Reviewerly/exHarmony huggingface dataset:
Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder

Existing anomaly detection models of mechanical systems often face challenges for the equipment under multiple working conditions: the learning model under a single working condition is challenging to adapt to new working conditions, and centralized learning of multicondition samples leads to too low detection accuracy. A multiworking condition variational auto-encoder (MW-CVAE) is proposed to solve the problem. Based on the variational auto-encoder model, the working conditions of the equipment are regarded as the input. The anomaly evaluation threshold of each independent working condition is established by the centralized learning of normal multiworking condition samples. At the same time, it is found that the representation of each working condition sample in the space of the hidden layer forms a distribution close to the prior probability, providing a theoretical basis for the separation and evaluation of working conditions. By comparing and verifying the CWRU, JNU, and PU datasets, the new method significantly improves anomaly detection (the F1-score value is increased by 18-19%) and can be widely used in anomaly detection mechanical systems with various discrete working conditions.
A Feature-Map-Based Method for Explaining the Performance Degradation of Ship Detection Networks

The unknowability of the inner workings limits the magnitude of performance improvement of ship target detection networks in synthetic aperture radar (SAR) images under Gaussian noise. However, none of the existing interpretation methods explain the phenomenon of network changes under noise. The feature map can visually reflect the changes in image delivery in the network, and some metrics can quantitatively characterize the degree of network performance degradation in a noise environment. So in this paper, we propose a comprehensive analysis method that integrates texture and brightness features of the internal feature map of the network to clarify the change process of target features under Gaussian noise. First, we analyzed the degradation of three target detection networks under different levels of Gaussian noise; then, the feature maps of four convolution layers were sampled and visualized for qualitative analysis; finally, the texture and brightness features were extracted for quantitative characterization of the feature amount changes. We experimentally validated the method on publicly available SSDD radar datasets. The networks were extremely sensitive to Gaussian noise, and the mean Average Precision decreased by up to 96.3%. The angular second moment and entropy texture feature values of the feature map could drop and rise 59.10% and 97.81%, respectively, while the brightness value could increase up to 100.92%. This indicates that noise changes the structure of feature maps and reduces the amount of effective information.
Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models

Recent research has found that neural networks for computer vision are vulnerable to several types of external attacks that modify the input of the model, with the malicious intent of producing a misclassification.With the increase in the number of feasible attacks, many defence approaches have been proposed to mitigate the effect of these attacks and protect the models.Mainly, the research on both attack and defence has focused on RGB images, while other domains, such as the infrared domain, are currently underexplored.In this paper, we propose two attacks, and we evaluate them on multiple datasets and neural network models, showing that the results outperform others established attacks, on both RGB as well as infrared domains.In addition, we show that our proposal can be used in an adversarial training protocol to produce more robust models, with respect to both adversarial attacks and natural perturbations that can be applied to input images.Lastly, we study if a successful attack in a domain can be transferred to an aligned image in another domain, without any further tuning.The code, containing all the files and the configurations used to run the experiments, is available https://github.com/jaryP/IR-RGB-domain-attackonline.
Mastering Trustful Artificial Intelligence

As a counter-thesis to the naive general narrative that artificial intelligence (AI) is a hyped super-technology which can solve all problems and even overtake human intelligence, this article discusses five essential problem areas associated with AI technology: modeling ability, how do we derive models of the real world from data and how do we create a model without prejudices and errors?; verifiability, how do we verify the AI algorithms?; explainability, how can we understand the decision-making process of AI systems?; ethics, how do we guarantee compliance with ethical principles and values?; and finally, responsibility, who is responsible for the decisions made by the AI system? We also discuss fundamental threat scenarios in the context of our information society, as well as the limits of AI technology compared with human intelligence. The article highlights that the development of AI technology, as well as related policies and regulations, must be organized to ensure its socially acceptable use and rule out any improper use. Furthermore, the article provides a philosophical discussion on the limits of AI and the diversity of life, and shows that we bear the ultimate responsibility for what machines do. This paper concludes by arguing that even the most sophisticated machine will probably never be able to match humans in terms of their multi-dimensionality of cognition, emotion, and physicality and in their sensual perception of the world.
Certifying Black-Box Policies With Stability for Nonlinear Control

Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approximations of nonlinear dynamics. We study the problem of certifying a black-box control policy with stability using model-based advice for nonlinear control on a single trajectory. We first show a general negative result that a naive convex combination of a black-box policy and a linear model-based policy can lead to instability, even if the two policies are both stabilizing. We then propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>-confident policy</i> , with a coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> indicating the confidence in a black-box policy, and prove its stability. With bounded nonlinearity, in addition, we show that the adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> -confident policy achieves a bounded competitive ratio when a black-box policy is near-optimal. Finally, we propose an online learning approach to implement the adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> -confident policy and verify its efficacy in case studies about the Cart-Pole problem and a real-world electric vehicle (EV) charging problem with covariate shift due to COVID-19.
Sales System at a Web-Based Cika Ciki Snack Shop

Cika Ciki Snack Shop is one of the agents that sells a variety of snacks from various brands, but the store owner has problems promoting the products he sells, the longer it takes, the more competitors there are other snack agents, then Cika Ciki Snack Shop utilizes technology to market products. Therefore, the author created a website-based sales information system to develop a business in terms of sales. to make it easier for store owners to promote snacks and make it easier for customers to make purchases. This research was made using a Website-based information system with the waterfall method, and MySQL as the database, which is the data collection of anything snack along with the brand name provided by Cika Ciki Snack Store.
Scheduling on a budget: Avoiding stale recommendations with timely updates

Recommendation systems usually create static models from historical data. Due to concept drift and changes in the environment, such models are doomed to become stale, which causes their performance to degrade. In live production environments, models are therefore typically retrained at fixed time-intervals. Of course, every retraining comes at a significant computational cost, making very frequent model updates unrealistic in practice. In some cases, the cost is worth it, but in other cases an update could be redundant and the cost an unnecessary loss. The research question then consists of finding an acceptable update schedule for your recommendation system, given a limited budget. This work provides a pragmatic analysis of model staleness for a variety of collaborative filtering algorithms in news and retail domains, where concept drift is a known impediment. We highlight that the rate at which models become stale is highly dependent on the environment they perform in and that this property can be derived from data. These findings are corroborated by empirical observations from four large-scale online experiments. Instead of retraining at regular intervals, we propose an adaptive scheduling method that aims to maximise the accuracy of the recommendations within a fixed resource budget. Offline experiments show that our proposed approach improves recommendation performance while keeping the cost constant. Our findings can guide practitioners to spend their available resources more efficiently.
Real‐time threat assessment based on hidden Markov models

Abstract An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime‐ and terrorism‐related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model‐based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.
Understanding Linked Data and the Potential for Enhanced Discoverability

Linked data emerged as a description model for Galleries, Libraries, Archives, Museums, and Repositories Management (GLAMR) institutions and has been increasing in popularity despite the lack of its widespread adoption in the community. Linked data offers solutions to common description pitfalls and shows potential for democratizing authoritative sources. However, the learning curve for workers, technical uplift required, and lack of support in commonly used vendor systems still present significant barriers to its use. This chapter provides an overview of linked data, the benefits of its use in digital repositories and discovery systems, practical approaches to getting started, and real-world examples of linked data projects being undertaken by GLAMR institutions.
Construction cost prediction based on adaptive boosting and artificial neural networks

The artificial bee colony algorithm and multilayer error back-propagation neural networks commonly used in construction project cost forecasting suffer from slow training speeds and high costs. A combination of the beetle antennae search, support vector machines, adaptive boosting and genetic algorithms was proposed to solve these problems. Support vector machine optimisation was accomplished using the beetle antennae search algorithm. The enhanced genetic algorithm was then used directly to swap out the fit solutions for the unfit ones. One hundred projects completed during the last 3 years were chosen from a network integration database to serve as the training data set after developing the prediction model. Using actual cost information and trial and error, appropriate parameters were chosen, and combinations of algorithms were selected for comparison. The maximum relative error of the improved method was 9.01%, which was 34.68% lower than that of the baseline method, while the smallest relative error was 0.59%, which was 1.58% lower than that of the baseline method. The ’innovation of the study lies in the addition of the beetle antennae search algorithm and enhancement of the genetic algorithm. The former significantly increased the search efficiency of the network, while the latter generally increased the population fitness and mitigated the drawback of the genetic algorithm which was prone to local convergence.
A Comparative Analysis of Numerical Methods for Solving the Leaky Integrate and Fire Neuron Model

The human nervous system is one of the most complex systems of the human body. Understanding its behavior is crucial in drug discovery and developing medical devices. One approach to understanding such a system is to model its most basic unit, neurons. The leaky integrate and fire (LIF) method models the neurons’ response to a stimulus. Given the fact that the model’s equation is a linear ordinary differential equation, the purpose of this research is to compare which numerical analysis method gives the best results for the simplified version of this model. Adams predictor and corrector (AB4-AM4) and Heun’s methods were then used to solve the equation. In addition, this study further researches the effects of different current input models on the LIF’s voltage output. In terms of the computational time, Heun’s method was 0.01191 s on average which is much less than that of the AB-AM4 method (0.057138) for a constant DC input. As for the root mean square error, the AB-AM4 method had a much lower value (0.0061) compared to that of Heun’s method (0.3272) for the same constant input. Therefore, our results show that Heun’s method is best suited for the simplified LIF model since it had the lowest computation time of 36 ms, was stable over a larger range, and had an accuracy of 72% for the varying sinusoidal current input model.
An Event-Driven Architectural Model for Integrating Heterogeneous Data and Developing Smart City Applications

Currently, many governments are gearing up to promote the development of smart cities in their countries. A smart city is an urban area using different types of sensors to collect data, which will then be used to manage assets and resources efficiently. Through smart technology, the quality of living and performance of urban services are enhanced. Recent works addressed a set of platforms aimed to support the development of smart city applications. It seems that most of them involved dealing with collecting, managing, analyzing, and correlating data to extract new information useful to a city, but they do not integrate a diversified set of services and react to events on the fly. Moreover, the application development facilities provided by them seem to be limited and might even increase the complexity of this task. We propose an event-based architecture with components that meet important requirements for smart city platforms, supporting increased demand for scalability, flexibility, and heterogeneity in event processing. We implement such architecture and data representation models, handling different data formats, and supporting a semantics-based data model. Finally, we discuss the effectiveness of a S mart Event-based Middleware (SEMi) and present empirical results regarding a performance evaluation of SEMi.
Neural Coreference Resolution for Dutch Parliamentary Documents with the DutchParliament Dataset

The task of coreference resolution concerns the clustering of words and phrases referring to the same entity in text, either in the same document or across multiple documents. The task is challenging, as it concerns elements of named entity recognition and reading comprehension, as well as others. In this paper, we introduce DutchParliament, a new Dutch coreference resolution dataset obtained through the manual annotation of 74 government debates, expanded with a domain-specific class. In contrast to existing datasets, which are often composed of news articles, blogs or other documents, the debates in DutchParliament are transcriptions of speech, and therefore offer a unique structure and way of referencing compared to other datasets. By constructing and releasing this dataset, we hope to facilitate the research on coreference resolution in niche domains, with different characteristics than traditional datasets. The DutchParliament dataset was compared to SoNaR-1 and RiddleCoref, two other existing Dutch coreference resolution corpora, to highlight its particularities and differences from existing datasets. Furthermore, two coreference resolution models for Dutch, the rule-based DutchCoref model and the neural e2eDutch model, were evaluated on the DutchParliament dataset to examine their performance on the DutchParliament dataset. It was found that the characteristics of the DutchParliament dataset are quite different from that of the other two datasets, although the performance of the e2eDutch model does not seem to be significantly affected by this. Furthermore, experiments were conducted by utilizing the metadata present in the DutchParliament corpus to improve the performance of the e2eDutch model. The results indicate that the addition of available metadata about speakers has a beneficial effect on the performance of the model, although the addition of the gender of speakers seems to have a limited effect.
Study the Test Method of Temperature and Humidity Monitoring Equipment in the Cold Chain Transportation Process

Abstract Transportation objects include food cold chain logistics, flower, plant cold chain logistics, chemical cold chain logistics and other cold chain logistics. In order to improve the overall performance of the temperature and humidity monitoring equipment in the cold chain process, and further promote and improve the regional metering service system. This study is to analyze its dewpoint temperature stability, relative humidity stability and temperature stability to evaluate the performance of the dew point instrument. The results showed that the standard deviation of temperature, humidity and dew-point in reproducible experiments are 0.00°C, 0.07%RH and 0.01°C. The standard deviation of temperature, humidity and dew-point in stability experiments are 0.02°C, 0.33%RH and 0.066°C. Results show that the measurement method of this experiment meets the requirements of the measurement standards.
Newfangled Design Thinking-Based Application for Blood Donation and Assistance

All lives are in the digital era, where everything is available online and mostly application. To enrich its strength, an application for blood donation has been developed in the name of “Blood World”. This application helps to overcome the knowledgelessness over blood donation and services by providing accurate information about the availability of blood and ways to donate blood.There are more centers to provide the information on blood donation camps, but in this era the human beings are busy with their periodic schedule. so make it available in the form of a mobile application. Requests can also be given which will be processed earlier than the available ones. The application also includes information about the cause and preventive measures about blood related problems and also includes options for making appointments with the doctors. The complete design is made up of XML and the powerful language JAVA is used in the construction of application. The app thus developed is highly reliable and is more user-friendly.
From Service Composition to Mashup Editor: A Multiperspective Taxonomy

Service-oriented computing has become a popular area of research, with a particular focus on service composition. There have been many developments in this field, such as new techniques for data engineering in service description languages, protocols for publication and discovery, the optimization of service selection and scheduling, and the deployment and monitoring of composed services. However, this diversity of approaches and methodologies can make it challenging to navigate between different proposed solutions and identify research gaps. In order to provide a clearer understanding of this body of work, this paper presents a comprehensive framework for the taxonomy of service composition approaches, methodologies, and tools. This framework proposes a structured view of different perspectives, such as formal, semantic, and automatic approaches, with a particular focus on the end-user’s perspective and tools such as Mashups.
Modeling and digital twin

This article considers various types of modeling and the concept of "digital double", as well as their examples of the concept of implementation. For example, computer simulations not only provide a prediction, but also identify which control effects on the system will lead to favorable developments between the link components of the model. In this case, digital simulation allows to optimize the service sequence of sensor elements SE with its time-bound, ensuring minimization of flight time and unconditional absence of complete battery discharge of any of the sensor elements. In this case, the model interacts with the network being modeled, constantly receiving from it up-to-date information about the state, which is characteristic of digital duplicates. Digital doubles can display not only the characteristics of individual devices, but also process characteristics. Modern production processes are very complex and distributed in space and time. Models of such processes should therefore consider the distributed nature of the modelling objects. The interaction of the digital model with the model object representing the production process is also analyzed. The interaction takes place through integration tools that link the model with the model object.