However, currently there is very little XAI for the arts. In this work, we demonstrate how a latent variable model for music generation can be made more explainable; specifically we extend MeasureVAE which generates measures of music. We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions. We suggest that in doing so we bridge the gap between the latent space and the generated musical outcomes in a meaningful way which makes the model and its outputs more explainable and more debuggable.
Is there progress in activity progress prediction?
Venue: arXiv cs.CV 2023
Year: 2023

Activity progress prediction aims to estimate what percentage of an activity has been completed. Currently this is done with machine learning approaches, trained and evaluated on complicated and realistic video datasets. The videos in these datasets vary drastically in length and appearance. And some of the activities have unanticipated developments, making activity progression difficult to estimate. In this work, we examine the results obtained by existing progress prediction methods on these datasets. We find that current progress prediction methods seem not to extract useful visual information for the progress prediction task. Therefore, these methods fail to exceed simple frame-counting baselines. We design a precisely controlled dataset for activity progress prediction and on this synthetic dataset we show that the considered methods can make use of the visual information, when this directly relates to the progress prediction. We conclude that the progress prediction task is ill-posed on the currently used real-world datasets. Moreover, to fairly measure activity progression we advise to consider a, simple but effective, frame-counting baseline.
Explorations in Subexponential Non-associative Non-commutative Linear Logic
Venue: arXiv cs.LO 2023
Year: 2023

In a previous work we introduced a non-associative non-commutative logic extended by multimodalities, called subexponentials, licensing local application of structural rules. Here, we further explore this system, exhibiting a classical one-sided multi-succedent classical analogue of our intuitionistic system, following the exponential-free calculi of Buszkowski, and de Groote, Lamarche. A large fragment of the intuitionistic calculus is shown to embed faithfully into the classical fragment.
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
Venue: arXiv cs.CV 2023
Year: 2023

Images taken under the low-light condition often contain blur and saturated pixels at the same time. Deblurring images with saturated pixels is quite challenging. Because of the limited dynamic range, the saturated pixels are usually clipped in the imaging process and thus cannot be modeled by the linear blur model. Previous methods use manually designed smooth functions to approximate the clipping procedure. Their deblurring processes often require empirically defined parameters, which may not be the optimal choices for different images. In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map. Based on the new model, the non-blind deblurring task can be formulated into a maximum a posterior (MAP) problem, which can be effectively solved by iteratively computing the latent map and the latent image. Specifically, the latent map is computed by learning from a map estimation network (MEN), and the latent image estimation process is implemented by a Richardson-Lucy (RL)-based updating scheme. To estimate high-quality deblurred images without amplified artifacts, we develop a prior estimation network (PEN) to obtain prior information, which is further integrated into the RL scheme. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art algorithms both quantitatively and qualitatively on synthetic and real-world images.
3D Modeling of a Guitar Using a Computer Tomography Scan
Venue: arXiv cs.CE 2023
Year: 2023

This paper describes the development of a detailed 3D geometric model of an acoustical guitar. Modeling an instrument is a sophisticated task considering the individual parts and their complex shapes. The geometry of the parts visible from the outside can be measured using appropriate tools, but it is very difficult to measure the details of the internal parts like bracing, heels, and other features by hand through the sound hole. Otherwise, it would be necessary to disassemble the guitar to measure the precise position and dimensions of the parts inside it. Reassembling the guitar could result in improper functioning. To avoid damaging the instrument by disassembling or taking inaccurate measurements through the sound hole, a computer tomography (CT) scan of the guitar body was performed. Using this method, cross-sectional images of the guitar body in all the three dimensions were extracted with 1 mm spacing between adjacent images. In total, approximately 2000 images were generated and used in developing the geometric model of the guitar. The 3D model will be further used to develop a vibro-acoustic simulation model of the guitar
Recent Advancements In The Field Of Deepfake Detection
Venue: arXiv cs.AI 2023
Year: 2023

A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.