Reviewerly/exHarmony huggingface dataset:
Resilience of Reed-Solomon Codes against Single-Frequency Electromagnetic Disturbances: Fault Mechanisms and Fault Elimination through Symbol Inversion

Modern safety-critical systems depend heavily on communication networks while operating in increasingly polluted electromagnetic environments. Forward Error Correction codes are increasingly being used in safety-critical applications; however, vulnerabilities can still be caused by undetected corrupted data. Within this paper, the effectiveness of primitive Reed–Solomon Codes under single-frequency electromagnetic disturbances is assessed. Additionally, the impact of various parameters including the message length, the Reed–Solomon Codes’ symbol size, and the amplitude of the induced voltages are also investigated. Simulations show that, at harmonics and some certain ratios of the bit-rate frequency, the susceptibility of Reed–Solomon Codes relative to this type of disturbance increases substantially. In worse-case scenarios, the rate of undetected corrupted data at these ratios could increase to values above 80%. It is shown that the main reason that Reed–Solomon Codes fail to detect such errors is due to the repetitive nature of code words’ symbols, as well as a special relation among the symbol size, the channel’s bit-rate, and the disturbance frequency. Accordingly, this paper proposes to add an extra inversion layer to the communication protocol to enhance the resiliency of these codes against single-frequency electromagnetic disturbances. Finally, it is shown that the proposed layer substantially mitigates the ratio of undetected corrupted data under the considered electromagnetic environment. By using the proposed approach, the rate of undetected corrupted data at the frequencies of concern decreased to values near 0%.
PLDA inspired Siamese networks for speaker verification

The deep learning methodologies in state-of-the-art speaker recognition systems are predominantly limited to the extraction of recording level embeddings. This is usually followed by generative modeling of the embeddings to output the verification score. In this paper, we explore a fully neural approach where the neural model outputs the verification score directly, given the acoustic feature inputs. This model, termed as Siamese neural network (SiamNN), combines the embedding extraction and back-end modeling into a single processing pipeline. The back-end modeling is achieved using a neural approach to PLDA modeling, called neural probabilistic linear discriminant analysis (NPLDA). In the NPLDA model, the verification score is computed as a discriminative similarity function. The development of the single neural SiamNN model allows the joint optimization of all the modules using a verification cost. Several speaker recognition experiments are performed using SITW, VOiCES, and NIST SRE datasets where the proposed SiamNN model is shown to significantly improve over the state-of-art x-vector PLDA baseline system (relative improvements of up to 35% in the primary cost metric). We also provide a detailed analysis of the influence of hyper-parameters, choice of loss functions, and data sampling strategies for training the model. In particular, we highlight that the proposed soft detection cost function based optimization improves over other loss functions considered.
Double chain networks for monocular 3D human pose estimation

The 2D-3D lifting task for Human Pose Estimation is a highly nonlinear mapping problem, which requires mutual constraints among human joints. In this paper, we mainly discuss how to represent the constraints among the joints by their global and local dependencies, and how to fuse them more effectively. Therefore, we propose a novel end-to-end neural network architecture named Double chain Networks (DCN), for monocular 2D-3D human pose lifting task. The DCN consists of two parts. One is a global constraint module based on full connection layer, which extracts the global dependency information of human body joints; the other is graph convolution based local constraint module, which represents the local dependency information of human body joints. The global and local constraint features are fused by interleaved addition to combine the global and local spatial constraint features of human joints in DCN regression, which outperforms the way of concatenating them. We perform abundant ablation experiments on Human3.6M dataset to verify the advantages of the constraint modules, and compare the performance of the single/double chain networks. Experimental results show that the DCN achieves state-of-the-art performance and exhibits strong generalization ability, and its performance is comparable to the methods based on temporal information. Finally, we apply the DCN to reconstruct 3D skeletons in 3D anime characters. The equipotential-joints connection mode to extract the constraint relationships between the human equipotential-joints; The DCN can effectively extract and combine the global and local constraints of human joints; The DCN achieves state-of-the-art performance and exhibits strong generalization ability; We apply the DCN to the posture construction of 3D virtual characters.
Integration of a Quantum Voting Scheme into Grayscale Images Using the Novel Enhanced Quantum Representation and Qiskit Framework

This paper illustrates the way a proposed quantum voting scheme can be designed in combination with a steganography technique called Least Significant Bit (LSB), by modifying a small number of pixels in multiple grayscale images. It combines the voting scheme with the novel enhanced quantum representation (NEQR) of an image, where the LSBs of these pixels represent the vote for each entity that takes part in the voting process. A server is also used, not only to count but also to guarantee the integrity of the votes (which is done inherently, by its design and quantum properties). The superdense coding circuit is part of the design, allowing each voter to use one qubit in order to transmit two classical bits (the vote value). The selected platform for testing this scheme is IBM Quantum Experience, together with the open-source framework called Qiskit (written in Python). This framework allows users to create various quantum circuits, using a wide selection of quantum gates, and then to simulate them, either on a simulator or on a real quantum device. The quantum circuits and the measurement results are also presented in this paper.
Hybrid Controller based on Null Space and Consensus Algorithms for Mobile Robot Formation

This work presents a novel hybrid control approach based on null space and consensus algorithms to solve the scalability problems of mobile robot formation and improve leader control through multiple control objectives. In previous works, the training of robots based on the null space requires a rigid training structure based on a geometric shape, which increases the number of agents in the formation. The scheme of the control algorithm, which does not make formation scalability possible, must be changed; therefore, seeking the scalability of training based on null space is a challenge that could be solved with the inclusion of consensus algorithms, which allow the control structure to be maintained despite increasing or decreasing the number of robot followers. Another advantage of this proposal is that the formation of the followers does not depend on any geometric figure compared to previous works based on the null space; this new proposal does not present singularities as if the structure is based on geometric shape, the latter one is crucial since the formation of agents can take forms that cannot be achieved with a geometric structure, such as collinear locations, that can occur in many environments. The proposed hybrid control approach presents three tasks: i) leader position task, ii) leader shape task, and iii) follower formation task. The proposed algorithm is validated through simulations, performing tests that use the kinematic model of non-holonomic mobile robots. In addition, linear algebra and Lyapunov theory are used to analyze the stability of the method. Doi: 10.28991/ESJ-2022-06-03-01 Full Text: PDF
SeRSS: a storage mesh architecture to build serverless reliable storage services

Cloud storage has been the solution for organizations to manage the exponential growth of data observed over the past few years. However, end-users still suffer from side-effects of cloud service outages, which particularly affect edge-fog-cloud environments. This paper presents SeRSS, a storage mesh architecture to create and operate reliable, configurable, and flexible serverless storage services for heterogeneous infrastructures. A case study was conducted based on-the-fly building of storage services to manage medical imagery. The experimental evaluation revealed the efficiency of SeRSS to manage and store data in a reliable manner in heterogeneous infrastructures.
Research on Distributed Multi-Sensor Cooperative Scheduling Model Based on Partially Observable Markov Decision Process

In the context of distributed defense, multi-sensor networks are required to be able to carry out reasonable planning and scheduling to achieve the purpose of continuous, accurate and rapid target detection. In this paper, a multi-sensor cooperative scheduling model based on the partially observable Markov decision process is proposed. By studying the partially observable Markov decision process and the posterior Cramer-Rao lower bound, a multi-sensor cooperative scheduling model and optimization objective function were established. The improvement of the particle filter algorithm by the beetle swarm optimization algorithm was studied to improve the tracking accuracy of the particle filter. Finally, the improved elephant herding optimization algorithm was used as the solution algorithm of the scheduling scheme, which further improved the algorithm performance of the solution model. The simulation results showed that the model could solve the distributed multi-sensor cooperative scheduling problem well, had higher solution performance than other algorithms, and met the real-time requirements.
Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.
Auditing Tokenomics: A Case Study and Lessons from Auditing a Stablecoin Project

Tokenomics is a vital part of any blockchain project.It is the study of how crypto tokens are used within the blockchain ecosystem, their role in the project, and how they are designed to incentivise certain behaviours.There are many ways that crypto tokens can be designed for use within an ecosystem.For example, they can be designed to have a fixed supply so that there is no inflation or deflation in the system.Founding teams can also create tokens that provide voting or governance rights to holders, thereby incentivising them to hold onto their tokens rather than sell them on exchanges.They can also be used simply to pay fees.The range of options that founding teams have when designing token economies often leaves them with more questions than answers.Even deciding whether the token economy design is robust can be a challenge.Furthermore, a blockchain project not only has to convince its founders but its prospective investors.As a result, innovative crypto-projects often create for themselves interesting narratives, but they are not always viable.For that reason, a recent trend in the industry is "the tokenomics audit."The goal of a tokenomics audit is similar to an audit in any other industry (e.g., accounting).The auditor has to assess the viability of a project, while also suggesting potential improvements.The end goal is to provide an independent view on whether a token economy is viable or not.This paper discusses general principles that can be followed when running a tokenomics audit.The paper uses as a case study a recent tokenomics audit, conducted for the BankX stablecoin (https://bankx.io/),by the author of this paper.The paper first discusses in general the different methods and mechanisms that a tokenomics auditor can employ to audit a project.The paper then proceeds to demonstrate how these methods were used in the audit of the BankX project.Tokenomic auditing is still a new area, and there is no set of established methods to conduct an audit.By reviewing this case study, this paper helps provide some lessons to the community, upon which future research can improve.
Vehicle Re-Identification with Spatio-Temporal Model Leveraging by Pose View Embedding

Vehicle re-identification (Re-ID) research has intensified as numerous advancements have been made along with the rapid development of person Re-ID. In this paper, we tackle the vehicle Re-ID problem in open scenarios. This research differs from the early-stage studies that focused on a certain view, and it faces more challenges due to view variations, illumination changes, occlusions, etc. Inspired by the research of person Re-ID, we propose leveraging pose view to enhance the discrimination performance of visual features and utilizing keypoints to improve the accuracy of pose recognition. However, the visual appearance information is still limited by the changing surroundings and extremely similar appearances of vehicles. To the best of our knowledge, few methods have been aware of the spatio-temporal information to supplement visual appearance information, but they neglect the influence of the driving direction. Considering the peculiar characteristic of vehicle movements, we observe that vehicles’ poses on camera views indicating their directions are closely related to spatio-temporal cues. Consequently, we design a two-branch framework for vehicle Re-ID, including a Keypoint-based Pose Embedding Visual (KPEV) model and a Keypoint-based Pose-Guided Spatio-Temporal (KPGST) model. These models are integrated into the framework, and the results of KPEV and KPGST are fused based on a Bayesian network. Extensive experiments performed on the VeRi-776 and VehicleID datasets related to functional urban surveillance scenarios demonstrate the competitive performance of our proposed approach.
Can I only share my eyes? A Web Crowdsourcing based Face Partition Approach Towards Privacy-Aware Face Recognition

Human face images represent a rich set of visual information for online social media platforms to optimize the machine learning (ML)/AI models in their data-driven facial applications (e.g., face detection, face recognition). However, there exists a growing privacy concern from social media users to share their online face images that will be annotated by unknown crowd workers and analyzed by ML/AI researchers in the model training and optimization process. In this paper, we focus on a privacy-aware face recognition problem where the goal is to empower the facial applications to train their face recognition models with images shared by social media users while protecting the identity of the users. Our problem is motivated by the limitation of current privacy-aware face recognition approaches that mainly prevent algorithmic attacks by manipulating face images but largely ignore the potential privacy leakage related to human activities (e.g., crowdsourcing annotation). To address such limitations, we develop FaceCrowd, a web crowdsourcing based face partition approach to improve the performance of current face recognition models by designing a novel crowdsourced partial face graph generated from privacy-preserved social media face images. We evaluate the performance of FaceCrowd using two real-world human face datasets that consist of large-scale human face images. The results show that FaceCrowd not only improves the accuracy of the face recognition models but also effectively protects the identity information of the social media users who share their face images.
Learning Visible Thermal Person Re-Identification via Spatial Dependence and Dual-Constraint Loss

Visible thermal person re-identification (VT Re-ID) is the task of matching pedestrian images collected by thermal and visible light cameras. The two main challenges presented by VT Re-ID are the intra-class variation between pedestrian images and the cross-modality difference between visible and thermal images. Existing works have principally focused on local representation through cross-modality feature distribution, but ignore the internal connection of the local features of pedestrian body parts. Therefore, this paper proposes a dual-path attention network model to establish the spatial dependency relationship between the local features of the pedestrian feature map and to effectively enhance the feature extraction. Meanwhile, we propose cross-modality dual-constraint loss, which adds the center and boundary constraints for each class distribution in the embedding space to promote compactness within the class and enhance the separability between classes. Our experimental results show that our proposed approach has advantages over the state-of-the-art methods on the two public datasets SYSU-MM01 and RegDB. The result for the SYSU-MM01 is Rank-1/mAP 57.74%/54.35%, and the result for the RegDB is Rank-1/mAP 76.07%/69.43%.
Achieving Long-Term Fairness in Sequential Decision Making

In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets.
Investigating Clutching Interactions for Touchless Medical Imaging Systems

Touchless input could transform clinical activity by allowing health professionals direct control over medical imaging systems in a sterile manner. Currently, users face the issues of being unable to directly manipulate imaging in aseptic environments, as well as needing to touch shared surfaces in other hospital areas. Unintended input is a key challenge for touchless interaction and could be especially disruptive in medical contexts. We evaluated four clutching techniques with 34 health professionals, measuring interaction performance and interviewing them to obtain insight into their views on clutching, and touchless control of medical imaging. As well as exploring the performance of the different clutching techniques, our analysis revealed an appetite for reliable touchless interfaces, a strong desire to reduce shared surface contact, and suggested potential improvements such as combined authentication and touchless control. Our findings can inform the development of novel touchless medical systems and identify challenges for future research.
Improving NMF clustering by leveraging contextual relationships among words

Non-negative Matrix Factorization (NMF) and its variants have been successfully used for clustering text documents. However, NMF approaches like other models do not explicitly account for the contextual dependencies between words. To remedy this limitation, we draw inspiration from neural word embedding and posit that words that frequently co-occur within the same context (e.g., sentence or document) are likely related to each other in some semantic aspect. We then propose to jointly factorize the document-word and word-word co-occurrence matrices. The decomposition of the latter matrix encourages frequently co-occurring words to have similar latent representations and thereby reflecting the relationships among them. Empirical results, on several real-world datasets, provide strong support for the benefits of our approach. Our main finding is that we can drastically improve the clustering performance of NMF by leveraging the contextual relationships among words explicitly.
Comparison of continuity equation and Gaussian mixture model for long-term density propagation using semi-analytical methods

This paper compares the continuum evolution for density equation modelling and the Gaussian mixture model on the 2D phase space long-term density propagation problem in the context of high-altitude and high area-to-mass ratio satellite long-term propagation. The density evolution equation, a pure numerical and pointwise method for the density propagation, is formulated under the influence of solar radiation pressure and Earth's oblateness using semi-analytical methods. Different from the density evolution equation and Monte Carlo techniques, for the Gaussian mixture model, the analytical calculation of the density is accessible from the first two statistical moments (i.e., the mean and the covariance matrix) corresponding to each sub-Gaussian distribution for an initial Gaussian density distribution. An insight is given into the phase space long-term density propagation problem subject to nonlinear dynamics. The efficiency and validity of the density propagation are demonstrated and compared between the density evolution equation and the Gaussian mixture model with respect to standard Monte Carlo techniques.
Addressing Traffic Prediction Uncertainty in Multi-Period Planning Optical Networks

Deep-quantile regression is leveraged to capture traffic prediction uncertainty over future network planning intervals. We show that quantile predictions, acting as discriminative margins, result to significant spectrum savings compared to empirically estimated myopic margins considered.
A Convolutional Neural Network for Nonrigid Structure from Motion

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.