Reviewerly/exHarmony huggingface dataset:
A new two-axis solar tracker based on the online optimization method: Experimental investigation and neural network modeling

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model. In this research, four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles, which determine the position of the sun. The magnitude of the sunray is considered as the cost function of all algorithms. Then, several experiments are carried out to find the best optimization algorithm with optimal population size, number of iterations, and also the best initialization method. Uniform initialization leads to faster convergence compared to random initialization. The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms, with a convergence time of less than 40 s. The average fitness value or voltage received by the tracker is 2.4 Volts in this method, which is higher than other methods. TLBO also performs well with a population size of 15 and 7 iterations. Afterward, the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO. Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling. The performance of the proposed ANN model is evaluated using regression plots, demonstrating a strong correlation between predicted and target outputs. Finally, the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise

We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink communications. Such imperfect communication scenarios arise in the practical deployment of FL in emerging communication systems and protocols. The analysis developed in this paper demonstrates, for the first time, that there is an asymmetry in the detrimental effects of uplink and downlink communications in FL. In particular, the adverse effect of the downlink noise is more severe on the convergence of FL algorithms. Using this insight, we propose improved Signal-to-Noise (SNR) control strategies that, discarding the negligible higher-order terms, lead to a similar convergence rate for FL as in the case of a perfect, noise-free communication channel while incurring significantly less power resources compared to existing solutions. In particular, we establish that to maintain the $O(\frac{1}{\sqrt{K}})$ rate of convergence like in the case of noise-free FL, we need to scale down the uplink and downlink noise by $\Omega({\sqrt{k}})$ and $\Omega({k})$ respectively, where $k$ denotes the communication round, $k=1,\dots, K$. Our theoretical result is further characterized by two major benefits: firstly, it does not assume the somewhat unrealistic assumption of bounded client dissimilarity, and secondly, it only requires smooth non-convex loss functions, a function class better suited for modern machine learning and deep learning models. We also perform extensive empirical analysis to verify the validity of our theoretical findings.
Quantum phase transition of the Jaynes-Cummings model

Herein, we propose an experimentally feasible scheme to show the quantum phase transition of the Jaynes-Cummings (JC) model by modulating the transition frequency of a two-level system in a quantum Rabi model with strong coupling. By tuning the modulation frequency and amplitude, the ratio of the effective coupling strength of the rotating terms to the effective cavity (atomic transition) frequency can enter the deep-strong coupling regime, while the counter-rotating terms can be neglected. Thus, a deep-strong JC model is obtained. The ratio of the coupling strength to resonance frequencies in the deep-strong JC model is two orders of magnitude larger than the corresponding ratio in the original quantum Rabi model. Our scheme can be employed in atom-cavity resonance and off-resonance cases, and it is valid over a broad range. The nonzero average cavity photons of the ground state indicate the emergence of a quantum phase transition. Further, we demonstrate the dependence of the phase diagram on the atom-cavity detuning and modulation parameters. All the parameters used in our scheme are within the reach of current experimental technology. Our scheme provides a new mechanism for investigating the critical phenomena of finite-sized systems without requiring classical field limits, thereby opening a door for studying fundamental quantum phenomena occurring in the ultrastrong and even deep-strong coupling regimes.
Comparison of regmed and BayesNetty for exploring causal models with many variables

Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as regmed is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of regmed can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying regmed to the resulting "filled-in" data set.
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data

Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program consisting of math and table operations to answer a question, state-of-the-art methods use a retriever-generator pipeline. However, their retrieval results are static, while different generation steps may rely on different sentences. To attend to the retrieved information that is relevant to each generation step, in this paper, we propose DyRRen, an extended retriever-reranker-generator framework where each generation step is enhanced by a dynamic reranking of retrieved sentences. It outperforms existing baselines on the FinQA dataset.
Self-Attention Over Tree for Relation Extraction With Data-Efficiency and Computational Efficiency

Dependency trees parsed from natural language sentences have been proven to be beneficial for the relation extraction task by deep neural networks. However, effectively and efficiently utilizing the structural information of dependency trees remains a challenging research problem for neural networks. Existing methods either struggle to facilitate interaction between nodes at different levels that are distant in the dependency tree or face limitations due to computational inefficiency. In this article, we propose a novel model, named Self-Attention over Tree for Relation Extraction (SATRE), designed for tree-structured data and adept at fully utilizing the structural information implied in the subtrees of dependency trees. SATRE enables interactions among nodes in each subtree, even when they are on widely separated layers. As a result, nodes are learned across multiple subtrees, making SATRE data-efficient; that is, SATRE remains efficient even when data is scarce. Furthermore, SATRE is implemented using a parallel mechanism. Specifically, SATRE parallelly computes node representations of a tree in two levels: across all subtrees of the tree and across all nodes within a subtree. Empirically, SATRE consistently outperforms the compared methods on two real-world benchmarks: the TACRED and SemEval-2010 Task 8 datasets, which shows the effectiveness of SATRE. Meanwhile, extensive experiments indicate SATRE's data-efficiency in utilizing the training data and its computational efficiency in running time.
Two-Step Strategy for Domain Adaptation Retrieval

Conventional hash-based retrieval method rely on the assumption that the query and database are of the identical domain. However, cross-domain problem often occurs in real-world applications, leading to the unsatisfactory performance of existing hashing methods. Recently, some researchers have put forward domain adaptation retrieval (DAR) under the perspective of domain adaptation (DA) and achieved promising results. But the following limitations still exist: 1) a single function is used to handle two challenges, i.e., domain adaptation and hashing, which is not flexible to explore enough underlying information for simultaneously accomplishing these challenges well; 2) non-dominant features in the sample are ignored; 3) the dissimilarity structure of dissimilar samples is not taken into account. To address the above problems, we propose a novel framework named two-step strategy (TSS) for domain adaptation retrieval, which advocates dividing DAR into two steps: DA step and hashing step. A DA function and a hash function are learned to handle the above two challenges, respectively, making the process more reasonable. Additionally, a discriminant semantic fusion loss is proposed to improve the discriminative ability among classes. Unlike other works that focus on discovering dominant features, we exploit the neglected non-dominant features and assign them attention with sinusoidal semantic embedding, actively creating a clear separation between classes. At last, we present an adaptive similarity preserving loss to preserve the similarity structure of the original data in all intra-domain and inter-domain hash codes. Extensive experiments on various datasets demonstrate that the proposed TSS achieves state-of-the-art performance.
Feature Decomposition for Reducing Negative Transfer: A Novel Multi-Task Learning Method for Recommender System (Student Abstract)

We propose a novel multi-task learning method termed Feature Decomposition Network (FDN). The key idea of the proposed FDN is to reduce the phenomenon of feature redundancy by explicitly decomposing features into task-specific features and task-shared features with carefully designed constraints. Experimental results show that our proposed FDN can outperform the state-of-the-art (SOTA) methods by a noticeable margin on Ali-CCP.
GOHSP: A Unified Framework of Graph and Optimization-Based Heterogeneous Structured Pruning for Vision Transformer

The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foundation model. However, ViTs are typically constructed with large-scale sizes, which then severely hinder their potential deployment in many practical resources constrained applications. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. However, unlike its current popularity for CNNs and RNNs, structured pruning for ViT models is little explored. In this paper, we propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models. We first develop a graph-based ranking for measuring the importance of attention heads, and the extracted importance information is further integrated to an optimization-based procedure to impose the heterogeneous structured sparsity patterns on the ViT models. Experimental results show that our proposed GOHSP demonstrates excellent compression performance. On CIFAR-10 dataset, our approach can bring 40% parameters reduction with no accuracy loss for ViT-Small model. On ImageNet dataset, with 30% and 35% sparsity ratio for DeiT-Tiny and DeiT-Small models, our approach achieves 1.65% and 0.76% accuracy increase over the existing structured pruning methods, respectively.
Efficient Off-chain Micro-payment Systems for Blockchain-based P2P Energy Trading

P2P energy trading often utilizes a permissionless blockchain platform for energy credit tokenization and trading. However, permissionless blockchain suffers from slow transaction confirmation time, poor scalability, high transaction fees for micro-payment, and the need for persistent online connections. Therefore, we develop a cost-effective off-chain micro-payment solution to support offline transactions of energy credit tokens without persistent connections to blockchain, in a similar vein as off-chain payment channels for cryptocurrencies (e.g., Lightning Network). But unlike cryptocurrencies, off-chain trading of energy credit tokens faces a new challenge. Since energy credit tokens are usually generated ex-post from delayed smart meter reporting, real-time P2P energy applications would need to cope with yet-to-be-realized energy credit tokens at the moment of trading, which significantly increases the counterparty risks among untrusted users. Therefore, we propose a secure off-chain payment channel protocol to effectively mitigate the counterparty risks in P2P energy applications.
Probabilistic Analysis of LLL-based Decoder of Interleaved Chinese Remainder Codes

To date, Li et al. have presented the only decoder for Interleaved Chinese Remainder (ICR) codes [1]. The core of their ICR decoder is to find a short vector in a lattice using the LLL algorithm [2]. However, their analysis of the decoding failure is partially heuristic. In this work, we present a new analysis of their LLL-based decoder that gives a proved upper bound on its decoding failure probability.
Using Security Metrics to Determine Security Program Effectiveness

Security objectives serve as the foundation for security metrics, which are used to guide decisions on how to increase the security of all parts engaged in providing services and processing data. Numerous data breaches are re-vealed each week, some of which may have affected tens or even hundreds of millions of people. Customers and regulators are both becoming more concerned about firms' information security procedures and their plans for preventing security breaches and protecting sensitive data. As a result, sever-al laws and regulations have been enacted to enhance cybersecurity risk management and to protect personal information that may be held or trans-mitted among businesses. The majority of these industry-specific and general data protection laws are complex, requiring ongoing oversight to maintain compliance throughout your business and the companies of your vendors. To gauge the effectiveness of and involvement in the usage of security con-trols, it is crucial to define a set of security metrics. A carefully defined set of metrics will help direct future security decisions and strengthen your or-ganization's security posture. In our study, we proposed to review security metrics to determine security program effectiveness for a company which is fictional for the scope of study. Firstly, we defined security metrics and their key indicators successfully. We discussed different scenarios for Trivest Technologies Limited company, which is fictional, we just used it for our scope of study. We successfully discussed, developed, and used KPIs, KRIs and KGIs; which are security metrics for the Trivest Technologies Limited company, and we found out that these security metrics help us determine the security program effectiveness for a company successfully. By implementation of its successful results, it also aligns with one of the United Nations Sustainable Development Goals i.e., 8th: Decent work and Economic Growth.
BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated Learning

Federated learning (FL) is a machine learning technique that allows multiple devices to train a model collaboratively without sharing their data with a central server. It has advantages such as increased privacy, reduced communication costs, and improved scalability, making it useful in scenarios where data is distributed across multiple devices and privacy is a concern, such as in healthcare or finance. However, the potential for participants to behave selfishly in FL can be a challenge, and incentive mechanisms are needed to encourage them to participate in the training process. Incentives can take many strategies, such as financial rewards or reputation-based systems, and can be tailored to specific needs. In defense technology, FL can be used for predictive maintenance, target recognition, and intelligence analysis. However, the existing incentive mechanisms in FL have limitations, such as being complex to design and implement, and raising privacy concerns. To improve the incentive mechanisms in FL, we propose an incentive mechanism based on blockchain called BTIMFL that ensures transparency and effectiveness. The proposed mechanism includes DAO (Decentralized Autonomous Organizations) and smart contracts for the automatic distribution of profits to ensure fairness.
Chatbot Feedback on Students’ Writing: Typology of Comments and Effectiveness

Providing feedback is time-consuming. A chatbot has the potential to facilitate provision of detailed feedback saving instructors time and energy. However, the effectiveness of chatbot feedback has not been determined. In this study, chatbot end-comments on 28 students’ written papers were classified by topic and function. The comments were further evaluated for whether they reflected the five characteristics of effective feedback. Results demonstrated the ability of the chatbot to provide a variety of comments. The characteristics of these comments nevertheless, did not seem to reflect feedback best practices. The study provides recommendations for further chatbot feedback research, and in particular calls for more partnership between artificial intelligence developers and writing researchers.
Privacy and Ethical Considerations of Smart Environments: A Philosophical Approach on Smart Meters

This paper delves into the privacy and ethical concerns related to smart devices in IoT, particularly smart meters. It explores the regulatory framework governing data protection through the lens of GDPR. Our analysis identifies relevant GDPR articles and presents a case study that exposes the privacy risks associated with smart meters. Ethical considerations are explored using deontology and consequentialism, offering a thorough analysis of privacy and ethical issues for consumers, manufacturers, and policymakers. The paper emphasizes the complexity of these concerns and the need for a comprehensive assessment of potential benefits and harms to individuals and society. It also stresses the importance of ethical considerations and compliance with GDPR requirements when designing smart building systems to guarantee adequate privacy measures. Our concluding remarks advocate that manufacturers and suppliers prioritize consumer privacy and security over profit and consider stronger privacy protections, increased transparency, and alternative business models to address these concerns. The importance of IoT privacy concerns cannot be underestimated, given their impact on personal and organizational privacy.
CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness

Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks. However, the structured sparse models obtained by the existing works suffer severe performance degradation for both benign and robust accuracy, thereby causing a challenging dilemma between robustness and structuredness of compact DNNs. To address this problem, in this paper, we propose CSTAR, an efficient solution that simultaneously impose Compactness, high STructuredness and high Adversarial Robustness on the target DNN models. By formulating the structuredness and robustness requirement within the same framework, the compressed DNNs can simultaneously achieve high compression performance and strong adversarial robustness. Evaluations for various DNN models on different datasets demonstrate the effectiveness of CSTAR. Compared with the state-of-the-art robust structured pruning, CSTAR shows consistently better performance. For instance, when compressing ResNet-18 on CIFAR-10, CSTAR achieves up to 20.07% and 11.91% improvement for benign accuracy and robust accuracy, respectively. For compressing ResNet-18 with 16x compression ratio on Imagenet, CSTAR obtains 8.58% benign accuracy gain and 4.27% robust accuracy gain compared to the existing robust structured pruning.
Combined simulation and experimental study on spectral absorbance of partially disordered MoSe<sub>2</sub> nanospheres

Abstract It is important to clarify the role and possible applicability of partially disordered structures in photonics, but there is still a lack of an effective method for it. Here, we investigate partially disordered MoSe 2 nanospheres experimentally regarding their morphology and absorption spectrum in broadband wavelengths and propose an optical simulation with three-dimensional finite-difference time-domain method to explain the crucial impacts of morphological parameters on optical responses. The experimental spectral absorbance of MoSe 2 nanospheres reveals a strong light-absorbing character in broadband wavelengths. The simulated spectral curves coincide with the experimental results by adjusting morphological parameters, i.e. the statistics of size and the number of layer, and the linear correlation coefficient between the simulated and experimental spectral curves is up to 0.94. The disorder plays a key role in the high light-absorption feature, and the feature originates from anti-reflection, defective state absorption, multiple light scattering and coherent diffusion effects. The results not only deepen the understanding of disordered photonics in semiconductor nanostructures, but also provide a simulation approach to optimize experimental designs.
Image detail enhancement of nanocomposites based on deep convolution neural networks

Research on the image detail enhancement algorithm of nanocomposites can better improve the image quality of nanocomposites and facilitate researchers to understand the structural characteristics of materials. Due to the problems of low image quality and difficulty to retain the details of traditional nanocomposite image details enhancement algorithms, a new nanocomposite image details enhancement algorithm based on deep convolution neural networks (DCNN) is designed. Through histogram equalization, linear gray-scale stretching and median filtering, the image of nanocomposite is preprocessed, and then FCM algorithm is used for edge detection and image segmentation. Using color model transformation algorithm and DCNN, an image detail enhancement model including feature extraction and nonlinear mapping is constructed. Finally, the idea of image detail reconstruction is introduced, and image detail reconstruction is realized through a convolution layer. The results show that compared with the experimental comparison algorithm, the image processed by the proposed algorithm contains more detailed information and has higher image quality. The PSNR value reaches 27.1 dB, which indicates that the nanocomposite image has a better detail enhancement effect and can be widely used in the field of nanocomposite analysis and research.