Reviewerly/exHarmony huggingface dataset:
PF-DA: Pairing Free and Secure Data Aggregation for Energy Internet-Based Smart Meter-to-Grid Communication

The amalgamation of energy, economics and information technology has evolved the paradigm of Energy Internet (EI). This evolution of EI has transformed houses or buildings into home networks (HN) to ease communication between consumer-to-grid. Smart meters deployed at various sites (of HNs) send load, consumption and related information to the control center of the distribution utility. However, data sharing using wireless channels creates associated privacy and security issues and challenges. For example, adversary can capture family life-style from consumption data and may try to commit crime. Besides, data received at control center from various sites must be processed in an efficient manner. Therefore, an efficient protocol for such sharing and processing of data is aggregation scheme. Even though there is a considerable number of data aggregation schemes relying on public-key cryptography have been proposed in the literature. However, most of them are based on the expensive pairing computation and hence are not efficient. To address this issue, in this article we present an efficient and new pairing free and highly secure data aggregation protocol in certificate-based infrastructure. Due to pairing free design, the method’s computational cost is adequately efficient for any resource-constrained devices such as smart appliances or smart meters. The security and performance analysis has been presented to support the deployability of the scheme.
A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]

Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this context, the objective of this paper is to develop a method for detecting the beginning of hard drive malfunction using streaming SMART data, allowing the user to take actions before the breakdown occurs. This is a challenging task for two main reasons. First, there are not usually many examples of failed hard drives. Second, in these few available examples, hard drives are only identified and labeled as failed after complete breakdown occurs, but the exact moment when they begin to malfunction is usually unknown. Both these aspects significantly complicate the supervised learning of hard drive failure prediction models. To cope with these issues, the problem is addressed as a multidimensional time series streaming classification problem based on sliding windows. Moreover, as a solution to the highly imbalanced situation, the learned classifier is optimized to maximize the minimum recall of classes. Experimental results using the Backblaze benchmark dataset show that the proposed method reliably anticipates hard drive failures and obtains a higher balance between the recall values of both classes, failed and correct disks, compared to other state-of-the-art solutions.
Online multimedia traffic classification from the QoS perspective using deep learning

In the next generation of communication systems, data traffic is expected to increase dramatically and continuously. Particularly for multimedia traffic, it has a dominant share in the increasing traffic. Therefore, there is an urgent need to develop an effective and accurate scheme to achieve online and automatic traffic management. To this end, this paper proposes an online multimedia traffic classification framework based on a Convolutional Neural Network (CNN), capable of conducting fast and early classification as well as class incremental learning. First, the sliding window technique is applied to capture the flow slices for further feature extraction. Then, the 3-dimensional flow representation is extracted based on the probability distribution function. After that, according to the specific structure of features, a deeply adapted structure of CNN is devised to better learn the knowledge from the representation. Besides, to better support the addition of new services, a class incremental learning model is developed with the techniques of knowledge distillation and bias correction to achieve continuous learning without retraining from scratch. Our experimental results reveal that the proposed method achieves faster and more accurate traffic classification compared with the state-of-the-art. Additionally, the deployed scheme using incremental learning achieves drops by about 50% in both time and memory consumptions compared with existing methods, while guaranteeing the accurate classification after adding new classes.
Ensembling perturbation-based oversamplers for imbalanced datasets

Oversampling is effective to handle imbalanced data and is often combined with ensemble learning to boost its performance, for examples the synthetic minority oversampling technique (SMOTE) and its ensemble variants SMOTEBagging and SMOTEBoost. However, SMOTE-based methods often introduce unnecessary noises when classes are not well separated, which may impinge upon classifier training and hinder the classification performance. In addition, the aggressive generation of SMOTE-based methods would further complicate the learning process. Therefore, we propose a simple yet effective approach to generate diverse instances to balance the class distribution in a more conservative way in this work. Major differences among the proposed method and existing methods, which also highlight the originality of this work, are listed as follows. Firstly, unlike existing methods which utilize nearest neighbors-based methods to determine regions of interest, we analyze and compute the impact of class imbalance quantitatively for each minority instance in order to guide the data generation process. This ensures instances can be synthesized in a safe and informative region to avoid noise generation. Secondly, new instances are generated via perturbing a random subsets of input features of given seed instances. We show that this process generates instances that share the same asymptotic mean and covariance as given seed instances, thus classifiers with high generalization capability can be achieved. The generation process is repeated to train multiple base classifiers which are then fused via a majority voting to further improve its performance. Nonparametric Wilcoxon test and Friedman test confirm that the proposed method significantly outperforms most reference methods over thirty-five imbalanced datasets in terms of five metrics. The important finding of this work is that we show that ensembling simple perturbation-based oversamplers can yield better performance than many advanced ensemble methods for imbalanced datasets.
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.
Sequence graph transform (SGT): a feature embedding function for sequence data mining

Sequence feature embedding is a challenging task due to the unstructuredness of sequences—arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT’s properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.
Reinforcement Learning for Cart Pole Inverted Pendulum System

Recently, reinforcement learning considered to be the chosen method to solve many problems. One of the challenging problems is controlling dynamic behaviour systems. This paper used policy gradient to balance cart pole inverted pendulum. The purpose of this paper is to balance the pole upright with the movement of the cart. The paper employed two main policy gradient-based algorithms. The results show that PG using baseline has faster episodes than reinforce PG in the training process, reinforce PG algorithm got higher accumulative reward value than PG using baseline.
Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems

Decentralized optimizations have been extensively applied in large-scale industrial cyber-physical systems to achieve control scalability. However, state-of-the-art methods heavily depend on explicit communications between participants, exposing the entire control framework to data confidentiality risks. To overcome this challenge, in this article, a privacy-preserving decentralized multi-agent cooperative optimization paradigm was developed via integrating cryptography into decentralized optimization. The proposed approach can effectively protect participants’ privacy against external eavesdroppers, honest-but-curious agents, and the system operator. Theoretical security and correctness analyses are provided. Simulations of numerical examples and experiments on a real-world platform are given to demonstrate the security, accuracy, and applicability of the proposed method.
Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter

This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements' distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA's loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver's tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.
Towards Generalizable and Robust Face Manipulation Detection via Bag-of-feature

Over the past several years, to solve the problem of malicious abuse of facial manipulation technology, face manipulation detection technology has obtained considerable attention and achieved remarkable progress. However, most existing methods have very impoverished generalization ability and robustness. In this paper, we propose a novel method for face manipulation detection, which can improve the generalization ability and ro-bustness by bag-of-feature. Specifically, we extend Transformers using bag-of-feature approach to encode inter-patch relation-ships, allowing it to learn forgery features without any additional mask supervision. Extensive experiments demonstrate that our method can outperform competing for state-of-the-art methods on FaceForensics++, Celeb-DF and DeeperForensics-l.0 datasets.
Empowering Web Applications with WebAssembly: Are We There Yet?

WebAssembly is the newest web standard. It defines a compact bytecode format that allows it to be loaded and executed fast. While WebAssembly is generally believed to be faster than JavaScript, there have been inconsistent results when it comes to showing which code is faster. Unfortunately, insufficient study has been conducted to understand the performance benefits of WebAssembly. In this paper, we investigate how browser engines optimize WebAssembly execution in comparison to JavaScript. In particular, we measure their execution time and memory usage with diverse programs. Our results show that (1) JIT optimization in Chrome significantly impacts JavaScript speed but has no discernible effect on WebAssembly speed; (2) WebAssembly uses much more memory than JavaScript. We hope that our findings can help WebAssembly virtual machine developers uncover optimization opportunities.
Vehicle Detection Based on Semantic-Context Enhancement for High-Resolution SAR Images in Complex Background

Small-scale target detection (such as vehicles) in complex synthetic aperture radar (SAR) image scenes has always been a pain point for the advanced convolutional neural network (CNN)-based target detectors because of the downsampling operations and the local receptive field characteristics of CNNs. To tackle these limitations, a vehicle detector named SCEDet for the small-scale vehicles in SAR images is proposed to improve the detection performance in this letter. SCEDet mainly consists of two parts: subaperture semantic feature extraction and subaperture semantic-context enhancement (SCE) with SCE module. First, ResNet34 with subaperture decomposition is used to efficiently exploit the latent subaperture semantic features. Then, the SCE module is proposed to balance the multiscale semantic information as well as aggregate the global context information for vehicle detection with a small number of parameters and computation costs. The experimental results on the FARAD dataset (0.1 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times0.1$ </tex-math></inline-formula> m, Ka-band) demonstrate that both the detection performance and the speed are much better than other detection methods under the same hardware conditions.
Learning to solve sequential physical reasoning problems from a scene image

In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion planning (TAMP). Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g., first-order logic) with continuous motion planning such as nonlinear trajectory optimization. The action sequences represent the discrete decisions on a symbolic level, which, in turn, parameterize a nonlinear trajectory optimization problem. Owing to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we introduce deep visual reasoning: based on a segmented initial image of the scene, a neural network directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. Our method generalizes to scenes with many and varying numbers of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene and the goal in (segmented) images as input to the neural network, instead of a fixed feature vector. We show that the framework can not only handle kinematic problems such as pick-and-place (as typical in TAMP), but also tool-use scenarios for planar pushing under quasi-static dynamic models. Here, the image-based representation enables generalization to other shapes than during training. Results show runtime improvements of several orders of magnitudes by, in many cases, removing the need to search over the discrete action sequences.
Cooperative and Interactive Learning to estimate human behaviours for energy applications

A new supervised learning approach is proposed to classify different instances related to human behaviour in a office context. It is based on a combination of Interactive and Cooperative Learning relying only on timed data from a set of sensors and feedback from occupants to collect the training data. The privacy of occupants is granted by avoiding the use of cameras and it can be easily adapted to different non-residential and residential contexts, many profiles of users and different types of data. A new method is introduced to correct inconsistent labels, which assesses the errors and requests new information to the same context (updates). The methodology was tested for both occupancy and activity recognition, settling that using non-numerical labels leads to a more subjective environment but still reliable. Further, two different interaction criteria were analysed: density and spread rate. It was confirmed that the optimal sensors/features selected depend not only on the inhabitants characteristics but also on the interaction methodology applied. This approach can be used to promote sustainable behavioural changes by sending suggestions to the occupants and to study the impact of those suggestions (flexibility). It can be applied to energy management systems improving energy efficiency and performing active demand-side management.
Measurement of a Large-Scale Short-Video Service Over Mobile and Wireless Networks

Short-video sharing services have seen explosive growth in recent years. Compared to conventional video sharing platforms, these have very different characteristics which are far from well-understood. This work aims at filling the gap by measuring and analyzing detailed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-level</i> performance data from a top-10 short video service in China. The application-level data offered detailed and rare insights into many performance metrics of the service, which are otherwise inaccessible to external measurements. The service has a scale of over one billion daily views just for the mobile and wireless segments of the service. Our datasets covered over 22 billion video playbacks, over 100 million video files, served by over 5,000 servers to users across 35 provinces and 13 ISPs in China. We analyzed three aspects of the service: (a) video content characteristics; (b) network analytics; and (c) video streaming analytics. Our results revealed significant differences from conventional video-sharing platforms. These findings will have implications for system designs at all levels. The data also enabled us to conduct an indirect network performance measurement of mobile and wireless network services across China, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">as experienced</i> by the service. These results offer rare insights into mobile and wireless networks' real-world performance in a large country.
DS-Chain: A secure and auditable multi-cloud assisted EHR storage model on efficient deletable blockchain

In recent years, cloud storage service has widely attracted the healthcare industry. Healthcare organizations are gradually outsourcing the massive-scale electronic healthcare records (EHRs) on cloud paradigm. This specialized cloud-assisted EHR storage architecture facilitates the healthcare organizations with high scalability, flexibility, low-cost operations, and availability to outsourced EHRs. As EHR often contains patients’ sensitive medical information, it becomes essential to ensure the confidentiality and proper secrecy of the outsourced EHRs. Moreover, in the cloud-assisted EHR storage model, the patients no longer have direct control over their outsourced EHRs, which may also cause severe integrity and confidentiality related security issues. Presently, the healthcare industry observes that the emerging immutable blockchain technology properly attains the basic security needs for developing a secure cloud assisted EHR storage framework. Therefore, in this paper, we introduce a model named “DS-Chain”1 - a novel deletable consortium blockchain based secure EHR storage model in a multi-cloud paradigm. The model’s key idea is to integrate each EHR outsourcing’s operations into a transaction on a novel efficient consortium blockchain. The proposed model ensures the confidentiality of the EHR’s sensitive medical information and guarantees the proper integrity and correctness of the outsourced EHRs. On the other hand, we employ the collaborative multi-cloud storage model that offers reasonable durability and availability for the outsourced EHRs in the cloud paradigm. Furthermore, we also arrange all the transactions according to their corresponding EHR’s type over the blockchain network, ensuring efficient block deletion. The detailed security analysis with proper formal proofs guarantees the computational intractability of the proposed model. Finally, the extensive performance analysis with test-simulations illustrates the feasibility and practicability of the proposed model.