Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency. Moreover, most of the existing distributed framework are proposed for single-agent reinforcement learning and not suitable for multi-agent. In this paper, we design an distributed MARL framework based on the actor-work-learner architecture. In this framework, multiple asynchronous environment interaction modules can be deployed simultaneously, which greatly improves the sample collection speed and sample diversity. Meanwhile, to make full use of computing resources, we decouple the model iteration from environment interaction, and thus accelerate the policy iteration. Finally, we verified the effectiveness of propose framework in MaCA military simulation environment and the SMAC 3D realtime strategy gaming environment with imcomplete information characteristics.
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers

Self-supervised learning (SSL) methods such as masked language modeling have shown massive performance gains by pretraining transformer models for a variety of natural language processing tasks. The follow-up research adapted similar methods like masked image modeling in vision transformer and demonstrated improvements in the image classification task. Such simple self-supervised methods are not exhaustively studied for object detection transformers (DETR, Deformable DETR) as their transformer encoder modules take input in the convolutional neural network (CNN) extracted feature space rather than the image space as in general vision transformers. However, the CNN feature maps still maintain the spatial relationship and we utilize this property to design self-supervised learning approaches to train the encoder of object detection transformers in pretraining and multi-task learning settings. We explore common self-supervised methods based on image reconstruction, masked image modeling and jigsaw. Preliminary experiments in the iSAID dataset demonstrate faster convergence of DETR in the initial epochs in both pretraining and multi-task learning settings; nonetheless, similar improvement is not observed in the case of multi-task learning with Deformable DETR. The code for our experiments with DETR and Deformable DETR are available at https://github.com/gokulkarthik/detr and https://github.com/gokulkarthik/Deformable-DETR respectively.
Crossover in Extended Newtonian Gravity Emerging from Thermodynamics

Recently, it has been discovered that a scalar field coupled to a fluid and allowed to be a thermodynamic variable in consistency with the second law of thermodynamics is only of gravity, and, accordingly, the emergence of extended Newtonian gravity has been predicted. The resulting field equation for the potential of this emergent force is nonlinear and admits the logarithmic potential as a singular solution, suggesting its relevance to the dark matter conundrum. Here, a general analysis of the nonlinear field equation is performed. It is found that the emergent force field exhibits an unsharp crossover between the 1/r and 1/r2 forces outside the fluid, depending on a spatial scale characteristic of the present theory to be observationally tested in the context of the dark matter conundrum. Then, the action functional is constructed for the potential of the emergent field, and the field energy is shown to be free from an infrared divergence. A comment is also made on the difference of the present theory to MOND (modified Newtonian dynamics).
Recommending Research Papers to Chemists: A Specialized Interface for Chemical Entity Exploration

Researchers and scientists increasingly rely on specialized information retrieval (IR) or recommendation systems (RS) to support them in their daily research tasks. Paper recommender systems are one such tool scientists use to stay on top of the ever-increasing number of academic publications in their Field. Improving research paper recommender systems is an active research Field. However, less research has focused on how the interfaces of research paper recommender systems can be tailored to suit the needs of different research domains. For example, in the Field of biomedicine and chemistry, researchers are not only interested in textual relevance but may also want to discover or compare the contained chemical entity information found in a paper’s full text. Existing recommender systems for academic literature do not support the discovery of this non-textual, but semantically valuable, chemical entity data. We present the First implementation of a specialized chemistry paper recommender system capable of visualizing the contained chemical structures, chemical formulae, and synonyms for chemical compounds within the document’s full text. We review existing tools and related research in this Field before describing the implementation of our ChemVis system. With the help of chemists, we are expanding the functionality of ChemVis, and will perform an evaluation of recommendation performance and usability in future work. CCS CONCEPTS • Human-centered computing $\rightarrow$ Graphical user interfaces; • Information systems $\rightarrow$ Recommender systems
Bottleneck Bandwidth Handling Noise and Ensuring Security Using ECC

Abstract — The capacity of a path estimated by the packet pair technique. This technique has been used in numerous applications as well network management and end-to end admission control up to date this method can be used to fingerprint Internet paths. On the other hand it performed in open environment, so the end hosts try to modify these measurements to increase their network gain. Using packet pair technique we examine the major coercion against adjacent with bandwidth estimation. These implementations of the technique are susceptible to a wide range of bandwidth manipulation attack. Bandwidth is often used as a synonym for data transfer rate, the amount of data that can be carried from one point. So the end hosts can accurately change their claimed bandwidth. To discriminate such attacks, some possible countermeasures are used. Our technique can detect whether delays were inserted within the transmission of a packet-pair (e.g., by bandwidth shapers). Further a novel scheme for remote path identification using the distribution of packet-pair dispersions and we evaluate its accuracy, robustness, and potential use. In this paper we handle noise message and to ensure security during message transmission. It further prevents denial of service attack and finds alternate path to avoid delay packet dispersion. To secure the packets it uses elliptic curve cryptography (ECC) algorithm for high end security.
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields

Dynamic imaging is essential for analyzing various biological systems and behaviors but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition times require severe undersampling, which leads to data incompleteness. Multiple images may then be compatible with the data, thus requiring special techniques (regularization) to ensure the uniqueness of the reconstruction. Computational and memory requirements are particularly burdensome for three-dimensional dynamic imaging applications requiring high resolution in both space and time. Exploiting redundancies in the object's spatiotemporal features is key to addressing both challenges. This contribution investigates neural fields, or implicit neural representations, to model the sought-after dynamic object. Neural fields are a particular class of neural networks that represent the dynamic object as a continuous function of space and time, thus avoiding the burden of storing a full resolution image at each time frame. Neural field representation thus reduces the image reconstruction problem to estimating the network parameters via a nonlinear optimization problem (training). Once trained, the neural field can be evaluated at arbitrary locations in space and time, allowing for high-resolution rendering of the object. Key advantages of the proposed approach are that neural fields automatically learn and exploit redundancies in the sought-after object to both regularize the reconstruction and significantly reduce memory storage requirements. The feasibility of the proposed framework is illustrated with an application to dynamic image reconstruction from severely undersampled circular Radon transform data.
Construction of knowledge and development of collective work: training workers of an operational centre for coordination and control of forest fires in Chile

1 Construction of knowledge and development of collective work: training workers of an operational centre for coordination and control of forest fires in Chile Fabiola Maureira, Tahar-Hakim Benchekroun, Pierre Falzon, Elias Apud Centre de recherche sur le travail et le developpement (CRTD), Conservatoire national des arts et metiers (Cnam), Paris, FRANCE ; Ergonomics Unit,Universidad de Concepcion, Concepcion, CHILE
Fractional Order Control of Micro Electro-Mechanical Systems

This paper addresses the problem of the fractional sliding mode control (FSMC) for a MEMS optical switch. The proposed scheme utilizes a frac tional sliding surface to describe dynamic behavior of the system in the sliding mode stage. A fter a comparison with the classical integer-order counterpart, it is seen that the control system wit h the proposed sliding surface displays better transient performance. The claims are justified thr ough a set of simulations and the results obtained are found promising. Overall, the contribution of t his paper is to demonstrate that the response of the system under control is significantly better fo r the fractional-order differentiation exploited in the sliding surface design stage than that for the classical integer-order one, under the same conditions.
A Longitudinal Study of Cryptographic API - a Decade of Android Malware

Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy. Various system-based and third-party libraries for Android provide cryptographic functionalities, and previous works mainly explored the misuse of cryptographic API in benign applications. However, the role of cryptographic API has not yet been explored in Android malware. This paper performs a comprehensive, longitudinal analysis of cryptographic API in Android malware. In particular, we analyzed $603\,937$ Android applications (half of them malicious, half benign) released between $2012$ and $2020$, gathering more than 1 million cryptographic API expressions. Our results reveal intriguing trends and insights on how and why cryptography is employed in Android malware. For instance, we point out the widespread use of weak hash functions and the late transition from insecure DES to AES. Additionally, we show that cryptography-related characteristics can help to improve the performance of learning-based systems in detecting malicious applications.
Raw Filtering of JSON Data on FPGAs

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of such formats is that an application may spend a significant amount of processing time just on unselectively parsing all data. To relax this issue, the concept of raw filtering is proposed with the idea to remove data from a stream prior to the costly parsing stage. However, as accurate filtering of raw data is often only possible after the data has been parsed, raw filters are designed to be approximate in the sense of allowing false-positives in order to be implemented efficiently. Contrary to previously proposed CPU-based raw filtering techniques that are restricted to string matching, we present FPGA-based primitives for filtering strings, numbers and also number ranges. In addition, a primitive respecting the basic structure of JSON data is proposed that can be used to further increase the accuracy of introduced raw filters. The proposed raw filter primitives are designed to allow for their composition according to a given filter expression of a query. Thus, complex raw filters can be created for FPGAs which enable a drastical decrease in the amount of generated false-positives, particularly for IoT workload. As there exists a trade-off between accuracy and resource consumption, we evaluate primitives as well as composed raw filters using different queries from the RiotBench benchmark. Our results show that up to 94.3% of the raw data can be filtered without producing any observed false-positives using only a few hundred LUTs.
Utilizing coarse-grained data in low-data settings for event extraction

Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtain, instead of annotating more documents. We utilize a multi-task model with two auxiliary tasks, document and sentence binary classification, in addition to the main task of token classification. We perform a series of experiments with varying data regimes for the aforementioned integration. Results show that while introducing extra coarse-grained data offers greater improvement and robustness, a gain is still possible with only the addition of negative documents that have no information on any event.
Regression-Based Projection for Learning Mori-Zwanzig Operators

We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism. We present a principled method to extract the Markov and memory operators for any regression models. We show that the choice of linear regression results in a recently proposed data-driven learning algorithm based on Mori's projection operator, which is a higher-order approximate Koopman learning method. We show that more expressive nonlinear regression models naturally fill in the gap between the highly idealized and computationally efficient Mori's projection operator and the most optimal yet computationally infeasible Zwanzig's projection operator. We performed numerical experiments and extracted the operators for an array of regression-based projections, including linear, polynomial, spline, and neural-network-based regressions, showing a progressive improvement as the complexity of the regression model increased. Our proposition provides a general framework to extract memory-dependent corrections and can be readily applied to an array of data-driven learning methods for stationary dynamical systems in the literature.
Quantum Self-Attention Neural Networks for Text Classification

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have opened the door to research in Quantum NLP (QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum self-attention neural network (QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

Counterfactual explanations (CFEs) highlight changes to a model’s input that alter its prediction in a particular way. s have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of plausibility for automatically generated s, enhancing their robustness by exclusively creating plausible explanations. However, practical benefits of this constraint on user experience are yet unclear. In this study, we evaluate objective and subjective usability of plausible s in an iterative learning task. We rely on a game-like experimental design, revolving around an abstract scenario. Our results show that novice users benefit less from receiving plausible rather than closest s that induce minimal changes leading to the desired outcome. Responses in a post-game survey reveal no differences for subjective usability between both groups. Following the view of psychological plausibility as comparative similarity, users in the closest condition may experience their s as more psychologically plausible than the computationally plausible counterpart. In sum, our work highlights a little-considered divergence of definitions of computational plausibility and psychological plausibility, critically confirming the need to incorporate human behavior, preferences and mental models already at the design stages of XAI. All source code and data of the current study are available: https://github.com/ukuhl/PlausibleAlienZoo
Wind power forecasting approach using neuro-fuzzy system combined with wavelet packet decomposition, data preprocessing, and forecast combination framework

Wind power forecasting is presently one of the challenging tasks to deal with supply-demand balance in modern electric power systems. Accurate wind power predictions are needed to reduce the risk in uncertainty and enable for better dispatch, scheduling, and power system integration. This article deals with the challenge of wind power forecasting by proposing the application of the forecasting methodology using the wavelet packet decomposition principles, neuro-fuzzy systems, as well as the benefits of data preprocessing and forecast combination framework. The used data consist of the quarter-hourly observations of wind power generation in France, and the proposed method is used to ensure forecasts for a time horizon of an hour-ahead. The obtained results indicate the superior accuracy of the proposed model with an average mean absolute percentage error of 3.408%, which means there is possibility to construct a high-precision method using only the historical wind power data.
Inﬂationary Cosmology with a scalar-curvature mixing term ξRφ 2

We use the PLANCK 2018 and the WMAP data to constraint inﬂation models driven by a scalar ﬁeld φ in the presence of the non-minimal scalar-curvature mixing term 12 ξRφ 2 . We consider four distinct scalar ﬁeld potentials φ p e − λφ , (1 − φ p ) e − λφ , (1 − λφ ) p and αφ 2 1+ αφ 2 to study inﬂation in the non-minimal gravity theory. We calculate the potential slow-roll parameters and predict the scalar spectral index n s and the tensor-to-scalar ratio r , in the parameters ( λ, p, α ) space of the potentials. We have compared our results with the ones existing in the literature, and this indicates the present status of the non-minimal inﬂation after the release of the PLANCK 2018 data.
ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the research in this area, we present ALIGNMEET, a comprehensive tool for meeting annotation, alignment, and evaluation. The tool aims to provide an efficient and clear interface for fast annotation while mitigating the risk of introducing errors. Moreover, we add an evaluation mode that enables a comprehensive quality evaluation of meeting minutes. To the best of our knowledge, there is no such tool available. We release the tool as open source. It is also directly installable from PyPI.
A human inspired cognitive wheelchair navigation system

This paper proposes a novel approach to impart cognition to wheelchair navigation system (WNS). The cognitive engineering for WNS is to incorporate advance wheelchair user assistance features for autonomous functions. The simplified top level schematic human brain model for navigation computing is developed. From this developed model, a novel human inspired cognitive cycle (HICS) is proposed that consists of six acts. On the basis of HICS, a theoretical model for cognitive wheelchair navigation system (CWNS) is developed. The proposed top level theoretical CWNS model provides general framework for technical implementation of autonomous wheelchair navigation system. The three new approaches to artificial intelligence, namely: HICS, SIE and CWNS are introduced in this paper. The prototype realisation of proposed CWNS theoretical model has been done using 8051 microcontroller as cognitive embedded system (CES). It has intelligent of obstacle avoidance and distress user command avoidance using logics of the 8051 microcontroller coding. The working response of proposed model has been compared with existing models on the basis of possible health condition and impairments associated with wheelchair users.
Muon g-2 Anomaly from Vectorlike Leptons in TeV scale Trinification and $E_6$ models

We revisit an economical model for the g-2 anomaly featuring a vector-like charged fermion and a scalar singlet that arise naturally in TeV scale Trinification and $E_6$ unification models. The phenomenological implications for lepton-flavor universality, Higgs decays and charged lepton-flavor violating decays are discussed in detail.
Effectual and Lossless Electrical Power Generation Methodology Using Wind-Lens Technology

Wind energy is playing a critical role in the establishment of an environmentally sustainable low carbon economy. This paper presents an overview of wind turbine generator technologies and compares their advantages and drawbacks used for wind energy utilization. Traditionally, DC machines, synchronous machines and squirrel-cage induction machines have been used for small scale power generation. For medium and large wind turbines (WTs), the doubly-fed induction generator (DFIG) is currently the dominant technology while permanent magnet (PM), switched reluctance and high temperature superconducting generators are all extensively researched and developed over the years. In this paper, the topologies and features of these machines are discussed with special attention given to their practical considerations involved in the design, control and operation. It is hoped that this paper provides quick reference guidelines for developing wind turbine generation systems.