Reviewerly/exHarmony huggingface dataset:
Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of nonlinear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are nonlinearly dependent. We show that this model, called “receptron”, provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.
New hybrid segmentation algorithm: UNet-GOA

The U-Net architecture is a prominent technique for image segmentation. However, a significant challenge in utilizing this algorithm is the selection of appropriate hyperparameters. In this study, we aimed to address this issue using an evolutionary approach. We conducted experiments on four different geometric datasets (triangle, kite, parallelogram, and square), with 1,000 training samples and 200 test samples. Initially, we performed image segmentation without the evolutionary approach, manually adjusting the U-Net hyperparameters. The average accuracy rates for the geometric images were 0.94463, 0.96289, 0.96962, and 0.93971, respectively. Subsequently, we proposed a hybrid version of the U-Net architecture, incorporating the Grasshopper Optimization Algorithm (GOA) for an evolutionary approach. This method automatically discovered the optimal hyperparameters, resulting in improved image segmentation performance. The average accuracy rates achieved by the proposed method were 0.99418, 0.99673, 0.99143, and 0.99946, respectively, for the geometric images. Comparative analysis revealed that the proposed UNet-GOA approach outperformed the traditional U-Net architecture, yielding higher accuracy rates.
Data-Augmentation-Based Federated Learning

With the rapid growth of the number of devices generating and collecting data, dispersion becomes an important feature of data in Internet of Things. Federated learning (FL) provides a feasible way to mine information in such distributed data. It involves training machine learning models over multiple distributed participants without raw data transmission. However, due to the data heterogeneity among participants, the performance of the FL model degrades dramatically. Currently, improved methods mainly reduce data heterogeneity from the perspective of modifying the process of model training, which usually have problems, such as high-resource consumption or the need for auxiliary data. In this article, we enhance FL model from another perspective, focusing on data rather than model training. We reduce data heterogeneity by enhancing the trained local data to improve FL performance. Specifically, we propose an FL method based on data augmentation (abbreviated as FedM-UNE), implementing the classic data augmentation method MixUp in federated scenarios without transferring raw data. Furthermore, in order to adapt this method to regression tasks, we first modify MixUp by bilateral neighborhood expansion (MixUp-BNE), and then propose a federated data augmentation method named FedM-BNE based on it. Compared with the conventional FL method, both FedM-UNE and FedM-BNE increase negligible overhead. To demonstrate the effectiveness, we conduct exhaustive experiments on six data sets employing a variety of loss functions. The results indicate that FedM-UNE and FedM-BNE consistently improve the performance of the FL model. Moreover, our methods are compatible with existing FL enhancements, which yield further improvements in performance.
Normative Framework for Deriving Neural Networks with Multicompartmental Neurons and Non-Hebbian Plasticity

This study derives an online algorithm implementable in a neural network with local updating rules to solve a specific class of complex tasks establishing a precise relationship between realistic synaptic learning rules and underlying computational principles that guide their design.
Influence of technical, cognitive and socio-emotional factors on digital literacy in nursing students assessed using structural equation modeling

Digital literacy is one of the 21st-century skills that nursing students should develop and acquire. Several factors influencing digital literacy have been identified, but the predictive role of technical, cognitive, and socio-emotional factors in digital literacy remains unexplored in nursing students. This study aimed to explore the digital literacy level and determine the predictive role of technical, cognitive, and socio-emotional factors in digital literacy among nursing students in Turkey using a structural equation modeling (SEM)–based approach. A web-based, predictive, cross-sectional study. This study was conducted among first-, second-, third- and fourth-year students in a nursing faculty located in the capital of Turkey in the academic year 2021–2022. Overall, 210 nursing students were recruited for this study. The use of a digital literacy model guided data collection. Data were collected between 8 and 28 June 2022 using online Google Forms. The data were analyzed using descriptive statistics and SEM. Nursing students' digital literacy mean score was 67.29 (13.60). Internet self-efficacy (β = 0.31, p < 0.001), online information search strategies (β = 0.20, p = 0.003), and online privacy concerns (β = 0.14, p = 0.024) were positive predictors, whereas social media use (β = −0.13, p = 0.032) was a negative predictor of digital literacy. Among these variables, Internet self-efficacy had the most significant influence on the digital literacy level. The four variables contributed to 22 % of variance in the digital literacy level. This study shows nursing students' digital literacy level is above average. Our findings suggest that technical, cognitive, and socio-emotional factors influence digital literacy.
AutoAssign+: Automatic Shared Embedding Assignment in streaming recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems.
In-The-Wild Deepfake Detection using Adaptable CNN Models with Visual Class Activation Mapping for Improved Accuracy

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evaluate four different CNN models (DenseNet121, ResNet18, SqueezeNet, and VGG11) at different batch sizes and with various performance metrics. Results show that the adapted VGG11 model with a batch size of 32 achieved the highest accuracy of 94.46% in detecting Deepfakes, outperforming the other models, with DenseNet121 as the second-best performer achieving an accuracy of 93.89% with the same batch size. Grad-CAM techniques are utilized to visualize the decision-making process within the models, aiding in understanding the Deepfake classification process. These findings provide valuable insights into the performance of different deep learning models and can guide the selection of an appropriate model for a specific application.
Deep Dehazing Powered by Image Processing Network

Image processing is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. Recent dehazing methods also refrain from using conventional image processing techniques, whereas only focusing on the development of new deep neural network (DNN) architectures. Unlike this recent trend, we show that image processing techniques are still competitive, if they are incorporated into DNNs. In this paper, we utilize conventional image processing techniques (i.e. curve adjustment, retinex decomposition, and multiple image fusion) for accurate dehazing. Moreover, we employ direct learning for stable dehazing performance. The proposed method can perform with low computational cost and easy to learn. The experimental results demonstrate that the proposed method produces accurate dehazing results compared to recent algorithms.
Backpropagation-free 4D continuous ant-based neural topology search

Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a neural architecture search space. Synthetic ant agents explore CANTS’ continuous search space based on the density and distribution of pheromones, strongly inspired by how ants move in the real world. This continuous search space allows CANTS to automate the design of artificial neural networks (ANNs) of any size, removing a key limitation inherent to many current NAS algorithms that must operate within structures of a size that is predetermined by the user. This work expands CANTS by adding a fourth dimension to its search space representing potential neural synaptic weights. Adding this extra dimension allows CANTS agents to optimize both the architecture as well as the weights of an ANN without applying backpropagation (BP), which leads to a significant reduction in the time consumed in the optimization process: at least an average of 96% less time consumption with very competitive optimization performance, if not better. The experiments of this study – using real-world data – demonstrate that the BP-Free CANTS algorithm exhibits highly competitive performance compared to both CANTS and ANTS while requiring significantly less operation time.
Single-photon sources based on incomplete binary-tree multiplexers with optimal structure

We consider novel types of spatially multiplexed single-photon sources based on output-extended incomplete binary-tree multiplexers containing general asymmetric routers where the construction of the multiplexers takes into account the total transmission efficiencies of the multiplexer arms at which a novel router can be added to the system. After selecting the multiplexer that outperforms the others, we identify the ranges of the loss parameters for which the application of the selected multiplexer leads to single-photon sources with higher single-photon probabilities and lower multiphoton noise than that can be achieved by using asymmetric multiplexers. We show that using the selected multiplexer is especially advantageous in the case of single-mode sources characterized by thermal statistics of the input photon pairs. We also reveal that the application of this multiplexer yields high performance single-photon sources even for suboptimal system sizes that is a typical situation in current experiments.
Towards a TDD maturity model through an anti-patterns framework

Agile software development has been adopted in the industry to quickly react to business change. Since its inception both academia and industry debate the different shades that agile processes and technical practices play in the day-to-day of students and professional developers. Efforts have been made to understand the pros and cons of the Test Driven Development (TDD) practice to develop software as part of a professional environment. Despite the effort of practitioners to list the TDD anti-patterns that unveil undesired effects in the code when practicing TDD, work is needed to understand the causes that lead to that. In that sense, this paper proposes a research project that explores the TDD anti-patterns context and what leads practitioners to face them in the software development context. As a result, we expect to offer a TDD maturity framework to help practitioners in the process of writing code guided by tests and prevent the addition of anti-patterns.
Inverse Problems for Physics-Based Process Models

We describe and compare two formulations of inverse problems for a physics-based process model in the context of uncertainty and random variability: the Bayesian inverse problem and the stochastic inverse problem. We describe the foundations of the two problems in order to create a context for interpreting the applicability and solutions of inverse problems important for scientific and engineering inference. We conclude by comparing them to statistical approaches to related problems, including Bayesian calibration of computer models. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
When the Data Are Out: Measuring Behavioral Changes Following a Data Breach

This paper investigates users’ reactions to a massive and highly sensitive data breach via an array of causal inference methodologies.
A method for detecting floating objects on water based on edge computing

With the development and application of computer vision, many target detection networks are applied to the detection of floating objects in rivers. For the detection problems such as small targets easily missed and misdetected in water surface floating object detection tasks and difficult to deploy models. An edge computing-oriented approach to river floater detection is proposed. First, a four-fold down-sampling feature layer is added to the YOLOv5 network which enhances more target detail features and improves the detection capability of small objects. Second, CA (Coordinate Attention) is added to the Backbone to suppress background noise interference, and different pooling is used to accommodate different hierarchical features. Then, a bilinear interpolation method is adopted for up-sampling to avoid the loss of small object features. Design a data enhancement algorithm for small targets based on Mosaic to increase the number of small objects and enrich the training background. Finally, for the edge computing architecture platform, the channel pruning algorithm is used to prune and compress the model structure to adapt to the computing capability of edge devices. The experimental results show that the method can effectively improve the detection capability of the network for floating objects on the water surface. The detection accuracy can reach 93.6%, and the detection speed can be maintained at 36 frames per second, which can achieve high-precision real-time detection of floating objects on the water surface.
LAMDA-SSL: a comprehensive semi-supervised learning toolkit

In this study, we present an easy-to-use, powerful, and open-source toolkit in Python for SSL with comprehensive functionality, simple interfaces, complete documentation, and the best support for algorithms, data types, and tasks compared with related toolkits. Our aim is to facilitate SSL research and applications by addressing the challenge of limited labeled data. Going forward, we plan to incorporate advanced algorithms into LAMDA-SSL and expand its application scope in open environments [9].
An Anonymous and Efficient Multimessage and Multireceiver Certificateless Signcryption Scheme for VANET

In future intelligent transportation systems, vehicular ad hoc network (VANET) is a popular application. They provide early warning of dangers through wireless communication to improve road safety. Therefore, we should protect messages from leakage and changes during transmission. To ensure the security issues in the communication process, we propose a secure and effective certificateless signcryption scheme with multiple messages and multiple receivers and prove its confidentiality and unforgeability based on the hardness of the discrete logarithm problem and the computational Diffie Hellman problem. Our scheme implements the signature and encryption of multiple messages for different receivers and achieves anonymity for the receiver. Based on our signcryption scheme, we design an identity authentication and key agreement protocol applying the construction in VANET. In addition, we also point out that through comprehensive performance analysis, our proposal has higher security and efficiency of computation and communication compared to other signcryption constructions.
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states, as well as periods characterized by continuous transition among these pure states. To capture this transitory behavior, the dynamical Wasserstein barycentre (DWB) model of [1] associates with each pure state a datagenerating distribution and models the continuous transitions among these states as a Wasserstein barycenter of these distributions with dynamically evolving weights. Focusing on the univariate case where Wasserstein distances and barycenters can be computed in closed form, we extend [1] specifically relaxing the parameterization of the pure states as Gaussian distributions. We highlight issues related to the uniqueness in identifying the model parameters as well as uncertainties induced when estimating a dynamically evolving distribution from a limited number of samples. To ameliorate non-uniqueness, we introduce regularization that imposes temporal smoothness on the dynamics of the barycentric weights. A quantile-based approximation of the pure state distributions yields a finite dimensional estimation problem which we numerically solve using cyclic descent alternating between updates to the pure-state quantile functions and the barycentric weights. We demonstrate the utility of the proposed algorithm in segmenting both simulated and real world human activity time series.
Satellite Unsupervised Anomaly Detection Based on Deconvolution-Reconstructed Temporal Convolutional Autoencoder

Anomaly detection for orbiting satellites has become a paramount research focus in the aerospace domain. Data-driven methodologies, employing high-dimensional telemetry data, have exhibited significant potential for timely anomaly detection without the need for pre-existing models or rules. In this paper, we introduce a novel satellite anomaly detection method, the Deconvolution-reconstructed Temporal Convolutional Autoencoder (DRTCAE), which utilizes preprocessed telemetry data. The DRTCAE consists of a convolutional encoder and a deconvolutional decoder, each leveraging the parallelism of stacked Advanced Dilated Causal Convolutional (ADCC) blocks. This innovative architecture facilitates efficient multi-scale nonlinear transformations, which are crucial for extracting time-dependent features from various telemetry data sources and enabling accurate anomaly detection. Through extensive experiments with telemetry data from the Zhuhai-1 OVS3A satellite and an unspecified GEO satellite, our findings demonstrate that the DRTCAE-based approach outperforms traditional multivariate regression techniques. In conclusion, our results underscore the DRTCAE as an exceptionally proficient method for detecting anomalies in orbiting satellites, thereby contributing to the improvement of overall reliability and safety in space missions.
Design of Asynchronous Non-blocking Network Crawler Based on Node.js

In order to improve the crawling efficiency of network crawler, make full use of CPU and realize the processing of high concurrency data, this paper proposes an asynchronous non-blocking network crawler design based on Node.js. It uses a single-threaded model to deal with concurrent data. When a client requests a connection, it triggers an internal event. Through the non-blocking I/O and event-driven mechanism, Node. js program can be parallelized macroscopically. At the same time, it constructs many asynchronous I/O APIs at the bottom, and there is no need to wait between each call. After the operation is completed, the data are processed through callback, so as to reduce the cost and complexity. It is used to write lightweight web crawlers. By acquiring the page, grasping the target content, storing it in JSON files and arrays, and then jumping to the next page, the crawling of web data is realized through several steps of obtaining detailed information. Through practical application test, it is very accurate and efficient to crawl nearly 1000 pieces of data from the webpage.