Reviewerly/exHarmony huggingface dataset:
Security and Privacy Challenges and Solutions in IoT Data Analytics

With a number of IoT devices expected to reach above 30 billion by 2025, the security and privacy challenges related to the exchange of data are increasingly becoming a key concern for all stakeholders involved in the ecosystem of IT and allied industries. In this particular context, our research aims to explore, comprehend, analyze, and recommend the solutions for effective and secure data management in the field of IoT data analytics with their specific applications for smart cities. With the help of extant literature and real-world scenarios, the study identifies the key challenges faced by IoT data analytics firms while they handle, process, execute, and transfer enormous data through trillions of interconnected devices. The identification is then followed by the analysis of the key variables based on which authors conceptualize a theoretical framework to connect the stakeholders in the IoT data analytics segment. The framework suggests the strategies to successfully manage the security and privacy challenges related to data while specifying areas that require specific attention from the industry. In addition, the research offers crucial recommendations for industry, customers, and policymakers and has important implications for researchers as well as practitioners.
Application of Machine Learning in Mineral Mapping Using Remote Sensing

The machine learning is an effective approach toward acquiring patterns on voluminous data popularly termed big data. Remote sensing is one such field that can be employed with the ML concepts to ascertain solutions to several environmental problems. With the raw spatial data captured by the sensors like LANDSAT, meaningful insights can be drawn adhering to the specifics of the arena. The images are captured in the form of electromagnetic waves often termed as spectral signatures based on the reflectance properties of elements on the earth’s surface. The paper intends to showcase the relevance of machine learning concepts pertaining to a specific area of application in geosciences with the identification of potential mineral mapping areas as the key objective. To derive the most appropriate results, the key indicators on the earth’s surface are focused where the dataset is band mapped with multispectral data. The spectral resolution is a key concept that provides an unambiguous picture of mineral spectra across the spectral regions. The image classification provides the classifiers which can be further accurately assessed to determine the several regions including vegetation, soil, and water. The paper intends to delve into the intricacies of remote sensing as an effective tool of data capturing in the form of spectral signatures and the role of machine learning algorithms for effective geospatial analysis to derive regions of exploration interest with extensive scrutiny of work under the study arena.
Detecting and understanding correlations among data

This chapter, Detecting and understanding correlations among data, develops techniques for quantifying correlations within datasets, and especially within and among time series. Several different manifestations of correlation are explored and linked together: from probability theory, covariance; from time series analysis, cross-correlation; and from spectral analysis, power spectral density, and coherence. The effect of smoothing and band-pass filtering on the statistical properties of the data and their spectra is also discussed.
Detecting fishing URLs using fuzzy clustering algorithms with global optimization

An algorithm for detecting phishing URLs (classifier) using fuzzy clustering is proposed, which includes choosing the type of intelligent classifier and justifying its parameters using global optimization methods. The following were studied as intellectual classifiers: subtractive clustering and fuzzy clustering of C-means. To find (adjust) the optimal (for a specific task) parameters of intelligent classifiers, the use of global optimization methods is justified, including genetic algorithm, direct random search, annealing simulation method, multicriteria optimization and threshold acceptance method. As a criterion of global optimization, a combined criterion was used, which includes the definition of the regularity criterion calculated on the test sample and the bias (minimum shift) criterion based on the analysis of solutions. By modeling in the Matlab environment with the help of standard and developed programs, the evaluated efficiency of using the proposed algorithm is evaluated on the example of experimental data – a set of 150 phishing and 150 secure URLs. The set of experimental data included information about the domain name registrar, the lifetime of the domain, the geolocation of the hosting server, the presence of a secure connection with a valid certificate. By simulation it is established that the fuzzy classifier with the subtractive clustering algorithm and using the Sugeno structure and 6 clusters meets the minimum of the combined criterion. All phishing URLs that were mistakenly classified as secure were found to have a secure con-nection with a valid certificate. Thus, further research should be aimed at exploring additional informative attributes (features) that could allow better separation of phishing and secure URLs.
Data as “Brickolage”

Data visualization is a reliable tool for professional communication practitioners for synthesizing and presenting data to a variety of audiences and attention should be given to preparing and teaching our students how to design effective visualizations. In this experience report, the author presents finding from teaching a data visualization activity using LEGO blocks and Indigenous maker practices. Students can then transfer such skills to making and critiquing their own data visualizations following this foray into material practices involving manipulating and playing with data through the activity. The use of hands-on making, alongside the study of good data visualization design, helps to instill maker practices of community and creativity in students as evidenced in the full visualization projects produced by students.
Routing single photons from a trapped ion with photonic integrated circuits

Trapped ions excel as quantum networking nodes. Here, we use integrated photonic circuits to route and directionally switch single photons emitted by an ion. This is a key step towards scalable trapped ion quantum networks.
A Simple Algorithm Selector for Continuous Optimisation Problems

A large number of algorithms has been proposed for solving continuous optimisation problems. However, there is limited theoretical understanding of the strengths and weaknesses of most algorithms and their individual applicability. Furthermore, the performance of these algorithms is highly dependent on their control parameters, which need to be configured to achieve a peak performance. Automating the processes of selecting the most suitable algorithm and the right control parameters can help in solving continuous optimisation problems effectively and efficiently. In this paper, a simple online algorithm selector is proposed. It decides on selecting the right algorithm based on the current state of the search process to solve a given problem. Each algorithm in the portfolio of the algorithm selector competes with others and utilises the results of other algorithms to locate the global optimum. The proposed algorithm selector and the algorithms of the portfolio as stand-alone algorithms were benchmarked on the noise-free BBOB-2009 testbed. The results show that the performance of the simple algorithm selector is better than the performances of the individual algorithms in general. It was also able to solve eleven out of twenty-four functions of the test suite to the ultimate accuracy of 10-8.
FMEA Implementation Based on Design Activities

Purpose: To implement FMEA effectively, it must be used as a reliability design tool for enabling design engineers to formulate preventive design plans for potential failure modes and not as a tool for evaluating the reliability of known failure modes and improvement measures. Accordingly, the authors propose an implementation method that includes FMEA among the various design activities implemented in the field.BRMethods: This study examines the relationship between general design activities in the field and FMEA. Moreover, a new format and procedure that can be included in the implementation of FMEA are devised. These are based on the various preferences of design engineers as regards materials, tolerances, and interfaces. Furthermore, the presentation of the implementation of the foregoing to different cases is anticipated to be advantageous in the field.BRResults: The use of FMEA in customer submissions only describes known failure modes. In addition to the foregoing, drawing design improvements and inferring potential failure modes, which may occur in actual customer use environment, are possible. Moreover, a practical FMEA implementation guide for discovering effective failure modes while performing various analysis processes to prepare the FMEA form is proposed.BRConclusion: The conduct of a study that can support failure mode analysis during the design activity process such that brainstorming can be replaced by intelligent machine operation is deemed necessary.
Investigating the effect of augmented reality technology on traditional culture experience motivation by using a novel Tosenkyo application

In this study, we proved that augmented reality (AR) technology increases the motivation to experience traditional culture. We developed an AR Tosenkyo application to simulate "Tosenkyo," a traditional Japanese game, and conducted experiments with 47 participants, including foreigners. Because Tosenkyo AR application components can be categorized into play equipment and backgrounds, we prepared three applications with different degrees of AR environmental conditions to investigate their effect on motivation. The results of the experiment revealed that, before the AR experience, Japanese people were less motivated to experience traditional culture compared with Chinese people; however, after the AR experience, their motivation increased considerably. This tendency was pronounced in a harmonic AR environment, which is considered a moderate fusion of reality and AR. Furthermore, the path analysis of the questionnaire results revealed that higher quality of AR application considerably improves motivation.
IoT-Based Electronic Health Records (EHR) Management System Using Blockchain Technology

Health care is the most important factor for the quality of life and IoT has provided us with the capabilities to improve the standards of health care. With the advancement in IoT, the capabilities to incorporate the mobility of patients has seen a great leap. With the prior knowledge of a patient's medical records, the doctor can make quick and efficient decisions. However, current electronic health record systems that are used to manage and store medical records of the patients suffer from issues including heavy bookkeeping, security, data integrity, and privacy. There is also no standard inter-connection to store and retrieve medical records of patients among the majority of the hospitals. In this book chapter, we present a permissioned blockchain-based system that could be used to store the patient records in a secure way i.e., the system will be patient-centric. Also in this system the patient's health can be monitored using IoT sensors and that data will be stored in the cloud and accessed using blockchain only. In this system, the patient will have control over the access to their medical records i.e., this system will be patient-centric which is not the case with current electronic health record systems. Hyperledger Fabric Blockchain was used to implement this project and to store medical records of patients and AWS storage has been used. To receive data from IoT sensors and send that to the cloud, mobile devices were used.
Formal language hierarchy reflects different levels of cognitive complexity.

Formal language hierarchy describes levels of increasing syntactic complexity (adjacent dependencies, nonadjacent nested, nonadjacent crossed) of which the transcription into a hierarchy of cognitive complexity remains under debate. The cognitive foundations of formal language hierarchy have been contradicted by two types of evidence: First, adjacent dependencies are not easier to learn compared to nonadjacent; second, crossed nonadjacent dependencies may be easier than nested. However, studies providing these findings may have engaged confounds: Repetition monitoring strategies may have accounted for participants' high performance in nonadjacent dependencies, and linguistic experience may have accounted for the advantage of crossed dependencies. We conducted two artificial grammar learning experiments where we addressed these confounds by manipulating reliance on repetition monitoring and by testing participants inexperienced with crossed dependencies. Results showed relevant differences in learning adjacent versus nonadjacent dependencies and advantages of nested over crossed, suggesting that formal language hierarchy may indeed translate into a hierarchy of cognitive complexity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Technique to harmonize Booking.com's dual rating systems

• Booking.com changed its rating system gradually from Sept 2019 through April 2020. • This can lead to misleading results regarding the impact of the Covid-19 pandemic. • We studied the level of data contamination by analysing over half a million reviews. • A generalized cleaning and estimation procedure is developed and demonstrated. • Enables study of the pandemic as well as impact of changes to rating systems
Software Size Estimation: A survey

Software size prediction is a first step to compute information for software project development such as: project cost, effort, schedules, duration and so on. This paper presents the software prediction methods and the efforts of the researchers in this era. Methods such as Source line of Code, Model based estimation, function point and estimation from UML diagrams. For every method, the strength and weakness is presented. Also, it presents the evaluation criteria that is necessary for the researcher to evaluate the accuracy of the methods.This information will help the researcher in the future to choose the most effect method that fit to his/her software project.
Optical ReLU using membrane lasers for an all-optical neural network

In this study, we propose low power consumption, programmable on-chip optical nonlinear units (ONUs) for all-optical neural networks (all-ONNs). The proposed units were constructed using a III-V semiconductor membrane laser, and the nonlinearity of the laser was used as the activation function of a rectified linear unit (ReLU). By measuring the relationship of the output power and input light, we succeeded in obtaining the response as an activation function of the ReLU with low power consumption. With its low-power operation and high compatibility with silicon photonics, we believe that this is a very promising device for realizing the ReLU function in optical circuits.
Distilling Resolution-robust Identity Knowledge for Texture-Enhanced Face Hallucination

The main focus of most existing face hallucination methods is to generate visually pleasing results. However, in many applications, the final goal is to identify the person in the low-resolution (LR) image. In this paper, we propose a texture and identity integration network (TIIN) to effectively incorporate identity information into face hallucination tasks. TIIN consists of an identity-preserving denormalization module (IDM) and an equalized texture enhance module (ETEM). The IDM exploits the identity prior and the ETEM improves image quality through histogram equalization. To extract identity information effectively, we propose a resolution-robust identity knowledge distillation network (RIKDN). RIKDN is specifically designed for LR face recognition and can be of independent interest. It employs two teacher-student streams. One stream narrows the performance gap between high-resolution (HR) and LR images. The other distills correlation information from the HR-HR teacher stream to guide learning in the LR-HR student stream. We conduct extensive experiments on multiple datasets to demonstrate the effectiveness of our methods.
Analyzing XAI Metrics: Summary of the Literature Review

&lt;p&gt;The widespread adoption of Artificial Intelligence (AI) models by various industries in recent years have made Explainable Artificial Intelligence (XAI) an active field of research. This adoption can cause trust and effectiveness to suffer if the results of these models are not favorable in some way. XAI has advanced to the point where many metrics have been proposed as reasons for the outputs of many AI models. However, there is little consensus about what technical metrics are most important, nor is there a consensus on how best to analyze explainable methods and models. A discussion of varying attempts at this is brought forth, but the paper also goes into the ethics of AI and its societal impact. Given the modern ubiquity with which AI exists and the immensely multidisciplinary approach, AI has evolved into, using only technical metrics cannot fully describe XAI’s effectiveness. This paper explores several approaches to measuring the ethical effects of XAI, whether it has any bearing on modern research, as well as how the impacts of AI and XAI are measured on society. The full attempt at quantifying XAI models’ effectiveness is explored from a technical and non-technical point of view.&lt;/p&gt;
Alexa, let's work together! How Alexa Helps Customers Complete Tasks with Verbal and Visual Guidance in the Alexa Prize TaskBot Challenge

In this talk, I will present the Alexa Prize TaskBot Challenge, which allows selected academic teams to develop TaskBots. TaskBots are agents that interact with Alexa users who require assistance (via "Alexa, let's work together") to complete everyday tasks requiring multiple steps and decisions, such as cooking and home improvement. One of the unique elements of this challenge is its multi-modal nature, where users receive both verbal guidance and visual instructions, when a screen is available (e.g., on Echo Show devices). Some of the hard AI challenges the teams addressed included leveraging domain knowledge, tacking dialogue state, supporting adaptive and robust conversations and probably the most relevant to this conference: handling multi-modal interactions.
Prediction of traffic accident based on optimal exponential smoothing model with Markov modification

To predict the development of road traffic accident scientifically and accurately, traffic fatalities number of death in China from 2002 to 2015 are taken as a sample. The optimal smoothing coefficient and the optimal initial smoothing value are determined by nonlinear programming method and curve fitting method respectively. Then optimal cubic smoothing model is established to predict the traffic fatalities number of death from 2016 to 2018. Taking −2%, 4%, 10.5% and 17% as the state partition thresholds, and the Markov model to revise the prediction results. Finally, the results are compared with Markov's modified traditional exponential smoothing model. Results show that the prediction accuracy has improved by 0.2%, with the average relative error decreases from −5.01% of the traditional cubic exponential smoothing model to −4.81% of the optimal cubic exponential smoothing model. And the prediction accuracy has even improved by 1.16%, with the average relative error decreases from −3.27% of the Markov's modified traditional cubic exponential smoothing model to −2.11% of the proposed model. The proposed model presents superiority and can be used to provide scientific and accurate reference for traffic safety management in reality.
Engineering nonclassical SU(1,1) coherent states of light by multiphoton excitation

Abstract Quantum optical systems with nonclassical features play a vital role in the physical implementation of a large variety of quantum technologies, such as quantum metrology, quantum information processing, and quantum computation protocols, and hence are important to exploit the quantum advantage. In this work, we construct a general class of nonclassical coherent states (CSs) of light and present a scheme to enhance their nonclassicality by multiphoton excitation. In particular, using various optical realizations of <?CDATA $su(1, 1)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>s</mml:mi> <mml:mi>u</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> Lie algebra, we construct CSs of light following Barut-Girardello formalism and then perform the multiphoton discrete excitation on these continuous-variable optical CSs. We investigate the nonclassical features by analyzing the photon-counting probability distribution, Mandel <?CDATA $Q-$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>Q</mml:mi> <mml:mo>−</mml:mo> </mml:math> parameter, quadrature squeezing, and Wigner quasi-probability distribution. Our numerical results show that, for a particular set of parametric values, these multiphoton excited states exhibit sub-Poisson photon-counting statistics, quadrature squeezing, and negativity of Wigner distribution, which are indicators of nonclassicality. Moreover, it is shown that the nonclassical nature of these states gets enhanced as the photon-excitation number increases.
Realtime Sign Language Detection and Recognition

The real-time sign language recognition system is developed for recognizing the gestures of Indian Sign Language (ISL). Generally, sign languages consist of hand gestures. For recognizing the signs, the Regions of Interest (ROI) are identified and tracked using the skin segmentation feature of OpenCV. Then by using [1] Media Pipe, it captures the landmarks of the hands and the key points of landmarks are stored in an NumPy array. Then we can train the model on it by using TensorFlow, Keras and LSTM. Lastly the model can be tested Realtime by taking live feed from the webcam.Realtime Sign Detection and Recognition is one of the potential applications for the deaf and dumb people as it help them to connect with the world. Previous approaches to sign detection and recognition were done by using the Machine Learning Algorithm by training it on the images but now we are using Deep Learning Models to enhancing Realtime sign detection and recognition.