OpenRaiser/Intern-Atlas huggingface dataset:
Bounds for Synchronizing Markov Decision Processes
Venue: arXiv cs.LO 2022
Year: 2022

We consider Markov decision processes with synchronizing objectives, which require that a probability mass of $1-ε$ accumulates in a designated set of target states, either once, always, infinitely often, or always from some point on, where $ε= 0$ for sure synchronizing, and $ε\to 0$ for almost-sure and limit-sure synchronizing. We introduce two new qualitative modes of synchronizing, where the probability mass should be either positive, or bounded away from $0$. They can be viewed as dual synchronizing objectives. We present algorithms and tight complexity results for the problem of deciding if a Markov decision process is positive, or bounded synchronizing, and we provide explicit bounds on $ε$ in all synchronizing modes. In particular, we show that deciding positive and bounded synchronizing always from some point on, is coNP-complete.
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
Venue: arXiv cs.RO 2022
Year: 2022

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.
Rationality in current era -- A recent survey
Venue: arXiv cs.CY 2022
Year: 2022

Rationality has been an intriguing topic for several decades. Even the scope of definition of rationality across different subjects varies. Several theories (e.g., game theory) initially evolved on the basis that agents (e.g., humans) are perfectly rational. One interpretation of perfect rationality is that agents always make the optimal decision which maximizes their expected utilities. However, subsequently this assumption was relaxed to include bounded rationality where agents have limitations in terms of computing resources and biases which prevents them to take the optimal decision. However, with recent advances in (quantum) computing, artificial intelligence (AI), science and technology etc., has led to the thought that perhaps the concept of rationality would be augmented with machine intelligence which will enable agents to take decision optimally with higher regularity. However, there are divergent views on this topic. The paper attempts to put forward a recent survey (last five years) of research on these divergent views. These viewsmay be grouped into three schools of thoughts. The first school is the one which is sceptical of progress of AI and believes that human intelligencewill always supersede machine intelligence. The second school of thought thinks that advent of AI and advances in computing will help in better understanding of bounded rationality. Third school of thought believes that bounds of bounded rationality will be extended by advances in AI and various other fields. This survey hopes to provide a starting point for further research.
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Venue: arXiv cs.CV 2022
Year: 2022

Spatial-Spectral Total Variation (SSTV) can quantify local smoothness of image structures, so it is widely used in hyperspectral image (HSI) processing tasks. Essentially, SSTV assumes a sparse structure of gradient maps calculated along the spatial and spectral directions. In fact, these gradient tensors are not only sparse, but also (approximately) low-rank under FFT, which we have verified by numerical tests and theoretical analysis. Based on this fact, we propose a novel TV regularization to simultaneously characterize the sparsity and low-rank priors of the gradient map (LRSTV). The new regularization not only imposes sparsity on the gradient map itself, but also penalize the rank on the gradient map after Fourier transform along the spectral dimension. It naturally encodes the sparsity and lowrank priors of the gradient map, and thus is expected to reflect the inherent structure of the original image more faithfully. Further, we use LRSTV to replace conventional SSTV and embed it in the HSI processing model to improve its performance. Experimental results on multiple public data-sets with heavy mixed noise show that the proposed model can get 1.5dB improvement of PSNR.
Gleo-Det: Deep Convolution Feature-Guided Detector with Local Entropy Optimization for Salient Points
Venue: arXiv cs.CV 2022
Year: 2022

Feature detection is an important procedure for image matching, where unsupervised feature detection methods are the detection approaches that have been mostly studied recently, including the ones that are based on repeatability requirement to define loss functions, and the ones that attempt to use descriptor matching to drive the optimization of the pipelines. For the former type, mean square error (MSE) is usually used which cannot provide strong constraint for training and can make the model easy to be stuck into the collapsed solution. For the later one, due to the down sampling operation and the expansion of receptive fields, the details can be lost for local descriptors can be lost, making the constraint not fine enough. Considering the issues above, we propose to combine both ideas, which including three aspects. 1) We propose to achieve fine constraint based on the requirement of repeatability while coarse constraint with guidance of deep convolution features. 2) To address the issue that optimization with MSE is limited, entropy-based cost function is utilized, both soft cross-entropy and self-information. 3) With the guidance of convolution features, we define the cost function from both positive and negative sides. Finally, we study the effect of each modification proposed and experiments demonstrate that our method achieves competitive results over the state-of-the-art approaches.
A Graph Diffusion Scheme for Decentralized Content Search based on Personalized PageRank
Venue: arXiv cs.SI 2022
Year: 2022

Decentralization is emerging as a key feature of the future Internet. However, effective algorithms for search are missing from state-of-the-art decentralized technologies, such as distributed hash tables and blockchain. This is surprising, since decentralized search has been studied extensively in earlier peer-to-peer (P2P) literature. In this work, we adopt a fresh outlook for decentralized search in P2P networks that is inspired by advancements in dense information retrieval and graph signal processing. In particular, we generate latent representations of P2P nodes based on their stored documents and diffuse them to the rest of the network with graph filters, such as personalized PageRank. We then use the diffused representations to guide search queries towards relevant content. Our preliminary approach is successful in locating relevant documents in nearby nodes but the accuracy declines sharply with the number of stored documents, highlighting the need for more sophisticated techniques.
Towards A COLREGs Compliant Autonomous Surface Vessel in a Constrained Channel
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we look at the role of autonomous navigation in the maritime domain. Specifically, we examine how an Autonomous Surface Vessel(ASV) can achieve obstacle avoidance based on the Convention on the International Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world environments. Our ASV is equipped with a broadband marine radar, an Inertial Navigation System (INS), and uses official Electronic Navigational Charts (ENC). These sensors are used to provide situational awareness and, in series of well-defined steps, we can exclude land objects from the radar data, extract tracks associated with moving vessels within range of the radar, and then use a Kalman Filter to track and predict the motion of other moving vessels in the vicinity. A Constant Velocity model for the Kalman Filter allows us to solve the data association to build a consistent model between successive radar scans. We account for multiple COLREGs situations based on the predicted relative motion. Finally, an efficient path planning algorithm is presented to find a path and publish waypoints to perform real-time COLREGs compliant autonomous navigation within highly constrained environments. We demonstrate the results of our framework with operational results collected over the course of a 3.4 nautical mile mission on the Charles River in Boston in which the ASV encountered and successfully navigated multiple scenarios and encounters with other moving vessels at close quarters.
Trainable Compound Activation Functions for Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

Activation functions (AF) are necessary components of neural networks that allow approximation of functions, but AFs in current use are usually simple monotonically increasing functions. In this paper, we propose trainable compound AF (TCA) composed of a sum of shifted and scaled simple AFs. TCAs increase the effectiveness of networks with fewer parameters compared to added layers. TCAs have a special interpretation in generative networks because they effectively estimate the marginal distributions of each dimension of the data using a mixture distribution, reducing modality and making linear dimension reduction more effective. When used in restricted Boltzmann machines (RBMs), they result in a novel type of RBM with mixture-based stochastic units. Improved performance is demonstrated in experiments using RBMs, deep belief networks (DBN), projected belief networks (PBN), and variational auto-encoders (VAE).
An Iterative Labeling Method for Annotating Fisheries Imagery
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises of 2,026 completely unlabeled images, while the second consists of 21,968 images that were point annotated by experts. Our results indicate that training with a small subset and iterating on that to build a larger set of labeled data allows us to converge to a fully annotated dataset with a small number of iterations. Even in the case of a dataset labeled by experts, a single iteration of the methodology improves the labels by discovering additional complicated examples of labels associated with fish that overlap, are very small, or obscured by the contrast limitations associated with underwater imagery.
Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate
Venue: arXiv cs.SI 2022
Year: 2022

Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.
Edge-PRUNE: Flexible Distributed Deep Learning Inference
Venue: arXiv cs.DC 2022
Year: 2022

Collaborative deep learning inference between low-resource endpoint devices and edge servers has received significant research interest in the last few years. Such computation partitioning can help reducing endpoint device energy consumption and improve latency, but equally importantly also contributes to privacy-preserving of sensitive data. This paper describes Edge-PRUNE, a flexible but light-weight computation framework for distributing machine learning inference between edge servers and one or more client devices. Compared to previous approaches, Edge-PRUNE is based on a formal dataflow computing model, and is agnostic towards machine learning training frameworks, offering at the same time wide support for leveraging deep learning accelerators such as embedded GPUs. The experimental section of the paper demonstrates the use and performance of Edge-PRUNE by image classification and object tracking applications on two heterogeneous endpoint devices and an edge server, over wireless and physical connections. Endpoint device inference time for SSD-Mobilenet based object tracking, for example, is accelerated 5.8x by collaborative inference.
Relevance-based Margin for Contrastively-trained Video Retrieval Models
Venue: arXiv cs.CV 2022
Year: 2022

Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \url{https://github.com/aranciokov/RelevanceMargin-ICMR22}.
Systematic Literature Review: Anti-Phishing Defences and Their Application to Before-the-click Phishing Email Detection
Venue: arXiv cs.CR 2022
Year: 2022

Most research into anti-phishing defence assumes that the mal-actor is attempting to harvest end-users' personally identifiable information or login credentials and, hence, focuses on detecting phishing websites. The defences for this type of attack are usually activated after the end-user clicks on a link, at which point the link is checked. This is known as after-the-click detection. However, more sophisticated phishing attacks (such as spear-phishing and whaling) are rarely designed to get the end-user to visit a website. Instead, they attempt to get the end-user to perform some other action, for example, transferring money from their bank account to the mal-actors account. These attacks are rarer, and before-the-click defence has been investigated less than after-the-click defence. To better integrate and contextualize these studies in the overall anti-phishing research, this paper presents a systematic literature review of proposed anti-phishing defences. From a total of 6330 papers, 21 primary studies and 335 secondary studies were identified and examined. The current research was grouped into six primary categories, blocklist/allowlist, heuristics, content, visual, artificial intelligence/machine learning and proactive, with an additional category of "other" for detection techniques that do not fit into any of the primary categories. It then discusses the performance and suitability of using these techniques for detecting phishing emails before the end-user even reads the email. Finally, it suggests some promising areas for further research.
Can deep learning match the efficiency of human visual long-term memory in storing object details?
Venue: arXiv cs.LG 2022
Year: 2022

Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology. For example, Standing (1973) showed that humans could recognize with high accuracy thousands of pictures that they had seen only once a few days prior to a recognition test. In deep learning, the primary mode of incorporating new information into a model is through gradient descent in the model's parameter space. This paper asks whether deep learning via gradient descent can match the efficiency of human visual long-term memory to incorporate new information in a rigorous, head-to-head, quantitative comparison. We answer this in the negative: even in the best case, models learning via gradient descent require approximately 10 exposures to the same visual materials in order to reach a recognition memory performance humans achieve after only a single exposure. Prior knowledge induced via pretraining and bigger model sizes improve performance, but these improvements are not very visible after a single exposure (it takes a few exposures for the improvements to become apparent), suggesting that simply scaling up the pretraining data size or model size might not be a feasible strategy to reach human-level memory efficiency.
What is in #P and what is not?
Venue: arXiv cs.CC 2022
Year: 2022

For several classical nonnegative integer functions, we investigate if they are members of the counting complexity class #P or not. We prove #P membership in surprising cases, and in other cases we prove non-membership, relying on standard complexity assumptions or on oracle separations. We initiate the study of the polynomial closure properties of #P on affine varieties, i.e., if all problem instances satisfy algebraic constraints. This is directly linked to classical combinatorial proofs of algebraic identities and inequalities. We investigate #TFNP and obtain oracle separations that prove the strict inclusion of #P in all standard syntactic subclasses of #TFNP-1.
Attention Mechanism in Neural Networks: Where it Comes and Where it Goes
Venue: arXiv cs.LG 2022
Year: 2022

A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through a long development period. Today, many works have been devoted to this idea in a variety of tasks. Remarkable performance has recently been demonstrated. The goal of this paper is to provide an overview from the early work on searching for ways to implement attention idea with neural networks until the recent trends. This review emphasizes the important milestones during this progress regarding different tasks. By this way, this study aims to provide a road map for researchers to explore the current development and get inspired for novel approaches beyond the attention.
MetFI: Model-driven Fault Simulation Framework
Venue: arXiv cs.SE 2022
Year: 2022

Safety-critical designs need to ensure reliable operations under hostile conditions with a certain degree of confidence. The continuously higher complexity of these designs makes them more susceptible to the risk of failure. ISO26262 recommends fault injection as the proper technique to verify and measure the dependability of safety-critical designs. To cope with the complexity, a lot of effort and stringent verification flow is needed. Moreover, many fault injection tools offer only a limited degree of controllability. We propose MetaFI, a model-driven simulator-independent fault simulation framework that provides multi-purpose fault injection strategies such as Statistical Fault Injection, Direct Fault Injection, Exhaustive Fault Injection, and at the same time reduces manual efforts. The framework enables injection of Stuck-at faults, Single-Event Transient faults, Single-Event Upset faults as well as Timing faults. The fault simulation is performed at the Register Transfer Level (RTL) of a design, in which parts of the design targeted for fault simulation are represented with Gate-level (GL) granularity. MetaFI is scalable with a full System-on-Chip (SoC) design and to demonstrate the applicability of the framework, fault simulation was applied to various components of two different SoCs. One SoC is running the Dhrystone application and the other one is running a Fingerprint calculation application. A minimal effort of 2 persondays was required to run 38 various fault injection campaigns on both the designs. The framework provided significant data regarding failure rates of the components. Results concluded that Prefetcher, a component of the SoC processor, is more susceptible to failures than the other targeted components on both the SoCs, regardless of the running application.