Reviewerly/exHarmony huggingface dataset:
Synthetically generated microscope images of microtopographies using stable diffusion

This study presents a method to generate synthetic microscopic surface images by adapting the pre-trained latent diffusion model Stable Diffusion and the pre-trained text encoder OpenCLIP-ViT/H. A confocal laser scanning microscope was used to acquire the dataset for transfer learning. The measured samples include metallic surfaces processed with different abrasive machining methods like grinding, polishing, or honing. The network is trained to generate microtopographies with these machining methods, with different materials (for example, aluminum, PVC, and steel) and roughness values (for example, milling with Ra=0.4 to Ra =12.5). The performance of the network is evaluated through visual inspection, and the objective image quality measures Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Frechet Inception Distance (FID). The results demonstrate that the proposed method can generate realistic microtopographies, albeit with some limitations. These limitations may be due to the fact that the original training data for the Stable Diffusion network used mostly images from the Internet, which often show people or landscapes. It was also found that the lack of post-processing of the synthetic images may lead to a reduction in perceived sharpness and less finely detailed structures. Nevertheless, the performance of the model demonstrates a promising and effective approach to surface metrology and materials science, contributing to fields such as materials science and surface engineering.
Adapting synthetic training data in deep learning-based visual surface inspection to improve transferability of simulations to real-world environments

Learning models from synthetic image data rendered from 3D models and applying them to real-world applications can reduce costs and improve performance when using deep learning for image processing in automated visual inspection tasks. However, sufficient generalization from synthetic to real-world data is challenging, because synthetic samples only approximate the inherent structure of real-world images and lack image properties present in real-world data, a phenomenon called domain gap. In this work, we propose to combine synthetic generation approaches with CycleGAN, a style transfer method based on Generative Adversarial Networks (GANs). CycleGAN learns the inherent structure from real-world samples and adapts the synthetic data accordingly. We investigate how synthetic data can be adapted for a use case of visual inspection of automotive cast iron parts and show that supervised deep object detectors trained on the adapted data can successfully generalize to real-world data and outperform object detectors trained on synthetic data alone. This demonstrates that generative domain adaptation helps to leverage synthetic data in deep learning-assisted inspection systems for automated visual inspection tasks.
One for all: Efficient verifiable dynamic multi-user searchable encryption in the presence of corrupted users

Dynamic multi-user searchable symmetric encryption (DMUSSE) enables the data owner to dynamically outsource encrypted database to the cloud server and selectively share the search capability with data users. However, inter-user state synchronization is expensive but often necessary in multi-user setting. In addition, only a few works are proposed against the malicious servers that achieve verifiability (guarantee the integrity of query results) by deploying a Merkle tree for all database entries in a straightforward way, which is inevitably subject to logarithmic communication overhead. In this work, we propose a new verifiable DMUSSE scheme with improved performance, while maintaining user-server collusion resistance. Concretely, we first design an efficient and forward-and-backward-secure DMUSSE scheme with the aid of Hyperledger Fabric (a permissioned blockchain), greatly reducing synchronous communication and computation. In particular, our scheme does not reveal any information about uncorrupted users when corrupted users collude with an adversarial server. Then, we achieve efficient verifiability of query results based on multiset hash functions with O(1) communication cost. Finally, we compare our scheme with the state-of-the-art solutions O-μSE and Q-μSE (Chamani et al. TDSC 2021). Experimental results show that our scheme brings at least 90.9% communication savings, 2.75×, 109.2×, and 11.67× speedup in search, update, and verify time, respectively.
Geostatistical assessment of soils in Ibadan, Southwest Nigeria: Focus on agricultural lands

Food production relies on agricultural fields, which can accumulate hazardous materials. This study examined multi-element concentration of agricultural soils in Ibadan, located in Southwest Nigeria. A systematic sampling approach was employed where 39 soil samples were collected and analysed for their elemental composition with plasma-mass spectrometry (ICP-MS) atomic fluorescence spectrometer (AFS). Pb (27.96 ppm), Ag (75.68 ppm), As (0.87 ppm), Ba (37.30 ppm) and Co (3.8 ppm) recorded average values below the recommended baseline values. While 20% of samples for Cr and all of the samples for Co reported values above the recommended baseline values of 80.74 ppm and 0.40 ppm respectively. The soils had a Relative Contamination and Cumulative Index (RCCI) greater than 75%–100%. This shows that some soils were highly contaminated with various elements. The Empirical Orthogonal function (EOF) analysis also indicated that the soils were loaded with Cu, Co, Mn, As, Cr, and Fe. This study provides valuable insight on the multi-element concentration in agricultural soils in Ibadan. These findings may help policymakers, agricultural stakeholders, and environmental authorities manage and remediate soil in the study area.
Few Shot Spatio-Temporal Anomaly Detection Model For Suspicious Activities

Convolutional Neural Network (CNN) has performed better for recent application of object recognition and object detection especially for image data but problem with CNN is that they require labels as learning signals. It is quite impossible to label all types of anomalies in a particular environment. Unsupervised methods used for video anomaly detection has drawback that they require too much data so that accurate results should be produced using unlabeled data which in turn increases computational cost. For this research a Few shot anomaly detection method is introduced using spatio-temporal autoencoder model for detecting suspicious activities in videos is proposed which doesn’t require any labels during training and also has very less computational cost then traditional unsupervised deep learning methods. Spatiotemporal autoencoder model has two components. Spatial autoencoder is used for spatial feature representation while temporal autoencoder extracts features from temporal dimensions. Few shot anomaly detection technique comprises the fact that it takes few images in each batch of training loop and trains the model on those images. At last averages the learning of all images and compute the loss for reconstruction by taking average loss of all batches. Experimental results on Avenue Dataset gives better results and achieves much lesser computational cost then other unsupervised anomaly detection methods.
Blockchain-FRL for Vehicular Lane Changing: Toward Traffic, Data, and Training Safety

Reinforcement learning has been adopted to improve the efficiency of vehicular lane changing (LC) decisions. However, since the local vehicular data needs to be uploaded to the edge node for accomplishing the learning and decision tasks, the data and traffic safety are under critical threat. Illegal data usage or attacks can generate misleading LC decisions such as collisions or rollover, which severely degrades the traffic safety. Therefore, this article investigates a blockchain-federated reinforcement learning (FRL) approach for the LC decisions, which jointly accounts for the traffic, data, and training safety of the LC decision making. A vehicular reputation model based on the Bayesian risk situation is constructed and combined with the FRL. The proposed model can help evaluate the traffic safety, select the vehicles for participating in the FRL training, and adjust the FRL aggregation weights. The FRL can protect the data safety by enabling the exchange and aggregation of the LC decision network parameters. The blockchain can ensure the FRL training safety by recording the FRL task information. In addition, a Proof-of-Work (PoW) consensus scheme is devised to increase the FRL robustness, where the vehicles can collaboratively join the blockchain consensus and accomplish the FRL aggregation in a distributed manner. Two typical scenarios are selected for the experimental evaluation, including the highway scenario and the merging scenario. The experimental results indicate that the proposed method shows better efficiency, convergence, and message safety delivery ratio by comparing with the existing studies.
Learning to Perturb for Contrastive Learning of Unsupervised Sentence Representations

Recently, contrastive learning has been shown effective in fine-tuning pre-trained language models (PLM) to learn sentence representations, which incorporates perturbations into unlabeled sentences to augment semantically related positive examples for training. However, previous works mostly adopt heuristic perturbation methods that are independent of the sentence representations. Since the perturbations are unaware of the goal or process of sentence representation learning during training, it is likely to lead to sub-optimal augmentations for conducting constrative learning. To address this issue, we propose a new framework <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2P-CSR</b> that adopts a learnable perturbation strategy for improving contrastive learning of sentence representations. In our L2P-CSR, we design a safer perturbation mechanism that only weakens the influence of tokens and features on the sentence representation, which avoids dramatically changing the semantics of the sentence representations. Besides, we devise a gradient-based algorithm to generate adaptive perturbations specially for the dynamically updated sentence representation during training. Such a way is more capable of augmenting high-quality examples that guide the sentence representation learning. Extensive experiments on diverse sentence-related tasks show that our approach outperforms competitive baselines.
Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion

LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however suffer from the domain shift problem as other deep learning approaches. To address this, we propose a novel Unsupervised Domain Adaptation (UDA) technique for multi-modal LiDAR segmentation. Unlike previous works in this field, we leverage depth completion as an auxiliary task to align features extracted from 2D images across domains, and as a powerful data augmentation for LiDARs. We validate our method on three popular multi-modal UDA benchmarks and we achieve better performances than other competitors.
Multigoal Reinforcement Learning via Exploring Entropy-Regularized Successor Matching

Multigoal reinforcement learning (RL) algorithms tend to achieve and generalize over diverse goals. However, unlike single-goal agents, multigoal agents struggle to break through the exploration bottleneck with a fair share of interactions, owing to rarely reusable goal-oriented experiences with sparse goal-reaching rewards. Therefore, well-arranged behavior goals during training are essential for multigoal agents, especially in long-horizon tasks. To this end, we propose efficient multigoal exploration on the basis of maximizing the entropy of successor features and Exploring entropy-regularized successor matching, namely, E <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> SM. E <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> SM adopts the idea of a successor feature and extends it to entropy-regularized goal-reaching successor mapping that serves as a more stable state feature under sparse rewards. The key contribution of our work is to perform intrinsic goal setting with behavior goals that are more likely to be achieved in terms of future state occupancies as well as promising in expanding the exploration frontier. Experiments on challenging long-horizon manipulation tasks show that E <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> SM deals well with sparse rewards and in pursuit of maximal state-covering, E <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> SM efficiently identifies valuable behavior goals toward specific goal-reaching by matching the successor mapping.
Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning

Metaverse is envisioned to be the next-generation human-centric Internet which can offer an immersive experience for users with a broad application in healthcare, education, entertainment, and industries. These applications require the analysis of massive data that contains private and sensitive information. A potential solution to preserving privacy is deploying distributed learning frameworks, including federated learning (FL) and split learning (SL), due to their ability to address privacy leakage and analyze personalised data without sharing raw data. However, it is proven that FL and SL are still susceptible to adversarial poisoning attacks. In this paper, we analyse such critical issues for the privacy-preserving mechanism in Metaverse services. We develop a novel poisoning attack based on Bayesian optimisation to emulate the adversarial behaviour against FL (BO-FLPA) and SL (BO-SLPA) which is important for the development of effective defense algorithms in the future. Specifically, we develop a layer optimisation method using the intuition of black-box optimisation with assuming that there is a function between the prediction’s uncertainty and layer optimisation parameters. The result of this optimisation provides the optimal weight parameters for the hidden layer, such as the first or second layer for FL, and the first layer for SL. Numerical results demonstrate that in both FL and SL, the poisoned hidden layers have the ability to increase the susceptibility of the model to adversarial attacks in terms of prediction with low confidence or having a larger deviation of the probability density function of the predictions.
Efficient Model Extraction by Data Set Stealing, Balancing, and Filtering

Model extraction replicates the functionality of machine learning models deployed as a service. Recently, generative adversarial networks (GANs)-based methods have achieved remarkable performance in data-free model extraction. However, previous methods generate random data in every training batch, resulting in slow convergence and redundant queries. We propose to tackle the task with a much simpler paradigm. Specifically, we steal a data set with GAN before training the clone model rather than during every training batch. Benefiting from full use of the generated data, the proposed paradigm needs less training time and query cost. To improve the class distribution of data, a balancing strategy is applied. Furthermore, the balanced data set is filtered based on adversarial robustness for better quality. Combining the above strategies, we propose an efficient model extraction by data set stealing, balancing, and filtering (DSBF). Experiments on three widely used data sets show that DSBF outperforms previous methods while converging faster and costing fewer queries.
High-Speed LFSR Decoder Architectures for BCH and GII Codes

In literature, PIBMA, a linear-feedback-shift-register (LFSR) decoder, has been shown to be the most efficient high-speed decoder for Reed-Solomon (RS) codes. In this work, we follow the same design principles and present two high-speed LFSR decoder architectures for binary BCH codes, both achieving the critical path of one multiplier and one adder. We identify a key insight of the Berlekamp algorithm that iterative discrepancy computation involves only even-degree terms. The first decoder separates the even and odd-degree terms of the error-locator polynomial to iterate homogeneously with discrepancy computation. The resulting LFSR decoder architecture, dubbed PIBA, has ⌊3t2⌋+1 processing elements (PEs), each containing two registers, two multipliers, one adder, and two multiplexers (same as that of PIBMA), which compares favorably against the best existing architecture composed by 2t+1 PEs. The second one, dubbed pPIBA, squeezes the entire error-locator polynomial into the even-term array of the first one to iterate along with discrepancy computation, which comes at the cost of a controlled defect rate. pPIBA employs t+1+f systolic units with a defect probability of 2-q(f+1), where q denotes the finite field dimension and f is a design parameter, which significantly reduces the number of PEs for a large correcting power t. The proposed architectures can be arbitrarily folded to trade off complexity with latency, due to the systolic nature. GII decoding has been notorious for the composition of many seemly irrelevant functional blocks. We are motivated by the unified framework UPIBA which can be reconfigured to carry out both error-only and error-and-erasure decoding of RS codes in the most efficient manner. We devise a unified LFSR decoder for GII-RS, GII-ERS (referring to erasure correction of GII-RS codes), and GII-BCH codes, respectively. Each LFSR decoder can be reconfigured (but not multiplexed) to execute different functional blocks, and moreover achieves the same critical path of one multiplier, one adder, and one multiplexer. The resulting GII-RS/BCH decoder contains only four functional blocks, which are literally the same as the decoder for single RS/BCH codes. For GII-RS and GII-BCH decoding, we also incorporate the original mechanism by Tang and Kötter to minimize the miscorrection rate, which comes surprisingly at a negligible cost. Our proposed high-speed low-complexity GII-ERS decoder renders the multi-layer GII codes highly attractive against other locally recoverable codes.
The Effect of Video See-Through HMD on Peripheral Visual Search Performance

A video see-through head mounted display (VSHMD) has a digital camera set attached to the front of the HMD, which is a promising personal display device bringing a user into a realistic mixed world on demand. However, VSHMDs have an inherent risk of diminishing visual performance owing to the limited field of view (FOV) and poor display resolution etc. We investigated influences of VSHMD on the peripheral visual search performance of wearers. Nineteen participants performed a covert visual feature search task. All participants performed the tasks in four FOV conditions: 15°, 30°, 45°, and 60° with three types of viewing devices including VSHMD. The results showed that the VSHMD deteriorated the user’s peripheral visual search performance worse than the HMD, whereas no deterioration found in the FOV restriction or the low resolution of stimuli. This result implies that low sharpness of display and limited refresh rate of VSHMD cameras may induce diminished peripheral visual perception. Designers should consider this effect when developing any augmented reality or mixed reality system. Such investigations will raise an important guideline for the practical usage of VSHMD and tele-operation.
Designing a Forensic-Ready Wi-Fi Access Point for the Internet of Things

Recent advances in the Internet of Things are leading to a proliferation of smart devices in our daily life. Having so many connected devices around us potentially introduces new witnesses that can be a reference for forensic investigations. For these reasons, IoT Forensics has become a popular research area with the goal of extracting information from IoT devices to be used as potential evidence. This work presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-Sniffer</i> , a framework to be installed in Wi-Fi access points with the aim of facilitating the extraction of network traffic information from IoT devices, to be later used for forensic purposes. The tool allows the on-the-fly computation of traffic features from connected IoT devices by using a simple user interface for its configuration. After presenting the tool logic and its implementation details, we present an accurate analysis of the tool computational impact on two different consumer Wi-Fi access points. Finally, we present four different IoT forensics use cases, in which network traffic features extracted with the proposed tool from consumer IoT devices are analyzed with machine learning techniques with the goal of 1) identifying the device producing the traffic; 2) recognizing the activity performed by the user; 3) detecting the user’s passage through a room door; and 4) detecting and classifying user interactions with a smart speaker. We conclude the work by presenting an analysis of possible storage optimization for evidence preservation with the use of lossy compression techniques.
Rate-Convergence Tradeoff of Federated Learning Over Wireless Channels

In this article, we consider a federated learning (FL) problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modeled as packet erasure channels (PECs), where the probability of erasure is determined by block length, code rate, and signal-to-noise ratio (SNR). In spite of fluctuations in instantaneous loss of FL, we prove that the expectation of loss converges even in the presence of packet erasure. To mitigate the impact of packet erasure on FL performance, we suggest a paradigm in which the central node (CN) makes use of memory. In particular, we propose two schemes in which, in the event of packet erasure, the CN retains either the most recent local updates or the most recent global parameters. We investigate the impact of coding rate, SNR, and the CN memory on the convergence of FL. For both short- and long-packet communications, we examine a realistic scenario of a massive IoT under the assumption of error-prone transmissions. Our simulation results demonstrate that even a single memory unit has a considerable effect on the FL’s efficiency in erroneous communication.