Reviewerly/exHarmony huggingface dataset:
Chaos LiDAR Based RGB-D Face Classification System With Embedded CNN Accelerator on FPGAs

Face classification is important in many applications such as surveillance, border control, and security systems. However, wide variations in environments such as insufficient light, large distances or pose angles make the task challenging. Depth sensors are added with RGB cameras for improving classification accuracy but commercial RGB-D sensors are most targeted for indoors applications. In this paper, we present and design a Chaos LiDAR depth sesnor that provides high-precision depth images through intelligent correlation processing for both indoors and outdoors applications. Our Chaos LiDAR depth sensor detects range from 2 to 40 meters with precision around 8mm at 20-meter. With the Chaos LiDAR depth as input, we design a RGB-D based face classification embedded CNN (eCNN) model for wide range applications such as dim illumination, various distances and large poses. Our Chaos LiDAR increases around 14.27% classification accuracy compared to RealSense D435i for distance from 3 to 5 meter. The eCNN face classification subsystem is implemented in Xilinx ZCU 102 and achieves 11.11 ms inference time. The eCNN engine achieves a peak throughput at 614.4 GOPS. The overall system including Chaos LiDAR, correlation and eCNN FPGA achieves face classification inference rate of 10fps.
In-motion continuous point cloud measurement based on bundle adjustment fused with motion information of triple line-scan images

The point cloud continuous measurements in some in-motion circumstances, such as quality inspection of products on assembly lines or rail traffic, have been requiring higher measurement speed, accuracy, and point cloud density. With the advantages of high acquisition rates and ultrahigh resolution, line-scan cameras have been developing gradually for dynamic measurements. However, because of non-coplanar installation and unidimensional images, the measurement based on line-scan cameras is affected by movement. In this article, a dynamic scanning point cloud measurement based on triple line-scan images is present. The point cloud optimization is based on bundle adjustment fused with motion information. The epipolar constraint of line-scan images in dynamic conditions is researched for matching. The effect of motion on matching error is analyzed. A triple line-scan cameras experimental setup validates the proposed method.
Online Learning of Wearable Sensing for Human Activity Recognition

This article presents a novel semisupervised learning method for wearable sensors to recognize human activities. The proposed method is termed a tri-very fast decision tree (VFDT). The proposed method is a more efficient version of the Hoeffding tree and three VFDTs are generated from the original labeled example set and refined using unlabeled examples. Based on the heuristic growth characteristics of VFDT, a tri-training framework is proposed which uses unlabeled data to update the model without labeled data. This significantly reduces the computational time and storage of the data processing. In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update the model. The experiment collects data stream of 16 min with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using a desktop computer. The obtained results show that only minor difference in terms of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f1$ </tex-math></inline-formula> -score rendered by the proposed method online or offline. This is a prominent characteristic for wearable computing within the Internet of Things (IoT). Data set can be linked as <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm</uri> .
Automated sarcasm detection and classification using hyperparameter tuned deep learning model for social networks

Abstract In recent digital era, social media sites have been commonly used by majority of people to generate massive quantities of textual data. Sarcasm can be treated as a kind of sentiment, which generally expresses the opposite of what has been anticipated. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). The recent advancements of deep learning (DL) models influence neural networks (NN) in learning the lexical as well as contextual features, eradicating the necessity of hand‐crafted features for sarcasm detection. With this motivation, this article designs an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC‐HPTDL) model for social media. The proposed ASDC‐HPTDL technique primarily involves pre‐processing stage to transform the data into useful format. At the next stage of pre‐processing, the pre‐processed data is converted into the feature vector by Glove Embedding's technique. Followed by, attention bidirectional gated recurrent unit (ABiGRU) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the ABiGRU technique, a hyperparameter tuning process using improved artificial flora algorithm (IAFO) is employed, shows the novelty of the work. The proposed model is validated using the benchmark dataset and the results are examined interms of precision, recall, accuracy, and F1‐score.
SHREC 2022 track on online detection of heterogeneous gestures

This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low.
Design of robot automatic navigation under computer intelligent algorithm and machine vision

This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields.
The network of the subjective experience in embodiment phenomena

Body illusions are designed to temporarily alter body representation by embodying fake bodies or part of them. Despite their large use, the embodiment questionnaires have been validated only for the embodiment of fake hands in the rubber hand illusion (RHI).With the current study, we aimed at (1) extending the validation of embodiment questionnaires to a different illusory situation e.g., the full-body illusion (FBI); (2) comparing two methods to explore the questionnaires structures: a classic exploratory factor analysis (EFA) and a modern exploratory graph analysis (EGA). 118 healthy participants completed an FBI procedure where the subjective experience of embodiment was measured with a standard questionnaire.The EFA results in two-factor structures. However, the confirmatory factor analysis (CFA) fit indices do not show a good fit with the data. Conversely, the EGA identified four communities: ownership, agency, co-location and disembodiment; the solution was confirmed by a CFA.Overall, the EGA seems to be the best fitting method for the present data. Our results confirm the EGA as a suitable substitute for a more classical EFA. Moreover, the emerged structure suggests that the FBI induces similar effects to the RHI, implying that the embodiment sensations are common to different illusory methods.
A New Clustering Method Based on the Inversion Formula

Data clustering is one area of data mining that falls into the data mining class of unsupervised learning. Cluster analysis divides data into different classes by discovering the internal structure of data set objects and their relationship. This paper presented a new density clustering method based on the modified inversion formula density estimation. This new method should allow one to improve the performance and robustness of the k-means, Gaussian mixture model, and other methods. The primary process of the proposed clustering algorithm consists of three main steps. Firstly, we initialized parameters and generated a T matrix. Secondly, we estimated the densities of each point and cluster. Third, we updated mean, sigma, and phi matrices. The new method based on the inversion formula works quite well with different datasets compared with K-means, Gaussian Mixture Model, and Bayesian Gaussian Mixture model. On the other hand, new methods have limitations because this one method in the current state cannot work with higher-dimensional data (d &gt; 15). This will be solved in the future versions of the model, detailed further in future work. Additionally, based on the results, we can see that the MIDEv2 method works the best with generated data with outliers in all datasets (0.5%, 1%, 2%, 4% outliers). The interesting point is that a new method based on the inversion formula can cluster the data even if data do not have outliers; one of the most popular, for example, is the Iris dataset.
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data

With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the original data. However, many SVD-based applications cannot be well supported by existing federated SVD solutions. The crux is that these solutions, adopting either differential privacy (DP) or homomorphic encryption (HE), suffer from accuracy loss caused by unremovable noise or degraded efficiency due to inflated data.
QoS-aware and multi-objective virtual machine dynamic scheduling for big data centers in clouds

Abstract Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.
Image dehazing based on polarization information and deep prior learning

(objective)Generally, images taken under hazy weather have lower visibility and contrast. Therefore, how to improve their visibility and contrast and recover the image information is critical. Various dehazing algorithms have been proposed based on light intensity information. However, these methods mostly lack sufficient scattering information about atmospheric particles. Polarization supplies extra information independent of intensity, which can be used to effectively remove scattered light from haze particles, making it possible to help image dehazing. (methods)Therefore, this paper proposes a new image dehazing algorithm based on polarization information and deep prior learning (polarization-based unsupervised dehazing network, PUDN). Polarization information can provide more information about the image, while prior knowledge can constrain and optimize the network. This algorithm can remove haze without pretraining or corresponding ground-truth image. (result)Experimental results show that introducing polarization information and prior knowledge can improve the visibility and contrast of haze images, significantly improve haze image quality. Among them, the visible edge of heavy-haze image is improved by 71% on average, and the contrast is improved by 31 %. The dehazing performance is superior to that of traditional and supervised learning methods. Our method provides a new direction for image dehazing processing.
Consensus graph learning for auto-weighted multi-view projection clustering

Graph-based learning approaches have achieved remarkable success in clustering prevalent multi-view data owing to their capacities to reveal the relation between data and discover its underlying structure. However, real multi-view data is not only simply high-dimensional, but also contains noise and redundant information, so the learned affinity graphs may be unreliable, let alone optimal, and produce inaccurate clustering results. Moreover, existing graph learning based multi-view projection models only learn a common graph or a shared low-dimensional embedding matrix, which fails to preserve the flexible local manifold geometry of each view. To alleviate these problems, a novel consensus graph-based auto-weighted multi-view projection clustering (CGAMPC) is developed, which performs dimensionality reduction, manifold structure preservation and consensus structured graph learning simultaneously. To be specific, the ℓ2,1-norm is leveraged to resist noise and adaptively select discriminative features. Meanwhile, to preserve the manifold structure information of all views, we construct informative similarity graphs for the projection data, and fuse them into a consensus structured graph via an auto-weighted synthesis strategy. Furthermore, an effective alternating iterative algorithm is presented to optimize our CGAMPC. Finally, numerical studies on several multi-view benchmark datasets justify the superiority of the proposed approach over other state-of-the-art clustering approaches.
An efficient approach for mining maximized erasable utility patterns

As a method of extracting important information in the real world, the field of pattern mining has been actively studied. In particular, in industrial fields such as factories, it is also important to find patterns with low value and remove them in order to overcome the economic crisis. Erasable pattern mining has emerged to solve the concern. Traditional erasable pattern mining targeted a binary database containing only information on the existence of items. However, in the real world, data has a variety of properties, such as quantity and profit. Considering these properties makes it possible for more meaningful result patterns to be mined. Therefore, we propose a maximized erasable pattern mining algorithm that uses a list structure as a data structure and takes into account the quantity and price of the item. The proposed method considers the amount and relative benefit of each item to estimate the profit of a transaction and extracts the maximized erasable patterns considering the user’s preference. Moreover, the list structure utilized in the proposed method contains only the essential information for the mining process, which not only allows the mining to be performed efficiently, but also reduces memory usage. Using various real-world and synthetic datasets, we evaluate runtime, memory efficiency, and scalability of the suggested technique and state-of-the-art competitors. The evaluation results demonstrate that the proposed method performs better.
Q-VIPER: Quantitative Vertical Bitwise Algorithm to Mine Frequent Patterns

As a popular big data analytics and knowledge discovery task, frequent pattern mining aims to discover frequently occurring sets of items (e.g., merchandise items, events) from big data. Frequent patterns can be discovered horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of their mining direction (horizontal or vertical), traditional frequent pattern mining algorithms aim to discover Boolean frequent patterns in the sense that patterns capture the presence (or absence) of items within the discovered patterns. However, there are many real-life situations, in which quantities of items within the patterns are important. For example, the quantity of items may also affect profits of selling the items within the discovered patterns. Hence, in this paper, we present a quantitative vertical bitwise algorithm to mine frequent patterns. This Q-VIPER algorithm first represents the big data as a collection of bitmaps. Each item-centric bitmap captures the presence or absence of a transaction containing the item, as well as the quantity of that item in each transaction. With this representation, our algorithm then vertically mines quantitative frequent patterns. When compared the existing MQA-M algorithm (which was built for quantitative frequent pattern mining), evaluation results show that our quantitative vertical bitwise Q-VIPER algorithm takes shorter runtime to mine quantitative frequent patterns.
Robust Control Performance for Open Quantum Systems

Robust performance of control schemes for open quantum systems is investigated under classical uncertainties in the generators of the dynamics and nonclassical uncertainties due to decoherence and initial state preparation errors. A formalism is developed to measure performance based on the transmission of a dynamic perturbation or initial state preparation error to the quantum state error. This makes it possible to apply tools from classical robust control such as structured singular value analysis. A difficulty arising from the singularity of the closed-loop Bloch equations for the quantum state is overcome by introducing the #-inversion lemma, a specialized version of the matrix inversion lemma. Under some conditions, this guarantees continuity of the structured singular value at s = 0. Additional difficulties occur when symmetry gives rise to multiple open-loop poles, which under symmetry-breaking unfold into single eigenvalues. The concepts are applied to systems subject to pure decoherence and a general dissipative system example of two qubits in a leaky cavity under laser driving fields and spontaneous emission. A nonclassical performance index, steady-state entanglement quantified by the concurrence, a nonlinear function of the system state, is introduced. Simulations confirm a conflict between entanglement, its log-sensitivity and stability margin under decoherence.
SocialFi

Decentralized Finance (DeFi) is considered a new vision of banking and financial services based on Peer-to-Peer technology, and in particular on the blockchain technology. Since 2020, DeFi has started to develop in the cryptocurrency markets. Then, with the increase in popularity of digital platforms such as Twitter, Facebook and Instagram, and with the launch of the Metaverse, DeFi has been introduced into the Social environment by the combination of social networking and finance in the blockchain. This new shape of DeFi has been called Social Finance (SocialFi). SocialFi can be seen as the social influence on any medium that can be tokenized, allowing to get monetary benefits from being part of SocialFi platforms. In detail, SocialFi has a special feature that is decentralized, open and user-controlled, and it started to be adopted by the communities. The main component of SocialFi is the Social token used to reward people. In this paper, we describe in detail the concept of SocialFi, and we discuss its application on future Social Media platforms.
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. The original sample is shifted towards a target sample, yielding an adversarial sample, by using the modified activations to reconstruct the original sample. A human might (and possibly should) notice differences between the original and the adversarial sample. Depending on the attacker-provided constraints, an adversarial sample can exhibit subtle differences or appear like a "forged" sample from another class. Our approach and goal are in stark contrast to common attacks involving perturbations of single pixels that are not recognizable by humans. Our approach is relevant in, e.g., multistage processing of inputs, where both humans and machines are involved in decision-making because invisible perturbations will not fool a human. Our evaluation focuses on deep neural networks. We also show the transferability of our adversarial examples among networks.