Reviewerly/exHarmony huggingface dataset:
Semantic Communications: A New Paradigm for Networked Intelligence

Wireless communications are envisioned to shift from “connected things” to “connected/networked intelligence”, which is featured by proliferating intelligent objects, intelligent interaction, and autonomous evolution. The resulting massive connections, stringent and variable requirements impose great challenges on 6G to constantly provide optimal service for networked intelligence (NI). We envision semantic communications as a promising roadmap to communications for NI. Specifically, orienting at the interaction intent, semantic communications develop compact representation frameworks, efficient transmission links, and concise network architecture for the networked information interaction and management, enhancing the efficiency, autonomy, intelligence, and scalability of communication systems. In this paper, the key functionalities of semantic-enabled NI and related works are overviewed. Some open issues are discussed at the end.
Analysis of the NFT's Potential Impact in an E-commerce platform: A systematic review

NFT or non-fungible token is a blockchain-based unique digital asset. This systematic review paper gathered research papers and organized them into NFT implementations and NFT discussions. The researcher conducted a survey to assess the current perspective of people towards NFTs. The result suggests that most of them believe that NFTs can be helpful for e-commerce. Some of the concepts provided by the respondents as to why NFTs will be beneficial for e-commerce are authentication, trust, giving value to customers, commodity, security, proof of ownership, and a lot more. This paper then proposed a simple framework for adopting NFTs in e-commerce.
GOCA: Guided Online Cluster Assignment for Self-supervised Video Representation Learning

Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant features. While this works well for image-based self-supervision, it often fails for videos, which require understanding motion rather than focusing on background. Using optical flow as complementary information to RGB can alleviate this problem. However, we observe that a naïve combination of the two views does not provide meaningful gains. In this paper, we propose a principled way to combine two views. Specifically, we propose a novel clustering strategy where we use the initial cluster assignment of each view as prior to guide the final cluster assignment of the other view. This idea will enforce similar cluster structures for both views, and the formed clusters will be semantically abstract and robust to noisy inputs coming from each individual view. Additionally, we propose a novel regularization strategy to address the feature collapse problem, which is common in cluster-based self-supervised learning methods. Our extensive evaluation shows the effectiveness of our learned representations on downstream tasks, e.g., video retrieval and action recognition. Specifically, we outperform the state of the art by 7% on UCF and 4% on HMDB for video retrieval, and 5% on UCF and 6% on HMDB for video classification (Code available at https://github.com/Seleucia/goca ).
Localizing Fake Segments in Speech

Accelerated progress in voice cloning technology is making phone scams easier and exposing potential threats to politicians. Previous spoofing detection technology focused more on the fully faked speech. In this work, we create a Partial Synthetic Detection (Psynd) dataset and propose a fake segments localization system of the partially faked speech. Psynd dataset is a multi-speaker English corpus of approximately 13 hours in total at 24kHz sampling rate read English speech injected with synthetic speech. The fake segments are generated by state-of-art multi-speaker text-to-speech models with high similarity to the real speech to be injected. Our fake segments localization system consists of 3 parts: acoustic feature extraction, classification and post-processing. Frame level CQCC features are extracted and forwarded to a spoofing-discriminant ANN to predict real or fake label sequence. Assuming that the fake or real segments in the partially faked speech cannot be shorter than the duration of a phoneme, the labels of extreme short fake or real segments are flipped. We use 1-D IoU to evaluate the localization performance and get the result of 98.58% during the test, much higher than a random guess of $\frac{1}{3}$. We also explore extreme cases like fully faked, fully real and multi-fake-segments speech and degraded partially faked audio. Some benchmark results are presented on this dataset and show that a more robust detector is needed.
Function Recognition Of Multi-function Radar Via CNN-GRU Neural Network

In the field of cognitive electronic reconnaissance, recognizing the function (A variety of work modes arranged in temporal sequence) of the multi-function radar (MFR) is critical for electronic warfare equipment to develop effective countermeasures. However, research in this field is still very lack. Therefore, this paper proposes a convolutional neural network and gated recurrent units (CNN-GRU) to achieve MFR function recognition. The one-dimension convolutional neural network (1D-CNN) structure can be adapted to significantly reduce the computation time when processing a long input sequence, as well two 1D-CNNs are utilized to extract the higher-order sequential features of pulse repetition frequency (PRF) and pulse width (PW) in intercepted pulse stream sequence, respectively, while the GRU learns the higher-order sequential features to output the recognition results. The advantages of the proposed method in recognition accuracy and testing time are all verified by extensive experiments with ablation studies.
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes

Safety-critical technical systems operating in un-known environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from the data stream generated during operation. Gaussian process-based learning is particularly well suited for safety-critical applications as it ensures bounded prediction errors. While there exist computationally efficient approximations for online inference, these approaches lack guarantees for the prediction error and have high memory requirements, and are therefore not applicable to safety-critical systems with tight memory constraints. In this work, we propose a novel networked online learning approach based on Gaussian process regression, which addresses the issue of limited local resources by employing remote data management in the cloud. Our approach formally guarantees a bounded tracking error with high probability, which is exploited to identify the most relevant data to achieve a certain control performance. We further propose an effective data transmission scheme between the local system and the cloud taking bandwidth limitations and time delay of the transmission channel into account. The effectiveness of the proposed method is successfully demonstrated in a simulation.
Dual-Channel Early Warning Framework for Ethereum Ponzi Schemes

Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.
Motion Estimation for Complex Fluid Flows Using Helmholtz Decomposition

In this paper, we proposed a novel motion model with Helmholtz decomposition for complex fluid flows in a filtering-based optical flow framework, where the optimization of the regularization term is treated as a filtering process, and different motion patterns can be captured by finding appropriate filter kernels based on the designed filters. In this framework, we introduce a novel optical flow method with a joint spatial filter, which is based on the Helmholtz decomposition theorem that assumes a local motion field is composed of a curl field and a divergence field. By adjusting the scale of the weights in the filter kernels and combining a curl filter with a divergence filter in a certain ratio, it can simulate different motion patterns. In addition, if the correlation between the horizontal and vertical components of the optical flow field in the filter kernel is eliminated, it will be transformed into a linear motion model. Based on this linear motion model, we also develop a novel optical flow method with an adaptive guided filter. By finding an adaptive filter kernel driven by both the input image and the guided motion field for the designed filter, it can successfully capture different motion patterns, and yield an edge-preserving smoothing optical flow field. Most importantly, the proposed optical flow model provides a new way to design the regularizers for capturing different motion patterns in complex fluid flow. In particular, the designed optical flow method with an adaptive guided filter significantly outperforms the current state-of-the-art optical flow methods in predicting complex fluid flows.
A Transformer-Based Three-Branch Siamese Network For Hyperspectral Object Tracking

Hyperspectral videos can provide more information for the object tracking task. Due to the limited training samples, most current hyperspectral trackers do not fully use hyperspectral information to improve the tracking performance. To solve this problem, we propose a Transformer-based three-branch Siamese network (TrTSN) for hyperspectral object tracking. First, we construct a three-branch structure based on the Siamese network to obtain the semantic information of hyperspectral data fully. Second, we design a Transformer-based fusion module (TFM) and use two TFMs to adaptively combine the information obtained by different branches to obtain more robust features. Finally, the two sets of classification response and regression response generated by two fusion features are corresponding merged to improve the tracking network’s ability to predict the object’s position. Experimental results show that the TrTSN tracker is superior to the state-of-the-art trackers, demonstrating the effectiveness of this method.
Dissecting Mining Pools of Bitcoin Network: Measurement, Analysis and Modeling

Bitcoin network is one of the most popular blockchain systems. Mining pools are the main components of the Bitcoin network that invest a large amount of computing power to maximize their expected mining payoffs, which guarantees the security of the Bitcoin network. Although many existing works about mining pools are available, the long-term evolution of mining pools, and their effects on both the Bitcoin system and end-users, remain to be investigated. To fill this gap, we trace over 2.54 hundred thousand blocks from Feb 2016 to Nov 2020 and collect over 12 million unconfirmed transactions from Mar 2018 to Nov 2020. We then conduct a broad range of analyses, including the pool evolution, labeled transactions, and labeled blocks. We make the following observations from our measured data: 1) A few mining pools control most of the peer-to-peer network's computing power. 2) The long-term computing power of top mining pools grows exponentially while its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">continuous-time mining strategy</i> decreases linearly. 3) The computing power of the Bitcoin network converges to the Nash equilibrium. We then propose game-based strategies for mining activity analysis, i.e., the best-response strategies for mining pools when the mining revenue increases or decreases sharply. Moreover, we study the transaction fee dilemma of mining pools and the transaction fee strategies for end-users. Our models and analysis can help to understand and improve the Bitcoin system quantitatively.
A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the search space. SE is very powerful for optimization, but still has room for improvement due to some promising solutions usually fail to survive into the next generation. To alleviate this issue, we innovatively design a novel lottery-based elite retention strategy and propose a lottery elite spherical evolution algorithm (LESE). To verify the effectiveness of the proposed LESE, we experimentally compare it with the original SE algorithm and other representative meta-heuristics algorithms. We use 30 benchmark functions from the IEEE CEC2017 benchmark as the test set for our experiments. The effectiveness of LESE is demonstrated by analyzing the experimental results from perspectives of solution accuracy, convergence speed, solution distribution, and search dynamics.
Hybrid Artificial Intelligence for Knowledge Representation and Model-Based Medical Image Understanding - Towards Explainability

In this paper, we advocate that combining several frameworks in artificial intelligence, adopting a hybrid point of view for both knowledge data representation and reasoning, offers opportunities towards explainability. This idea is illustrated on the example of image understanding, in particular in medical imaging, formulated as a spatial reasoning problem.
Multipath Canceled RF Fingerprinting for Wireless OFDM Devices Based on Hammerstein System Parameter Separation

Radio frequency fingerprint (RFF) based authentication of wireless devices can be used in the fields of the access security at the physical-layer of wireless networks and radio spectrum management. However, the stability of RFF is easily damaged by the wireless multipath fading channel in mobile communications. An RFF fingerprinting method with the nonlinearity and in-phase and quadrature (IQ) imbalance of the transmitter is proposed for chunk-based wireless orthogonal frequency division multiplexing (OFDM) devices based on a Hammerstein system parameter separation technique, which cancels the adverse influence of the time-varying multipath channel. First, the parameters of the nonlinear model of the transmitter and finite impulse response (FIR) of the wireless multipath channel are estimated with the Hammerstein system parameter separation technique. Second, the best IQ imbalance parameter combination is obtained with the FIR estimation of the channel. Finally, the estimated parameters of the nonlinear model and IQ imbalance are used as RFFs to classify the transmitters. Theoretical analyses and numerical experiments demonstrate that the obtained RFFs are stable and the fusion authentication of transmitters with subtle differences from the same model and same series is feasible using the novel method.
Development of VR Motion Sickness Test Platform Based on UE

Virtual Reality (VR) technology is considered to be an important technical support for Metaverse. However, the discomfort caused by VR, especially VR motion sickness, greatly affects the user experience. Therefore, it’s particularly important to study the comfort for VR. In this project, a motion sickness test platform based on Unreal Engine (UE) was developed to measure and improve the comfort for VR. In the platform, virtual three-dimensional scenes are created and the platform can adjust parameters like rotation angular velocity and axis, height of the virtual camera, quantities of black and white stripes. Rotation angular velocity and axis of the virtual camera is set to verify usability of the platform. It can be concluded that VR motion sickness to some extent is aggravated with angular velocity going up. It’s more intense when rotating around X and Y axes than when rotating around Z. And women are more likely to get VR motion sickness than men.
Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption

Stream clustering is a technique capable of identifying homogeneous groups of observations that continuously arrive in a digital stream. In this work, we inherently refine a TF-IDF-based text stream clustering algorithm by the introduction of an automated distance threshold adaption technique for document insertion and cluster merging, improving the performance during distributional changes in the data stream. By conducting a thorough evaluation study, we show that our new fast approach outperforms state-of-the-art one-pass and batch-based stream clustering algorithms on various existing benchmarking datasets as well as a newly introduced dataset that poses additional challenges to the community. Moreover, we find that current evaluation approaches in the field of textual stream clustering are not adequate for a sound clustering performance assessment of evolving distributions. We thus demand the utilization of time-based evaluation.
Dynamic Free-Space Roadmap for Safe Quadrotor Motion Planning

Free-space-oriented roadmaps typically generate a series of convex geometric primitives, which constitute the safe region for motion planning. However, a static environment is assumed for this kind of roadmap. This assumption makes it unable to deal with dynamic obstacles and limits its applications. In this paper, we present a dynamic free-space roadmap, which provides feasible spaces and a navigation graph for safe quadrotor motion planning. Our roadmap is constructed by continuously seeding and extracting free regions in the environment. In order to adapt our map to environments with dynamic obstacles, we incrementally decompose the polyhedra intersecting with obstacles into obstacle-free regions, while the graph is also updated by our well-designed mechanism. Extensive simulations and real-world experiments demonstrate that our method is practically applicable and efficient.
Strong-motion Earthquake Prediction Model using Convolutional Extreme Learning Machine

The earthquake occurrence is difficult to predict. However, the ground motion can be measured by using the network of seismic telemetry stations. In this case, the sensing seismic wave data can be used as the input to predict the strong-motion earthquake in the target area. In this paper, a strong-motion earthquake prediction model, called C-ELM, has been developed by applying the integration of the extreme learning machine and convolutional neural network. The prediction model applies the convolutional neural network method to extract features from the initial P-wave. The extreme learning machine method is applied to minimize the loss function. The proposed prediction model has been evaluated by comparing its predicted results to the traditional convolutional neural network. The result shows that the strong-motion prediction accuracy of the C-ELM is close to the result of the traditional convolutional neural network. However, the C-ELM can extensively reduce the computation training time.
An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

AlphaCode is a code generation system for assisting software developers in solving competitive programming problems using natural language problem descriptions. Despite the advantages of the code generating system, the open source com-munity expressed concerns about practicality and data licensing. However, there is no research investigating generated codes in terms of code clone and performance. In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes. The results show that (i) the generated codes from AlphaCode are similar to human codes (i.e., the average maximum similarity score is 0.56) and (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage. Moreover, AlphaCode tends to generate more similar codes to humans for low-difficulty problems (i.e., four cases have the exact same codes). It also employs excessive nested loops and unnecessary variable declarations for high-difficulty problems, which cause low performance regarding our manual investigation. The replication package is available at https:/doi.org/10.5281/zenodo.6820681
Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition

This paper presents an unsupervised feature learning approach based on 3D-skeleton data for human action and human discrete emotion recognition.Relying on the time series of skeleton data analysis to perform such tasks is effective and important to preserve the individual's privacy better.Besides, such methods can represent a viable alternative to emotion recognition applications, in which most works use frontal or profile facial images disclosing the subject's appearance.On the other hand, current unsupervised methods are able to encode the high variety of contexts and nature of the data, but often at the expense of a higher model complexity or longer computational time.To lessen these shortcomings, this paper proposes a convolutional residual autoencoder that models the skeletal geometry across the temporal dynamics of the data without relying on computationally expensive recurrent architectures.Our approach also implements a Graph Laplacian Regularization leveraging upon the implicit skeleton joints connectivity, further improving the robustness of the feature embeddings learned without using action or emotion labels.It was validated on large-scale datasets, having variability in the domain, the input skeleton data (e.g. the number of joints, adjacency matrices), and sensor technology.The results show its effectiveness by notably surpassing the performance of the state-of-the-art unsupervised methods while also achieving better recognition scores compared to the several fully supervised approaches.Extensive experimental analysis proves the usefulness of the proposed method under various evaluation protocols with observed higher-quality feature representations, even if when it is trained with fewer data.The results highlight the proposed method's remarkable transfer-ability across various domains, and its faster inference time.
Formal representation of natural language elements in multi-agent system based of self-organization of distributed neurocognitive architectures

The paper proposes an approach to the formal representation of natural language elements based on multi-agent recursive architectures for solving problems of speech recognition, understanding and synthesis. This approach, based on intelligent software agents with a developed cognitive architecture, makes it possible to represent linguistic information at all levels of the language: from specific morphological to abstract semantic.