Reviewerly/exHarmony huggingface dataset:
Unsupervised Deep Tensor Multitask Anomaly Detection With Rule Adaptation for Online Early Fault Evaluation

This article tries to solve the problem of online early fault detection (EFD) with unlabeled streaming data by addressing the following challenges: 1) no state information is available for online data; 2) online working condition is not determined in advance; and 3) false alarm should be avoided. This article first proposes a deep tensor multitask anomaly detection (Tensor-MAD) model with rule adaptation for the online EFD. Running on multitask learning architecture, Tensor-MAD builds a new tensorized pooling filter to keep the essential information of each task’s operating status from noisy data. With hypersphere-based one-class detection rule representation, Tensor-MAD further constructs a new rule adaptation mechanism to transfer the detection rule from offline labeled data to the online unlabeled data. A training algorithm with an alternating minimization scheme is also provided to update tensor decomposition and rule adaptation. Then, the optimal information filter level and the rule adaptation degree can be determined. Based on the obtained anomalies, this article proposes a nonparametric alarm threshold setting method based on the sequential accumulation of anomaly probability. This threshold can be adaptively chosen once an expected false alarm rate (FAR) is given. A rationality proof is also provided. Experimental results on the IEEE PHM Challenge 2012 bearing dataset demonstrate that the proposed approach can adaptively and accurately evaluate the early fault occurrence from unlabeled streaming data. More importantly, the proposed approach has a much lower FAR and a faster convergence speed, providing an easy-to-deploy and reliable solution for online EFD.
Restricting the distribution of visual attention reduces cybersickness

Abstract This study investigated whether increased attention to the central or peripheral visual field can reduce motion sickness in virtual reality (VR). A recent study found that increased attention to the periphery during vection was correlated with lower self-reported motion sickness susceptibility, which suggests that peripheral attention might be beneficial for avoiding cybersickness. We tested this experimentally by manipulating visual attention to central vs. peripheral fields during VR exposure. We also measured attention to the periphery during vection and motion sickness susceptibility to attempt to replicate the previous results. In Experiment 1, task-relevant cues to target locations were provided in the central or peripheral field during navigation in VR, and we found no differences in motion sickness. In Experiment 2, attention to the center or periphery was manipulated with a dot-probe task during passive VR exposure, and we found that motion sickness was greater in the condition that required attention to the periphery. In both experiments, there was no correlation between baseline attentional allocation and self-reported motion sickness susceptibility. Our results demonstrate that restricting attention to the central visual field can decrease cybersickness, which is consistent with previous findings that cybersickness is greater with large FOV.
LAS: Locality-Aware Scheduling for GEMM-Accelerated Convolutions in GPUs

This article presents a graphics processing unit (GPU) scheduling scheme that maximizes the exploitation of data locality in deep neural networks (DNNs). Convolution is one of the fundamental operations used in DNNs and accounts for more than 90% of the total execution time. To leverage massive thread-level parallelism (TLP) in a GPU, deeply nested convolution loops are lowered (or unrolled) into large matrix multiplication, which trades memory capacity and bandwidth for TLP augmentation. A large workspace matrix is split into tiles of general matrix multiplication (GEMM) and concurrently executed by many thread blocks. Notably, the workspace is filled with a number of duplicate data that originate from the same sources in the input feature map during the lowering process. However, conventional GPU scheduling is oblivious to data duplication patterns in the workspace, and thread blocks are assigned to streaming multiprocessors (SMs) irrespective of data similarity between GEMM tiles. Such scheduling misses a significant opportunity to exploit data locality manifested in the DNN convolution. This article proposes a GPU scheduling technique called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Locality-Aware Scheduling</i> (LAS) that i) identifies which thread blocks share the largest amount of identical data based on the lowered patterns of a DNN convolution and ii) allocates such thread blocks showing the greatest data similarity to the same SM. In this way, small caches in SMs can efficiently utilize the data locality of the DNN convolution. Experimental results show that LAS with tensor cores achieves 20.1% performance improvements on average with 14.8% increases in L1 cache hit rates.
Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Aiming at the problems of difficult-to-extract effective information and insufficient feature extraction in the existing intelligent question answering robot environment, a personalized course resource recommendation algorithm based on deep learning is proposed. Firstly, the potential preferences of users are obtained through course-related data. Secondly, the authors use one-hot coding and embedding to convert word vectors into low-dimensional, dense real-valued vectors and input them into the CIN-GRU model. Finally, the attention mechanism is used to improve the attention of some words and the accuracy of personalized course recommendation. The experiment shows that when the recommended list is 35, the precision, recall, and F1 value of the proposed personalized course recommendation method are 0.862, 0.851, and 0.857, respectively, which are higher than those of the comparison method. Therefore, the performance of the proposed method in sustainable personalized course resource recommendation is better than that of the comparison method.
A Study on the Application of Sentiment-Support Words on Aspect-Based Sentiment Analysis

Aspect-based sentiment classification is currently an important research direction to identify the sentiment expressed by sentences in different aspects. The primary approach for performing aspect-level sentiment analysis involves extracting both grammatical and semantic information. However, analyzing the grammatical connection between aspect words and other words within a review sentence using morphological features like part of speech can be exceedingly complex. This paper proposes the concept of sentiment-supporting words, dividing sentences into aspectual words, sentiment-supporting words and non-sentiment-supporting words, which simplifies the core task of sentiment analysis. Three rules are designed for determining the “sentiment-support words” of the text in different aspects. Subsequently, the application of sentiment-support words in sentiment analysis models is given, and five classical sentiment analysis models are improved accordingly. According to the experimental outcomes on two publicly available datasets, the “sentiment-support words” and corresponding sentiment support rules proposed in this paper are capable of significantly enhancing aspect-based sentiment analysis.
Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.
An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion

For the reliability and safety of engine equipment, real-time anomaly detection through monitoring signals from multi-source sensors is essential. However, signal coupling caused by complicated interactions between numerous components raises a challenge. Additionally, due to the extreme operating environment and severe malfunction result, the failure data is difficult to collect or simulate, leading to the lack of anomaly samples. This paper proposed an asymmetrical graph Siamese network (AGSN) for one-class anomaly detection with multi-source fusion. The network consists of two weights-shared graph encoders and an extra remapping block which prevents the model from collapsing when one-class training. Firstly, AGSN adaptively constructs the graph structure based on sensor signals to model the components of systems and fuse multi-source signals into graph data. Secondly, graph data of normal samples are input into the AGSN for graph contrastive learning, enabling the graph encoders to completely cluster normal samples in the feature space. Thus, anomalous samples can be distinguished from normal samples when anomaly detection. The AGSN is evaluated on two datasets of liquid rocket engine (LRE) multi-sensor signals and compared with baseline approaches. The experimental results demonstrate that the proposed model is efficient, lightweight, and reliable, outperforming existing methods.
Reviving the Dynamics of Attacked Reservoir Computers

Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir. Numerical experiments demonstrate the efficacy and broad applicability of the framework and reveal inspiring insights into the mechanisms. This work provides a vehicle to improve the robustness of reservoir computers and can be generalized to broader types of neural networks.
An efficient image compression architecture based on optimized 9/7 wavelet transform with hybrid post processing and entropy encoder module

Satellite communication is popular due to the advancement in storing and transmitting satellite images. Image compression can be defined as the science of lowering the number of bits necessary to represent an image. In satellite image processing, image degradation is considered a challenging problem. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is the transformation-dependent image compression method developed especially for usage in on-board space platforms. It holds de-correlation, quantization and entropy encoding phases. This paper introduces a new optimized memory organization for Discrete Wavelet Transform (DWT) to perform spatial de-correlation with fewer memory requirements on an FPGA device. Also, the proposed optimized DWT is integrated with a hybrid post-processing and entropy encoder module to reduce the spatial redundancies between the wavelet coefficients and compress the de-correlated data with high compression performance. A high-throughput hardware implementation of the Binary arithmetic entropy Coder (BAEC) is also provided to perform lossless compression with low implementation complexity. To provide the convenience and compactness, the proposed system will be implemented in the Xilinx working platform by developing a Verilog code. The proposed model is then evaluated on the Arty Z7–20 development board. The proposed approach is analyzed on different performance parameters such as throughput and frequency. The proposed design allowed a maximum operating frequency of 250 MHz, leading to a throughput of 156.25 Msamples/sec on Zynq. In addition, the structure of the proposed method overtakes the conventional design in terms of memory requirements and area.
A Novel Weight-Assignment Load Balancing Algorithm for Cloud Applications

Abstract Web applications commonly suffer from flash crowds and resource failure, resulting in performance degradation. Flash crowds are large, sudden, yet legitimate influxes of user requests that constitute a critical problem because of their potential economic damage. For cloud providers, resource estimation is challenging, while distributing workload and sustaining performance. To alleviate flash crowds and resource failure problems, we propose a novel weight assignment load balancing algorithm that combines five carefully selected server metrics to efficiently distribute the workload of three-tier web applications among virtual machines. We experimentally characterised, using a private cloud running OpenStack, the load distribution ability of our proposed novel algorithm, as well as a baseline algorithm and round-robin algorithm. We compared the performance of the three algorithms by simulating resource failures and flash crowds, while carefully measuring response times. Our experimental results show that our approach improves average response times by 12.5% when compared to the baseline algorithm and 22.3% when compared to the round-robin algorithm in the flash crowds’ situation. In addition, average response time was improved by 20.7% when compared to the baseline algorithm and 21.4% when compared to the round-robin algorithm in resource failure situations. These experiments show that our novel algorithm is more resilient to fluctuating loads and resource failures than baseline algorithms.
Cloud-edge data encryption in the internet of vehicles using Zeckendorf representation

Abstract Cloud-edge data security is a key issue in the internet of vehicles (IoV), as the potential for data breaches increases as more vehicles are connected. As vehicles become smarter and more connected, the risk of unauthorized access to the data generated by the vehicles also increases. Data encryption is a highly effective security measure that is widely used to protect the IoV from malicious actors. By encrypting data, it becomes virtually impossible for unauthorized individuals to access the information. This ensures that only the intended parties can access the data, allowing for secure communication between cloud and edge. Data encryption is a cost-effective and reliable security measure that is essential for any organization that relies on the IoV. The IoV is characterized by the large volume of data that is exchanged between devices in cloud and edge. This necessitates the use of a strong encryption method, such as stream ciphering, which is particularly well-suited to this type of environment. Stream ciphering provides the highest levels of security, making it the ideal choice for securing data transmission in the IoV. Many stream ciphering algorithms use bitwise exclusive or (XOR) to encrypt the data stream, so the core is the generation of a pseudo-random key stream. This paper proves that the probability of the number 1 appearing in the middle part of the Zeckendorf representation is constant, which can be used to generate pseudo-random key stream sequences. The pseudo-random sequence generated by the linear feedback shift register (LSFR) is periodic, and the key sequence will be duplicated. The logistic chaos (LC) sequence is too sensitive to the disturbance of initial value, and its stability is poor. In this paper, our proposed ZPKG (key generator based on Zeckendorf presentation) algorithm solves these two main problems in stream ciphering. The generated key sequence not only has strong randomness, but also is infinitely long, and it is robust to the minor disturbance of the initial value. Graphical Abstract
Research on the Optimization of A/B Testing System Based on Dynamic Strategy Distribution

With the development of society, users have increasing requirements for the high-quality experience of products. The pursuit of a high profit conversion rate also gradually puts forward higher requirements for product details in the competition. Product providers need to iterate products fast and with a high quality to enhance user viscosity and activity to improve the profit conversion rate efficiently. A/B testing is a technical method to conduct experiments on target users who use different iterative strategies, and observe which strategy is better through log embedding and statistical analysis. Usually, different businesses of the same company are supported by different business systems, and the A/B tests of different business systems need to be operated in a unified manner. At present, most A/B testing systems cannot provide services for more than one business system at the same time, and there are problems such as high concurrency, scalability, reusability, and flexibility. In this regard, this paper proposes an idea of dynamic strategy distribution, based on which a configuration-driven traffic-multiplexing A/B testing model is constructed and implemented systematically. The model solves the high-concurrency problem when requesting experimental strategies by setting message middleware and strategy cache modules, making the system more lightweight, flexible, and efficient to meet the A/B testing requirements for multiple business systems.
Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities

Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens. However, the massive data generated in these cities also poses significant privacy risks, particularly in de-anonymization and re-identification. This survey focuses on the privacy concerns and commonly used techniques for data protection in smart cities, specifically addressing geolocation data and video surveillance. We categorize the attacks into linking, predictive and inference, and side-channel attacks. Furthermore, we examine the most widely employed de-identification and anonymization techniques, highlighting privacy-preserving techniques and anonymization tools; while these methods can reduce the privacy risks, they are not enough to address all the challenges. In addition, we argue that de-identification must involve properties such as unlikability, selective disclosure and self-sovereignty. This paper concludes by outlining future research challenges in achieving complete de-identification in smart cities.
RSETP: A Reliable Security Education and Training Platform Based on the Alliance Blockchain

Improving the training quality of education and training institutions is one of the important reasons for the sustainable development of training institutions. However, traditional education and training institutions lack supervision, and the process system is not standardized, resulting in a lack of authenticity and reliability of information in the training process. Because most of the traditional education and training systems are centralized, it is difficult to share training data between institutions and relevant departments. Therefore, in view of the problems of data tampering and resource sharing in the field of education and training, combined with the node complexity of education and training scenarios, we propose an education and training blockchain platform suitable for multi-node scenarios to ensure the authenticity of the training data and traceability. In this consortium chain, we design specific ledger structures and corresponding smart contracts to meet different business logic requirements. According to different business data types, we optimize the ledger storage method. To improve chaincode utilization and system transaction throughput, we explore different system configuration rules and provide useful insights. Finally, several experiments are performed to evaluate the security and efficiency of the proposed system.
A Semantic View on Planetary Mapping—Investigating Limitations and Knowledge Modeling through Contextualization and Composition

The concept of planetary mapping constitutes different activities within different contexts. Much like the field of cartography, it is an amalgamation of science, techniques, and artistic disciplines. It has undergone considerable changes over the last decades to cope with increasing demands related to data management, analysis, and visualization. Planetary mapping employs abstraction, which involves simplifications and generalizations. It aims to produce accessible visualization of planetary surfaces to gain insights and knowledge. Here, we show that different manifestations of this concept are interdependent and we discuss how different mapping concepts relate to each other semantically. We reason that knowledge gain can only be achieved through thematic mapping. The reasoning for systematic mapping and exploration is an intellectual product of thematic mapping. In order to highlight these relationships, we (a) develop in-depth definitions for different types of planetary mapping, (b) discuss data and knowledge flow across different mapping concepts, and (c) highlight systemic limitations related to data that we acquire and attempt to abstract through models. We finally develop a semantic proto-model that focuses on the transformation of information and knowledge between mapping domains. We furthermore argue that due to compositionality, map products suffer not only from abstraction but also from limitations related to uncertainties during data processing. We conclude that a complete database is needed for mapping in order to establish contextualization and extract knowledge. That knowledge is needed for reasoning for planning and operational decision making. This work furthermore aims to motivate future community-based discussions on functional semantic models and ontologies for the future development of knowledge extraction from thematic maps.