Reviewerly/exHarmony huggingface dataset:
Fault Analysis of Intelligent Substation Secondary System Based on Improved Random Forest

To solve the problem of low efficiency of manual analysis due to noise data in secondary system fault analysis of intelligent station, a fault data analysis method based on particle swarm optimization and improved random forest was proposed. Before the analysis, the fault data of the secondary system is collected first, and then the specific communication state of each loop and each terminal is determined by combining the secondary loop resolved by the SCD file, setting the fault feature values to attribute values, the analysis results to labels, using the improved random forest algorithm carries on the classification, combined with particle swarm algorithm optimize the key parameters, Weight is set for each tree according to the classification accuracy of each tree, and finally, the data is classified by voting. Through the test set verification, the improved method has higher accuracy and stronger anti-noise ability compared with the traditional random forest, SVM, and BP neural network, and improves the efficiency of fault analysis.
Quantum Walk Circuits to Analyze Networks

Quantum walks are studied extensively using various networks. The quantum version of the walk involves using the properties found in quantum computing. These properties can include quantum interference, no-cloning theorem, and restriction on an intermediate measurement. Here, in the proposed work, we focus on finding out the properties associated with the networks using quantum walks while also verifying its findings. One of the properties includes identifying critical nodes in a network vital for its robustness and survivability. So, the results related to this and some other properties are analyzed. The proposed work requires the implementation of quantum walk on different networks. Therefore, this research paper presents an algorithm that takes an undirected network, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> , as input and constructs the quantum circuit to perform quantum walk on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> . The working of the proposed algorithm is shown using various networks, from simple to complex.
Deep Genetic Programming Trees Are Robust

We sample the genetic programming tree search space and show it is smooth, since many mutations on many test cases have little or no fitness impact. We generate uniformly at random high-order polynomials composed of 12,500 and 750,000 additions and multiplications and follow the impact of small changes to them. From information theory, 32 bit floating point arithmetic is dissipative, and even with 1,501 test cases, deep mutations seldom have any impact on fitness. Absolute difference between parent and child evaluation can grow as well as fall further from the code change location, but the number of disrupted fitness tests falls monotonically. In many cases, deeply nested expressions are robust to crossover syntax changes, bugs, errors, run time glitches, perturbations, and so on, because their disruption falls to zero, and so it fails to propagate beyond the program.
The impact of weather and panel characteristics features on photovoltaic power forecasting

The intermittence of solar energy is causing an enormous problem in smart grid management. The energy needed is not always granted due to the unbalance distribution during the day. Thus, accurate solar energy forecasting becomes a necessity to satisfy the demand. In the present work, Artificial Neural Network was deployed using a brute force search algorithm to select the set of features that fit the model accurately, using data generated from photo-voltaic (PV) plants installed in Eugene, Oregon as well as weather data of the same location. Several combinations of features were tested and evaluated based on two error metrics to conclude that weather variables and predetermined panel characteristics are the most effective features for PV solar energy forecasting.
Pre-Design Stage Cost Estimation for Cloud Services

Cloud computing is being increasingly employed in the development of systems. For such systems, rapid cost estimation is required to start new business. This paper proposes a “Pay-Per-Use” billing model, and providers offer various strategic pricing options. There are many cost structures, and hence, users need to consider many pricing options when estimating system costs. Although companies often want to estimate the cost of their systems, it is difficult to obtain a comprehensive estimate that considers all components. Previously, researchers have implemented simulation-based and analytical approaches to solve this problem. In this study, we develop a cost-estimation method that employs directed acyclic graph (DAG)-based representation and matrix operations. Our method emphasizes simplicity through the use of a systematic procedure that can estimate the costs of new services in the pre-design stage.
A Novel Password Secure Mechanism using Reformation based Optimized Honey Encryption and Decryption Technique

The exponential rise of online services has heightened awareness of safeguarding the various applications that cooperate with and provide Internet users. Users must present their credentials, such as user name and secret code, to the servers to be authorized. This sensitive data should be secured from being exploited due to numerous security breaches, resulting in criminal activity. It is vital to secure systems against numerous risks. This article offers a novel approach to protecting against brute force attacks. A solution is presented where the user obtains the keypad on each occurrence. Following the establishment of the keypad, the webserver produces an encrypted password for the user's Computer/device authentication. The encrypted password will be used for authentication; users must type the amended one-time password (OTP) every time they access the website. This research protects passwords using reformation-based encryption and decryption and optimal honey encryption (OH-E) and decryption.
Fast identification method of malicious code based on extreme learning machine

A data balancing strategy based on conditional generative confrontation network and particle swarm algorithm can effectively improve the model's ability to detect malicious code. Nonetheless for some malicious code families with similar characteristics, there are still large errors in the model's detection results. This paper proposes a method for rapid identification of malicious code based on extreme learning machine(ELM). First, this paper introduce traditional single-hidden-layer feed forward neural networks (SLFNs) and analyze the weakness of the backpropagation algorithm. Then explaining the principle and advantages of the ELM algorithm. Finally, Introducing the detection method of malicious code based on the feature migration strategy and the ELM in detail. Experimental comparisons verify that the rapid detection model of malicious code proposed in this paper can effectively reduce the recognition time while ensuring high classification accuracy, and attain the requirements for real-time detection of malicious code.
Design for Domestic Uncertainty: Envisioning Future Technologies in the Work-From-Home Era that Support Reflective Practices

The COVID-19 pandemic fundamentally changed the nature of work by shifting most in-person work to a predominantly remote modality. As boundaries between home and workspaces became blurred, people have to renegotiate various aspects of work and personal life. In my thesis, drawing on reflective design that emphasizes the value of human interpretation in meaning-making, I investigate how technologies can leverage reflection to support remote workers’ wellness. My thesis consists of a set of interrelated studies, including empirical and design research, which will help me explore the role of technology for the art of living in an uncertain world.
Ramifications of incorrect image segmentations; emphasizing on the potential effects on deep learning methods failure

Abstract Introduction Detecting failure cases is critical to ensure a secure self-driving system. Any flaw in the system directly results in an accident. In genuine class, the model’s probability reflects better-reflected model confidence. As a result, the confidence distributions of failed predictions were changed to lower values. In contrast, accurate predictions were remained associated with high values, allowing for considerably more excellent separability between such prediction types. The study investigates the association of ramifications with computational color constancy that can negatively influence CNN’s image classification and semantic segmentation. Methodology Image datasets were used to conduct different scales and complexity experiments. For instance, minimal and straightforward images of digits were comparatively provided through MNIST and SVHN datasets. The dataset’s standard validation set was employed to test and compute additional metrics because ground truth that is not publicly available for some test sets. Results The results depicted that baseline methods were outperformed through the proposed approach with a considerable variant on minimal datasets or models in every context. Therefore, Transmission Control Protocol (TCP) is appropriate in failure prediction, and ConfidNet is competent to be fulfilled as confidence criterion. Further, one of the solutions would be to elevate the validation set size, but this would influence the prediction performance of a failure model. On the contrary, the confidence estimation was based on models with test predictive performance levels, similar to baselines. Conclusions The gap between validation accuracy and training accuracy was significant on CIFAR-100, which indicates the modest enhancement for failure detection via the validation set.
EdgeTune

Deep Neural Networks (DNNs) have demonstrated impressive performance on many machine-learning tasks such as image recognition and language modeling, and are becoming prevalent even on mobile platforms. Despite so, designing neural architectures still remains a manual, time-consuming process that requires profound domain knowledge. Recently, Parameter Tuning Servers have gathered the attention o industry and academia. Those systems allow users from all domains to automatically achieve the desired model accuracy for their applications. However, although the entire process of tuning and training models is performed solely to be deployed for inference, state-of-the-art approaches typically ignore system-oriented and inference-related objectives such as runtime, memory usage, and power consumption. This is a challenging problem: besides adding one more dimension to an already complex problem, the information about edge devices available to the user is rarely known or complete. To accommodate all these objectives together, it is crucial for tuning system to take a holistic approach to parameter tuning and consider all levels of parameters simultaneously into account. We present EdgeTune, a novel inference-aware parameter tuning server. It considers the tuning of parameters in all levels backed by an optimization function capturing multiple objectives. Our approach relies on inference estimated metrics collected from our emulation server running asynchronously from the main tuning process. The latter can then leverage the inference performance while still tuning the model. We propose a novel one-fold tuning algorithm that employs the principle of multi-fidelity and simultaneously explores multiple tuning budgets, which the prior art can only handle as suboptimal case of single type of budget. EdgeTune outputs inference recommendations to the user while improving tuning time and energy by at least 18\% and 53\% when compared to the baseline.
Eurobonds, debt sustainability and macroeconomic performance in Africa: Synthetic control experiments

There has been a strong wave of Eurobond issuances by Africa's frontier market economies since the start of the century. But it is not clear how these issuances have affected economic performance. This paper uses synthetic control experiments to conduct comparative case study analyses of the impacts of Eurobond issuances on economic growth, debt sustainability, and domestic capital markets. Ex post, we compare the trajectories of the relevant macroeconomic outcomes against their synthetically constructed business-as-usual counterfactual in an environment with no Eurobond issuance. The results show that, on average, sovereign Eurobond issuances have led to improvements in per capita GDP in Africa by about 10 percent above the counterfactual, business-as-usual scenario. Although most issuances were within 3 percent of GDP, they potentially led to about 13 percentage point acceleration in the debt-to-GDP ratios after ten years, compared to the counterfactual. Public capital accumulation is, on average, faster in the first two to three years following an issuance in countries with positive correlations.
Increasing the Competitiveness of the University Educational Programs through Scientific and Methodological Support Using Modern Intellectual Technologies

This paper considers the problem of increasing the competitiveness of university educational programs. To solve this problem, it is proposed to improve scientific and methodological content that ensure the implementation of appropriate educational technologies. It is highlighted that in order to solve applied problems of various levels of complexity, it is necessary to use specialized developed software along with classical programs within the framework of the laboratory facilities. Such software should take into account not only specifics and features of the real sector of the economy, but also modern scientific approaches to solving relevant practical problems. In this case, solving these problems, students operate with professional terms; acquire the ability to analyze emerging situations typical for future professional activities. As an example, the scientific and methodological support of laboratory work on discipline Operation of automated information processing and control systems (AIPCS) is considered. Brief objective settings are given, the novelty of their solution methods based on the use of artificial intelligence methods in the form of integration of heterogeneous cognitive models (HCM) and a system of hierarchical production rules with fuzzy logic is noted. The on-screen forms of the developed software implementing the proposed methods are also presented. The purposeful introduction of applied objectives into practice of teaching results in the formation of skills in operating with professional terms and the ability to analyze situations typical for future professional activities, thereby forming the graduate’s professional competence.
Controlling the Pandemic of COVID-19 based on Telecommunication Data

Mobile phones as a pervasive tool may have several applications in the field of preventing the spread of infectious diseases. Many countries used it in recent years to control Ebola and SARS in Africa, and today controlling COVID-19 in China and Singapore. Since now an effective way to reduce or stop the spread of the disease is social isolation and quarantine while the mobile phone could be an effective tool for patient control. In this paper, a non-invasive method presented for controlling prevalence of COVID-19 disease. It is based on the records of subscriber’s transactions in the mobile network. Many valuable data could be obtained by analyzing these records, such as identifying quarantine offenders during illness, the number of interprovince trips while restrictions were imposed, and the density of patients in different parts of the city or country.
Comparative Analysis on Ontology-Based Contextual Senses Approaches for Search Engine

Computers and the Internet have changed how we live our lives in ways that are often difficult to grasp, therefore it is extremely helpful to utilise computers and the Internet for the purposes of making computers and the Internet easier to use. An ontology-based search engine will assist in more accurate searches by utilising artificial intelligence to improve natural language processing and analysing keywords to more precisely target data (NLP). A general evaluation of some of the existing search engine design methodologies was introduced in this document, including with some information about the difficult areas of development. Text analysis with artificial intelligence NLP techniques could be used to build an engine that generates intelligent responses based on user requests. This research is all about intelligent ontology-based semantic search engines and evaluating their research in terms of creating methodologies. Furthermore, elements of artificial intelligence technology that are increasingly difficult to deal with, including such deep learning and machine learning, are investigated to influence development of search engines. These publications demonstrate considerable progress in the decade-long drive towards AI-infused ontology-driven search engine via use of ontologies.
Optimizing Performance and Resource Consumption of Cloud-Native Logging Application Stacks

Nowadays cloud-based applications and Internet of Things use-cases are becoming more and more common in the field of IT benefiting from the virtually limitless resources and microservice-based deployment options available in the cloud. Observability in such environments is key for tracing application execution to detect possible malfunctions and anomalies. Collecting logs can greatly help in this regard, however, a high volume of logging data can add huge costs for the maintenance of the infrastructure gathering monitoring data. In order to increase the profitability of the application, monitoring-related infrastructure needs to have the lowest cost possible while still being able to fully serve the application’s monitoring needs. In this work, we investigate this aspect and provide an evaluation of the resource footprint of one of the most prominent log collection services, Elastic Stack, from the perspective of its write path.
Successive Low-Light Image Enhancement Using an Image-Adaptive Mask

Low-light images are obtained in dark environments or in environments where there is insufficient light. Because of this, low-light images have low intensity values and dimmed features, making it difficult to directly apply computer vision or image recognition software to them. Therefore, to use computer vision processing on low-light images, an image improvement procedure is needed. There have been many studies on how to enhance low-light images. However, some of the existing methods create artifact and distortion effects in the resulting images. To improve low-light images, their contrast should be stretched naturally according to their features. This paper proposes the use of a low-light image enhancement method utilizing an image-adaptive mask that is composed of an image-adaptive ellipse. As a result, the low-light regions of the image are stretched and the bright regions are enhanced in a way that appears natural by an image-adaptive mask. Moreover, images that have been enhanced using the proposed method are color balanced, as this method has a color compensation effect due to the use of an image-adaptive mask. As a result, the improved image can better reflect the image’s subject, such as a sunset, and appears natural. However, when low-light images are stretched, the noise elements are also enhanced, causing part of the enhanced image to look dim and hazy. To tackle this issue, this paper proposes the use of guided image filtering based on using triple terms for the image-adaptive value. Images enhanced by the proposed method look natural and are objectively superior to those enhanced via other state-of-the-art methods.
Complex Texture Features Learned by Applying Randomized Neural Network on Graphs

Since the 1960s, texture has become one of the most-studied visual attribute of images for analysis and classification tasks. Among many different approaches such as statistical, spectral, structural and model-based, there are also methods that rely on analyzing the image complexity and learning techniques. These recent approaches are receiving attention for its promising results in the past few years. This paper proposes a method that combines complex networks and randomized neural networks. In the proposed approach, the texture image is modeled as a complex network, and the information measures obtained from the topological properties of the network are then used to train the RNN in order to learn a representation of the modeled image. Our proposal has proven to perform well in comparison to other literature approaches in two different texture databases. Our method also achieved a high performance in a very challenging biological problem of plant species recognition. Thus, the method is a promising option for different tasks of image analysis.
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation

Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of distributional shifts (e.g., rotations of images), and alignment regularization is a technique often used together with data augmentation to further help the model learn representations invariant to the shifts used to augment the data. In this paper, motivated by a proliferation of options of alignment regularizations, we seek to evaluate the performances of several popular design choices along the dimensions of robustness and invariance, for which we introduce a new test procedure. Our synthetic experiment results speak to the benefits of squared ℓ2 norm regularization. Further, we also formally analyze the behavior of alignment regularization to complement our empirical study under assumptions we consider realistic. Finally, we test this simple technique we identify (worst-case data augmentation with squared ℓ2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods. We also release a software package in both TensorFlow and PyTorch for users to use the method with a couple of lines at https://github.com/jyanln/AlignReg.
Privacy-preserving Collaborative Filtering by Distributed Mediation

Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [ 38 ] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation.