Reviewerly/exHarmony huggingface dataset:
Neuroevolution of recurrent neural networks for time series forecasting of coal-fired power plant operating parameters

This work presents how the Evolutionary eXploration of Augmenting Memory Models (EXAMM) neuroevolution algorithm is incorporated into Microbeam Technologies' condition-based monitoring power plant optimization software using a workflow that integrates coal-fired power plant data collection, evolved RNN predictions and analytic performance indices predictions. To the authors' knowledge, it is the first use of a neuroevolution strategy to evolve recurrent neural networks (RNNs) for forecasting of power plant parameters where the evolved networks have been incorporated into production software used at a coal-fired power plant. A preliminary exploration of the plant's performance shows that after incorporating this software, the amount of revenue lost due to power plant derates and outages decreased by $7.3 million, a savings of 42%, and increased efficiency under medium and low load conditions. A further investigation of the effect of training sequence length and time series data normalization methods on evolving and training RNNs for this system is given, providing practical results useful for real world time series forecasting. It is shown that dividing long time series sequences up into shortened training sequences can dramatically speed up training, and that using different normalization methods (min-max vs. z-score) can provide statistically significant results, dependent on the data sets.
Gradient Descent and Newton's Method with Backtracking Line Search in Linear Regression

The accuracy and efficiency of optimization algorithms remain an essential challenge in supervised machine learning. We used gradient descent and Newton's method, along with backtracking line search, to determine optimal parameter values in linear regression. The obtained results for both models are accurate and are close to the straightforward solutions using the least squares method. Newton's method with backtracking line search performs better with less iteration steps and execution time, suggesting that this method has a higher tolerance for controlling and selecting learning rate alpha. Optimization algorithms with backtracking line search method can be applied to solve diverse regression problems with high precision and efficiency.
IoT Enabled Geofencing for Covid-19 Home Quarantine

The novel coronavirus pandemic has brought our concern towards the necessity of digital infrastructure for remote monitoring of infected people as well as the people who are with close contact with them. Though, many nations have finished the clinical trials period of vaccination and started vaccination to people, still there are many uncertainties such as how these vaccines will work to the people with severe illness, which vaccine is more effective compared to others etc. Moreover, vaccination may not provide a full protection against the current situation since there are new variants of corona virus and the number of affected people increasing again. Therefore, a proper diseases detection and individual monitoring system to maintain a smart quarantine facility is still very much needed to minimise the rapid spread of covid-19 and other contagious virus. Here, wearable tools could play important roles to provide valuable information of tracking people such as real-time remote monitoring, contact tracing symptoms prediction etc. to establish and maintain a proper quarantine system. In this manuscript we proposed a geofencing system with a wristband, integrating it with IOT and GPS to trace Covid-19 patients inside a boundary. This design could be an effective quarantine solution as a home isolation system for covid-19 patients as well as the travellers who cross borders. The performance of our proposed system was measured with respect to received signal strength indication (RSSI), GPS accuracy and coverage.
Assessing Perceived Sentiment in Pull Requests with Emoji: Evidence from Tools and Developer Eye Movements

The paper presents an eye tracking pilot study on understanding how developers read and assess sentiment in twenty-four GitHub pull requests containing emoji randomly selected from five different open source applications. Gaze data was collected on various elements of the pull request page in Google Chrome while the developers were tasked with determining perceived sentiment. The developer perceived sentiment was compared with sentiment output from five state-of-the-art sentiment analysis tools. SentiStrength-SE had the highest performance, with 55.56% of its predictions being agreed upon by study participants. On the other hand, Stanford CoreNLP fared the worst, with only 5.56% of its predictions matching that of the participants'. Gaze data shows the top three areas that developers looked at the most were the comment body, added lines of code, and username (the person writing the comment). The results also show high attention given to emoji in the pull request comment body compared to the rest of the comment text. These results can help provide additional guidelines on the pull request review process.
Attention Guided Multi-Scale Regression for Scene Text Detection

Scene text detection plays an important role in terms of its position as the first step of many methods such as text recognition. However, despite of the tremendous improvement made for scene text detection, in the context of this deep learning era, there still remains many challenges such as the variety of text scales and the distinguish difficulty due to the disturbance of background. This paper introduces an attention guided multi-scale regression method for detecting scene text, which achieves promising performance. The pipeline gathers feature maps from two different scales, which enhance its perceptive ability, especially to large and long texts. Moreover, the introduction of attention module further improved the prediction performance in terms of separate the text region from background more accurately. Experiments conducted on ICDAR 2015 and MSRA-TD500 show that comparable results have been achieved by the proposed text detector in terms of accuracy and precision.
A Survey of Adversarial Example Toolboxes

At present, the development of artificial intelligence has attracted more and more attention, as one of the important branches of machine learning, deep learning has developed rapidly in recent years and received wide attention. However, in recent years, the concept of adversarial example has been put forward, more and more people began to pay attention to the security of deep learning, because the generation of adversarial example is helpful to improve the security of the model, so a variety of attack algorithms have been proposed and implemented by researchers. To make it easier for developers to use, many companies have built libraries to generate adversarial examples. This paper introduces these algorithms and tool libraries, and compares the configuration process of the tool library with the implementation of attack engineering, and puts forward a comprehensive selection proposal to the readers.
Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Abstract This paper presents Specularizer , a framework for uncovering speculative execution attacks using performance tracing features available in commodity processors. It is motivated by the practical difficulty of eradicating such vulnerabilities in the design of CPU hardware and operating systems and the principle of defense-in-depth. The key idea of Specularizer is the use of Hardware Performance Counters and Processor Trace to perform lightweight monitoring of production applications and the use of machine learning techniques for identifying the occurrence of the attacks during offline forensics analysis. Different from prior works that use performance counters to detect side-channel attacks, Specularizer monitors triggers of the critical paths of the speculative execution attacks, thus making the detection mechanisms robust to different choices of side channels used in the attacks. To evaluate Specularizer , we model all known types of exception-based and misprediction-based speculative execution attacks and automatically generate thousands of attack variants. Experimental results show that Specularizer yields superior detection accuracy and the online tracing of Specularizer incur reasonable overhead.
Deceiving neural source code classifiers

This work presents an evolutionary approach for assessing the robustness of a system trained in the detection of software vulnerabilities. By applying a Grammatical Evolution genetic algorithm, and using the output of the system being assessed as the fitness function, we show how we can easily change the classification decision (i.e. vulnerable or not vulnerable) for a given instance by simply injecting evolved features that in no wise affect the functionality of the program. Additionally, by means of the same technique, that is by simply modifying the program instances, we show how we can significantly decrease the accuracy measure of the whole system on the dataset used for the test phase.
ReMP: Rectified Metric Propagation for Few-Shot Learning

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.
Deep convolutional correlation iterative particle filter for visual tracking

This work proposes a novel framework for visual tracking based on the integration of an iterative particle filter, a deep convolutional neural network, and a correlation filter. The iterative particle filter enables the particles to correct themselves and converge to the correct target position. We employ a novel strategy to assess the likelihood of the particles after the iterations by applying K-means clustering. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Experimental results on two different benchmark datasets show that our tracker performs favorably against state-of-the-art methods.
Development of R-Shiny interface for implementation of backpropagation neural network model in breast cancer classification

Abstract Artificial Neural Network or Neural Network (NN) is an information processing system that has similar characteristics to the neural network in living things. One type of NN that is often used in classification is Backpropagation Neural Network (BPNN). BPNN is an NN model that is often used for classification because it does not need to use assumptions and has high accuracy. One of the classification problems that can be solved with BPNN is the classification of breast cancer. The breast cancer data used in this study came from the UCI Machine Learning website. The problem with BPNN is that programming is difficult for users who do not understand the program, especially the R program. Therefore, to make it easier for users to analyze BPNN, an R-Shiny application or interface is created using the RStudio program. The application or R-Shiny interface that has been created has several advantages, namely the application process that is fast in displaying classification results, the use of user-friendly applications and the use of applications that are more comfortable when compared to having to write syntax such as in the R program. BPNN classification results use The R-Shiny interface has a different level of accuracy for each experiment due to the random distribution of training &amp; testing data. The experiments conducted in this study resulted in a range of accuracy values ranging from 58.33% to 91.67% with an average accuracy of 74.17%.
Mirroring Vector Space Embedding for New Words

Most embedding models used in natural language processing require retraining of the entire model to obtain the embedding value of a new word. In the current system, as retraining is repeated, the amount of data used for learning gradually increases. It is thus very inefficient to retrain the entire model whenever some new words emerge. Moreover, since a language has a huge number of words and its characteristics change continuously over time, it is not easy to embed all words. To solve both problems, we propose a new embedding model, the Mirroring Vector Space (MVS), which enables us to obtain a new word embedding by using the previously built word embedding model without retraining it. The MVS embedding model has a convolutional neural networks (CNN) structure and presents a novel strategy to obtain word embeddings. It predicts the embedding value of a word by learning the vector space of an existing embedding model using the explanations of the word. It also provides flexibility for external resources, reusability for training times, and portability in the point that our model can be used with any models. We verify these three attributes and the novelty in our experiments.
Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)

Nowadays criminal activities are growing at an exponential rate. Crime prevention by effective identification of criminals is the main issue before the police and on the other hand, the availability of police officers is inadequate. There are various technological solutions for detecting criminals but they are not up to the mark. In this project, a face detection and recognition system for criminal identification is developed using the multi-task cascade neural network. This system will be able to detect faces and recognize faces of criminals automatically in real-time. This system would also just require a single image of the criminal to recognize him, also known as one-shot learning. The purpose is to identify the criminal face, retrieve the information stored in the database for the identified criminal and a notification is sent to the police personnel with all the details and the location at which he was under the surveillance of the camera.
The Quantum Optics of Asymmetric Mirrors With Coherent Light Absorption

The local observables of the quantised electromagnetic field near a mirror-coated interface depend strongly on the properties of the media on {\em both} sides. In macroscopic quantum electrodynamics, this fact is taken into account with the help of optical Green's functions which correlate the position of an observer with all other spatial positions and photon frequencies. Here we present an alternative, more intuitive approach and obtain the local field observables with the help of a quantum mirror image detector method [Furtak-Wells et al., Phys. Rev. A 97, 043827 (2018)]. In order to correctly normalise electric field operators, we demand that spontaneous atomic decay rates simplify to their respective free space values far away from the reflecting surface. Our approach is interesting, since mirror-coated interfaces constitute a common basic building block for quantum photonic devices.
IR for Children 2000-2020: Where Are We Now?

Over 20 years ago, Information Retrieval (IR) researchers began their quest for sound IR systems for children. The path was not straightforward. Challenges posed by interface design, relevance determination, diverse contexts, ethics, and many more, were taken up and explored from different perspectives. Large projects such as Puppy-IR and the International Children's Digital Library gave this field a certain boost; still, there is neither a sound solution for children in the search area in 2021 nor a roadmap to get there. What is the reason for this? Does the field cry out for specific IR solutions developed on a small scale for very small sub-fields and specific target groups? Are there some significant unforeseen barriers that hinder researchers? What about obstacles natural to areas of study such as this one that require a multidisciplinary approach or involve protected populations? With this workshop, we want to bring together as many key experts as possible from research and industry who focus on IR for children to understand why, unlike other IR areas, this one has not flourished and look for the biggest challenges for the next 10 years. We are not only thinking of traditional researchers and designers but also of those who develop and use IR systems for fields, such as in music, film, and education, as a way to push past this immobility and look at the problem from new, and perhaps more stimulating, perspectives.
Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity

Active contour model is one of the most widely used image segmentation tools at present, but the existing methods only utilize single feature information of image to minimize the energy function, which is easy to cause false segmentations in infrared (IR) images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient computed by local range to incorporate the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms typical models in terms of precision rate and overlapping rate in IR test images. The code is available at: https://github.com/MinjieWan/MFDACM .
VIoLET

Internet of Things (IoT) deployments have been growing manifold, encompassing sensors, networks, edge, fog, and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose a virtual environment for validating Internet of Things at large scales (VIoLET), an emulator for defining and launching large-scale IoT deployments within cloud VMs. It allows users to declaratively specify container-based compute resources that match the performance of native IoT compute devices using Docker. These can be inter-connected by complex topologies on which bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation as well. We also incorporate models for CPU resource dynamism, and for failure and recovery of the underlying devices. We offer a detailed comparison of VIoLET’s compute and network performance between the virtual and physical deployments, evaluate its scaling with deployments with up to 1, 000 devices and 4, 000 device-cores, and validate its ability to model resource dynamism. Our extensive experiments show that the performance of the virtual IoT environment accurately matches the expected behavior, with deviations levels within what is seen in actual physical devices. It also scales to 1, 000s of devices and at a modest cloud computing costs of under 0.15% of the actual hardware cost, per hour of use, with minimal management effort. This IoT emulation environment fills an essential gap between IoT simulators and real deployments.
A differential particle scheme and its application to PID parameter tuning of an inverted pendulum

Gradient-free stochastic optimization algorithms are well-known for finding suitable parameter configurations over independent runs ubiquitously. Attaining low variability of convergence performance through independent runs is crucial to allow further generalization over distinct problem domains. This paper investigates the performance of a differential particle system in stabilizing a nonlinear inverted pendulum under diverse and challenging initial conditions. Compared to the relevant algorithms in the literature, our experiments show the feasibility of achieving lower convergence variability to stabilize a nonlinear pendulum over independent runs and initial conditions within a reasonable computational load.
Open-Set Hypothesis Transfer With Semantic Consistency

Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and target domain data to perform domain alignment, which greatly limits their applications when source domain data are restricted due to privacy concerns. This paper addresses the challenging hypothesis transfer setting for UODA, where data from source domain are no longer available during adaptation on target domain. We introduce a method that focuses on the semantic consistency under transformation of target data, which is rarely appreciated by previous domain adaptation methods. Specifically, our model first discovers confident predictions and performs classification with pseudo-labels. Then we enforce the model to output consistent and definite predictions on semantically similar inputs. As a result, unlabeled data can be classified into discriminative classes coincided with either source classes or unknown classes. Experimental results show that our model outperforms state-of-the-art methods on UODA benchmarks.
Pure Recall-Based Graphical User Authentication Schemes: Perspectives from a Closer Look

In an era of mobile, embedded and ubiquitous computing, activities of hackers and cybercriminals has metamorphosed into a global pandemic. Resulting effects cuts across most sectors of human endeavor given the high penetration level of technology. Successful unauthorized access leading to information and identity theft, system infiltration, intellectual property theft, financial crimes, extortion, carding and much more are on the increase, consequently making user authentication an important process, ensuring systems and services are accessed by their intended users. Text passwords are the most widely deployed user authentication scheme today. However, are hardly human-friendly for the vast majority, leaving humans with a memorability problem and consequently a security problem. Graphical User Authentication (GUA) schemes, on the other hand, are proven by state-of-the-art research with compelling evidence to perform better in memorability and potentially by implication security. However currently available GUA schemes provide theoretical entropy levels far from that offered by text password scheme. Thus the research community constantly is seeking to improve GUAs to position them as possible alternatives to Text passwords. This study is a first of two planned studies. It seeks to take a closer look at Pure Recall-based GUAs with emphasis on a user authentication design factor contextual parameter. The study aims at a better understanding of Pure Recall-based GUAs developed between the first 20 years (1996 to 2016), then others in a later study in an attempt to better position Pure Recall-based GUAs as alternatives to text passwords.