Reviewerly/exHarmony huggingface dataset:
Addi-Reg: A Better Generalization-Optimization Tradeoff Regularization Method for Convolutional Neural Networks

In convolutional neural networks (CNNs), generating noise for the intermediate feature is a hot research topic in improving generalization. The existing methods usually regularize the CNNs by producing multiplicative noise (regularization weights), called multiplicative regularization (Multi-Reg). However, Multi-Reg methods usually focus on improving generalization but fail to jointly consider optimization, leading to unstable learning with slow convergence. Moreover, Multi-Reg methods are not flexible enough since the regularization weights are generated from a definite manual-design distribution. Besides, most popular methods are not universal enough, because these methods are only designed for the residual networks. In this article, we, for the first time, experimentally and theoretically explore the nature of generating noise in the intermediate features for popular CNNs. We demonstrate that injecting noise in the feature space can be transformed to generating noise in the input space, and these methods regularize the networks in a Mini-batch in Mini-batch (MiM) sampling manner. Based on these observations, this article further discovers that generating multiplicative noise can easily degenerate the optimization due to its high dependence on the intermediate feature. Based on these studies, we propose a novel additional regularization (Addi-Reg) method, which can adaptively produce additional noise with low dependence on intermediate feature in CNNs by employing a series of mechanisms. Particularly, these well-designed mechanisms can stabilize the learning process in training, and our Addi-Reg method can pertinently learn the noise distributions for every layer in CNNs. Extensive experiments demonstrate that the proposed Addi-Reg method is more flexible and universal, and meanwhile achieves better generalization performance with faster convergence against the state-of-the-art Multi-Reg methods.
A parameterized geometric fitting method for ellipse

This study presents an accurate and robust method for fitting noisy and occlusion elliptic data. The nonlinear issue of ellipse fitting is interpreted as a set of Levenberg–Marquardt iterations (LMIs) by minimizing the geometric distance. For each iteration of the geometric fitting, the representations of the parameterized ellipses are mapped to the geometric error distances, which are implemented latently by an orthogonal angle segmentation of the ellipses. Moreover, dimension reduction is utilized by the LMIs to avoid misconvergence and expensive computations. The method based on two recent representative algorithms is verified by simulation and real-world experiments. The results suggest that the saliency capability of the new method to fit ellipse-specific profiles with severe noise and occlusion, which is better than or equal to those of the reference approaches, has potential applications in quality monitoring, three-dimensional reconstruction, and instrument calibration.
Digital Transformation of Education at School

Digital transformation of education at schools is inevitable condition for digital transformation of educational system. In the paper, we discuss features of “digital school” from the perspective of school as a community of actors and from the perspective of school as an educational space.
Deep and Confident Image Analysis for Disease Detection

This paper proposes an efficient deep learning classifier built on Bayesian deep neural network framework for general probabilistic disease detection along with reliable principled uncertainty estimation. Specifically we harness the expressiveness and temporal nature of Seq-2-Seq Convolutional neural networks (CNNs) to model explicitly disease detection problem via deep and confident image processing. The work in this paper shows that the uncertainty informed decision making can improve the diagnostic performance considerably. Furthermore, we deploy a Memory Network in order to memorize detected images representing infected cells in historical records. We demonstrate and validate empirically the effectiveness of the proposed framework via extensive experimental and rigorous evaluation on large-scale real world data sets. Experiments across different tasks and datasets show robust generalization, accurate and superior performance of proposed method compared to the well-known state-of-the-art diseases detectors.
Development of IoT Based Logistic Vehicle Maintenance System

The efficient maintenance and service system is crucial to ensure the vehicle always in perfect condition. Conventional periodic maintenance has been proven to lack in term of efficiency and could cause major damage inside the vehicle system. An approach of predictive maintenance is proposed in this project. This project focuses on the logistics industry because transportation is very crucial in their environment. They must ensure their process of supplying the product run smoothly without any problems. A prototype of the Development of IoT Based Logistic Vehicle Maintenance system has been developed using both hardware and software. Two main microcontrollers which are Arduino UNO and NodeMCU will be used to collect the sensor reading from DHT11 sensor and two pressure transducer sensors and then send the data to the MySQL database. There will be two methods of displaying the data which are through a private webpage that acts as the data log and through a visualization method by using Grafana dashboard. The prototype system was implemented successfully for the intended application.
An improved multi-leader comprehensive learning particle swarm optimisation based on gravitational search algorithm

Multi-leader comprehensive learning particle swarm optimiser possesses strong exploitation ability, by randomly selecting and assigning best-ranked particles as leaders during optimisation. However, it lacks the ability to preserve diversity by mainly focusing on exploitation, and adopting random selection to choose leaders also hinders its performance. To overcome these deficiencies, an improved multi-leader comprehensive learning particle swarm optimiser is proposed based on Karush-Kuhn-Tucker proximity measure and Gravitational Search Algorithm. Karush-Kuhn-Tucker proximity measure is employed to determine the best-ranked particles’ contribution to the swarm’s convergence to influence their selection as guides for other particles. Gravitational Search Algorithm is introduced to preserve the algorithm’s ability to maintain diversity. To curb premature convergence and particles getting trapped in a local optimum, an adaptive reset velocity strategy is incorporated to activate stagnated particles. Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. The results verify that our proposed algorithm possesses a better capability to elude local optima with faster convergence than other algorithms. Furthermore, to prove the efficacy of the application of our proposed algorithm in real-life, the algorithms are used to train a Feedforward neural network for epilepsy detection, of which our proposed algorithm outperforms the others.
Discovering interactions using covariate informed random partition models

Combination chemotherapy treatment regimens created for patients diagnosed with childhood acute lymphoblastic leukemia have had great success in improving cure rates. Unfortunately, patients prescribed these types of treatment regimens have displayed susceptibility to the onset of osteonecrosis. Some have suggested that this is due to pharmacokinetic interaction between two agents in the treatment regimen (asparaginase and dexamethasone) and other physiological variables. Determining which physiological variables to consider when searching for interactions in scenarios like these, minus a priori guidance, has proved to be a challenging problem, particularly if interactions influence the response distribution in ways beyond shifts in expectation or dispersion only. In this paper we propose an exploratory technique that is able to discover associations between covariates and responses in a general way. The procedure connects covariates to responses flexibly through dependent random partition distributions and then employs machine learning techniques to highlight potential associations found in each cluster. We provide a simulation study to show utility and apply the method to data produced from a study dedicated to learning which physiological predictors influence severity of osteonecrosis multiplicatively.
(Dis)connected parenting: other-tracking in the more-than-human sensorium

The range of available tracking technologies that target parents and children has increased dramatically over the last decade, providing functionalities such as location and activity tracking. Situated in emerging conversations on the more-than-human sensorium, this paper investigates tracking practices among Australian parents of children aged between two and eight. In only rare cases had parents adopted tracking apps and sensor-enabled devices. Parents experienced digital sensors as misleading and an interruption to the desired parent-child relationship. Parents instead leaned on their own observations and other sensory cues about their child's health and wellbeing. These findings emphasize how sensed and sensored ways of knowing can be out of sync rather than mutually instructive where the technology is used to track another body. It also highlights the relevance of sensing in parent-child interaction orders.
LegalOps: A Summarization Corpus of Legal Opinions

We present a new, large-scale corpus for training and evaluating text summarization systems on legal opinions, called LegalOps. The corpus includes <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sub> 14K opinions together with their summaries from U.S. Federal Courts, e.g. the Supreme Court and Federal Appeals Courts. The aim of this paper is to provide a novel data source of sufficient variety that it will advance theoretical work on modeling the particular patterns within legal discourse, but also of sufficient size that it will provide a new challenging testbed for state-of-the-art automatic summarization models.
Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

In this paper, we describe an overarching ML system with a simple programming interface that leverages existing AI and ML frameworks to make the task of model exploration easier. The proposed system introduces a new programming construct namely pipeline graph (a directed acyclic graph) consisting of multiple machine learning operations provided by different ML repositories. End user uses the pipeline graph as a common interface for modeling different ML tasks such as classification, regression, and timeseries prediction, while enabling efficient execution on different environments (Spark, Celery and Cloud). We further annotated the pipeline graph with a hyper-parameter grid and an option to try-out a wide range of optimization strategies (i.e., Random, Bayesian, Bandit, AutoLearn, etc). Given a large pre-defined pipeline graph along with its hyper-parameters, we provided a general-purpose, scalable and efficient pipeline-graph exploration technique to provide the automated solutions to a variety of ML tasks. We compare our automated approach to several state-of-the-art automated AI systems and find that we achieve performance comparable to the best results, while often producing simpler pipelines using off the shelf components. Our evaluation suite consists of experiments on 60+ classifications and regressions datasets.
Improving visual communication of discriminative accuracy for predictive models: the probability threshold plot

To propose a visual display-the probability threshold plot (PTP)-that transparently communicates a predictive models' measures of discriminative accuracy along the range of model-based predicted probabilities (Pt).We illustrate the PTP by replicating a previously-published and validated machine learning-based model to predict antihyperglycemic medication cessation within 1-2 years following metabolic surgery. The visual characteristics of the PTPs for each model were compared to receiver operating characteristic (ROC) curves.A total of 18 887 patients were included for analysis. Whereas during testing each predictive model had nearly identical ROC curves and corresponding area under the curve values (0.672 and 0.673), the visual characteristics of the PTPs revealed substantive between-model differences in sensitivity, specificity, PPV, and NPV across the range of Pt.The PTP provides improved visual display of a predictive model's discriminative accuracy, which can enhance the practical application of predictive models for medical decision making.
Algorithms of Machine Learning in Recognition of Trolls in Online Space

The Internet is becoming more and more accessible and widespread. With a growing user base, we also encounter more often the anti-social behavior of users. One of these forms of behavior is a trollism. The problem of troll's comments regulation becomes more and more important. One of possible solutions is a recognition of trolls by machine learning models. The work deals with the behavior of trolls on the Internet, the possibilities of the trollism detection and types of data, which can be used to it. The proposed approach is based on the use of Support Vectors Machine, Multinomial Naïve Bayes, and Logistic Regression. The best results were achieved by Multinomial Naïve Bayes model up to 0,92 of Recall.
A Conditional GAN Based Approach for Distorted Camera Captured Documents Recovery

Many of the existing documents are digitized using smart phone’s cameras. These are highly vulnerable to capturing distortions (perspective angle, shadow, blur, warping, etc.), making them hard to be read by a human or by an OCR engine. In this paper, we tackle this problem by proposing a conditional generative adversarial network that maps the distorted images from its domain into a readable domain. Our model integrates a recognizer in the discriminator part for better distinguishing the generated images. Our proposed approach demonstrates to be able to enhance highly degraded images from its condition into a cleaner and more readable form.
Single Image Deraining via Multi-scale Gated Feature Enhancement Network

With the widespread popularity of computer vision applications, single image deraining problem has attracted more and more attentions. Though various deep-learning based algorithms are designed for single image rain steak removal, deraining performance is still limited due to the insufficient utilization of multi-scale features, which either fails to remove rain steaks completely or damages the original image content. In our paper, a novel deraining network called Multi-scale Gated Feature Enhancement Network (MGFE-Net) is proposed to deal with different types of rain streaks meanwhile achieve a satisfied restoration effect. In MGFE-Net, a multi-scale gated module (MGM) is first utilized between the encoder and decoder to extract multi-scale features according to image content and keep the consistence between high-level semantics and low-level detail features. Furthermore, to cope with diverse rain streaks with different representative characteristics, we integrate the receptive field block (RFB) into encoder and decoder branches to enhance extracted rain features. Multi-level outputs of decoder are fused to obtain a final refined result. Extensive deraining results on synthetic and realistic rain-streak datasets present that our MGFE-Net performs better than recent deraining methods.
Wearables: Educational Projects Made with the BBC micro:bit

BBC micro:bit is becoming more and more spread in Slovakia last years. As it is spreading around the world, extensions allow to be more creative. One of extensions for BBC micro: bit is wearables. Wearable technologies in general can be smartwatches, fitness trackers, VR. As a BBC micro:bit extension it means to make wearable technologies from the very beginning. It is an opportunity for pupils to understand how things work in our everyday life. The article deals with BBC micro: bit and its extension, wearables, to create projects such as plushie toy, headband and postcard.
GPS-based fire detection system (Global Positioning System) and SMS Gateway

Abstract This research aims to produce a GPS-based fire detection system (Global Positioning System) and SMS Gateway. The benefits of this detection system can detect early fire occurrence based on the detection of temperature conditions by accommodating the nature of the fire and able to detect any rise in temperature caused by the existence of the fire. This detection system must also be able to read any smoke produced by a fire. To realize the system, required sensors capable of reading the temperature and smoke. The Arduino Uno microcontroller is the brain control system of the system. At a temperature of&gt; 35 C, the system will activate the DHT 11 and MQ 2 sensors that detect smoke&gt; 50 ppm from fire. The system will activate Buzzer as a warning in the form of the next alarm sound Global Positioning System (GPS) will provide information in the form of coordinates of the location of the point of fire through GSM SIM900 Module Short Message Service (SMS) to the user. The results obtained mq2 = 128 ppm and temperature value = 38 ° C and GPS data with latitude of -3.04798388 and a longitude of 104.78263092. From the data it is seen that the mq2 value reaches&gt; 50ppm and the temperature value reaches&gt; 35 ° C, and the detector outputs buzzer sound and warning notification of coordinate point in the form of SMS containing the message “FIRE available” with the coordinates of the location of the fire detected by GPS.
Influencing Factors of Infection Anxiety, Impulsivity, Decision Making Type on Health Information Seeking Behavior on the Internet among College Students in COVID-19

Purpose: This study investigated the influencing factors of COVID-19 infection anxiety, impulsivity, and decisionmaking type on health information-seeking behavior on the internet. Methods: This study was conducted by 178 college students of 5 cities in Korea using a self-reporting survey with structured questionnaires of COVID-19 anxiety, impulsiveness, decision-making style, and health information-seeking behavior on the internet. SPSS 25 was used for the statistical analysis. Results: The results showed that non-planning impulsivity(ß=-.25, p<.001), dependent decision-making types(ß=.23, p<.001), and COVID-19 infection anxiety(ß=.22, p=.001) had statistically significant effects on health information-seeking behavior on the internet with 33.6% overall explanatory power. Conclusion: Findings of this study indicate that college students’ dependent decision-making types, COVID-19 infection anxiety had a positive effect, and non-planning impulsivity had a negative effect on health information-seeking behavior on the internet. Based on the results of this study, it is necessary to identify and compare health information-seeking behavior on the internet based on infection and self-quarantine experiences.
Chaos-Based Interleave Division Multiple Access Scheme with Physical Layer Security

Interleave division multiple access (IDMA) is a multiple-access scheme and it is expected to improve frequency efficiency. Meanwhile, the damage caused by cyberattacks is increasing yearly. To solve this problem, we propose a method of applying radio-wave encryption to IDMA based on chaos modulation to realize physical layer security and the channel coding effect. We show that the proposed scheme ensures physical layer security and obtains channel coding gain by numerical simulations.
Vehicle Routing Problem Considering Reconnaissance and Transportation

Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for wartime troop movements. The VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements and reconnaissance, and transportation vehicle routes were determined simultaneously in the VRPCRT. For this paper, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed, and computational experiments were conducted to compare the ACO algorithm’s performance and that of the mixed-integer programming model. The performance of the ACO algorithm was shown to yield excellent results even for the real-size problem. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.
Scalable recommendations using decomposition techniques based on Voronoi diagrams

Collaborative filtering based recommender systems typically suffer from scalability issues when new users and items join the system at a very rapid rate. We tackle this concerning issue by employing a decomposition based recommendation approach. We partition the users in the recommendation domain with respect to location using a Voronoi Diagram and execute the recommender algorithm individually in each partition (cell). This results in a much reduced recommendation time as we eliminate the need for running the algorithm using the entire user set. We further address the problem of improving the recommendation quality of the users residing in the peripheral region of a Voronoi cell. The primary objective of our approach is to bring down the recommendation time without compromising the accuracies of recommendations much, which is rightly addressed by our proposed method. The outcomes of the experiments performed demonstrate the scalability as well as efficacy of our method by reducing the runtime of the baseline CF algorithm by at least 65% for each of these four publicly available datasets of varying sizes — MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor datasets. The accuracies of recommendations in terms of MAE, RMSE, Precision, Recall and F1 metrics also hold good.
WordNet: A Lexical Database Organized on Psycholinguistic Principles*

This chapter describes WordNet, an on-line lexical reference system whose design is based on psycholinguistic theories of human lexical organization and memory. English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Different kinds of lexical relations link the synonym sets. Some syntactic properties of verbs and adjectives are also specified. We briefly discuss existing and potential applications of WordNet.