Reviewerly/exHarmony huggingface dataset:
Verifying dynamic trait objects in rust

Rust has risen in prominence as a systems programming language in large part due to its focus on reliability. The language's advanced type system and borrow checker eliminate certain classes of memory safety violations. But for critical pieces of code, teams need assurance beyond what the type checker alone can provide. Verification tools for Rust can check other properties, from memory faults in unsafe Rust code to user-defined correctness assertions. This paper particularly focuses on the challenges in reasoning about Rust's dynamic trait objects, a feature that provides dynamic dispatch for function abstractions. While the explicit dyn keyword that denotes dynamic dispatch is used in 37% of the 500 most-downloaded Rust libraries (crates), dynamic dispatch is implicitly linked into 70%. To our knowledge, our open-source Kani Rust Verifier is the first symbolic modeling checking tool for Rust that can verify correctness while supporting the breadth of dynamic trait objects, including dynamically dispatched closures. We show how our system uses semantic trait information from Rust's Mid-level Intermediate Representation (an advantage over targeting a language-agnostic level such as LLVM) to improve verification performance by 5%--15× for examples from open-source virtualization software. Finally, we share an open-source suite of verification test cases for dynamic trait objects.
CATtalk: An IoT-Based Interactive Art Development Platform

Interactive art has been significant advanced by the Internet of Things (IoT) and cyber physical interaction technologies, which enables the participants to engage with the art devices.Several tools and platforms have been proposed to create the art devices.However, the interactive artworks are typically developed with these art devices in ad hoc approaches, and the artists need to spend significant programming efforts to integrate the art devices.This paper proposes CATtalk, a platform to create and maintain interactive artworks.The novel idea is to treat all art devices in an interactive artwork as IoT devices that can be transparently reused by reconfiguration in CATtalk.Therefore, the artworks developed independently by individual artists can be quickly integrated to create new interactive applications.Through CATtalk's no-code and low-code mechanisms, the artists can manipulate CATtalk with little or no programing efforts.CATtalk offers a built-in mechanism so that any person in the audience can play with an interactive artwork with his/her smartphone.We also conduct analytic analysis, simulation and measurements to ensure that the interactive art performance in cross-country remote stages are not affected by the communications delays.In our measurements, the average local and remote communication delays are about 0.01 and 0.05 seconds, respectively.If the art performance is designed such that the average delay between two actions of a local (remote) performer is longer than 0.1 seconds, then the probability of out-of-sequence actions is less than 0.01%.That is, the local dancer should perform slower than the remote dancer.Such delay analysis for remote interactive art performance has not been conducted in the literature.
Phase Unwrapping-Free Fringe Projection Profilometry for 3D Shape Measurement

Phase unwrapping is always the necessary procedure in fringe projection profilometry (FPP). This letter presents an efficient phase unwrapping-free FPP method. Both the wrapped phase maps of the measured object and the reference plane are extracted by phase-shifting algorithm. If the depth range of the measured object is within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi $ </tex-math></inline-formula> in phase domain, the three-dimensional (3D) shape can be directly reconstructed by converting the wrapped phase difference into absolute phase difference without phase unwrapping process. Simulations and experiments are carried out, and their results demonstrate the effectiveness and robustness of the proposed method.
Implicit Neural Representations for Variable Length Human Motion Generation

We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distributions of INRs, from which one can easily sample representations to generate novel human motion sequences. Our method offers variable-length sequence generation by construction because a part of INR is optimized for a whole sequence of arbitrary length with temporal embeddings. In contrast, previous works reported difficulties with modeling variable-length sequences. We confirm that our method with a Transformer decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC datasets in terms of realism and diversity of generated motions. Surprisingly, even our method with an MLP decoder consistently outperforms the state-of-the-art Transformer-based auto-encoder. In particular, we show that variable-length motions generated by our method are better than fixed-length motions generated by the state-of-the-art method in terms of realism and diversity. Code at https://github.com/PACerv/ImplicitMotion .
Learning Object Placement via Dual-Path Graph Completion

Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem and propose a novel graph completion module (GCM). The background scene is represented by a graph with multiple nodes at different spatial locations with various receptive fields. The foreground object is encoded as a special node that should be inserted at a reasonable place in this graph. We also design a dual-path framework upon the structure of GCM to fully exploit annotated composite images. With extensive experiments on OPA dataset, our method proves to significantly outperform existing methods in generating plausible object placement without loss of diversity.
All That’s Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms.
Drive&amp;Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation

This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city. Our contributions are threefold. First, we propose a novel method for cross-modal unsupervised learning of semantic image segmentation by leveraging synchronized LiDAR and image data. The key ingredient of our method is the use of an object proposal module that analyzes the LiDAR point cloud to obtain proposals for spatially consistent objects. Second, we show that these 3D object proposals can be aligned with the input images and reliably clustered into semantically meaningful pseudo-classes. Finally, we develop a cross-modal distillation approach that leverages image data partially annotated with the resulting pseudo-classes to train a transformer-based model for image semantic segmentation. We show the generalization capabilities of our method by testing on four different testing datasets (Cityscapes, Dark Zurich, Nighttime Driving and ACDC) without any finetuning, and demonstrate significant improvements compared to the current state of the art on this problem.
AAKE-BIVT: Anonymous Authenticated Key Exchange Scheme for Blockchain-Enabled Internet of Vehicles in Smart Transportation

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment.Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats.To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT.AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement.A cloud server (CS) then securely aggregates data from related RSUs and generates transactions.The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method.By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment.Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation.Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.
The Reader and the Library in the Context of Communication between Science and Society

The dissemination of scientific knowledge is one of the urgent social tasks today. As part of the interaction between science and society, representatives of the scientific environment create information content, the dissemination of which becomes the task of social institutions, including libraries. The article deeply analyzes part of the results of a survey of readers of public libraries concerning the possibility of the latter’s participation in the communication of science and society in any possible form. The material for the study was a questionnaire survey on the attitude of readers of public libraries to science, conducted in March – April 2022 within the framework of the research project “The current state and trends in the development of communications between Russian science and society”. The opinion of readers of Novosibirsk public libraries is studied, concerning priority ways of obtaining information about scientific achievements, attending popular scientific events and opportunities for access to modern scientific knowledge through the library, while considerable attention is paid to mass events as a form of interaction of communicators in the field of science with society. The following conclusions were made in the study: readers of public libraries are loyal to the potential of libraries for the development of communication between science and society; for them, the library as a means of obtaining information is second after Internet in importance; the library can be considered as one of the platforms for visiting popular science events, and its efforts to strengthen communication links between science and society. They should be aimed at more active cooperation with representatives of the scientific world.
Image-Based Visual Servoing of Unmanned Aerial Manipulators for Tracking and Grasping a Moving Target

In this article, an image-based visual servoing (IBVS) control strategy is proposed for the unmanned aerial manipulator (UAM) system to track and grasp a moving target. Specifically, a robust-adaptive velocity observer is designed to estimate the relative velocity between the tracked target and the UAM platform. Based on the velocity observer, an IBVS controller using onboard camera of the UAM platform is proposed for moving target tracking without velocity measurement. Then, the barrier Lyapunov function is introduced into the UAM platform IBVS controller to ensure the safety of target tracking. Besides, another virtual camera is constructed on manipulator end-effector to compensate for the tracking error of the UAM platform. As a benefit, the eye-to-hand onboard camera ensures the global view of the UAM, and the eye-in-hand virtual camera of the manipulator ensures the accuracy of the grasping task. Finally, the stability of the proposed IBVS control strategy is analyzed through Lyapunov theory. The comparative simulations are provided to illustrate the target tracking performance of the proposed method. The experimental results demonstrate that the proposed method can be applied to the UAM with a low-cost sensor suite to realize the tasks of tracking and grasping a moving target.
Real-time wearable-device based activity recognition using machine learning methods

Classification of activities from body-worn accelerometer data to help monitor and take care of health attracts much attention from the research community.This paper proposed to design a real-time monitoring device that can identify people's actions from the accelerometer's data worn on the waist with five activities, including lying, sitting, standing, walking, and jogging.From the collected acceleration data, it is necessary to extract suitable features for real-time classification with high performance.These features are trained with machine learning algorithms that improve the efficiency of action classification.Consequently, a decision tree algorithm was embedded in the microcontroller.This programmed waist-mounted device was connected to the monitoring system via WiFi protocol.Users could monitor activities and managed data on a computer, a website, or a smartphone.The results were optimistic when the overall accuracy for the activities dataset reached 99.3% when training and classifying the activities on the computer.When experimenting with real-time wearable devices, the overall accuracy when classifying activities decreased but was still very good, reaching over 90%.
The Phenomenon of Education in the Context of an Intercultural Philosophical Approach

The article discusses the principles of coordination of intercultural philosophy with the main vectors of development of modern spheres of education. The mechanisms of the formation of intercultural philosophy since the end of the 20th century have been investigated. The etymological analysis of the concepts of “dialogue”, “discussion”, “contact”, “interaction”, “polylogue”, “speech of many” and “multiple contacts” is carried out. The authors analyze the processes of correlation, mutual influence and communication of cross-cultural discourse in the context of comparative philosophy and synthesis of cultures both from a general theoretical and methodological perspective. Special attention is paid to the prospects of studying the phenomenon of intercultural and interregional interactions of the philosophy of education, comparative and intercultural philosophy.
Multi-Client Functional Encryption with Fine-Grained Access Control

Multi-Client Functional Encryption ( $$\textsf {MCFE}$$ ) and Multi-Input Functional Encryption ( $$\textsf {MIFE}$$ ) are very interesting extensions of Functional Encryption for practical purpose. They allow to compute joint function over data from multiple parties. Both primitives are aimed at applications in multi-user settings where decryption can be correctly output for users with appropriate functional decryption keys only. While the definitions for a single user or multiple users were quite general and can be realized for general classes of functions as expressive as Turing machines or all circuits, efficient schemes have been proposed so far for concrete classes of functions: either only for access control, i.e. the identity function under some conditions, or linear/quadratic functions under no condition. In this paper, we target classes of functions that explicitly combine some evaluation functions independent of the decrypting user under the condition of some access control. More precisely, we introduce a framework for $$\textsf {MCFE}$$ with fine-grained access control and propose constructions for both single-client and multi-client settings, for inner-product evaluation and access control via Linear Secret Sharing Schemes (LSSS), with selective and adaptive security. The only known work that combines functional encryption in multi-user setting with access control was proposed by Abdalla et al. (Asiacrypt ’20), which relies on a generic transformation from the single-client schemes to obtain $$\textsf {MIFE} $$ schemes that suffer a quadratic factor of n (where n denotes the number of clients) in the ciphertext size. We follow a different path, via $$\textsf {MCFE} $$ : we present a duplicate-and-compress technique to transform the single-client scheme and obtain a $$\textsf {MCFE}$$ with fine-grained access control scheme with only a linear factor of n in the ciphertext size. Our final scheme thus outperforms the Abdalla et al.’s scheme by a factor n, as one can obtain $$\textsf {MIFE}$$ from $$\textsf {MCFE}$$ by making all the labels in $$\textsf {MCFE}$$ a fixed public constant. The concrete constructions are secure under the $$\textsf{SXDH}$$ assumption, in the random oracle model for the $$\textsf {MCFE}$$ scheme, but in the standard model for the $$\textsf {MIFE}$$ improvement.
Detecting Semantic Code Clones by Building AST-based Markov Chains Model

Code clone detection aims to find functionally similar code fragments, which is becoming more and more important in the field of software engineering. Many code clone detection methods have been proposed, among which tree-based methods are able to handle semantic code clones. However, these methods are difficult to scale to big code due to the complexity of tree structures. In this paper, we design Amain, a scalable tree-based semantic code clone detector by building Markov chains models. Specifically, we propose a novel method to transform the original complex tree into simple Markov chains and measure the distance of all states in these chains. After obtaining all distance values, we feed them into a machine learning classifier to train a code clone detector. To examine the effectiveness of Amain, we evaluate it on two widely used datasets namely Google Code Jam and BigCloneBench. Experimental results show that Amain is superior to nine state-of-the-art code clone detection tools (i.e., SourcererCC, RtvNN, Deckard, ASTNN, TBCNN, CDLH, FCCA, DeepSim, and SCDetector).
Sampling QCD field configurations with gauge-equivariant flow models

Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories.A critical question is whether this success will translate to studies of QCD.This Proceedings presents a status update on advances in this area.In particular, it is illustrated how recently developed algorithmic components may be combined to construct flow-based sampling algorithms for QCD in four dimensions.The prospects and challenges for future use of this approach in at-scale applications are summarized.
Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks

If a patient can be discharged from an intensive care unit (ICU) is usually decided by the treating physicians based on their clinical experience. However, nowadays limited capacities and growing socioeconomic burden of our health systems increase the pressure to discharge patients as early as possible, which may lead to higher readmission rates and potentially fatal consequences for the patients. Therefore, here we present a long short-term memory-based deep learning model (LSTM) trained on time series data from Medical Information Mart for Intensive Care (MIMIC-III) dataset to assist physicians in making decisions if patients can be safely discharged from cardiovascular ICUs. To underline the strengths of our LSTM we compare its performance with a logistic regression model, a random forest, extra trees, a feedforward neural network and with an already known, more complex LSTM as well as an LSTM combined with a convolutional neural network. The results of our evaluation show that our LSTM outperforms most of the above models in terms of area under receiver operating characteristic curve. Moreover, our LSTM shows the best performance with respect to the area under precision-recall curve. The deep learning solution presented in this article can help physicians decide on patient discharge from the ICU. This may not only help to increase the quality of patient care, but may also help to reduce costs and to optimize ICU resources. Further, the presented LSTM-based approach may help to improve existing and develop new medical machine learning prediction models.
Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach

Designing a cost-effective, scalable, and flexible architecture that supports low latency and high quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-DN arcHfiTecture for livE video stReaming (RICHTER). We first introduce RICHTER's multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios, by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users' Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70% respectively.