Reviewerly/exHarmony huggingface dataset:
Energy Efficient and Secure Information Dissemination in Heterogeneous Wireless Sensor Networks Using Machine Learning Techniques

The extensive use of sensor technology in every sphere of life, along with the continuous digitization of society, makes it realistic to anticipate that the planet will soon be patched with small-sized devices all over the place in the not-too-distant future. These devices give monitoring and surveillance capabilities, as well as access to a vast digital universe of information, among other things. Finding data and information, as well as processing enquiries, is made much easier thanks to the seamless transmission of information over wireless media, which is based on the “anywhere, anytime, everywhere” paradigm that allows information to be sent anywhere, at any time. Sensing networks came into existence as a consequence of the downsizing of wireless devices that are capable of receiving information from a source, transferring it, and processing it. Sensor networks, although they share many of the features, uses, and limits of ad hoc networks, have their own set of capabilities that are unique to them. While performing their responsibilities, sensor networks must contend with a variety of security issues, including unsecured wireless channels, physical compromise, and reprogramming. Because of the small size and ubiquitous availability of wireless sensor networks, compromise attacks are the most severe kind of attack in this environment (WSNs). With the proliferation of wireless sensor networks (WSNs), it is becoming more difficult to rely only on machine learning techniques. We sought to tackle the security challenge by developing a key management system as well as a secure routing mechanism. We are building scalable key management approaches that are resistant to node compromise and node replication attacks, which we will demonstrate in our proposed study, by using deployment-driven localization of security information and leveraging distributed key management. Using a security-aware selection of the next hop on the route to the destination, we were able to design safe routing algorithms that were effective.
BDSS: Blockchain-based Data Sharing Scheme With Fine-grained Access Control And Permission Revocation In Medical Environment

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem.To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario.In this scheme, we separate the public part and private part of the electronic medical record (EMR).Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately.This guarantees better fine-grained access control and makes patients to share data at ease.In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update.Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.
SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds

This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cones and tori. The aim of the track is to evaluate the quality of automatic algorithms for fitting and recognizing geometric primitives on point clouds. Specifically, the goal is to identify, for each point cloud, its primitive type and some geometric descriptors. For this purpose, we created a synthetic dataset, divided into a training set and a test set, containing segments perturbed with different kinds of point cloud artifacts. Among the six participants to this track, two are based on direct methods, while four are either fully based on deep learning or combine direct and neural approaches. The performance of the methods is evaluated using various classification and approximation measures.
Hyper-tune

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck. In this paper, inspired by our experience when deploying hyper-parameter tuning in a real-world application in production and the limitations of existing systems, we propose Hyper-Tune, an efficient and robust distributed hyper-parameter tuning framework. Compared with existing systems, Hyper-Tune highlights multiple system optimizations, including (1) automatic resource allocation, (2) asynchronous scheduling, and (3) multi-fidelity optimizer. We conduct extensive evaluations on benchmark datasets and a large-scale real-world dataset in production. Empirically, with the aid of these optimizations, Hyper-Tune outperforms competitive hyper-parameter tuning systems on a wide range of scenarios, including XGBoost, CNN, RNN, and some architectural hyper-parameters for neural networks. Compared with the state-of-the-art BOHB and A-BOHB, Hyper-Tune achieves up to 11.2X and 5.1X speedups, respectively.
Quantification of the Impact of Fine Particulate Matter on Solar Energy Resources and Energy Performance of Different Photovoltaic Technologies

Ambient fine particulate matter (PM2.5) could be a potential environmental risk for decreasing the available solar energy resources and solar photovoltaic (PV) power generation. This study quantifies the attenuation effects of PM2.5 on surface solar irradiance and system performance of different solar PV technologies in Hong Kong. The analysis based on observational irradiation data shows that the global horizontal irradiance decreased by more than 5% in most months under the conditions of PM2.5 concentration exceeding 33.5 μg/m3. During the experiment, the average PM2.5-related losses in the energy output of crystalline silicon and thin-film PV systems could be up to 7.00 and 9.73%, respectively. The measured energy outputs of the experimental PV modules suggest that PM2.5 affects the energy performance of thin-film solar cells with a larger band gap more significantly than that of crystalline silicon PV modules. Moreover, an increasing trend in the performance ratio of monocrystalline silicon, polycrystalline silicon, and copper indium gallium selenide PV systems with the increase of PM2.5 concentration is observed. In contrast, the amorphous silicon and cadmium telluride PV systems with a narrower spectral response range show a decreasing trend in the performance ratio over the experiment. Results indicate that the losses in the available solar energy resources and PV energy potential are expected to increase in areas where heavier PM2.5 pollution exists.
SR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance

In this paper, we consider the difference in the abstraction level of features extracted by different perceptual layers and use a weighted perceptual loss-based generative adversarial network to deblur the UAV images, which removes the blur and restores the texture details of the images well. The perceptual loss is used as an objective evaluation index for training process monitoring and model selection, which eliminates the need for extensive manual comparison of the deblurring effect and facilitates model selection. The UNet jump connection structure facilitates the transfer of features across layers in the network, reduces the learning difficulty of the generator, and improves the stability of adversarial training.
Optimization of aluminum fluoride addition in aluminum electrolysis process based on pruned sparse fuzzy neural network

The aluminum fluoride (AF) addition in aluminum electrolysis process (AEP) can directly influence the current efficiency, energy consumption, and stability of the process. This paper proposes an optimization scheme for AF addition based on pruned sparse fuzzy neural network (PSFNN), aiming at providing an optimal AF addition for aluminum electrolysis cell under normal superheat degree (SD) condition. Firstly, a Gaussian mixture model (GMM) is introduced to identify SD conditions in which the operating modes of AEP are unknown. Then, PSFNN is proposed to establish the AF addition model under normal SD condition identified by GMM. Specifically, a sparse regularization term is designed in loss function of PSFNN to extract the sparse representation from nonlinear process data. A structure optimization strategy based on enhanced optimal brain surgeon (EOBS) algorithm is proposed to prune redundant neurons in the rule layer. Mini-batch gradient descent and AdaBound optimizer are then introduced to optimize the parameters of PSFNN. Finally, the performance is confirmed on the simulated Tennessee Eastman process (TEP) and real-world AEP. Experimental results demonstrate that the proposed scheme provides a satisfactory performance.
Grad-Align: Gradual Network Alignment via Graph Neural Networks (Student Abstract)

Network alignment (NA) is the task of finding the correspondence of nodes between two networks. Since most existing NA methods have attempted to discover every node pair at once, they may fail to utilize node pairs that have strong consistency across different networks in the NA task. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of either node pairs exhibiting strong consistency or prior matching information. Specifically, the proposed method gradually aligns nodes based on both the similarity of embeddings generated using graph neural networks (GNNs) and the Tversky similarity, which is an asymmetric set similarity using the Tversky index applicable to networks with different scales. Experimental evaluation demonstrates that Grad-Align consistently outperforms state-of-the-art NA methods in terms of the alignment accuracy. Our source code is available at https://github.com/jindeok/Grad-Align.
DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding

Along with the evolution of deep neural networks (DNNs) in many real-world applications, the complexity of model building has also dramatically increased. Therefore, it is vital to protect the intellectual property (IP) of the model builder and ensure the trustworthiness of the deployed models. Meanwhile, adversarial attacks on DNNs (e.g., backdoor and poisoning attacks) that seek to inject malicious behaviors have been investigated recently, demanding a means for verifying the integrity of the deployed model to protect the users. This paper presents a novel DNN authentication framework DeepAuth that embeds a unique and fragile signature to each protected DNN model. Our approach exploits sensitive key samples that are well crafted from the input space to latent space and then to logit space for producing signatures. After embedding, each model will respond distinctively to these key samples, which creates a model-unique signature as a strong tool for authentication and user identity. The signature embedding process is also designed to ensure the fragility of the signature, which can be used to detect malicious modifications such that an illegitimate user or an altered model should not have the intact signature. Extensive evaluations on various models over a wide range of datasets demonstrate the effectiveness and efficiency of the proposed DeepAuth.
Deep Multi-Representational Item Network for CTR Prediction

Click-through rate (CTR) prediction is essential in the modelling of a recommender system. Previous studies mainly focus on user behavior modelling, while few of them consider candidate item representations. This makes the models strongly dependent on user representations, and less effective when user behavior is sparse. Furthermore, most existing works regard the candidate item as one fixed embedding and ignore the multi-representational characteristics of the item. To handle the above issues, we propose a Deep multi-Representational Item NetworK (DRINK) for CTR prediction. Specifically, to tackle the sparse user behavior problem, we construct a sequence of interacting users and timestamps to represent the candidate item; to dynamically capture the characteristics of the item, we propose a transformer-based multi-representational item network consisting of a multi-CLS representation submodule and contextualized global item representation submodule. In addition, we propose to decouple the time information and item behavior to avoid information overwhelming. Outputs of the above components are concatenated and fed into a MLP layer to fit the CTR. We conduct extensive experiments on real-world datasets of Amazon and the results demonstrate the effectiveness of the proposed model.
VoLearn

Conventional motion tutorials rely mainly on a predefined motion and vision-based feedback that normally limits the application scenario and requires professional devices. In this paper, we propose VoLearn, a cross-modal system that provides operability for user-defined motion learning. The system supports the ability to import a desired motion from RGB video and animates the motion in a 3D virtual environment. We built an interface to operate on the input motion, such as controlling the speed, and the amplitude of limbs for the respective directions. With exporting of virtual rotation data, a user can employ a daily device (i.e., smartphone) as a wearable device to train and practice the desired motion according to comprehensive auditory feedback, which is able to provide both temporal and amplitude assessment. The user study demonstrated that the system helps reduce the amplitude and time errors of motion learning. The developed motion-learning system maintains the characteristics of high user accessibility, flexibility, and ubiquity in its application.
A Hybrid Smart Quantum Particle Swarm Optimization for Multimodal Electromagnetic Design Problems

Quantum behaved particle swarm optimization (QPSO) has been one of the most widely used algorithm in engineering world.Since its debut in 2004, QPSO has been used for resolving numerous complicated multimodal problems.Moreover, considering the adaptability and versatility, it has resolved a variety of real-world and test problems.To tackle numerical and engineering optimization problems, we introduce novel hybrid algorithm QPSODE.The novel hybrid algorithm integrates Quantum behaved particle swarm optimization (QPSO) with differential evolution (DE) strategy.A crossover and selection (influenced by DE) is used in the QPSODE's position updating mechanism.During the selection process, the Boltzmann operator is applied to the position vectors of two randomly chosen particles, not to their individual optimum placements.Therefore, unlike the QPSO, a particle is only relocated to a new position if it has a higher fitness value, implying the application of a selection strategy across the whole search space.Additionally, the hybrid algorithm is improved by introducing proper parameters tuning, control parameter, path disparity.The hybrid algorithm enhances the algorithm's performance by speeding up the convergence and avoiding the premature convergence, the main flaw in the earlier algorithms.The proposed algorithm is put to test, by using 19 well-known benchmark test functions and the engineering optimization problem for superconducting magnetic energy storage (SMES).In terms of the quality of the resulting outputs, QPSODE outperforms various state-of-the-art approaches.
Analysis of the Influence of De-hazing Methods on Vehicle Detection in Aerial Images

In recent years, object detection from space in adverse weather, incredibly foggy, has been challenging.In this study, we conduct an empirical experiment using two de-hazing methods: DW-GAN and Two-Branch, for removing fog, then evaluate the detection performance of six advanced object detectors belonging to four main categories: two-stage, one-stage, anchorfree and end-to-end in original and de-hazed aerial images to find the best suitable solution for vehicle detection in foggy weather.We use the UIT-DroneFog dataset, a challenging dataset that includes a lot of small, dense objects captured in various altitudes, as the benchmark to evaluate the effectiveness of approaches.After experiments, we observe that each de-hazing method has different impacts on six experimental detectors.
Wrapper Based Feature Selection Approach Using Black Widow Optimization Algorithm for Data Classification

Feature selection is a crucial preprocessing step for data analysis and further predictive modeling tasks. Various optimization algorithms have been used in the past to achieve better results in terms of evaluation metrics like classification accuracy by selecting an optimal subset of features from a given dataset. In this paper, we propose a wrapper-based method that builds upon the idea of the Black Widow Optimization (BWO) technique, along with certain changes to the originally proposed algorithm to improve its performance. The results of the proposed method have been compared with the typically used optimization algorithms for the task of feature selection, such as Feature Selection based on Genetic Algorithm (FSGA) and Feature Selection based on Particle Swarm Optimization (FSPSO), over different well-known classification algorithms. The results show significant improvement in terms of obtaining better classification accuracy and selecting a smaller and more optimal subset of features, as well. Further, it is inferred from the results that FSBWO converges to the global best score faster than other competing algorithms, hence making it a favorable choice with large datasets where the processing time can be of crucial importance.
Efficient Machine Translation Domain Adaptation

Machine translation models struggle when translating out-of-domain text, which makes domain adaptation a topic of critical importance.However, most domain adaptation methods focus on fine-tuning or training the entire or part of the model on every new domain, which can be costly.On the other hand, semi-parametric models have been shown to successfully perform domain adaptation by retrieving examples from an in-domain datastore (Khandelwal et al., 2021).A drawback of these retrievalaugmented models, however, is that they tend to be substantially slower.In this paper, we explore several approaches to speed up nearest neighbor machine translation.We adapt the methods recently proposed by He et al. ( 2021) for language modeling, and introduce a simple but effective caching strategy that avoids performing retrieval when similar contexts have been seen before.Translation quality and runtimes for several domains show the effectiveness of the proposed solutions. 1
Predicate-Argument Based Bi-Encoder for Paraphrase Identification

Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings.While cross-encoders have achieved high performances across several benchmarks, bi-encoders such as SBERT have been widely applied to sentence pair tasks.They exhibit substantially lower computation complexity and are better suited to symmetric tasks.In this work, we adopt a biencoder approach to the paraphrase identification task, and investigate the impact of explicitly incorporating predicate-argument information into SBERT through weighted aggregation.Experiments on six paraphrase identification datasets demonstrate that, with a minimal increase in parameters, the proposed model is able to outperform SBERT/SRoBERTa significantly.Further, ablation studies reveal that the predicate-argument based component plays a significant role in the performance gain.
Improving the Adversarial Robustness of NLP Models by Information Bottleneck

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory.Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the previously reported defense methods while suffering almost no performance drop in clean accuracy on SST-2, AGNEWS and IMDB datasets.
BET: black-box efficient testing for convolutional neural networks

It is important to test convolutional neural networks (CNNs) to identify defects (e.g. error-inducing inputs) before deploying them in security-sensitive scenarios. Although existing white-box testing methods can effectively test CNN models with high neuron coverage, they are not applicable to privacy-sensitive scenarios where full knowledge of target CNN models is lacking. In this work, we propose a novel Black-box Efficient Testing (BET) method for CNN models. The core insight of BET is that CNNs are generally prone to be affected by continuous perturbations. Thus, by generating such continuous perturbations in a black-box manner, we design a tunable objective function to guide our testing process for thoroughly exploring defects in different decision boundaries of the target CNN models. We further design an efficiency-centric policy to find more error-inducing inputs within a fixed query budget. We conduct extensive evaluations with three well-known datasets and five popular CNN structures. The results show that BET significantly outperforms existing white-box and black-box testing methods considering the effective error-inducing inputs found in a fixed query/inference budget. We further show that the error-inducing inputs found by BET can be used to fine-tune the target model, improving its accuracy by up to 3%.
Intrinsic parameter-free calibration of FPP using a ray phase mapping model

This Letter presents a ray phase mapping model (RPM) for fringe projection profilometry (FPP) that avoids calibrating intrinsic parameters. The novelty of the RPM, to the best of our knowledge, is the ability to characterize the imaging system with independent rays for each pixel, and to associate the rays with the projected phase in the illumination field for efficient 3D mapping, which avoids complex imaging-specific modeling about lens layout and distortion. Two loss functions are constructed to flexibly optimize camera ray parameters and mapping coefficients, respectively. As a universal approach, it has the potential to calibrate different types of FPP systems with high accuracy. Experiments on wide-angle lens FPP, telecentric lens FPP, and micro-electromechanical system (MEMS)-based FPP are carried out to verify the feasibility of the proposed method.