Reviewerly/exHarmony huggingface dataset:
Hybrid deep learning for power generation forecasting in active solar trackers

To meet the growing electricity demand for consumers, it is necessary to use more efficient systems. The solar trackers stand out among the applications that can improve the efficiency of photovoltaic panel generation by increasing their solar uptake. For solar trackers to be more efficient, they can base their position update on a generation forecast and thus perform the control action only when there is greater efficiency in this update. For generation forecast, the long–short-term memory (LSTM) can handle a large volume of non-linear data. Furthermore, to improve the analysis, it is possible to apply signal filtering techniques. The wavelet energy coefficient is a technique used to reduce signal noise and extract features; this technique performs the filter and preserves the signal characteristic. In this study, the authors present a combination of wavelet energy coefficient and LSTM, defined as wavelet LSTM, to perform photovoltaic power forecasting in the dual-axis solar trackers.
Activity recognition and anomaly detection in smart homes

Physical and cognitive impairments decline the ability of elderly in execution of daily activities, such as eating, sleeping or taking medication. The proposed approach recognizes the activities performed in a smart home, and separates the normal from the anomalous activities. Moreover, we identify the anomalous days based on the number of activities performed in a day. We perform activity recognition by applying probabilistic neural network on the pre-segmented activity-data obtained from the sensors deployed at different locations in a smart home. We use H2O autoencoder to identify the anomalous from the normal instances of activities. We further categorize the anomalies based on the criteria such as missing or extra subevents, and unusual duration of activity. Since the ground truth of the anomalies is unavailable, we generate the ground truth using the boxplots of the duration, and the number of subevents in an activity. We provide the quantified results of activity recognition and anomaly detection that can be further used by the research community. A comprehensive evaluation of the proposed approach on two publicly available CASAS smart home datasets demonstrates its ability in the activity recognition and the correct identification of anomalies.
Portable X‐ray fluorescence spectrometry analysis of soils

Abstract Portable X‐ray fluorescence (PXRF) spectrometry is a proximal sensing technique whereby low‐power X‐rays are used to make elemental determinations in soils. The technique is rapid, portable, and provides multi‐elemental analysis with results generally comparable to traditional laboratory‐based techniques. Elemental data from PXRF can then be either used directly for soil parameter assessment (e.g., total Ca, total Fe) or as a proxy for predicting other soil parameters of interest (e.g., soil cation‐exchange capacity [CEC], soil reaction, soil salinity) via simple or multiple linear regression. Importantly, PXRF does have some limitations that must be considered in the context of soil analysis. Those notwithstanding, PXRF has proven effective in numerous, agronomic, pedological, and environmental quality assessment applications.
Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series

Detecting anomalies in time series is a vital technique in a wide variety of industrial application in which sensors monitor expensive machinery. The complexity of this task increases when heterogeneous sensors provide information of different attributes, scales and characteristics from the same machine. Actually, the challenges of anomaly detection for industrial time series are to design effective pre-processing, feature extraction and overcome the lack of abnormal samples. Recent deep learning models have shown prominent abilities on raw multivariate time series, alleviating these previous works. In this work, a novel framework named multi-time scale deep convolutional generative adversarial network (MTS-DCGAN) is proposed to deal with anomaly detection of industrial time series. Firstly, multivariate time series are transformed into the multi-channel signature matrices via sliding window based cross-correlation computation, and therein forgetting mechanism is introduced to effectively avoid false alarms due to excessive influence of old sequences. Then the framework conducts an unsupervised adversarial training of multi-channel signature matrices and capture their hidden features via deep convolution structure. Besides, a novel threshold setting strategy is proposed to optimize anomaly detection performance under imbalance of normal and abnormal data. Finally, the proposed framework is assessed against the experiments on four datasets. Within this study, experimental results show our framework outperforms the comparison algorithms in terms of model performance and robustness, providing an effective anomaly detection method for industrial multivariate time series.
Pilot-assisted intradyne reception for high-speed continuous-variable quantum key distribution with true local oscillator

We present a pilot-assisted coherent intradyne reception methodology for CV-QKD with true local oscillator. An optically phase-locked reference tone, prepared using carrier-suppressed optical single-sideband modulation, is multiplexed in polarisation and frequency to the 250 Mbaud quantum signal in order to provide optical frequency- and phase matching between quantum signal and local oscillator. Our concept allows for high symbol rates and can be operated at an extremely low excess-noise level, as validated by experimental measurements.
A Pairwise Attentive Adversarial Spatiotemporal Network for Cross-Domain Few-Shot Action Recognition-R2

- Action recognition is a popular research topic in the computer vision and machine learning domains. Although many action recognition methods have been proposed, only a few researchers have focused on cross-domain few-shot action recognition, which must often be performed in real security surveillance. Since the problems of action recognition, domain adaptation, and few-shot learning need to be simultaneously solved, the cross-domain few-shot action recognition task is a challenging problem. To solve these issues, in this work, we develop a novel end-to-end pairwise attentive adversarial spatiotemporal network (PASTN) to perform the cross-domain few-shot action recognition task, in which spatiotemporal information acquisition, few-shot learning, and video domain adaptation are realised in a unified framework. Specifically, the Resnet-50 network is selected as the backbone of the PASTN, and a 3D convolution block is embedded in the top layer of the 2D CNN (ResNet-50) to capture the spatiotemporal representations. Moreover, a novel attentive adversarial network architecture is designed to align the spatiotemporal dynamics actions with higher domain discrepancies. In addition, the pairwise margin discrimination loss is designed for the pairwise network architecture to improve the discrimination of the learned domain-invariant spatiotemporal feature. The results of extensive experiments performed on three public benchmarks of the cross-domain action recognition datasets, including SDAI Action I, SDAI Action II and UCF50-OlympicSport, demonstrate that the proposed PASTN can significantly outperform the state-of-the-art cross-domain action recognition methods in terms of both the accuracy and computational time. Even when only two labelled training samples per category are considered in the office1 scenario of the SDAI Action I dataset, the accuracy of the PASTN is improved by 6.1%, 10.9%, 16.8%, and 14% compared to that of the TA3N , TemporalPooling, I3D, and P3D methods, respectively.
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefitting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and Chebyshev fitting, the proposed shape signature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalability (Source code at SSN and also available at mmdetection3d soon.).
FedCoin: A Peer-to-Peer Payment System for Federated Learning

Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly and quantitatively assess their contributions. However, the calculation of SV is time-consuming and computationally costly. In this chapter, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities “mine” new blocks by solving meaningless puzzles. Based on the computed SVs, we propose a scheme for dividing the incentive payoffs among FL participants with non-repudiation and tamper-resistance properties. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL participants through accurately computing SVs with an upper bound on the computational resources required for reaching block consensus. It opens opportunities for non-data owners to play a role in FL.
Bias-Based Universal Adversarial Patch Attack for Automatic Check-Out

Adversarial examples are inputs with imperceptible perturbations that easily misleading deep neural networks (DNNs). Recently, adversarial patch, with noise confined to a small and localized patch, has emerged for its easy feasibility in real-world scenarios. However, existing strategies failed to generate adversarial patches with strong generalization ability. In other words, the adversarial patches were input-specific and failed to attack images from all classes, especially unseen ones during training. To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models. Regarding the perceptual bias, since DNNs are strongly biased towards textures, we exploit the hard examples which convey strong model uncertainties and extract a textural patch prior from them by adopting the style similarities. The patch prior is more close to decision boundaries and would promote attacks. To further alleviate the heavy dependency on large amounts of data in training universal attacks, we further exploit the semantic bias. As the class-wise preference, prototypes are introduced and pursued by maximizing the multi-class margin to help universal training. Taking Automatic Check-out (ACO) as the typical scenario, extensive experiments including white-box/black-box settings in both digital-world (RPC, the largest ACO related dataset) and physical-world scenario (Taobao and JD, the worlds largest online shopping platforms) are conducted. Experimental results demonstrate that our proposed framework outperforms state-of-the-art adversarial patch attack methods.(Our code can be found at https://github.com/liuaishan/ModelBiasedAttack .)
TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification

Recently, Few-Shot Learning (FSL), or learning from very few (typically 1 or 5) examples per novel class (unseen during training), has received a lot of attention and significant performance advances. While number of techniques have been proposed for FSL, several factors have emerged as most important for FSL performance, awarding SOTA even to the simplest of techniques. These are: the backbone architecture (bigger is better), type of pre-training (meta-training vs multi-class), quantity and diversity of the base classes (the more the merrier), and using auxiliary self-supervised tasks (a proxy for increasing the diversity). In this paper we propose TAFSSL, a simple technique for improving the few shot performance in cases when some additional unlabeled data accompanies the few-shot task. TAFSSL is built upon the intuition of reducing the feature and sampling noise inherent to few-shot tasks comprised of novel classes unseen during pre-training. Specifically, we show that on the challenging miniImageNet and tieredImageNet benchmarks, TAFSSL can improve the current state-of-the-art in both transductive and semi-supervised FSL settings by more than $$5\%$$ , while increasing the benefit of using unlabeled data in FSL to above $$10\%$$ performance gain.
Distream

Video cameras have been deployed at scale today. Driven by the breakthrough in deep learning (DL), organizations that have deployed these cameras start to use DL-based techniques for live video analytics. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world deployments. In this work, we present Distream, a distributed live video analytics system based on the smart camera-edge cluster architecture, that is able to adapt to the workload dynamics to achieve low-latency, high-throughput, and scalable live video analytics. The key behind the design of Distream is to adaptively balance the workloads across smart cameras and partition the workloads between cameras and the edge cluster. In doing so, Distream is able to fully utilize the compute resources at both ends to achieve optimized system performance. We evaluated Distream with 500 hours of distributed video streams from two real-world video datasets with a testbed that consists of 24 cameras and a 4-GPU edge cluster. Our results show that Distream consistently outperforms the status quo in terms of throughput, latency, and latency service level objective (SLO) miss rate.
Combination of Steganography and Cryptography: A short Survey

Abstract The establishment of a secure communication between two communicating parties is becoming a difficult problem due to the likelihood of attacks and other unintentional changes during an active communication over an unsecured network. However, the security of secret information can be secured using either cryptography or steganography. Steganography refers to the practice of concealing a message (with no traceability) in a manner that it will make no meaning to anyone else except the intended recipient, while cryptography, on the other hand, refers to the art of converting a plaintext (message) into an unreadable format. Thus, steganography conceals the existence of a secret message while cryptography alters the message format itself. Both steganographic and cryptographic techniques are powerful and robust. In this paper, the major aim is to review several ways of combining steganographic and cryptographic techniques to achieve a hybrid system. Moreover, some of the differences between cryptographic and steganographic techniques were presented as well.
Reversible data hiding method for multi-histogram point selection based on improved crisscross optimization algorithm

Traditional prediction error expansion (PEE)-based reversible data hiding (RDH) schemes focus on generating a sharp prediction error histogram (PEH) in order to increase embedding performance, but they neglect the influence of local complexity on embedding performance. When the PEH is partitioned into multiple sub-histograms by means of the local complexity, the embedding performance can be increased by excluding the embedding of data in the sub-histograms located in the texture regions. However, multiple sub-histograms also cause a problem about how to identify the optimal embedding points that can achieve the best visual quality for a given payload. Violent iteration is often used in RDH schemes to traverse all possible values of the embedding points in order to identify the optimal points. Therefore, we conclude that violent iteration is a very time-consuming method and that it leads to unacceptable computational complexity. The traditional multiple sub-histogram methods usually reduce the solution space (i.e., all possible combinations of the embedding points) to decrease the computational complexity. However, the solution that is obtained from the aforementioned methods may significantly deviate from the global optimal solution. Instead of shrinking the solution space by abandoning most solutions, we improve the crisscross optimization algorithm in order to search for the optimal solution in the global solution space. In this paper, the K-means clustering algorithm is used to classify all prediction errors into multiple categories according to the local complexity. Each category would generate a PEH. Subsequently, the problem of selecting the embedding points of multiple sub-histograms is transformed into a typical and multi-choice knapsack problem. The improved crisscross optimization algorithm is used to determine the approximate optimal solution. The experimental results showed that our scheme provided effective performance.
Explainable Face Recognition

Explainable face recognition (XFR) is the problem of explaining the matches returned by a facial matcher, in order to provide insight into why a probe was matched with one identity over another. In this paper, we provide the first comprehensive benchmark and baseline evaluation for XFR. We define a new evaluation protocol called the "inpainting game", which is a curated set of 3648 triplets (probe, mate, nonmate) of 95 subjects, which differ by synthetically inpainting a chosen facial characteristic like the nose, eyebrows or mouth creating an inpainted nonmate. An XFR algorithm is tasked with generating a network attention map which best explains which regions in a probe image match with a mated image, and not with an inpainted nonmate for each triplet. This provides ground truth for quantifying what image regions contribute to face matching. Finally, we provide a comprehensive benchmark on this dataset comparing five state-of-the-art XFR algorithms on three facial matchers. This benchmark includes two new algorithms called subtree EBP and Density-based Input Sampling for Explanation (DISE) which outperform the state-of-the-art XFR by a wide margin.
Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM

Anomaly detection of satellite telemetry data has always been a significant issue in the development of aeronautics and astronautics. Timely and effective anomaly detection method of satellite telemetry data is a research hotspot in academia and aerospace industry. For satellite telemetry data, we propose an anomaly detection model based on Bayesian deep learning without domain knowledge. In this model, we show the feasibility of implementing MC-dropout on the Long Short-Term Memory Network (LSTM), and establish the Bayesian LSTM. First, we perform a preliminary anomaly detection task through our model—Monte Carlo Dropout Bidirectional Long Short-term Memory Network (MCD-BiLSTM). Then, Monte Carlo (MC) Sampling Variance, Prediction Entropy and Mutual Information are taken to measure the uncertainty of output through MCD-BiLSTM. What's more, we further explore and exploit the three types of uncertainties, and utilize the variational auto-encoder (VAE) to reevaluate the high uncertainty samples to improve the anomaly detection capability. To our knowledge, it is the first time that Bayesian neural networks have been introduced into the field of satellite telemetry data anomaly detection. The experimental results on an imbalanced satellite telemetry dataset show that our proposed model can add effective regularization constraints, and obtain great robustness on imbalanced data, which performs better than popular traditional neural networks and other Bayesian neural networks.
Returns and volume: Frequency connectedness in cryptocurrency markets

The present paper investigates the causal link between returns and volume in cryptocurrency markets. Relative to earlier empirical studies on the topic, it employs a flexible methodological approach that allows the intensity and the pattern of the link to vary with the traders’ planning horizon (shorter vs longer runs) and the nature of the preceding price shocks (positive vs negative). Causality is bi-directional implying that: (a) volume-based technical analysis can lead to higher profits and (b) trend-following is likely to be a common investment strategy among rational uninformed traders. There is, therefore, scope for establishing appropriate disclosure systems to deal with information asymmetry and the risk associated with it. In the longer-run, spillovers from returns to volume are stronger than those in the opposite direction. The same, generally, applies to spillovers from positive relative to negative returns. Consequently, returns (especially the positive ones) are more likely to induce fundamental changes in traders’ expectations compared to volume.
Efficient CP-ABE Scheme With Shared Decryption in Cloud Storage

Attribute-based encryption (ABE) is a preferred technology used to access control the data stored in the cloud servers. However, in many cases, the authorized decryption user may be unable to decrypt the ciphertext in time for some reason. To be on the safe side, several alternate users are delegated to cooperate to decrypt the ciphertext, instead of one user doing that. We provide a ciphertext-policy ABE scheme with shared decryption in this article. An authorized user can recover the messages independently. At the same time, these alternate users (semi-authorized users) can work together to get the messages. We also improve the basic scheme to ensure that the semi-authorized users perform the decryption tasks honestly. An integrated access tree is used to improve the efficiency for our scheme. The new scheme is proved CPA-secure in the standard model. The experimental result shows that our scheme is very efficient on both computational overhead and storage cost.