Reviewerly/exHarmony huggingface dataset:
BWFSO: Hybrid Black-widow and Fish swarm optimization Algorithm for resource allocation and task scheduling in cloud computing

Cloud computing has been booming technology in recent years. It is used to share resources over the internet. Though cloud has many advantages and is used worldwide. It also has some disadvantages and issues. The major problem in cloud computing is scheduling and resource allocation. Allocating resources and tasks is one of the highly critical challenges. There are no proper methods or techniques to improve task scheduling and resource allocation. Previous methods used Virtual machine (VM) instances for scheduling. The major drawback of using Virtual machine instances is that it takes a lot of startup time and consumes all the resources to perform the task. In this paper, we have proposed a solution with fuzzy C-means clustering hybrid algorithms of using Black widow optimization for task scheduling and fish swarm optimization for efficient resource allocation to reduce cost, energy, resource utilization.
Simple and Effective: Spatial Rescaling for Person Reidentification

Global average pooling (GAP) allows convolutional neural networks (CNNs) to localize discriminative information for recognition using only image-level labels. While GAP helps CNNs to attend to the most discriminative features of an object, e.g., head of a bird or one man's bag, it may suffer if that information is missing due to camera viewpoint changes and intraclass variations in some tasks. To circumvent this issue, we propose one new module to help CNNs to see more, namely, Spatial Rescaling (SpaRs) layer. It introduces spatial relations among the feature map activations back to the model, guiding the model to focus on a broad area in the feature map. With simple implementation, it can be inserted into CNNs of various architectures directly. SpaRs layer consistently improves the performance over the reidentification (re-ID) models. Besides, the new module based on different normalization methods also demonstrates the superiority of fine-grained and general image classification benchmarks. The visualization method shows the changes in activated regions when equipped with the SpaRs layer for better understanding. Our code is publicly available at https://github.com/HRanWang/Spatial-Re-Scaling.
snWF: Website Fingerprinting Attack by Ensembling the Snapshot of Deep Learning

The website fingerprinting (WF) attack enables a local eavesdropper to identify which website a client is visiting under encrypted network connections. By leveraging deep neural networks, the state-of-the-art WF attacks achieve high accuracy in classic experimental scenes. However, due to the high variance of neural networks, those attacks are sensitive to the specific information in the data and would result in less-than-ideal performance on data outside the training set or on data that is impacted by the effect of concept drift. In this paper, we present snWF, a novelWF attack, which leverages an out-of-the ordinary ensemble to reduce the variance of neural networks and improve the robustness of the attack. In a large open-world setting with 400,000 websites, snWF manages to determine whether a user is visiting a monitored website, with a true positive rate of 98.1% and a false positive rate of 5.7%. We also evaluated snWF in a more realistic attack scenario, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wide-world</i> , to examine whether snWF can correctly classify websites that even an adversary has not seen before, and we found that snWF achieves a higher classification accuracy than the state-of-the-art attacks in this new setting. In addition, in the face of concept drift, snWF is found to be more resilient than any other attacks. Moreover, we are the first to reveal that under concept drift WF attacks suffer more severe performance degradation in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open-world</i> setting than in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-world</i> setting.
A Real-Time Trajectory Optimization Method for Hypersonic Vehicles Based on a Deep Neural Network

Considering the high-efficient trajectory planning requirements for hypersonic vehicles, this paper proposes a real-time trajectory optimization method based on a deep neural network. First, the trajectory optimization model of the hypersonic vehicle reentry phase is developed. The pseudo-spectral method is used to perform the trajectory optimization offline, and multiple optimal trajectory data are obtained. In addition, based on the inherent relationship between the state and control variables of a trajectory, a neural network is established to predict the current control outputs. The sample library of optimal trajectory data is used to train the parameters of the deep neural network to obtain an optimal neural network model. Finally, the simulation verification of the hypersonic vehicle reentry phase is performed. The simulation results show that under the condition of the initial value deviation and environmental interference, the proposed deep learning-based method can achieve a fast generation of hypersonic vehicle optimal trajectories, while achieving the advantages of high computational efficiency and reliability. Compared to traditional trajectory optimization algorithms, the proposed method has the generalization capability that satisfies the accuracy requirements and meets the demands of online real-time trajectory optimization.
Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energyefficient architecture design.The designed device is based on Bluetooth low energy (BLE) and NFC connectivity.BLE is used to exchange data with a gateway, whereas NFC has been chosen as an energy-efficient wake-up mechanism.A temperature and humidity sensor is embedded to monitor storage conditions and an accelerometer for tool usage monitoring.The ARM Cortex-M4 core embedded in the BLE module is exploited to process the information at the edge.A Tiny Machine Learning (TinyML) algorithm is proposed to process the data directly on board and achieve low latency and high energy efficiency.The TinyML algorithm has been developed embedded in the proposed device to detect four different usage classes (tool transportation, no-load, metal, and wood drilling).A dataset containing more than 280 min of three-axis accelerations during different activities has been acquired with the device attached to a construction rotary hammer drill and used to train and validate the algorithm.A neural architecture search has been performed to optimize the trade-off between accuracy and complexity, achieving an accuracy of 90.6% with a model size of roughly 30 kB.The experimental results showed an ultralow power consumption in sleep mode of 550 nA and a peak power consumption of 8 mA while running TinyML on the edge.This results in a balanced combination of edge processing capabilities and low power consumption, enabling to obtain a smart Internet of Things (IoT) device in the field with a long lifetime of up to four years in operation and 17 years in shelf mode with a standard 250-mAh coin battery.This work enables a long battery lifetime operation of device degradation and utility analysis, further closing the gap between edge processing and fine granularity data evaluation.
ReDefender: Detecting Reentrancy Vulnerabilities in Smart Contracts Automatically

As one of the most complex types of vulnerabilities, reentrancy poses a significant threat to smart contract development. Indeed, millions of dollars have evaporated due to reentrancy vulnerabilities of smart contracts in past years. In this article, we propose a new approach to detect reentrancy vulnerabilities using fuzz testing and develop a novel tool named ReDefender. Our approach consists of three main steps: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">preprocess contract to be detected:</i> when a contract is uploaded, its source code will be preprocessed to extract candidate pool for fuzzing and dependency graph which guides the automatic deployment of contracts; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzing input generation:</i> fuzzing input is generated to constitute transactions which will be sent to an agent contract to stimulate attacks, where runtime information is collected and recorded in the execution log during each execution; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vulnerability verification:</i> the execution log is analyzed to determine whether a reentrancy process occurs and whether the reentrancy process is malicious. We conduct comparative experiments on 204 tagged smart contracts and 90 injected contracts. The results show higher accuracy and lower false negative rate of ReDefender than that of the other three famous tools. Moreover, we conduct an experiment on 4776 real-world contracts demonstrating the ability of ReDefender to find reentrancy vulnerabilities that really cause economic losses.
Improving Calibration and Out-of-Distribution Detection in Deep Models for Medical Image Segmentation

Convolutional Neural Networks (CNNs) have proved to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these models. Specifically, training of these models on small medical image datasets is still challenging, with many studies promoting techniques such as transfer learning. Moreover, these models are infamous for producing over-confident predictions and for failing silently when presented with out-of-distribution (OOD) test data. In this paper, for improving prediction calibration we advocate for multi-task learning, i.e., training a single model on several different datasets, spanning different organs of interest and different imaging modalities. We show that multi-task learning can significantly improve model confidence calibration. For OOD detection, we propose a novel method based on spectral analysis of CNN feature maps. We show that different datasets, representing different imaging modalities and/or different organs of interest, have distinct spectral signatures, which can be used to identify whether or not a test image is similar to the images used for training. We show that our proposed method is more accurate than several competing methods, including methods based on prediction uncertainty and image classification.
A Learned Stereo Depth System for Robotic Manipulation in Homes

We present a passive stereo depth system that produces dense and accurate point clouds optimized for human environments, including dark, textureless, thin, reflective and specular surfaces and objects, at 2560 × 2048 resolution, with 384 disparities, in 30 ms. The system consists of an algorithm combining learned stereo matching with engineered filtering, a training and data-mixing methodology, and a sensor hardware design. Our architecture is 15× faster than approaches that perform similarly on the Middlebury and Flying Things Stereo Benchmarks. To effectively supervise the training of this model, we combine real data labelled using off-the-shelf depth sensors, as well as a number of different rendered, simulated labeled datasets. We demonstrate the efficacy of our system by presenting a large number of qualitative results in the form of depth maps and point-clouds, experiments validating the metric accuracy of our system and comparisons to other sensors on challenging objects and scenes. We also show the competitiveness of our algorithm compared to state-of-the-art learned models using the Middlebury and FlyingThings datasets.
ActivFORMS: A Formally Founded Model-based Approach to Engineer Self-adaptive Systems

Self-adaptation equips a computing system with a feedback loop that enables it to deal with change caused by uncertainties during operation, such as changing availability of resources and fluctuating workloads. To ensure that the system complies with the adaptation goals, recent research suggests the use of formal techniques at runtime. Yet, existing approaches have three limitations that affect their practical applicability: (i) they ignore correctness of the behavior of the feedback loop, (ii) they rely on exhaustive verification at runtime to select adaptation options to realize the adaptation goals, which is time- and resource-demanding, and (iii) they provide limited or no support for changing adaptation goals at runtime. To tackle these shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation). ActivFORMS contributes an end-to-end approach for engineering self-adaptive systems, spanning four main stages of the life cycle of a feedback loop: design, deployment, runtime adaptation, and evolution. We also present ActivFORMS-ta, a tool-supported instance of ActivFORMS that leverages timed automata models and statistical model checking at runtime. We validate the research results using an IoT application for building security monitoring that is deployed in Leuven. The experimental results demonstrate that ActivFORMS supports correctness of the behavior of the feedback loop, achieves the adaptation goals in an efficient way, and supports changing adaptation goals at runtime.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequential recommender models consider the next item prediction task as the training signal. Unfortunately, there are two essential challenges for these methods: (1) the long-term preference is difficult to capture, and (2) the supervision signal is too sparse to effectively train a model. In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec. The basic contrastive predictive coding (CPC) serves as encoders of sequences and items. The memory module is designed to augment the autoregressive prediction in CPC to enable a flexible and general representation of the encoded preference, which can improve the ability to capture the long-term preference. For effective training of the MMInfoRec model, a novel multi-instance noise contrastive estimation (MINCE) loss is proposed, using multiple positive samples, which offers effective exploitation of samples inside a mini-batch. The proposed MMInfoRec framework falls into the contrastive learning style, within which, however, a further finetuning step is not required given that its contrastive training task is well aligned with the target recommendation task. With extensive experiments on four benchmark datasets, MMInfoRec can outperform the state-of-the-art baselines.
ISBFK-means: A new clustering algorithm based on influence space

The time overhead is huge and the clustering quality is unstable when running the K-means algorithm on massive raw data. To solve these problems, the concept of the influence space is introduced, and on this basis, a new clustering algorithm named ISBFK-means based on the influence space is proposed in this paper. First, the influence space divides the given data set into multiple small regions. Then, the representative data objects in each region are obtained to form a new data set, in which the class labels of representative data objects are those of all the data objects in the correlation influence space. Next, the K-means clustering is performed on the new data set, thereby obtaining the final clustering result. Theoretical analysis and experimental results show that this approach effectively reduces the amount of data in the clustering process and improves the stability of clustering quality. As a major feature of this work, the celestial spectral data observed by the LAMOST survey are especially employed to verify the algorithm ISBFK-means . The experimental results indicate that this algorithm has higher performance than other similar algorithms on the correctness, efficiency and sensitivity to the quality of spectral data. • The impact of outliers on clustering results is reduced using the influence space. • Representative data objects of the original datasets are extracted under the influence space. • A clustering algorithm called ISBFK-means is proposed based on influence space. • Valuable information hidden in LAMOST low-quality spectra is revealed by ISBFK-means algorithm.
Projected federated averaging with heterogeneous differential privacy

Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important design goals. Many existing efforts achieve rigorous privacy by ensuring differential privacy for intermediate model parameters, however, they assume a uniform privacy parameter for all the clients. In practice, different clients may have different privacy requirements due to varying policies or preferences. In this paper, we focus on explicitly modeling and leveraging the heterogeneous privacy requirements of different clients and study how to optimize utility for the joint model while minimizing communication cost. As differentially private perturbations affect the model utility, a natural idea is to make better use of information submitted by the clients with higher privacy budgets (referred to as "public" clients, and the opposite as "private" clients). The challenge is how to use such information without biasing the joint model. We propose &lt;u&gt;P&lt;/u&gt; rojected &lt;u&gt;F&lt;/u&gt; ederated &lt;u&gt;A&lt;/u&gt; veraging (PFA), which extracts the top singular subspace of the model updates submitted by "public" clients and utilizes them to project the model updates of "private" clients before aggregating them. We then propose communication-efficient PFA+, which allows "private" clients to upload projected model updates instead of original ones. Our experiments verify the utility boost of both algorithms compared to the baseline methods, whereby PFA+ achieves over 99% uplink communication reduction for "private" clients.
PMRQ: Achieving Efficient and Privacy-Preserving Multidimensional Range Query in eHealthcare

Healthcare data explosion and cloud computing booming have motivated healthcare centers to outsource their healthcare data and data-driven services to a powerful cloud. Nevertheless, due to privacy concerns, the data are usually encrypted before being outsourced, which will degrade the data utility and make it challenging to implement data-driven services. Although the multidimensional range query over encrypted data, as one of the most popular outsourced services in eHealthcare, has been extensively studied, existing solutions still have some limitations in efficiency, privacy, and practicality. Aiming at this challenge, in this article, we design an efficient and privacy-preserving multidimensional range query (PMRQ) scheme. We first build an R-tree to index the data set and reduce the R-tree-based range queries to the multidimensional range intersection problem. Then, by delicately designing a data comparison algorithm and a homomorphic encoding technique, we present an encoding-based range intersection algorithm. After that, by employing matrix encryption to protect the privacy of the encoding-based range intersection algorithm, we design a multidimensional range intersection predicate encryption (MRIPE) scheme. Based on the MRIPE scheme, we then propose our PMRQ scheme. A detailed security analysis illustrates that our PMRQ scheme is privacy preserving, and experimental results demonstrate that it is computationally efficient.
Edge-Compatible Convolutional Autoencoder Implemented on FPGA for Anomaly Detection in Vibration Condition-Based Monitoring

Estimating the state of the health of machines using vibration signals analyzed with complex deep-learning models poses memory and processing speed constraints for edge implementation, whereas cloud-based computing poses high latency, high cost of data transmission and storage, and privacy threats. Moreover, in most practical settings, a large amount of data may be available or easily obtainable for machines in healthy/normal operating condition, and not in anomalous conditions. Thus, there exists a need to develop lightweight unsupervised learning based algorithms that can be implemented on an embedded platform for data processing and estimating state of health. In this letter, we present a lightweight convolutional autoencoder (CAE) implemented on a low-cost FPGA platform (PYNQ-Z2, Xilinx Zynq-7020 SoC) to discern anomalies in vibration data sets. We present an extensive performance analysis of various encoding methods to represent 1-D time-series as images and the feasibility analysis of FPGA-implementation of CAE. The optimized model has less than 10 000 trainable parameters and shows the highest accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 88% on CPU, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 85% with fixed point arithmetic on FPGA) using scalogram encoding when trained and tested on the Airbus SAS helicopter accelerometer dataset.
A novel word sense disambiguation approach using WordNet knowledge graph

Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human's natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document's main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms' order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.