Reviewerly/exHarmony huggingface dataset:
Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.
Distributed Nonlinear Polynomial Adaptive Graph Filter Based on Diffusion Conjugate Gradient Strategy

This brief investigates distributed and adaptive estimation of streaming data based nonlinear graph filters. To begin with, a new distributed polynomial filter for nonlinear graphs is proposed based on the Hadamard product, which not only represents the nonlinear relationship between dynamically changing graph input and output signals, but also accounts for the time dimension. A diffusion least mean square algorithm is presented to estimate the nonlinear polynomial graph filter parameters in a distributed manner. The method of diffusion hybrid conjugate gradient is leveraged to further improve the convergence rate of the graph diffusion adaptive filter. The advantages of the proposed algorithms are demonstrated in simulation studies.
Design of a Low-Cost and Device-Free Human Activity Recognition Model for Smart LED Lighting Control

Human activity recognition (HAR) constitutes an integral part of occupant centric smart services, such as health monitoring and building energy management. In this paper, a simple and cost effective solution to HAR through passive sensing of WiFi channel state information (CSI), is proposed. WiFi CSI extracted from ESP32 was utilized to classify four different human activities, using ensemble machine learning models. A mean accuracy of 83.39% was achieved using gradient boosting classifier with Haar wavelet based denoising, in spite of using a single transmission link and in presence of coexisting wireless devices. The proposed model can be employed for the development of an IoT enabled smart LED lighting system, with minimal infrastructure changes. In this strategy, the illuminance level of LED lighting fixtures is adjusted according to the predicted occupant activity, thereby reducing power consumption, without compromising visual comfort. This method offers a potential energy savings of up to 36.42% and 29.45% per month, for a typical office and home scenario, respectively. The possibility of supplementing activity sensing with daylight harvesting, is also explored. An additional energy savings of up to 18.26% may be obtained using this method, during daytime. An evaluation of the annual electricity cost shows an estimated reduction between 29.43% and 62.13% using activity recognition and daylight harvesting.
Role of radial index in self-reconstruction of structured beams

We demonstrate that the self-reconstruction performance of structured light is positively correlated with its radial degrees of freedom; specifically, structured beams with larger radial indices have better self-reconstruction abilities and higher tolerance to obstacles. These results were determined by quantifying the self-reconstruction degree of the Laguerre-Gauss beams. After the beam is partially blocked by an obstacle, we present convincing evidence theoretically and experimentally that an increase in the radial index of the Laguerre-Gauss beam is accompanied by a better self-reconstruction ability. We provide a physical explanation for this result in terms of non-diffraction, traveling-wave solution, and transverse momentum. Finally, we theoretically demonstrate that the contribution of the radial index to the self-reconstruction ability of the Hermite-Gauss and Ince-Gauss modes, the results are consistent. We believe that our results are applicable to all beams with complex radial structure, which provides another perspective on the self-reconstruction of structured beams: the contribution of the radial structure index.
Rational local unitary invariants of symmetrically mixed states of two qubits

We compute the field of rational local unitary invariants for locally maximally mixed states and symmetrically mixed states of two qubits. In both cases, we prove that the field of rational invariants is purely transcendental. We also construct explicit geometric quotients and prove that they are always rational. All the results are obtained by working over the field of real numbers, employing methods from classical and geometric invariant theory over arbitrary fields of characteristic zero.
Scalable and Parallel Optimization of the Number Theoretic Transform Based on FPGA

In lattice-based postquantum cryptography (PQC), polynomial multiplication is complex and time-consuming, which affects the overall computational efficiency. In addition, the parameters of different lattice-based algorithms require different number theoretic transform (NTT) structures, which limits the versatility of hardware design. To this end, this article proposes scalable and parallel optimization of the NTT based on a field-programmable gate array (FPGA). By analyzing the algorithm flow of the NTT, inverse NTT (INTT), and pointwise multiplication (PWM), an FPGA loosely coupled structure is designed, which can be used to place butterfly units of multiple pipelines in parallel and supports various modulo operations of a polynomial. In addition, to improve computing efficiency and scalability, key algorithm modules such as multipliers and modular reduction are deeply optimized. Moreover, the storage optimization of multiple RAM channels is carried out, and the alternate access control of data and the multiplexing of RAM resources reduce resource consumption and improve data access efficiency. For the SHA-3 algorithm, the scalable Keccak algorithm is implemented in a serial–parallel hybrid manner and supports multiple hash modes. Finally, taking the Dilithium algorithm as an example, through the parallelization of SHA-3 and NTT, the calculation cycle of key generation, signature, and verification is shortened. The experimental results and analysis show that the scheme in this article shortens the NTT calculation period while ensuring a high frequency, and the calculation time is significantly better than that of other schemes. Furthermore, it can support the optimized parallelization of multiple moduli and give full play to the computing advantages of an FPGA.
Three-dimensional reconstruction of light field based on cubic B-spline curve fitting

Fringe projection profilometry has been widely used in three-dimensional (3D) reconstruction. In this paper, we replace the camera with a self-made light field camera to achieve structured light field 3D reconstruction. Our self-made light field camera boasts higher resolution and lower cost with the ability to adjust spatial and angular resolution as needed. A novel 3D reconstruction method based on cubic B-spline curve fitting is proposed to directly fit the relationship between phase and 3D coordinates, and the look up table method is utilized to accelerate the entire calculation process. The experimental results demonstrate that the proposed method can accomplish 3D reconstruction of the structured light field under various viewing angles. Specifically, it has respectively reduced the computation time for phase and modulation intensity by 64.03% and 41.43%, and the reconstruction time is decreased by 32.99% without compromising the reconstruction accuracy.
Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks

Temporal convolutional networks (TCNs) have shown remarkable performance in sequence modeling and surpassed recurrent neural networks (RNNs) in a number of tasks. However, performing exceptionally on extremely long sequences remains an obstacle due to the restrained receptive field of temporal convolutions and a lack of forgetting mechanism. Although RNNs can carry state transmission down the full sequence length and latch information by forgetting mechanism, the issues of information saturation and gradient vanishing or exploding still persist, which usually occur in back-propagation due to the effect of multiplicative accumulation. To benefit from both temporal convolutions and RNNs, we propose a neural architecture that merge temporal convolutional data into recurrent networks. The temporal convolutions are employed intermittently and fused into the hidden states of RNNs with the assistance of attention for providing long-term information. With this architecture, it is not needed for convolutional networks to cover the total length of the sequence, thus gradient and saturation issues in RNNs are ameliorated since convolutions are integrated into its cells and the state is updated with convolutional information. Extensive experiments illustrate the superiority of our network against other competitive counterparts, such as TCNs and the different variants of RNNs.
Differentially Private Federated Learning With Stragglers’ Delays in Cross-Silo Settings: An Online Mirror Descent Approach

Federated learning is a privacy-preserving machine learning paradigm to protect the data of clients against privacy breaches. A lot of work on federated learning considers the cross-device setting where the number of clients is large and the data sample size of each client is low. However, this work focuses on cross-silo settings, where clients are few and have large sample sizes. We consider a fully decentralized setting where clients communicate with their immediate time-varying neighbors without the need for a central aggregator prone to congestion and a single point of failure. Our goal is to address stragglers’ delays in cross-silo settings. Existing algorithms designed to overcome stragglers’ delays work with fixed data distributions. They cannot work in real-time settings, such as wireless communication, characterized by time-varying data distributions. Therefore, this paper proposes two online learning algorithms that work with time-varying data and address stragglers’ delays while guaranteeing differential privacy, strong convergence, and communication efficiency. Using the mirror descent technique, the first proposed algorithm addresses the case where the loss gradient is easily computed while the second proposed algorithm addresses the case where the loss gradient is difficult to compute. Simulation results show the performance of the proposed algorithms.
Totally-ordered Sequential Rules for Utility Maximization

High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this article, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.
Reliable Event Generation With Invertible Conditional Normalizing Flow

Event streams provide a novel paradigm to describe visual scenes by capturing intensity variations above specific thresholds along with various types of noise. Existing event generation methods usually rely on one-way mappings using hand-crafted parameters and noise rates, which may not adequately suit diverse scenarios and event cameras. To address this limitation, we propose a novel approach to learn a bidirectional mapping between the feature space of event streams and their inherent parameters, enabling the generation of reliable event streams with enhanced generalization capabilities. We first randomly generate a vast number of parameters and synthesize massive event streams using an event simulator. Subsequently, an event-based normalizing flow network is proposed to learn the invertible mapping between the representation of a synthetic event stream and its parameters. The invertible mapping is implemented by incorporating an intensity-guided conditional affine simulation mechanism, facilitating better alignment between event features and parameter spaces. Additionally, we impose constraints on event sparsity, edge distribution, and noise distribution through novel event losses, further emphasizing event priors in the bidirectional mapping. Our framework surpasses state-of-the-art methods in video reconstruction, optical flow estimation, and parameter estimation tasks on synthetic and real-world datasets, exhibiting excellent generalization across diverse scenes and cameras.
Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance

In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories). In order to remedy the information loss from box annotations to centers, our method makes use of synthetic 3D shapes to convert the position-level annotations into virtual scenes with box-level annotations, and in turn utilizes the fully-annotated virtual scenes to complement the real labels. Specifically, we first present a shape-guided label-enhancement method, which assembles 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we transfer the information contained in the virtual scenes back to real ones by applying a virtual-to-real domain adaptation method, which refines the annotated object centers and additionally supervises the training of detector with the virtual scenes. Since the shape-guided label enhancement method generates virtual scenes by human-heuristic physical constraints, the layout of the fixed virtual scenes may be unreasonable with varied object combinations. To address this, we further present differentiable label enhancement to optimize the virtual scenes including object scales, orientations and locations in a data-driven manner. Moreover, we further propose a label-assisted self-training strategy to fully exploit the capability of detector. By reusing the position-level annotations and virtual scenes, we fuse the information from both domains and generate box-level pseudo labels on the real scenes, which enables us to directly train a detector in fully-supervised manner. Extensive experiments on the widely used ScanNet and Matterport3D datasets show that our approach surpasses current weakly-supervised and semi-supervised methods by a large margin, and achieves comparable detection performance with some popular fully-supervised methods with less than 5% of the labeling labor.
Gray code based gradient-free optimization algorithm for parameterized quantum circuit

A Gray code based gradient-free optimization (GCO) algorithm is proposed to update the parameters of parameterized quantum circuits (PQCs) in this work. Each parameter of PQCs is encoded as a binary string, named as a gene, and a genetic-based method is adopted to select the offsprings. The individuals in the offspring are decoded in Gray code way to keep Hamming distance, and then are evaluated to obtain the best one with the lowest cost value in each iteration. The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached. The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets, and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm. The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks. Importantly, the GCO algorithm has a robust performance in the noise environment.
Differentiable Image Data Augmentation and Its Applications: A Survey

Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.
Graph embedding approach to analyze sentiments on cryptocurrency

This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.
Star algorithm for neural network ensembling

Neural network ensembling is a common and robust way to increase model efficiency. In this paper, we propose a new neural network ensemble algorithm based on Audibert’s empirical star algorithm. We provide optimal theoretical minimax bound on the excess squared risk. Additionally, we empirically study this algorithm on regression and classification tasks and compare it to most popular ensembling methods.
How is a “Kitchen Chair” like a “Farm Horse”? Exploring the Representation of Noun-Noun Compound Semantics in Transformer-based Language Models

Abstract Despite the success of Transformer-based language models in a wide variety of natural language processing tasks, our understanding of how these models process a given input in order to represent task-relevant information remains incomplete. In this work, we focus on semantic composition and examine how Transformer-based language models represent semantic information related to the meaning of English noun-noun compounds. We probe Transformer-based language models for their knowledge of the thematic relations that link the head nouns and modifier words of compounds (e.g., kitchen chair: a chair located in a kitchen). Firstly, using a dataset featuring groups of compounds with shared lexical or semantic features, we find that token representations of six Transformer-based language models distinguish between pairs of compounds based on whether they use the same thematic relation. Secondly, we utilize fine-grained vector representations of compound semantics derived from human annotations, and find that token vectors from several models elicit a strong signal of the semantic relations used in the compounds. In a novel “compositional probe” setting, where we compare the semantic relation signal in mean-pooled token vectors of compounds to mean-pooled token vectors when the two constituent words appear in separate sentences, we find that the Transformer-based language models that best represent the semantics of noun-noun compounds also do so substantially better than in the control condition where the two constituent works are processed separately. Overall, our results shed light on the ability of Transformer-based language models to support compositional semantic processes in representing the meaning of noun-noun compounds.