Reviewerly/exHarmony huggingface dataset:
What Do Users Feel? Towards Affective EEG Correlates of Cybersecurity Notifications

Security notifications attempt to change risky computer usage behaviour but often fail to achieve their desired effect. Though there are likely many causes for this phenomenon, information systems researchers have posited that emotional reactions to security notifications may play a role in its explanation. This work-in-progress paper descibes a study to create a baseline of electroencephalographic (EEG) and behavioral responses to security notification images by comparing them to known responses to the well-studied International Affective Picture System (IAPS). By creating such a baseline of affective responses to security notification images, future work can explore the effect of passive emotional reactions to security notification designs which would generate insight into effective design practices.
Improved circuits for a random pulse computer

We present improved circuits intended for building a universal computer based on Random Pulse Computing (RPC) paradigm, a biologically-inspired way of computation in which variable is represented by a frequency of a Random Pulse Train (RPT) rather than a logic state. The RPC we mention here is also known as “stochastic unipolar computation” in newer literature. Unlike in previous art, where randomness is obtained from electronics noise or a pseudorandom shift register while processing circuitry is deterministic, in our approach both variable generation and signal processing rely on the random flip-flop (RFF), whose randomness is derived from a fundamentally random quantum process. This offers better conceptual simplicity and may lead to better precision in chained operations due to the higher randomness of output RTPs.
Optimized Solution of TSP (Travelling Salesman Problem) Based on Mendelian Inheritance

Background: TSP problem has been the part of literature from many decades; it’s an important optimization issue in operation research. TSP problem always remain greedy for the better results especially if chosen working field are Genetic Algorithms (GA). Objective: This paper presents a TSP solution, which performed the modified selection and crossover operations as well as takes advantage of Mendelian inheritance while producing the generations. Methods: GA has very broad resolution scope for optimization problems and it is capable enough for generating well-optimized results if right GA technique has been applied on right point of issue in controlled manner. here the proposed agenda is to utilize the GA concept for TSP by applying mendels rules which is never applied before for the same issue. Here the proposed scheme applies some modification in traditional Mendel process. In general, full chromosome window has been utilized in mendel inheritance process but in presented scheme we have utilizes Most Significant Bits (MSB) only which helps in to control the convergence aptitude of the process. Results: The scheme uses advanced modified Mendel operation which helps in to control convergence aptitude of the operation. It efficiently minimizes the total travelled distance of the graph which was the ultimate objective of the problem and that has been successfully achieved. Conclusion: The validation of the scheme has been confirmed from the obtained results, which are better enough as comparison to traditional TSP-GA.
Blockchain and Fuzzy Logic Application in EV’s Charging

Nowadays many investments are globally done for the adoption of electric vehicles to improve the transport systems sustainable. The location of charging infrastructures, the battery limitations, the producing cost, charging time methodologies, and the impact of EV's on the grid are among the most focusing areas, in order to introduce electric vehicles as trustable and clean means of transportation. The increasing number of demands for EV's has raised concerns regarding the impact of them on the electric network. Increasing energy demands especially in peak loads hours will result in many problems such as blackouts, voltage drop, etc. On the other side, owners expect their cars to be charged at the minimum amount of time with the lowest possible prices. Therefore, implementing new methods of charging for electric vehicles capable of tackling mentioned issues, are appreciated by industrial and electrical sectors. In this paper, we have proposed a new method of EV's charging based on the blockchain technology and fuzzy logic. Applying this method offers the compensation of peak loads and is a fast and efficient way of charging electric vehicles. It also guarantees the security and the privacy of members (EV's) due to the consensus inheritance of Blockchain.
University Education Management Based on Internet of Things Technology

With the continuous promotion of Internet of Things technology, resources and tools, the informatization of education management in colleges and universities Construction is an irreversible trend. This experiment is aimed at the continuous development of the current Internet of Things technology and the research trend of education management informatization in my country's universities. This article takes the Internet of Things technology as the center, combines the Internet of Things and the educational management of colleges and universities, links the Internet of Things, schools, students, and teachers together, innovates in education management under the background of the Internet of Things, and builds things. Network education management model and build platform. Incorporate WSN (Wireless Sensor Network Technology) into the campus LAN. Use radio frequency identification technology (RFID) to read the identity information of teachers and students (campus card electronic tags), aiming at the current situation of college education management, and learning from the successful cases of other colleges and universities, and how to realize automatic and remote fault detection and equipment maintenance Perform basic analysis and improve various functions. The experimental research results show that. As a new technology and field, the Internet of Things has greatly strengthened the communication between people and things. In the cultivation of talents in universities, it is necessary to strengthen the construction of connotation and strengthen the construction of laboratory technical teams, with the goal of cultivating high-quality talents.
Novel Class Detection in Non-stationary Streaming Environment with a Discriminative Classifier

In a data streaming environment, one of the biggest challenges for a machine learning classifier is to detect the changes in the concepts that the data corresponds to - a phenomenon called Concept Drift. It can manifest in different modes: existing classes can continually evolve, experience a sudden shift, or a novel class can emerge. Algorithms for novel class detection using cluster-based techniques and statistical approaches applied to the model outputs rely on the assumption that the feature space of the data posses some distance metric governing the class affiliation. Therefore, the novel class will correspond to a significant distance from the known clusters. Most of the time, these assumptions are correct, but the resulting algorithms are challenging to apply on higher-dimensional data such as images. In this paper, we present a novel approach called Discriminative Classifier Detector (DCD) for detecting concept evolution. DCD trains alongside the classification model. The primary model used to evaluate our approach is a Convolutional Neural Network (CNN) classifier for which the proposed DCD is a densely layered neural network. DCD is applied to the outputs of CNN's penultimate layer to achieve its independence against the number of output classes, which enables the underlying CNN model to evolve independently. DCD requires no structural change when the detected novel class is added to the CNN classifier. We demonstrate the effectiveness of our approach on several well-known datasets.
A Deep Learning Approach for Symmetric-Key Cryptography System

Cryptography is the art and science of protecting information form intruders of data by making the information unintelligible (encryption), as well as, to retrieve the original data (decryption). Good cryptography means that the information is encrypted in such a way that a brute force attack against the key or cryptography algorithm are all impossible. Up to date, several ciphers utilizing complex mathematics have been proposed. But none of them are entirely secure and their vulnerabilities have been exposed. Therefore, novel cryptography algorithms, capable of provide superior protection, are highly desirable. In proposed work, a method for generating a key from an alphanumeric login password is introduced and implementation of symmetric-key encryption and decryption using an autoencoder neural network. Our experiments show that proposed method overcome traditional cryptography algorithms, at lest when small text file are used, and it is extremely hard to crack.
Error Performance Analysis of IMM-Kalman Filter for Maneuvering Target Tracking Application

Maneuvering target is one of the important issues in the tracking system algorithm development. Various techniques are used to improve algorithm performance in maneuvering target tracking. The most popular technique is the Interacting Multiple Model (IMM) algorithm. IMM algorithm uses a combination of several types of filter models in the tracking process. In this paper, the error performance of three types of IMM algorithms namely IMM - Kalman Filter (KF) constant velocity (CV) and constant acceleration (CA), IMM-KF constant velocity (CV) and constant turn (CT), and IMM - KF CV, CA, and CT will be compared. The three types of algorithm will also be compared with a single filter KF- CV to see an increase in the performance of the Kalman Filter algorithm on target maneuvers after being combined. The four types of algorithm will be tested in 3 types of generated target trajectories. Based on the simulation results, it is concluded that the IMM-KF that uses 2 types of filter models provide the best error performance compared to other algorithms. These four types of algorithms will also be tested on real measurement data, which is Automatic Dependent Surveillance - Broadcast (ADS-B) measurement data commonly used to support secondary surveillance radar (SSR) at the airports. From the results of the implementation (using ADS-B data), the same conclusions are obtained from the simulation results.
JettSen: A Mobile Sensor Fusion Platform for City Knowledge Abstraction

In the past years, mobility trends in cities around the world have been pushing for safer, greener, and more efficient transportation systems. This shift in mobility trends creates an opportunity for using mobile lightweight infrastructure, such as bicycles, as a generator of knowledge that will benefit commuters alongside the environmental and societal performance of cities. We propose a system architecture design for an open source mobile sensor fusion apace a platform with a knowledge abstraction framework that enables citizens, urban planners, researchers, and city officials to better address the complex issues that are innate to cities. The system is mounted on a commercial electric assist bike and is able to combine sensor input that describes the bicycle’s electro-mechanical, geospatial, and environmental states. The system proposes sensor flexibility and modularity as key characteristics, and the abstraction framework conceptualizes the way in which these characteristics can be best exploited for city improvement. We demonstrate the functionality of the system and framework through the creation of a use case implementation for clustering bike trip patterns using unsupervised learning clustering techniques. This platform outlines a way to migrate focus from providing solutions to asking the right questions in order to satisfy citizens’ needs.
Modelling Neural Dynamics with Optics: A New Approach to Simulate Spiking Neurons through an Asynchronous Laser

In this paper, we propose a novel approach for implementing spiking neurons through an optical system. Spiking neurons are a new approach to emulate the neural processes that occur in the brain, known as the third generation of artificial neural networks. They have been successfully used to build a new technology called neuromorphic engineering, which looks for a better performance than traditional computing in tasks usually performed by AI-based systems. Our optical system consists of a low-cost laser source, based on a microcontroller and a continuous-wave laser diode; the microcontroller allows producing synchronous or asynchronous pulses with complex time profiles. Here, through said system we have successfully reproduced most of the neural dynamics observed in biological neurons. These dynamics have been reproduced using a very simple optical array with a great potential for the development of neuromorphic systems. The optical system has been experimentally validated.
The Sugargotchi

The excessive consumption of free sugars - sugar added to food, drinks and existing in honey, syrup and fruit juice - are endemic to the diets of Western children and a leading factor in dental disease. Recent public health interventions and legislation have aimed to safeguard consumer health. However, children and their parents continue to face challenges in understanding the implications of product sugar content for their long-term dental health when weighed against contradictory nutritional benefits and child-friendly marketing. Building upon previous HCI research on serious play for nutritional awareness and leveraging the power of digital pets for enacting behaviour change, we introduce 'the Sugargotchi' an embodied digital companion to raise children's awareness of their dental health and the adverse impacts of free sugars. This paper describes the rationale for the Sugargotchi's development, an overview of our initial prototype, and highlights the key design challenges it faces for household and clinical adoption.
Characteristics of information sources in education from the perspective of their users

The article deals with an acute issue of describing, assessing and searching for sources of information in the educational sphere. The author distinguishes four complex characteristics which allow a user to navigate the information flow and gives the tool for a critical evaluation of the source of information. The need for development of such criteria is becoming topical due to the current changes connected with global informatization of society and digitalization of education.
Coherence Evaluation Method Based on Analyses of Phrases Graph

Coherence evaluation falls into a category of natural language processing tasks. The state-of-the-art methods utilize different machine learning techniques to estimate the coherence value of texts. In this paper, the graph based method of coherence evaluation based on the extraction of phrases and coreference resolution has been suggested. State-of-the-art graph-based methods of coherence evaluation have been analyzed. The approach based on the analysis of the set of phrases has been suggested. The extraction of phrases from a text has been implemented according to the features of Ukrainian language with the usage of a syntactic parser. The usage of a bipartite digraph to estimate the lexical connectivity of sentences has been proposed. The effectiveness of the method on the Ukrainian-language corpus has been examined using document discrimination and insertion tasks. The metrics retrieved have been compared with the calculated metrics of other analytical and graph-based methods. The results obtained may indicate that the method suggested can be used to perform the coherence evaluation of Ukrainian texts. The method can be adapted to features of other languages by the changing of several additional components.
An Open Framework for Infinite Walking With Saccadic Redirection

In this project we created an expandable framework for allowing infinite walking in virtual reality in a closed play area. A saccade is a rapid eye movement with a unique property: the eye temporarily gathers reduced information – saccadic suppression. We leverage the suppression to redirect the user’s walking towards the center of the play area by rotating the virtual world around the camera’s location. With the VR environment and corresponding pre and post experience questions we could already show an improvement in understanding on a set of participants. Modern VR hardware such as the Vive Eye Pro allows a reasonable sample rate of eye movement measurements. A self-developed VR testing environment was used and with corresponding pre and post experience questions we tested a group of participants regarding general motion and VR- sickness parameters. We found a certain angle for the maximum saccade rotation which was base of further testing. We found, that our framework and the default settings successfully allow saccadic redirection with only marginal discomfort for the users.
Sound, Senses, Musical Meaning, and Digital Performance: Epistemological Reframings

Drawing upon scholarship in sound studies, computer music, sensory ethnography, and musicology, this article argues that digital mediation has radically altered our understanding of the ‘musical work’ and the divisions of labour emerging from digital musical performance practice. It further argues that this opens up both an opportunity and a need, particularly in contexts involving artificial intelligence (AI)/machine learning or interactive systems, for new creative approaches that place embodied practices of listening, improvisatory musical expression, and new approaches to performative sense-making at the core of musical research and creation in a manner that traverses composition, performance, and technological development.
Custody Transfer Tank Calibration Technology

Abstract In this paper a new, semi-automated tank calibration method based on advanced robotics is presented that enables enhanced volumetric measurement and calibration of custody/royalty transfer tanks, with an emphasis on those used in the Oil and Gas Industry. This technology offers relevant facilities a rapid and straightforward solution that offers improved precision over traditionally used methods, thus minimizing revenue losses resulting from inaccurately calibrated tanks. The technology developed is a semi-autonomous robotic system capable of calibrating upright cylindrical tanks. To achieve this, a magnetic crawler was designed that incorporates a digital, laser-based ruler that collects data about the offset of the tank circumference along the tank height with reference to the tanks’ base circumference. The technology has been developed in accordance with the requirements of API MPMS 2.2B "Manual of Petroleum Measurement Standards Chapter 2—Tank Calibration Section 2B—Calibration of Upright Cylindrical Tanks Using the Optical Reference Line Method" to ensure alignment with industry standards. The paper will present trials carried out in both the laboratory, during the technology calibration phase, as well as preliminary tests carried out at a tank farm in Jeddah, Saudi Arabia. The feasibility of using a magnetic crawler and the laser ruler configuration was first demonstrated in the lab environment where measurement uncertainty of less than 0.2 mm was documented, compared to the current technique, which yields measurement uncertainties of at least 1mm. As such, due to increased measurement accuracy, the technology enables consequential calibration of tanks every 2 years as opposed to every 5 years using existing solutions. Apart from the measurement accuracy improvement, this method significantly reduced the total time required to carry out the calibration measurements, increasing the efficiency of the process and reducing the down-time of the asset. Other advantages are based on the digital nature of the technology that simplifies data storage, thus allowing significantly more data to be captured. Finally, remote control of the robotic solution enhances operator safety as it eliminates the need for operators to perform calibration activities at elevated heights such as the roof of the tank. The work performed and presented in this paper demonstrates a novel application of robotics in the Oil and Gas Industry that directly impacts operational efficiency while enhancing safety and minimizing revenue losses. Globally, the current lack of accurate and efficient calibration methods impacts the efficiency of the crude supply chain. The work presented here provides a suitable and cost-effective solution to enhance value realization from the sale of liquid petroleum products.
An Entropy Minimization Approach to Dialogue Segmentation

This paper introduces an approach to segmentation of short dialogue fragments, based on the entropy of linguistic cues. Starting from the assumption that a dialogue fragment consists of two non-overlapping segments, the segment boundary is determined to minimize the maximum interactional entropy amongst the segments. The approach is evaluated on a corpus of 4500 artificially generated two-segment dialogues, each of which containing from 8 to 12 dialogue acts. In 29.20 percent of the dialogues, the detected segment boundary coincides with the actual segment boundary, and in 69.27 percent of the dialogues, the detected segment boundary either coincides with the actual boundary or is immediately preceded or succeeded by it.
Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.
Heavy Metal Contamination in the Soils Collected From Al-Muthanna Storage Site

This paper introduces the hazard contamination in the storage area site using geomatics analysis technique. The contamination level of four primary heavy metal concentrations (Cu, Zn, Pb and Cd) of soils evaluated, analyzed and compared to the background reference points. Results indicate that copper level exceeds the permitted level by (1050) times, for zinc by (16000) times over the permitted level on the surrounding isolation protection on the north-west Wall, very high Contamination spot activity of lead was found in Destruction zone about (1350) times over the background level and there is no activity detected for the cadmium in the storage area site.
Towards the Management and Dissemination of Knowledge from Gaming Simulations

Nowadays, gaming simulations (games) are used for various different purposes and generate a wealth of knowledge. Yet, there is still lack of research on whether, and if so how, these games can be used by researchers and practitioners to build evidences on systems' behavior within a larger scheme and/or manage and exploit the knowledge produced by and in these games. This article proposes a knowledge management framework, which aims at enabling the development of a knowledge management system that can store, index, and disseminate the knowledge produced by and in games in an appropriate way. The proposed framework is built on the basis of several factors, like the type of knowledge and the prospective users, and is then validated with three case studies from the Dutch railway sector. Through the case studies, the proposed framework appears to be able to help the management and dissemination of knowledge derived from games. The framework is a proof of concept on the feasibility of developing a knowledge management system module for games.