Reviewerly/exHarmony huggingface dataset:
Home Activity Recognition Using Aggregated Electricity Consumption Data

In this paper, we propose a low-cost, non-invasive home activity recognition method using low-resolution power consumption data. Notably, we tackle the following two challenges. Firstly, we use only the time series of power consumption data aggregated per house and measured every few tens of seconds, which is usually used for demand monitoring by smart meters. We design a set of activities that can be recognized by such low-resolution data, and find out an appropriate feature set to train and test balanced random forest classifiers. Secondly, we consider the divergence of activity patterns seen in different households. Since supervised learning dedicated to each household is not a realistic solution, we arrange different classifiers trained by different household data in supervised learning, and present a method to automatically choose the best-fit classifier for the household of interest in the online phase. The experiment was conducted to collect aggregated power consumption data from eight real homes for 191 days. The result of activity recognition using the dataset shows that the proposed method achieved 70% recognition accuracy in identifying activities like cooking and sleeping, which is significant for non-invasive remote monitoring.
Charactering the photon statistics properties of a squeezed light with a modified-HBT interferometer

By replacing the two single-photon detectors in HBT experiment with a pair of homodyne detectors, a modified-HBT interferometer was constructed. We introduced the principle and procedure for the measurement of second-order correlation function g(2)(0) of light based on the modified-HBT interferometer. The subtraction of dark noise in the process was discussed in detail for the first time. By recording four permutations of the quadratures with the interferometer and then calculating the correlation, the measurement of second-order correlation function for displaced squeezed light was realized, hence the photon statistics properties were characterized. The behavior of g(2)(0) values was totally interpreted with a viewpoint of two-photon interference, which can provide a good physical image for understanding the photon statistics properties of the squeezed light. This technique based on homodyne detectors may provide another approach for the detection and characterization of nonclassical states in the fields of quantum communication processing and quantum metrology.
Fuzzy algorithm-based fault analysis for automated production lines

In this paper, an in-depth analysis of automated production line faults based on fuzzy algorithms is carried out and based on an in-depth investigation of the mechanism of equipment faults, research work on equipment state prediction and production line fault diagnosis is carried out, and the corresponding algorithm model workflow is given, which has some practical application value for improving the accuracy of production line fault prediction. The algorithm with data mining association rules is proposed to extract the confidence parameters of the conditional state fuzzy net model, and an inverse conditional state fuzzy net is established based on the conditional state fuzzy net for fault diagnosis and reasoning, and a dynamic confidence level reasoning mechanism is also established for reverse reasoning based on the iterative algorithm of maximum algebra. To monitor the operating status of the production line more intuitively, a production line fault prediction and analysis system is developed based on the platform, which mainly includes a data management module, state monitoring module, state prediction module, fault diagnosis module, and maintenance advice module, which can more easily realize the monitoring of the production line equipment state and fault early warning prompting, making the system more practical value.
PreCLAS: An Evolutionary Tool for Unsupervised Feature Selection

Several research areas are being faced with data matrices that are not suitable to be managed with traditional clustering, regression, or classification strategies. For example, biological so-called omic problems present models with thousands or millions of rows and less than a hundred columns. This matrix structure hinders the successful progress of traditional data analysis methods and thus needs some means for reducing the number of rows. This article presents an unsupervised approach called PreCLAS for preprocessing matrices with dimension problems to obtain data that are apt for clustering and classification strategies. The PreCLAS was implemented as an unsupervised strategy that aims at finding a submatrix with a drastically reduced number of rows, preferring those rows that together present some group structure. Experimentation was carried out in two stages. First, to assess its functionality, a benchmark dataset was studied in a clustering context. Then, a microarray dataset with genomic information was analyzed, and the PreCLAS was used to select informative genes in the context of classification strategies. Experimentation showed that the new method performs successfully at drastically reducing the number of rows of a matrix, smartly performing unsupervised feature selection for both classification and clustering problems.
Improved KD tree high dimensional index algorithm based on location information

The basic KD tree nearest neighbor search has better retrieval performance for low dimension feature space. However, when there is no time requirement to estimate the parameters from the training data, it is difficult to find the nearest neighbor time in the large training set. The number of observations required in the training data set increases exponentially with the increase of dimension. For the 128 dimensional SIFT feature vector nearest neighbor search in this study, simply using KD tree is not the best choice. So we adopt the method of hierarchical index to solve the problem of large image with global plane constraint information. For large original image, we can assist the next index according to the coarse position information returned by LSH index of upper thumbnail. First, for the lower layer, we need to construct an improved KD tree index structure based on location information. The main idea of KD tree index structure based on position information improvement is to establish a plane coordinate system with the upper left corner of the two-dimensional image plane as the coordinate origin, the horizontal direction as the X axis and the vertical direction as the Y axis. In the process of constructing the fourth resolution of the first five nodes of KD tree, X and Y coordinate information are used alternately to split the nodes. In this way, we can roughly locate the feature matching area to the size of the original image, so as to improve the retrieval accuracy and speed at the same time.
Kernel Function and Dimensionality Reduction Effects on Speaker Verification System

In this research work, we are dealing with an Automatic Speaker Verification problem, which consists on the determination if a person really is the person he/she claims to be, using his/her speech signal characteristics. Therefore, we conducted three series of experiments applied to a subset of Hub4 Broadcast-News database using a Support Vector Machine (SVM) as classifier and the Mel Frequency Spectral Coefficients (MFSC) as speakers’ features. In the first series of experiments, we investigated the effect of the number of features in order to obtain the minimum number that gives a Good Verification Score (GVScore). In the second series of experiments, as there are many types of kernel functions, the appropriate one for speaker verification is investigated. In the last series of experiments, we investigated the GVScore with regard to speaker gender (Male vs. Male, Female vs. Female and Male vs. Female). In our approach, we have used the MFSC as features extraction, which are calculated in both training and testing sessions. The obtained results of the proposed techniques are quite interesting.
Standard Cataloguing of Augmented Objects for a Design Museum

Abstract Demo-ethno-anthropological assets belonging to the cultural heritage are not “autonomous” but represent the nodes of a complex system of relations. Their narration – for the purposes of knowledge and cultural diffusion – cannot transcend this relational dimension that recognises an enormous potential for development in the application of digital culture and in the prudent use of augmented reality. In the perspective of a design museum, it is therefore necessary to accompany each artefact / asset with a digital matrix that provides accurate information in relation to its critical interpretation. The exposure of an augmented object, in fact, allows to read better its use and social value along specific interpretative lines. This essay summarizes the study of a new type of archive – the animated archive of cultural materials – intended as a system of standard cataloguing of objects for a museum on design. Moreover, it is based on the critical comparison between the contribution of ludic humanism and the augmented narrative. This comparison is necessary because the rhetorical and design systems of the playful narrative offers interesting information for the realization of virtual – and non – exhibits, since the type of involvement and the environment required by the spectator is very similar; but above all because games are an important experimentation of interactive models of learning and cultural production.
SDABS: A Secure Cloud Data Auditing Scheme Based on Blockchain and SGX

With the continuous growth of data resources, outsourcing data storage to cloud service providers is becoming the norm. Unfortunately, once data are stored on the cloud platform, they will be out of data owners’ control. Thus, it is critical to guarantee the integrity of the remote data. To solve this problem, researchers have proposed many data auditing schemes, which often employ a trusted role named Third Party Auditor (TPA) to verify the integrity. However, the TPA may not be reliable as expected. For example, it may collude with cloud service providers to hide the fact of data corruption for benefits. Blockchain has the characteristics of decentralization, non-tampering, and traceability, which provides a solution to trace the malicious behaviors of the TPA. Moreover, Intel SGX, as the popular trusted computing technology, can be used to protect the correctness of the auditing operations with a slight performance cost, which excellently serves as the of the blockchain-based solution. In this paper, we propose a secure auditing scheme based on the blockchain and Intel SGX technology, termed SDABS. The scheme follows the properties of storage correctness, data-preserving, accountability, and anti-collusion. The experiment results show that our scheme is efficient.
Object Tracking Through Residual and Dense LSTMs

Visual object tracking task is constantly gaining importance in several fields of application as traffic monitoring, robotics, and surveillance, to name a few. Dealing with changes in the appearance of the tracked object is paramount to achieve high tracking accuracy, and is usually achieved by continually learning features. Recently, deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative, bypassing the need to retrain the feature extraction in an online fashion. Inspired by the success of residual and dense networks in image recognition, we propose here to enhance the capabilities of hybrid trackers using residual and/or dense LSTMs. By introducing skip connections, it is possible to increase the depth of the architecture while ensuring a fast convergence. Experimental results on the Re$$^{3}$$ tracker show that DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances such as occlusions and out-of-view objects. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
Toward Better Planetary Surface Exploration by Orbital Imagery Inpainting

Planetary surface images are collected by sophisticated imaging devices onboard the orbiting spacecraft. Although these images enable scientists to discover and visualize the unknown, they often suffer from the `no-data' region because the data could not be acquired by the onboard instrument due to the limitation in operation time of the instrument and satellite orbiter control. This greatly reduces the usability of the captured data for scientific purposes. To alleviate this problem, in this article, we propose a machine learning-based `no-data' region prediction algorithm. Specifically, we leverage a deep convolutional neural network (CNN) based image inpainting algorithm to predict such unphotographed pixels in a context-aware fashion using adversarial learning on planetary images. The benefit of using our proposed method is to augment features in the unphotographed regions leading to better downstream tasks such as interesting landmark classification. We use the Moon and Mars orbital images captured by the JAXA's Kaguya mission and NASA's Mars Reconnaissance Orbiter (MRO) for experimental purposes and demonstrate that our method can fill in the unphotographed regions on the Moon and Mars images with good visual and perceptual quality as measured by improved PSNR and SSIM scores. Additionally, our image inpainting algorithm helps in improved feature learning for CNN-based landmark classification as evidenced by an improved F1-score of 0.88 compared to 0.83 on the original Mars dataset.COMP: Please replace colons appearing after figure numbers and table numbers with period in all figure and table captions.
Re-examining Einstein’s <i>B</i> coefficient and rate equations with the Rabi model

Starting from the Rabi Hamiltonian, which is useful in arriving at non-perturbative results within the rotating wave approximation, we have found Einstein's $B$ coefficient to be time-dependent: $B(t)\propto|J_0(\omega_\gamma t)|$ for a two-level system (atom or molecule) in thermal radiation field. Here $\omega_\gamma$ is the corresponding Rabi flopping (angular) frequency and $J_0$ is the zeroth order Bessel function of the first kind. The resulting oscillations in the $B$ coefficient---even for very small $\omega_\gamma$---drives the system away from thermodynamic equilibrium at any finite temperature contrary to Einstein's assumption. The time-dependent generalized $B$ coefficient facilitates a path to go beyond Pauli's formalism of non-equilibrium statistical mechanics involving the quantum statistical Boltzmann (master) equation. In this context, we have obtained entropy production of the two-level system by revising Einstein's rate equations, while considering the $A$ coefficient to be the original time-independent one and the $B$ coefficient to be time-dependent.
Towards Secure Application Development: A Cyber Security Centred Holistic Approach

As technology becomes increasingly intertwined with our lives, the adoption of secure development lifecycles- methodologies that aim to embed good security practices into the development process-become more and more important. The ability of secure development lifecycles to reduce the risk of cyber attacks should make them a valuable tool, integral to any organisation that develops new technologies. However, the practicality of secure development lifecycles remains unclear and there is evidence to suggest that their adoption is limited. Consequently, this paper considers the challenges secure development lifecycles present to organisations. More specifically, it considers whether the lack of automation, integration, and secure development approaches limit its adoption. Furthermore, a holistic approach to secure development is explored as a means to overcome the aforementioned challenges.
A Deep Learning Model for Early Prediction of Sepsis from Intensive Care Unit Records

Early and accurate prediction of sepsis could help physicians with proper treatments and improve patient outcomes. We present a deep learning framework built on a bidirectional long short-term memory (BiLSTM) network model to identify septic patients in the intensive care unit (ICU) settings. The fixed value data padding method serves as an indicator to maintain the missing patterns from the ICU records. The devised masking mechanism allows the BiLSTM model to learn the informative missingness from the time series data with missing values. The developed method can better solve two challenging problems of data length variation and information missingness. The quantitative results demonstrated that our method outperformed the other state-of-the-art algorithms in predicting the onset of sepsis before clinical recognition. This suggested that the deep learning based method could be used to assist physicians for early diagnosis of sepsis in real clinical applications.
Towards Forecasting Ground Temperature Along a Pipeline Route

The proposed approach of forecasting ground temperature within geotechnical monitoring systems allows to complex geological, geophysical, geochemical and technological information and to make informed production decisions compensating the impact of linear extended objects on the bearing ground.
Density Estimation Using Multiscale Local Polynomial Transforms

The estimation of a density function with an unknown number of singularities or discontinuities is a typical example of a multiscale problem, with data observed at nonequispaced locations. The data are analyzed through a multiscale local polynomial transform (MLPT), which can be seen as a slightly overcomplete, non-dyadic alternative for a wavelet transform, equipped with the benefits from a local polynomial smoothing procedure. In particular, the multiscale transform adopts a sequence of kernel bandwidths in the local polynomial smoothing as resolution level-dependent, user-controlled scales. The MLPT analysis leads to a reformulation of the problem as a variable selection in a sparse, high-dimensional regression model with exponentially distributed responses. The variable selection is realized by the optimization of the l1-regularized maximum likelihood, where the regularization parameter acts as a threshold. Fine-tuning of the threshold requires the optimization of an information criterion such as AIC. This paper develops discussions on results in [9].
A New Lightweight CRNN Model for Keyword Spotting with Edge Computing Devices

Keyword Spotting (KWS) is a significant branch of Automatic Speech Recognition (ASR), which has been widely used in edge computing devices. The goal of KWS is to provide high accuracy at a low false alarm rate (FAR) while reducing the costs of memory, computation, and latency. However, limited resources are challenging for KWS applications on edge computing devices. Lightweight models and structures for deep learning have achieved good results in the KWS branch while maintaining high accuracy, low computational costs, and low latency. In this paper, we present a new Convolutional Recurrent Neural Network (CRNN) architecture named EdgeCRNN for edge computing devices. EdgeCRNN is based on a depthwise separable convolution (DSC) and residual structure, and it uses a feature enhancement method. The experimental results on Google Speech Commands Dataset depict that EdgeCRNN can test 11.1 audio data per second on Raspberry Pi 3B+, which are 2.2 times that of Tpool2. Compared with Tpool2, the accuracy of EdgeCRNN reaches 98.05% whilst its performance is also competitive.
ESPQuery: An Enhanced Secure Scheme for Privacy-Preserving Query Based on Untrusted Devices in the Internet of Things

The development of the Internet of Things (IoT) has brought various IoT services, which facilitate and enrich human life. All these services are at risk of privacy leakage. The privacy-preserving issue of IoT query, which is a typical service, has attracted much attention. A superior candidate for solving the above issue is classical cryptographic schemes based on the computational difficulty. With the advent of quantum computation, the security of such schemes may be broken by the strong ability of some advanced quantum algorithms. How to design a secure scheme for privacy-preserving query under the threat of quantum computation is crucial. To this end, we construct a general architecture of privacy-preserving query in IoT scenarios. Besides trust or honesty-but-curious models, we present a scheme for privacy-preserving query which is also valid in the scenario of untrusted devices. We provide a detailed security analysis. The result shows our scheme achieves private preservation of both service providers and clients.
A Security Problem in Small Scale Private Ethereum Network

Ethereum is taken as the representative of the 2nd generation of blockchain system, and has been widely used in many applications. Different from Bitcoin system, Ethereum introduces the concept of account, so that the balance inquiry and transaction validation could be performed with lower complexity. Since the information of each account is not stored in the blockchain but in the local database, it is prone to be modified. This paper studied the balance inquiry process in Ethereum based on the source code, and found that the balance of an account could be easily modified, and the value will not be validated based on the state root in the block header when used for transaction validation. Tests based on a small scale private Ethereum network show that if the balance of an account is modified in the database on all participating nodes, the invalid transaction based on the modified balance would be taken as a valid one and packed in the blockchain. This could be a big problem for the application where a private blockchain is built based on Ethereum with small number of nodes.
Blind Evaluation of DCT Domain Spectral Characteristics of Signal-dependent Noise in Images

The problem of noise spatial correlation is relevant for many images acquired by real life systems, and for radar images particularly. Taking into account the characteristics of such noise spectrum can contribute to a significant increase in the efficiency of further processing of these images, for example, in such operations as filtering and compression. In this paper, we propose a blind method for estimation of spectrum of spatially correlated noise in images. The method operates in the DCT domain and is applicable for different types of signal-dependent and mixed noise. Testing carried out for dual-polarization Sentinel-1 radar images has confirmed rather high accuracy of the proposed method.