SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series

Time series classification (TSC) has emerged as a critical task in various domains, and deep neural models have shown superior performance in TSC tasks. However, these models are vulnerable to adversarial attacks, where subtle perturbations can significantly impact the prediction results. Existing adversarial methods often suffer from over-parameterization or random logit perturbation, hindering their effectiveness. Additionally, increasing the attack success rate (ASR) typically involves generating more noise, making the attack more easily detectable. To address these limitations, we propose SWAP, a novel attacking method for TSC models. SWAP focuses on enhancing the confidence of the second-ranked logits while minimizing the manipulation of other logits. This is achieved by minimizing the Kullback-Leibler divergence between the target logit distribution and the predictive logit distribution. Experimental results demonstrate that SWAP achieves state-of-the-art performance, with an ASR exceeding 50% and an 18% increase compared to existing methods.Comment: 10 pages, 8 figure

Efficient traffic congestion estimation using multiple spatio-temporal properties

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods

Creating two-dimensional solid helium via diamond lattice confinement

The universe abounds with solid helium in polymorphic forms. Therefore, exploring the allotropes of helium remains vital to our understanding of nature. However, it is challenging to produce, observe and utilize solid helium on the earth because high-pressure techniques are required to solidify helium. Here we report the discovery of room-temperature two-dimensional solid helium through the diamond lattice confinement effect. Controllable ion implantation enables the self-assembly of monolayer helium atoms between {100} diamond lattice planes. Using state-of-the-art integrated differential phase contrast microscopy, we decipher the buckled tetragonal arrangement of solid helium monolayers with an anisotropic nature compressed by the robust diamond lattice. These distinctive helium monolayers, in turn, produce substantial compressive strains to the surrounded diamond lattice, resulting in a large-scale bandgap narrowing up to ~2.2 electron volts. This approach opens up new avenues for steerable manipulation of solid helium for achieving intrinsic strain doping with profound applications

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The Design and Implementation of Web-based E-learning Examination System Based on J2EE

Abstract: In this paper, the architecture of web-based self-adapted examination system and its implement method of design were introduced. In the course of implementing this system, J2EE system structure was studied. Also, the CELTS standard was employed as a metadata standard, And relevant standard was used to design the question pool, and set up standardization and portability question pool structure of the examination question structure. Key words: e-learning, J2EE, self-adapted examination system 1

Overview of standard and technology development of underground explosion-proof electrical equipment

The paper summarized coal mine underground explosion-proof electrical technique and its development, emphatically introduced the actualities and development trend of coal mine underground explosion-proof electrical standards, analyzed and studied the difference between two domestic and international electrical standards IEC 60079 and GB 3836. At last, the paper pointed out that the intrinsically safe explosion-proof type will be the next major form of coal mine underground explosion-proof electrical equipment

Deep learning for heterogeneous medical data analysis

At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical treatment, and scientific research, has drawn more and more attention of researchers. Deep learning, as the focus of most concern by both academia and industry, has been effectively applied in many fields and has outperformed most of the machine learning methods. Under this background, deep learning based medical data analysis emerged. In this survey, we focus on reviewing and then categorizing the current development. Firstly, we fully discuss the scope, characteristic and structure of the heterogeneous medical data. Afterward and primarily, the main deep learning models involved in medical data analysis, including their variants and various hybrid models, as well as main tasks in medical data analysis are all analyzed and reviewed in a series of typical cases respectively. Finally, we provide a brief introduction to certain useful online resources of deep learning development tools

User relation prediction based on matrix factorization and hybrid particle swarm optimization

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. Matrix factorization is an effective method in relationship prediction, However, traditional matrix factorization link prediction methods can only be used for non-negative matrix. In this paper, a generalized framework, itelliPrediction, is presented that is able to deal with positive and negative matrix. The novel itelliPrediction framework is domain independent and with high precision. We validate our approach using two different data sources, an open data sets and a real-word dataset, the result demonstrated that the quality of our approach is comparable to, if not better than, exiting state of the art relation predication framework

Electrocatalytic oxidation of ethylene glycol and glycerol on nickel ion implanted-modified indium tin oxide electrode

AbstractThe electrochemical behaviour of direct ethylene glycol and glycerol oxidation on a novel nickel ion implanted-modified indium tin oxide electrode (NiNPs/ITO) was investigated. The investigation is used to verify the feasibility of using the NiNPs/ITO electrode in the ethylene glycol and glycerol fuel cells. The size and morphology of nickel nanoparticles (NiNPs) on the substrate surface was determined by scanning electron microscopy (SEM). The cyclic voltammetry (CV) technique was utilized to characterize the typical electrochemical behaviours of the NiNPs/ITO electrode. In alkaline medium (0.2 M NaOH), a good redox behaviour of Ni(III)/Ni(II) coupled at the surface of modified electrodes can be observed. Electrochemical performances were measured by current–time curve technology. We find that the NiNPs/ITO electrode exhibits a satisfactory electrocatalytic activity toward ethylene glycol and glycerol with good stability, making it a prime candidate for use in ethylene glycol and glycerol fuel cells

Insights into Chain Elongation Mechanisms of Weak Electric-Field-Stimulated Continuous Caproate Biosynthesis:Key Enzymes, Specific Species Functions, and Microbial Collaboration

Chain elongation (CE) technology can recover high-value biochemicals such as caproate, contributing to the realization of carbon neutrality. Electro-assisted fermentation shows extraordinary potential to enhance microbial activity in anaerobic fermentation systems. However, little is known of the effects of key functional genes of enzymes, specific species functions, and microbial collaboration on caproate biosynthesis in the continuously fed expanded granular sludge bed (EGSB) reactor with mixed culture under a weak electric field. In this study, caproate production was enhanced by 21% with 0.05 V electric field introduction in the CE bioreactor continuously operated for 150 days. Mechanism investigation revealed the abundance of functional genes involved in converting substrates to key intermediates (acetyl CoA and malonyl CoA) and in the fatty acid biosynthesis pathway (FAB), and the activities of transmembrane transport and energy metabolism were upregulated. Ruminococcaceae_bacterium played the most significant role in enhancing caproate biosynthesis with electric field introduction. Some essential functional genes were undetected within Ruminococcaceae_bacterium, which implied that harmonious microbial collaboration existed to supplement the lacking functional genes to complete CE processes. Firmicutes_bacterium, Pseudomonas_aeruginosa, Caproiciproducens_sp._NJN-50 and Candidatus_Neoanaerotignum_tabaqchaliae played complementary and collaborative roles in constructing the  microbial collaboration mechanisms in the CE process with the electric field. This study offered a deep understanding of the CE mechanisms in the mixed-culture continuously fed reactor and unveiled the respective roles of different species and the microbial interaction and collaboration mechanisms with weak electric field stimulation and provided a promising strategy for enhancing caproate biosynthesis