OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Policy Optimization in Deep Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

The policy represented by the deep neural network can overfit the spurious features in observations, which hamper a reinforcement learning agent from learning effective policy. This issue becomes severe in high-dimensional state, where the agent struggles to learn a useful policy. Data augmentation can provide a performance boost to RL agents by mitigating the effect of overfitting. However, such data augmentation is a form of prior knowledge, and naively applying them in environments might worsen an agent's performance. In this paper, we propose a novel RL algorithm to mitigate the above issue and improve the efficiency of the learned policy. Our approach consists of a max-min game theoretic objective where a perturber network modifies the state to maximize the agent's probability of taking a different action while minimizing the distortion in the state. In contrast, the policy network updates its parameters to minimize the effect of perturbation while maximizing the expected future reward. Based on this objective, we propose a practical deep reinforcement learning algorithm, Adversarial Policy Optimization (APO). Our method is agnostic to the type of policy optimization, and thus data augmentation can be incorporated to harness the benefit. We evaluated our approaches on several DeepMind Control robotic environments with high-dimensional and noisy state settings. Empirical results demonstrate that our method APO consistently outperforms the state-of-the-art on-policy PPO agent. We further compare our method with state-of-the-art data augmentation, RAD, and regularization-based approach DRAC. Our agent APO shows better performance compared to these baselines.
Reasoning about Choreographic Programs
Venue: arXiv cs.PL 2023
Year: 2023

Choreographic programming is a paradigm where a concurrent or distributed system is developed in a top-down fashion. Programs, called choreographies, detail the desired interactions between processes, and can be compiled to distributed implementations based on message passing. Choreographic languages usually guarantee deadlock-freedom and provide an operational correspondence between choreographies and their compiled implementations, but until now little work has been done on verifying other properties. This paper presents a Hoare-style logic for reasoning about the behaviour of choreographies, and illustrate its usage in representative examples. We show that this logic is sound and complete, and discuss decidability of its judgements. Using existing results from choreographic programming, we show that any functional correctness property proven for a choreography also holds for its compiled implementation.
Identifying Minimal Changes in the Zone Abstract Domain
Venue: arXiv cs.LO 2023
Year: 2023

Verification techniques express program states as logical formulas over program variables. For example, symbolic execution and abstract interpretation encode program states as a set of integer inequalities. However, for real-world programs these formulas tend to become large, which affects scalability of analyses. To address this problem, researchers developed complementary approaches which either remove redundant inequalities or extract a subset of inequalities sufficient for specific reasoning. For arbitrary integer inequalities, such reduction approaches either have high complexities or over-approximate. However, efficiency and precision of these approaches can be improved for a restricted type of logical formulas used in relational numerical abstract domains. While previous work investigated custom efficient redundant inequality elimination for Zones states, our work examines custom semantic slicing algorithms that identify a minimal set of changed inequalities in Zones states. The client application of the minimal changes in Zones is an empirical study on comparison between invariants computed by data-flow analysis using Zones, Intervals and Predicates numerical domains. In particular, evaluations compare how our proposed algorithms affect the precision of comparing Zones vs. Intervals and Zones vs. Predicates abstract domains. The results show our techniques reduce the number of variables by more than 70% and the number of inequalities by 30%, compared to full states. The approach refines the granularity of comparison between domains, reducing incomparable invariants between Zones and Predicates from 52% to 4%, and increases equality of Intervals and Zones, invariants from 27% to 71%. The techniques improve the comparison efficiency by reducing total runtime for all subject comparisons for Zones and Predicates from over 4 minutes to a few seconds.
LNMesh: Who Said You need Internet to send Bitcoin? Offline Lightning Network Payments using Community Wireless Mesh Networks
Venue: arXiv cs.CR 2023
Year: 2023

Bitcoin is undoubtedly a great alternative to today's existing digital payment systems. Even though Bitcoin's scalability has been debated for a long time, we see that it is no longer a concern thanks to its layer-2 solution Lightning Network (LN). LN has been growing non-stop since its creation and enabled fast, cheap, anonymous, censorship-resistant Bitcoin transactions. However, as known, LN nodes need an active Internet connection to operate securely which may not be always possible. For example, in the aftermath of natural disasters or power outages, users may not have Internet access for a while. Thus, in this paper, we propose LNMesh which enables offline LN payments on top of wireless mesh networks. Users of a neighborhood or a community can establish a wireless mesh network to use it as an infrastructure to enable offline LN payments when they do not have any Internet connection. As such, we first present proof-of-concept implementations where we successfully perform offline LN payments utilizing Bluetooth Low Energy and WiFi. For larger networks with more users where users can also move around, channel assignments in the network need to be made strategically and thus, we propose 1) minimum connected dominating set; and 2) uniform spanning tree based channel assignment approaches. Finally, to test these approaches, we implemented a simulator in Python along with the support of BonnMotion mobility tool. We then extensively tested the performance metrics of large-scale realistic offline LN payments on mobile wireless mesh networks. Our simulation results show that, success rates up to %95 are achievable with the proposed channel assignment approaches when channels have enough liquidity.
Neural Implicit Dense Semantic SLAM
Venue: arXiv cs.CV 2023
Year: 2023

Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its position over time. In this paper, we propose a novel RGBD vSLAM algorithm that can learn a memory-efficient, dense 3D geometry, and semantic segmentation of an indoor scene in an online manner. Our pipeline combines classical 3D vision-based tracking and loop closing with neural fields-based mapping. The mapping network learns the SDF of the scene as well as RGB, depth, and semantic maps of any novel view using only a set of keyframes. Additionally, we extend our pipeline to large scenes by using multiple local mapping networks. Extensive experiments on well-known benchmark datasets confirm that our approach provides robust tracking, mapping, and semantic labeling even with noisy, sparse, or no input depth. Overall, our proposed algorithm can greatly enhance scene perception and assist with a range of robot control problems.
Building the MSR Tool Kaiaulu: Design Principles and Experiences
Venue: arXiv cs.SE 2023
Year: 2023

Background: Since Alitheia Core was proposed and subsequently retired, tools that support empirical studies of software projects continue to be proposed, such as Codeface, Codeface4Smells, GrimoireLab and SmartSHARK, but they all make different design choices and provide overlapping functionality. Aims: We seek to understand the design decisions adopted by these tools--the good and the bad--along with their consequences, to understand why their authors reinvented functionality already present in other tools, and to help inform the design of future tools. Method: We used action research to evaluate the tools, and to determine a set of principles and anti-patterns to motivate a new tool design. Results: We identified 7 major design choices among the tools: 1) Abstraction Debt, 2) the use of Project Configuration Files, 3) the choice of Batch or Interactive Mode, 4) Minimal Paths to Data, 5) Familiar Software Abstractions, 6) Licensing and 7) the Perils of Code Reuse. Building on the observed good and bad design decisions, we created our own tool architecture and implemented it as an R package. Conclusions: Tools should not require onerous setup for users to obtain data. Authors should consider the conventions and abstractions used by their chosen language and build upon these instead of redefining them. Tools should encourage best practices in experiment reproducibility by leveraging self-contained and readable schemas that are used for tool automation, and reuse must be done with care to avoid depending on dead code.
A positive feedback method based on F-measure value for Salient Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary research objective is to develop novel algorithms that can outperform state-of-the-art models, a task that is extremely difficult and time-consuming. In contrast, this paper proposes a positive feedback method based on F-measure value for SOD, aiming to improve the accuracy of saliency prediction using existing methods. Specifically, our proposed method takes an image to be detected and inputs it into several existing models to obtain their respective prediction maps. These prediction maps are then fed into our positive feedback method to generate the final prediction result, without the need for careful decoder design or model training. Moreover, our method is adaptive and can be implemented based on existing models without any restrictions. Experimental results on five publicly available datasets show that our proposed positive feedback method outperforms the latest 12 methods in five evaluation metrics for saliency map prediction. Additionally, we conducted a robustness experiment, which shows that when at least one good prediction result exists in the selected existing model, our proposed approach can ensure that the prediction result is not worse. Our approach achieves a prediction speed of 20 frames per second (FPS) when evaluated on a low configuration host and after removing the prediction time overhead of inserted models. These results highlight the effectiveness, efficiency, and robustness of our proposed approach for salient object detection.
Knowledge Enhanced Model for Live Video Comment Generation
Venue: arXiv cs.CV 2023
Year: 2023

Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus on short video datasets while ignoring other important video types such as long videos like movies. In this work, we collect a new Movie Live Comments (MovieLC) dataset to support research on live video comment generation for long videos. We also propose a knowledge enhanced generation model inspired by the divergent and informative nature of live video comments. Our model adopts a pre-training encoder-decoder framework and incorporates external knowledge. Extensive experiments show that both objective metrics and human evaluation demonstrate the effectiveness of our proposed model. The MovieLC dataset and our code will be released.
Social Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious Design Choices
Venue: arXiv cs.SI 2023
Year: 2023

Social media has expanded in its use, and reach, since the inception of early social networks in the early 2000s. Increasingly, users turn to social media for keeping up to date with current affairs and information. However, social media is increasingly used to promote disinformation and cause harm. In this contribution, we argue that as information (eco)systems, social media sites are vulnerable from three aspects, each corresponding to the classical 3-tier architecture in information systems: asymmetric networks (data tier); algorithms powering the supposed personalisation for the user experience (application tier); and adverse or audacious design of the user experience and overall information ecosystem (presentation tier) - which can be summarized as the 3 A's. Thus, the open question remains: how can we 'fix' social media? We will unpack suggestions from various allied disciplines - from philosophy to data ethics to social psychology - in untangling the 3A's above.
Stretching Demi-Bits and Nondeterministic-Secure Pseudorandomness
Venue: arXiv cs.CC 2023
Year: 2023

We develop the theory of cryptographic nondeterministic-secure pseudorandomness beyond the point reached by Rudich's original work (Rudich 1997), and apply it to draw new consequences in average-case complexity and proof complexity. Specifically, we show the following: *Demi-bit stretch*: Super-bits and demi-bits are variants of cryptographic pseudorandom generators which are secure against nondeterministic statistical tests (Rudich 1997). They were introduced to rule out certain approaches to proving strong complexity lower bounds beyond the limitations set out by the Natural Proofs barrier (Rudich and Razborov 1997). Whether demi-bits are stretchable at all had been an open problem since their introduction. We answer this question affirmatively by showing that: every demi-bit $b:\{0,1\}^n\to \{0,1\}^{n+1}$ can be stretched into sublinear many demi-bits $b':\{0,1\}^{n}\to \{0,1\}^{n+n^{c}}$, for every constant $0<c<1$. >>> see rest of abstract in paper.
SGED: A Benchmark dataset for Performance Evaluation of Spiking Gesture Emotion Recognition
Venue: arXiv cs.CV 2023
Year: 2023

In the field of affective computing, researchers in the community have promoted the performance of models and algorithms by using the complementarity of multimodal information. However, the emergence of more and more modal information makes the development of datasets unable to keep up with the progress of existing modal sensing equipment. Collecting and studying multimodal data is a complex and significant work. In order to supplement the challenge of partial missing of community data. We collected and labeled a new homogeneous multimodal gesture emotion recognition dataset based on the analysis of the existing data sets. This data set complements the defects of homogeneous multimodal data and provides a new research direction for emotion recognition. Moreover, we propose a pseudo dual-flow network based on this dataset, and verify the application potential of this dataset in the affective computing community. The experimental results demonstrate that it is feasible to use the traditional visual information and spiking visual information based on homogeneous multimodal data for visual emotion recognition.The dataset is available at \url{https://github.com/201528014227051/SGED}
LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
Venue: arXiv cs.AI 2023
Year: 2023

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, all state-of-the-art approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD -- an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1)~persons living in New York having a certain age, and (2)~the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
Venue: arXiv cs.CR 2023
Year: 2023

This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour and characteristics of networks, which are often overlooked by most existing NIDSs. By capturing these complex patterns in network traffic, FlowTransformer offers a flexible and efficient tool for researchers and practitioners in the cybersecurity community who are seeking to implement NIDSs using transformer-based models. FlowTransformer allows the direct substitution of various transformer components, including the input encoding, transformer, classification head, and the evaluation of these across any flow-based network dataset. To demonstrate the effectiveness and efficiency of the FlowTransformer framework, we utilise it to provide an extensive evaluation of various common transformer architectures, such as GPT 2.0 and BERT, on three commonly used public NIDS benchmark datasets. We provide results for accuracy, model size and speed. A key finding of our evaluation is that the choice of classification head has the most significant impact on the model performance. Surprisingly, Global Average Pooling, which is commonly used in text classification, performs very poorly in the context of NIDS. In addition, we show that model size can be reduced by over 50\%, and inference and training times improved, with no loss of accuracy, by making specific choices of input encoding and classification head instead of other commonly used alternatives.
Non-Contact Heart Rate Measurement from Deteriorated Videos
Venue: arXiv cs.CV 2023
Year: 2023

Remote photoplethysmography (rPPG) offers a state-of-the-art, non-contact methodology for estimating human pulse by analyzing facial videos. Despite its potential, rPPG methods can be susceptible to various artifacts, such as noise, occlusions, and other obstructions caused by sunglasses, masks, or even involuntary facial contact, such as individuals inadvertently touching their faces. In this study, we apply image processing transformations to intentionally degrade video quality, mimicking these challenging conditions, and subsequently evaluate the performance of both non-learning and learning-based rPPG methods on the deteriorated data. Our results reveal a significant decrease in accuracy in the presence of these artifacts, prompting us to propose the application of restoration techniques, such as denoising and inpainting, to improve heart-rate estimation outcomes. By addressing these challenging conditions and occlusion artifacts, our approach aims to make rPPG methods more robust and adaptable to real-world situations. To assess the effectiveness of our proposed methods, we undertake comprehensive experiments on three publicly available datasets, encompassing a wide range of scenarios and artifact types. Our findings underscore the potential to construct a robust rPPG system by employing an optimal combination of restoration algorithms and rPPG techniques. Moreover, our study contributes to the advancement of privacy-conscious rPPG methodologies, thereby bolstering the overall utility and impact of this innovative technology in the field of remote heart-rate estimation under realistic and diverse conditions.
Visual Diagrammatic Queries in ViziQuer: Overview and Implementation
Venue: arXiv cs.DB 2023
Year: 2023

Knowledge graphs (KG) have become an important data organization paradigm. The available textual query languages for information retrieval from KGs, as SPARQL for RDF-structured data, do not provide means for involving non-technical experts in the data access process. Visual query formalisms, alongside form-based and natural language-based ones, offer means for easing user involvement in the data querying process. ViziQuer is a visual query notation and tool offering visual diagrammatic means for describing rich data queries, involving optional and negation constructs, as well as aggregation and subqueries. In this paper we review the visual ViziQuer notation from the end-user point of view and describe the conceptual and technical solutions (including abstract syntax model, followed by a generation model for textual queries) that allow mapping of the visual diagrammatic query notation into the textual SPARQL language, thus enabling the execution of rich visual queries over the actual knowledge graphs. The described solutions demonstrate the viability of the model-based approach in translating complex visual notation into a complex textual one; they serve as semantics by implementation description of the ViziQuer language and provide building blocks for further services in the ViziQuer tool context.
Estimation of Interference Correlation in mmWave Cellular Systems
Venue: arXiv cs.IT 2023
Year: 2023

We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or when using non-orthogonal multiple access (NOMA) techniques. Assuming that the BS treats this interference as additional noise, we focus on the problem of estimating the interference correlation matrix from received signal samples. We consider a BS equipped with multiple antennas and operating in the millimeter-wave (mmWave) bands and propose techniques exploiting the fact that channels comprise only a few reflections at these frequencies. This yields a specific structure of the interference correlation matrix that can be decomposed into three matrices, two rectangular depending on the angle of arrival (AoA) of the interference and the third square with smaller dimensions. We resort to gridless approaches to estimate the AoAs and then project the least square estimate of the interference correlation matrix into a subspace with a smaller dimension, thus reducing the estimation error. Moreover, we derive two simplified estimators, still based on the gridless angle estimation that turns out to be convenient when estimating the interference over a larger number of samples.