Reviewerly/exHarmony huggingface dataset:
Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow.The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations.Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models.The dataset was chronologically divided into training, validation, and testing data.The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations.Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance.In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.
Bitcoin Nation: The World’s New 17th Largest Economy

Suppose there is Bitcoin nation and its currency bitcoin; with market capitalization of $1.086 trillion on 21 February 2021, Bitcoin’s market value (17th among G20) has surpassed GDPs of Netherlands ($909 billion), Saudi Arabia ($793 billion), Turkey ($754 billion), and Switzerland ($703 billion). This historic milestone was still not enough to convince opponents who claim that Bitcoin is associated with extremely volatility. Is this because of the inherent aspects of Bitcoin that cause volatility or is it due to the fact that regulators and law makers insist on running headlong into backlash to Bitcoin? But opponents clueless of the inner workings of money’s natural evolution along with short-sighted politicians lack the understanding that life-changing (revolutionary) technologies like blockchain and its prodigy Bitcoin will forge ahead unabated regardless of countless obstacles and challenges. Unlike the bursting of the Internet bubble (dot.com crisis) at the onset of the new millennium, Bitcoin will not disappear anytime soon, but regrettably will be susceptible to even more severe price corrections as long as the existing regulatory hurdle keeps curbing Bitcoin’s true potential. In the face of the U.S. raging a war against Bitcoin to protect the dollar’s “exorbitant privilege” and hegemony, Bitcoin has still achieved unthinkable price valuations (i.e. intraday high of $58,330 on February 21, 2021). In the absence of a regulatory hurdle, imagine what price levels Bitcoin may have achieved, $1,000,000? Raging a war against Bitcoin and altcoins can potentially have adverse effects on future developments of innovative technologies that are essential to building great civilizations.
Mobile Application Development

Just what you need to get mobile messaging going on virtually any platfrom, in vitually any language, this guide provides a thorough tutorial in the SIM.
Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents

We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.
An Abstract Contract Theory for Programs with Procedures

Abstract When developing complex software and systems, contracts provide a means for controlling the complexity by dividing the responsibilities among the components of the system in a hierarchical fashion. In specific application areas, dedicated contract theories formalise the notion of contract and the operations on contracts in a manner that supports best the development of systems in that area. At the other end, contract meta-theories attempt to provide a systematic view on the various contract theories by axiomatising their desired properties. However, there exists a noticeable gap between the most well-known contract meta-theory of Benveniste et al. [5], which focuses on the design of embedded and cyber-physical systems, and the established way of using contracts when developing general software, following Meyer’s design-by-contract methodology [18]. At the core of this gap appears to be the notion of procedure : while it is a central unit of composition in software development, the meta-theory does not suggest an obvious way of treating procedures as components. In this paper, we provide a first step towards a contract theory that takes procedures as the basic building block, and is at the same time an instantiation of the meta-theory. To this end, we propose an abstract contract theory for sequential programming languages with procedures, based on denotational semantics . We show that, on the one hand, the specification of contracts of procedures in Hoare logic , and their procedure-modular verification, can be cast naturally in the framework of our abstract contract theory. On the other hand, we also show our contract theory to fulfil the axioms of the meta-theory. In this way, we give further evidence for the utility of the meta-theory, and prepare the ground for combining our instantiation with other, already existing instantiations.
Locating Steganalysis of LSB Matching Based on Spatial and Wavelet Filter Fusion

For the case of that only a single stego image of LSB (Least Significant Bit) matching steganography is available, the existing steganalysis algorithms cannot effectively locate the modified pixels. Therefore, an algorithm is... | Find, read and cite all the research you need on Tech Science Press
Deep structure learning using feature extraction in trained projection space

Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data. While learning data representations via convolutions is already well studied and efficiently implemented in various deep learning libraries, one often faces limited memory capacity and insufficient number of training data, especially for high-dimensional and large-scale tasks. To overcome these limitations, we introduce a network architecture using a self-adjusting and data dependent version of the Radon-transform (linear data projection), also known as x-ray projection, to enable feature extraction via convolutions in lower-dimensional space. The resulting framework, named PiNet, can be trained end-to-end and shows promising performance on volumetric segmentation tasks. We test proposed model on public datasets to show that our approach achieves comparable results only using fractional amount of parameters. Investigation of memory usage and processing time confirms PiNet's superior efficiency compared to other segmentation models.
Event-Driven Ontology Population - from Research to Practice in Critical Infrastructure Systems

In an interconnected world, the Systems-of-Systems (SoS) paradigm is prevalent in various domains, particularly in large-scale environments as being found in the area of critical infrastructures. Due to the characteristics of SoS and those of critical infrastructures, the realization of high level services for Operational Technology Monitoring (OTM), such as failure cause reasoning, is challenging, whereas interoperability and evolvability are most pressing. In this realm, the contribution of this paper is twofold: Firstly, we conduct a systematic literature review focusing on semantic technologies in areas like (i) semantic annotations, (ii) event log focused work in the IoT, (iii) organizational process mining, and (iv) complex event processing. Based thereupon, we elaborate towards a hybrid (semi)-automatic ontology population approach in the context of OTM by combining inductive and deductive methods.
SiamCPN: Visual tracking with the Siamese center-prediction network

Abstract Object detection is widely used in object tracking; anchor-free object tracking provides an end-to-end single-object-tracking approach. In this study, we propose a new anchor-free network, the Siamese center-prediction network (SiamCPN). Given the presence of referenced object features in the initial frame, we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations. Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction, SiamCPN directly obtains all information required for tracking, greatly simplifying the model. A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net. The model can accurately predict object location, implement appropriate corrections, and regress the size of the target bounding box. Compared to other leading Siamese networks, SiamCPN is simpler, faster, and more efficient as it uses fewer hyperparameters. Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks, and is comparable to other excellent trackers on LaSOT, VOT2016, and OTB-100 while improving inference speed 1.5 to 2 times.
WEKA Ortamını Kullanarak Derin Öğrenme ile Kimlik Hırsızı Web Sitelerinin Tahmini

COVID-19 (Coronavirus) disease, observed in the city of Wuhan, China, on December 30, 2019, spread worldwide and caused a global epidemic.Since this epidemic can be transmitted very quickly and easily, some precautions and voluntary quarantine practices that governments have to take have significantly changed the habits of world communities in a short time.This change has especially increased distance activities, such as distance working, distance education, and distance shopping (e-commerce).Therefore, people have felt the need to quickly move the physical platforms they use to digital platforms to meet their daily needs.In this case, web phishing targeting digital platforms has led to a significant increase in online cyber attack types.The increase in phishing and the increasing volume of phishing websites have resulted in greater exposure of the world's information and organizations to various cyberattacks.Thus, after the COVID-19 pandemic in 2019, it has become more important than ever to detect phishing website analysis.In this study, performs the web phishing analysis and makes a comparison of classification performances among five popular methods: Random Forest (RF), Support Vector Machine (SVM), Multilayer Perception (MLP), k-Nearest Neighbour (k-NN), and Deep Learning (DL) by utilizing a Waikato Environment for Knowledge Analysis (WEKA) graphical user interface (GUI).In the experiments conducted with the data set divided into two as training and test, the RF and DL methods were more successful than the other methods compared, but k-NN, achieved a better performance when cross-validation was used.The possible reason for this is a simple approach toward deep learning.We hope the current study can provide guidance in investigating WEKA deep learning for web phishing classification.
MaSMT4: The AGR Organizational Model-Based Multi-Agent System Development Framework for Machine Translation

A framework is an essential tool for agent-based programming that saves a programmer's time and provides development standards. There are few multi-agent frameworks and models available for agent-based programming. The AGR organizational model is a successful agent development model that builds artificial societies through agents. MaDKit is one of the successful frameworks that use the AGR organizational model. However, the English to Sinhala agent-based machine translation system needs a lightweight framework and the fastest message-passing capabilities. These features are currently not available on the existing frameworks at a sufficient level. Thus, the Java-based multi-agent system development framework has been developed using the AGR organizational model with the required modifications. This paper presents a multi-agent system development framework, MaSMT, which is specially designed to handle English to Sinhala agent-based machine translation. The MaSMT has been developed using the AGR organizational model that provides an infrastructure of the agents, communication methods for agents, agent status controlling, and a tool for agent monitoring. Different types of multi-agent systems have already developed through the MaSMT framework, including, Octopus, AgriCom, and RiceMart. The framework is freely available and can be downloaded from the source forge.
Post-quantum MACsec in Ethernet Networks

The demand on MACsec in Ethernet is increasing substantially since MACsec fits well for industrial applications which require strong security as well as efficiency. To provide a long-term security, the MACsec protocol should be resistant to future attacks including quantum attacks. In this paper, MACsec is analysed under a quantum attack scenario. To achieve 128-bit quantum security, AES (Advanced Encryption Standard) algorithms defined in MACsec should mandate to use 256-bit keys. On the other hand, classical public-key cryptosystems in MKA are not secure at all against quantum attacks so that they need to be replaced by post-quantum crypto schemes in a quantum world. We propose an authenticated post-quantum key establishment protocol which is suitable for long-term secure MACsec. The proposed protocol is used in the hybrid mode, an ephemeral key exchange, and an end-to-end encryption. We verified by experiments that the proposed protocol can be deployed in existing a MACsec-enabled Ethernet network.
Human resource management practices and total quality management in insurance companies: Evidence from Jordan

This study aims to extend the argument about the association between HRM and Total Quality Management (TQM) in insurance companies in Jordan. It is argued that the consideration of TQM can be supported in terms of configuration as an effect of Human Resource Management (HRM) in the presence or absence of other HRM practices. Data for this study were collected from 24 insurance companies listed on the Amman Stock Exchange and 342 employees. PLS-SEM was used to analyze the data collected through path analysis. Structural equation modeling was used to examine a causal relationship between the variables. The overall effect of HRM on TQM was explored to improve quality orientation and people criteria in insurance companies. The findings have indicated a significant and positive effect of teamwork, staffing, performance appraisals, and compensation on TQM. However, training and development have a negative, but significant impact on TQM. In addition, TQM significantly and positively affects the HRM of an organization. Performance was also positively and significantly affected by HRM. TQM is an important factor of HRM in insurance companies. AcknowledgmentThe authors are very thankful to all the associated personnel in any reference that contributed to the purpose of this study.
Real-Time Object Detection for Visually Challenged

There are about 39 million completely blind people and about 246 million people having low vision across the world. Without visual perception, it is quite difficult for blind people to do their daily jobs, which makes them dependent on someone. Visually impaired people usually carry a stick to identify obstacles in front of them. The proposed system can identify objects in front of the blind person and also calculate the distance at which the object is present. The output is in the form of audio, and once the object is identified the system gives audio of the object label with a warning as the person approaches the object. The proposed system detects an object as well as humans which will help to maintain social distancing in public.
Conditional preparation of non-Gaussian quantum optical states by mesoscopic measurement

Abstract Non-Gaussian states of an optical field are important as a proposed resource in quantum information applications. While conditional preparation is a highly successful approach to preparing such states, their quality is limited by detector non-idealities such as dead time, narrow dynamic range, limited quantum efficiency and dark noise. Mesoscopic photon counters, with peak performance at higher photon number, offer many practical advantages over single-photon level conditioning detectors. Here we propose a novel approach involving displacement of the ancilla field into the regime where mesoscopic detectors can be used. We explore this strategy theoretically and present simulations accounting for experimental non-idealities such as loss and amplification noise, showing that precise photon-number resolution is not necessary to herald highly nonclassical states. We conclude that states with strong Wigner negativity can be prepared at high rates by this technique under experimentally attainable conditions.
Smart Contract Code Generation from Platform Specific Model for Hyperledger Go

Model Driven Architecture (MDA) together with Unified Modelling Language (UML) presents a framework which transfers the emphasis of development from source code to the higher level of abstraction i.e., models. In this paper, we demonstrate the application of MDA principles for generating smart contract code executed on a blockchain. Even though blockchain smart contracts are not in all cases a classic type of object-oriented software, which UML is intended for, we demonstrate the possibility to adapt to this specific implementation platform. MDA Platform Specific Model (PSM) is used as an input for transformation algorithm which maps PSM metamodel elements to Go Chaincode elements and produces Go chaincode. In PSM, UML class and sequence diagrams are used for specifying structural and behavioural aspects of the smart contract. MOFM2T transformation language and Acceleo tool are employed for the implementation of this algorithm. The results of the algorithm execution were demonstrated using example chaincode for machine learning model validation.
LASSO-based high-frequency return predictors for profitable Bitcoin investment

This article explores the Bitcoin return predictability of variables constructed from one-minute high-frequency Bitcoin trading data. During the training period of 2012–2018, LASSO is used to pick out the most powerful predictors. We then use predictors selected by LASSO to predict the Bitcoin returns in the 2018–2019 test sample. An investment strategy based on the return predictions outperforms a simple buy-and-hold strategy and other strategies based on the prediction of Ordinary Least Squares and Neural Networks.
Inteligencia Artificial: Un Futuro Inteligible

As an editorial, this short essay aimed to reflect on the present and future of artificial intelligence, in the context of the pandemic of the new coronavirus. It is based on the hypothesis that observes in artificial intelligence a factor of the first order that has accelerated the technological s processes, which in turn drive continuous improvements in all fields of human action. However, despite its many benefits, it is concluded, on the one hand, that this form of non-human intelligence will increasingly play an important role in all cultural, labour, military and recreational human relations and; on the other hand, that modern and civilized nations therefore have a moral duty to create l eyes that establish mechanisms of regulation and balance between artificial intelligence and human condition, as a condition of possibility so that its widespread use does not become a distinction in the style of what was planted at the time by some sci-fi narratives, in film and literature.
Seeking an Empathy-abled Conversational Agent

A fairly novel area of research, at the conjunction between Artificial Intelligence (AI) and Human-Computer Interaction (HCI), resides in developing conversational agents as more users prefer this type of interaction to conventional interfaces.In this paper, we present an open-domain empathic chatbot, encompassing two of the biggest challenges of dialog systems: understanding emotions and offering appropriate responses.Although these tasks are trivial for a human, it is difficult to create a system that can recognize others' feelings in a discussion.The proposed model is developed based on the Generative Pre-Trained Transformer and it uses two datasets, PersonaChat and Empathetic Dialogues to achieve an empathic chatbot with a cordial personality.The measured performance -18.20 perplexity, 6.56 BLEU score, and 6.56 accuracy -comes close to the state-of-the-art models, while offering a further refined dialogue persona.
Development of a second-order sliding innovation filter for an aerospace system

The sliding innovation filter is a new type of predictor-corrector estimation method. The strategy is used to estimate relevant states of interests and has been found to be robust to modeling uncertainties and disturbances. In this paper, a second-order formulation of the sliding innovation filter is presented to improve its estimation performance in terms of accuracy while maintaining robustness. The strategy is applied to an aerospace system that has been designed for experimentation. The results are compared with the well-known Kalman filter, and future work is considered.