Reviewerly/exHarmony huggingface dataset:
Computational of Distribution of Wind Speed as Preliminary Information for Fishers: Case Study in Lombok Sea

Wind speed and direction are two important things needed for safety by fishermen when going for fish in the sea.The shifting season in Indonesia is very visible nowadays, while the fishermen still use traditional techniques in determining the time will be at sea, so there is not a slight accident experienced by fishermen when it is in the middle of the ocean.Therefore, it is the responsibility of the relevant Government to convey early information to the fishermen for their safety.The purpose of this research is to provide alternative information in determining the wind speed and direction using the Artificial Neural Network type Back Propagation (ANN-BP) method.Prediction result with monthly data input for the last 12 years can be used as a reference by the Government to be presented to the fishermen in order to determine when to find fish and more anticipatory in seeing the speed of the wind is influential on the wave speed and height in the sea.The predicted result with the architecture of the 144 layers input, the 10 layers hidden-1, 5 layers hidden-2, and 1 output layer resulted in an average minimum wind speed of 2,996 knots, the average maximum wind speed of 31,500 knots, and the average wind direction of 207,824
Optimization of the ANOVA Procedure for Support Vector Machines

Feature selection is a powerful tool to identify the important characteristics of data for prediction. Feature selection, therefore, can be a tool for avoiding overfitting, improving prediction accuracy and reducing execution time. The applications of feature selection procedures are particularly important in Support vector machines, which is used for prediction in large datasets. The larger the dataset, the more computationally exhaustive and challenging it is to build a predictive model using the support vector classifier. This paper investigates how the feature selection approach based on the analysis of variance (ANOVA) can be optimized for Support Vector Machines (SVMs) to improve its execution time and accuracy. We introduce new conditions on the SVMs prior to running the ANOVA to optimize the performance of the support vector classifier. We also establish the bootstrap procedure as alternative to cross validation to perform model selection. We run our experiments using popular datasets and compare our results to existing modifications of SVMs with feature selection procedure. We propose a number of ANOVA-SVM modifications which are simple to perform, while at the same time, boost significantly the accuracy and computing time of the SVMs in comparison to existing methods like the Mixed Integer Linear Feature Selection approach.
INTERNET OF THINGS (IOT) MEDICAL SECURITY: TAXONOMY AND PERCEPTION

With technology advances, IoT applications are proliferating in the medical field.Integration of endpoint devices, sensors, communications, and applications is leading to improved data availability and tools to manage positive healthcare outcomes.However, in healthcare failures and security leaks can damage or be lethal to patients and jeopardize trust in providers and health care organizations.This study empirically develops an IoT Medical taxonomy with implications for categorizing and matching IoT security in the healthcare field to users by: (1) identifying the characteristics of IoT security and (2) describing and testing users' perceptions.The research analyzes the users' perceptions of IoT Medical security in terms of the fundamental issues such as confidentiality, integrity, availability, authentication, authorization, and audit.In addition to the proposed taxonomy of IoT Medical security, we summarize the key security factors influencing the users making the decision to use IoT.The study will help improve IoT security architecture design, IoT product development, and inform decision-making on IoT medical technology.
A Genetic Algorithm based Key Management Approach for Enhancing Data Security in Cloud Environment

More enterprises are moving sensitive data to the cloud, increasing the need to secure that data.With cloud technology becoming a larger and more important part of running a digital business, cloud computing (CC) platforms are rapidly limiting the effectiveness of the traditional security model.Since data and applications in the cloud reside the old enterprise boundaries, they must now be protected in new ways.As more and more users connect directly to the cloud applications, it is imperative to secure cloud assets to meet compliance, privacy and security requirements.Verification and key management are the key challenges within the cloud environment whereas trading the secret data.As of now, security is the main apprehension in the cloud security environment.Ensuring the costumers information may be a basic errand in cloud.Conventional cryptographic symmetric algorithms are reasonable for putting away information in a most secured way.Asymmetric algorithms are favored for encrypting the keys instead of the information since of its fewer speediness.Key management taxonomy comprises of three parts.Part 1 gives common direction and best practices for the administration of cryptographic keying fabric.Part 2 gives direction on approach and security arranging necessities for government organizations.At long last, Part 3 gives direction when utilizing the cryptographic highlights of current systems.The strategy for selecting or producing the key plays an imperative part in securing the data of cloud environment.Genetic algorithm (GA) is a powerful tool for understanding the foremost of the enhancement issues.The proposed Cloud Key Generation Genetic Algorithm (CKGGA) is utilized for producing the finest key which fulfills the required fitness function.The ideal key produced from the proposed CKGGA is encrypted with Asymmetric Addition Chaining Cryptographic Algorithm (ACCA) to form the key solid.This fortified key can be utilized for encrypting information.
Machine Learning Framework To Analyze Against Spear Phishing

The objective of this paper is to design and implement machine learning based ensemble algorithm on dataset to fit into the models that can be understood and executed by machines. In this paper we discussed different algorithms and machine learning concepts that can be implemented on the datasets, we taken email spam filter dataset for experiment and analysis, as the Advanced persistent threat the latest threat is intruded using the emails and major intrusion is done through spam emails. Machine learning uses different datamining techniques and mechanisms and accepts the input-data and gives the output as the statistical analysis. We implemented different email classification algorithms on the datasets bsed on spam and ham emails where spear phishing methods are identified and implemented different classification and regression methods to get the accurate results. In this paper for the better results in spite of existing algorithms we introduced the ensemble methods such as boosting, bagging, stacking and voting for much accuracy and higher level of classification and combining different algorithm. This paper will measure different machine learning algorithms performance on spam email filtering on the huge datasets. The framework provides implementation of learning algorithms that you can apply to larger datasets. An obvious approach to making decisions more reliable is to combine the output of different models. We even compared the existing algorithms and proposed algorithm; comparison tables are drawn along with the statistical analysis, data and graphical analysis is given.
A Low-cost Webcam-based Eye Tracker and Saccade Measurement System

Eye movements are integrated with cognitive processes, which indeed make it a helpful research basis for the investigation of human practices. Eye movements can be deployed in discovering several cognitive processes of the brain. This research utilizes low-resolution webcam to develop an eye tracker and saccades measurement tool to extensively lower the gadgets expenses. A consistent algorithm is developed to suit the quality of the webcam using open-source software (Python) to record the time series of the eye location. Likewise, several algorithms are proposed to extract high-level eye movement saccadic measurements from the raw gaze outputs. A pilot study is performed on ten normal participants and Multiple Sclerosis (MS) patients. Experimental results demonstrate that the proposed system is quick, simple and efficient for eye tracking and saccade measurement. The developed tool can be used by clinicians and medical physicians for the diagnosis and identification of neurological disorders
Work in Progress: A Comparative Long-Term Study of Fallback Authentication

Fallback authentication, the process of recovering access to an account if the primary authenticator is forgotten or lost, is of significant importance in real-world applications.A variety of mechanisms are deployed, ranging from secondary channels (such as email and SMS), over personal knowledge questions (such as the "mother's maiden name") to social authentication (such as vouching-based approaches).One central difference with primary authentication is that the elapsed time between enrollment and authentication can be much longer, typically in the range of years.However, few of the mechanisms used today have been studied over such long time-spans, making claims about their usability difficult to generalize to real-world applications.Additionally, most past studies have considered one or two mechanisms only, and deriving a meaningful comparison of a relevant number of mechanisms from the individual datapoints is not easy.In this work in progress paper, we report on the design of a usability study that we will use to study the usability of authentication mechanisms over a more realistic time-frame of up to 18 months, and will provide a fair comparison of the four most widely used fallback authentication schemes.We present results of a pre-study with 74 participants that ran over 4 weeks and indicates that schemes based on email and SMS are more usable.Mechanisms based on designated trustees and personal knowledge questions, on the other hand, fall short, both in terms of convenience and efficiency.
extended review on internet of things (IoT) and its characterisation

The Internet of Things (IoT) Physical objects (or Groups of such materials) sensors, processing Skills, software and the Internet or other Communication connects with other devices and systems and other in exchange via networks Described as technologies. Invalid due to Internet of Things Devices Considered by name, they are associated with the public Internet. No need to connect, just the network Should only be connected and can be addressed individually. The ability to provide Machinery and digital machines, Objects, animals or personal Identifiers (UIDs) and from man to man or without the need for man-to-computer Data transfer over a network communication. In the last few years, IoT too it is too much 21st century Has become one of the most important technologies. Nowadays, everyday items combine Kitchen appliances, cars, Thermostats, baby screens through devices embedded on the Internet, making seamless communication between people, processes and objects possible. With Low-cost computing, cloud, big data, analytics and mobile technologies, physics minimal human intervention data share and let’s collect. In this high-connected world, digital Between system connected objects collaborate.
“There Is Not Enough Information”: On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantiate an ADS in the area of automated loan approval and generate different explanations that are commonly used in the literature. We randomize the amount of information that study participants get to see by providing certain groups of people with the same explanations as others plus additional explanations. From our quantitative analyses, we observe that different amounts of information as well as people's (self-assessed) AI literacy significantly influence the perceived informational fairness, which, in turn, positively relates to perceived trustworthiness of the ADS. A comprehensive analysis of qualitative feedback sheds light on people's desiderata for explanations, among which are (i) consistency (both with people's expectations and across different explanations), (ii) disclosure of monotonic relationships between features and outcome, and (iii) actionability of recommendations.
Implicit particle filtering via a bank of nonlinear Kalman filters

The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions. This study is motivated by the need to enhance computational tractability in implementing this approach. We investigate the connection of the particle update step in the implicit particle filter with that of the Kalman filter and then formulate a novel realization of the implicit particle filter based on a bank of nonlinear Kalman filters. This realization is more amenable and efficient computationally.
Transformation-interaction-rational representation for symbolic regression

Symbolic Regression searches for a function form that approximates a dataset often using Genetic Programming. Since there is usually no restriction to what form the function can have, Genetic Programming may return a hard to understand model due to non-linear function chaining or long expressions. A novel representation called Interaction-Transformation was recently proposed to alleviate this problem. In this representation, the function form is restricted to an affine combination of terms generated as the application of a single univariate function to the interaction of selected variables. This representation obtained competing solutions on standard benchmarks. Despite the initial success, a broader set of benchmarking functions revealed the limitations of the constrained representation. In this paper we propose an extension to this representation, called Transformation-Interaction-Rational representation that defines a new function form as the rational of two Interaction-Transformation functions. Additionally, the target variable can also be transformed with an univariate function. The main goal is to improve the approximation power while still constraining the overall complexity of the expression. We tested this representation with a standard Genetic Programming with crossover and mutation. The results show a great improvement when compared to its predecessor and a state-of-the-art performance for a large benchmark.
Multivariable quantum signal processing (M-QSP): prophecies of the two-headed oracle

Recent work shows that quantum signal processing (QSP) and its multi-qubit lifted version, quantum singular value transformation (QSVT), unify and improve the presentation of most quantum algorithms. QSP/QSVT characterize the ability, by alternating ans\"atze, to obliviously transform the singular values of subsystems of unitary matrices by polynomial functions; these algorithms are numerically stable and analytically well-understood. That said, QSP/QSVT require consistent access to a single oracle, saying nothing about computing joint properties of two or more oracles; these can be far cheaper to determine given an ability to pit oracles against one another coherently. This work introduces a corresponding theory of QSP over multiple variables: M-QSP. Surprisingly, despite the non-existence of the fundamental theorem of algebra for multivariable polynomials, there exist necessary and sufficient conditions under which a desired stable multivariable polynomial transformation is possible. Moreover, the classical subroutines used by QSP protocols survive in the multivariable setting for non-obvious reasons, and remain numerically stable and efficient. Up to a well-defined conjecture, we give proof that the family of achievable multivariable transforms is as loosely constrained as could be expected. The unique ability of M-QSP to obliviously approximate joint functions of multiple variables coherently leads to novel speedups incommensurate with those of other quantum algorithms, and provides a bridge from quantum algorithms to algebraic geometry.
Switching of the information backflow between a helical spin system and non-Markovian bath

The dissipative dynamics of the spin chain coupled to the non-Markovian magnonic reservoir was studied. The chirality of the chain is formed due to the magnetoelectric coupling. We explored the sign of the trace distance derivative and found the alternating positive/negative periods in system’s time evolution. The negative sign is associated with the flow of information from the system to the bath and decrease in states distinguishability, while the positive sign is related to the flow of the information in the opposite direction and increase in distinguishability. We found the distinct effect of the applied electric and magnetic fields. While the Dzyaloshinskii–Moriya interaction and external electric field lead to reshuffling of the periods, the applied magnetic field leads to the swift positive–negative transitions. Thus, in the helical quantum rings coupled to the non-Markovian magnonic baths, it is possible to control the directions of information flow through the external fields.
Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541]

This commentary concerns two recently developed metaheuristic algorithms, namely the Sooty Tern Optimization Algorithm, and the Tunicate Swarm Algorithm. Both of these algorithms claim computational superiority over other methods based on experimental results on a certain benchmark set. The aim of this note is to aware researchers that this claim is not valid: the proposed algorithms use a zero-bias operator and many of the studied benchmark functions on which they were found superior have optimal solutions located in the zero vector. Moreover, the codes for the methods provided by the authors are not achieving the results reported in the respective publications.
Context-Aware Abbreviation Expansion Using Large Language Models

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters.Our approach is to expand the abbreviations into full-phrase options by leveraging conversation context with the power of pretrained large language models (LLMs).Through zero-shot, few-shot, and fine-tuning experiments on four public conversation datasets, we show that for replies to the initial turn of a dialog, an LLM with 64B parameters is able to accurately expand over 70% of phrases with abbreviation length up to 10, leading to an effective keystroke saving rate of up to 77% on these expansions.Including a small amount of context in the form of a single conversation turn more than doubles abbreviation expansion accuracies compared to having no context, an effect that is more pronounced for longer phrases.Additionally, the robustness of the models against typo noise can be enhanced through fine-tuning on noisy data.
Non-Fungible Tokens as a Mechanism for Representing Patient Consent

In recent years we have seen the adoption of distributed ledger technology (DLT), originally the mechanism underpinning the operation of the Bitcoin crypto currency, across a wider range of technology sectors including healthcare. DLT allows for the design of informatics systems with the properties of immutability, security, and decentralization. One recent innovation in the space has been the specification and development of Non-Fungible Tokens (NFTs). NFTs are decentralized DLT-based records that represent ownership of a unique digital asset. The predominant current use case for NFTs has been in the representation and sale of digital artwork, however the features offered by NFTs, unique-ness, immutability, transferability, and verifiability, are directly applicable to the design of health informatics systems. In this paper we explore these properties and describe a reference architecture for using NFTs as a means of representing and transferring records of patient's consent for medical data use.
EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction

Events are inter-related in documents.Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document.However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation.In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting.To improve consistency we introduce the Event-Aware Argument Extraction (EA 2 E) model with augmented context for training and inference.Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA 2 E compared to baseline methods 1 .
Learning Invariant Visual Representations for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize the attributes attached to any object reliably. Similarly, attribute-invariant features can also be learned when recognizing the objects with attributes as domains. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. Experiments on three CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art.