Reviewerly/exHarmony huggingface dataset:
Computational Fluid Dynamics-Based Programming of Mechanical Power Generation Applications

Electricity has a vital impact on human production and life, and energy storage systems are regarded as an important part of the "generation -transmission -distributionuse -storage" process.Mechanical elastic energy storage technology is a new type of energy storage technology recently proposed, which uses mechanical vortex spring as the energy storage medium and permanent magnet synchronous motor as the actuator to achieve energy storage and power generation.The aim of this paper is to investigate the design of a mechanical power generation application based on computational fluid dynamics.In this paper, a mechanical rectifier wave energy generation device is designed and the main work is as follows: the general structure of the device is designed, which mainly consists of three parts: the energy take-off mechanism (Power take-off, PTO for short), the float and the generator.The design of the structural parameters of the magnetically coupled force transfer system is completed according to the magnitude of the PTO force applied to the system.Based on the simulation model of the device, the effect of damping on the performance of the PTO system is analysed, and the input and output powers under different load resistance conditions are obtained to verify the optimal damping theory.The effect of wave period on the input-output power is obtained by varying the wave conditions.
In memory of Peter Carr

Demonstrates Peter den Iseger�s paper that combines Gaussian quadrature with Fast Fourier Transforms to produce Laplace Transform inversion that is extremely fast and approaching machine precision
IS DIGITAL KILLING PRODUCTIVITY?

Bogni presents a few egregious examples of the pervasive idiocy of many digital processes and procedures
Cryptocurrencies � Hype or Here to Stay?

Cryptocurrencies are an innovative alternative to the traditional banking system. Currently the primary application is the execution and verification of financial transactions. Arguably the most valuable property of cryptocurrencies is the underlying Blockchain technology, a decentralized ledger, which executes and verifies financial transactions without an expensive and slow intermediary. Bitcoin increased from $354 in 2016 to $67,500 in November 2021, so there is definitely a hype. Cryptocurrencies need a big network to fend off criminal 51% network attacks. Therefore, only a few cryptocurrencies will survive, but those will be a disruptive technology, challenging the existing banking system.
Alice Through The Crypto Glass Part 2 � Gold For Nerds: The World of Crypto-currencies

Despite considerable self-promotion, cryptocurrencies remain a small part of financial markets. In the second part of this series a deep look beyond the hype.
Incontri italoamericani: Identità, letteratura, riflessi dell’emigrazione

Book Review| February 01 2019 Incontri italoamericani: Identità, letteratura, riflessi dell’emigrazione Incontri italoamericani: Identità, letteratura, riflessi dell’emigrazione, edited by Michele Bottalico. Bari: Edizioni del Sud, 2017. 245pp. Valerio Massimo De Angelis Valerio Massimo De Angelis University of Macerata Search for other works by this author on: This Site Google Italian Americana (2019) XXXVII (1): 75–76. https://doi.org/10.5406/2327753X.37.1.25 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Valerio Massimo De Angelis; Incontri italoamericani: Identità, letteratura, riflessi dell’emigrazione. Italian Americana 1 February 2019; XXXVII (1): 75–76. doi: https://doi.org/10.5406/2327753X.37.1.25 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressItalian Americana Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 Carla Anne Simonini & Carol Bonomo Albright2016 Article PDF first page preview Close Modal Issue Section: Reviews You do not currently have access to this content.
Echoes of Casandrino: a la Vicina di Napole

Other| August 01 2021 Echoes of Casandrino: a la Vicina di Napole Robert Chianese Robert Chianese Search for other works by this author on: This Site Google Italian Americana (2021) XXXIX (2): 167–168. https://doi.org/10.5406/2327753X.39.2.20 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Robert Chianese; Echoes of Casandrino: a la Vicina di Napole. Italian Americana 1 August 2021; XXXIX (2): 167–168. doi: https://doi.org/10.5406/2327753X.39.2.20 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressItalian Americana Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 Carla Anne Simonini & Carol Bonomo Albright2016 Article PDF first page preview Close Modal You do not currently have access to this content.
Mitigation for cloud computing security risks and governance

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active.
Rule based design using clustering for knowledge acquisition

Data analysis can be done by expert system decisions on system status according to system input and output data. For the purpose of data analysis, there is often a need to classify data or to find regularities therein. The results of the regularity search can be expressed by the IF-THEN production rules. The use of different approaches - with clustering algorithms, neural networks - makes it possible to obtain rules that characterise data. Knowledge acquisition in this paper is the process of extracting knowledge from numerical ata in form of rules. Rules acquisition in this context is based on clustering methods. With the help of the K-means clustering algorithm, rules are derived from trained neural networks. The rule-making methodology is demonstrated on a sample basis of IRIS data. The effectiveness of the obtained rules is evaluated.
On the black swan risk dynamical evaluation

The paper was inspired by comprehensive researches in the field of video data information security. Video data is considered as a long range of frames. This work is targeted on the problem of correlations between the already happened and upcoming incidents in terms of their probability. The main idea of the paper is to take into account some hidden events that provoke the incidents. The possible incident is modelled as a finite sequence of those events. The statistical ensemble approach is used in order to evaluate an incident probability if there were no registered incidents in the system yet. It is shown that the probability can be evaluated dynamically if there is enough data about previous incidents in the statistical ensemble. Thus, it is supposed that the risk manager can operatively gather information from the similar information systems.
Allocation of conference hall booking in Android application using cloud

In each school association, there is a constant need of meeting rooms for direct different occasions. It is discovered that there is one gathering hall in each institution, regardless of whether it is a school or university. A lot of divisions need to share this single meeting hall for directing its occasion. Henceforth there is dependably a plausibility of the hall being reserved by at least two departments around the same time. The conflict in timing will be known to the departments just when the day of the occasion comes, at that point it will be past the point of no return and no time left for a substitute course of action. The framework proposed is additionally being produced as an Android application, since today, numerous individuals utilise Android. Consequently a proficient and easy to use application is required to reserve the hall in advance and with the data accessible to others, it shall be easy to check the status of the hall before booking. This procedure is dependent on the information stored in the cloud and the client gets the notifications when they book the hall via SMS or mail.
Semi convergent matrix-based neural predictive classifier for big data analytics and prediction in cloud services

Big data analytics is a technique concerning gathering, organising and analysing huge units of records to find out patterns or useful information. Recently, many research works has been designed for large records analytics of climate data. In system in accordance with overcome certain challenge, semi convergent matrix-based neural predictive classifier (SCM-NPC) procedure is recommended that gives productive big data calculation and data partaking in cloud services. Initially, the SCM-NPC technique constructs semi convergent matrix on distributed big data for improving the search accuracy of user requested information, next, the SCM-NPC technique is used neural predictive classifier for improving the prediction rate of climate data on cloud big data. Finally, the SCM-NPC technique applies MapReduce function on neural class that provides efficient predictive analytics about climate data conditions on cloud big data. The proposed SCM-NPC system conducts test takes a shot at parameter, for example, forecast rate, computation time and classification accuracy by using Amazon EC2 cloud big datasets. The results show that SCM-NPC technique is to build the expectation level of atmosphere realities conditions on big data and furthermore decreases the calculation time when contrasted with best in class works.
For mobile services in the cloud of things: a model-driven development pattern

Cloud computing has recent advances when it comes to storing and processing the information in a server, away from the end user. Numerous users are using the services offered by cloud for various applications. Some use cloud platforms to build their own applications while some use the entire application running on the cloud. An enormous amount of information is being processed; the cloud environment needs to be secured in all the aspects. Any data leaks could lead to serious problems. To overcome this challenge, a mobile-driven model for securing the information in the cloud using the secured key is proposed. The key is shared only to the authenticated user and not with unauthorised ones. The services are available only when the user is verified with the unique password generated for him. Various parameters were evaluated for the proposed model and were observed that it performed better than the traditional models. The computation speed was minimal with highly secured key generation.
Virtual reality as applied to the fields of education, transportation and exploration

This paper examines information on potential applications of virtual reality toward three grand challenges: education, transportation and exploration. Virtual reality's unique ability to immerse users in artificial environments has several potential uses. The paper briefly examines virtual reality's current state and limitations, before delving into possible applications by examining existing literature. In education, virtual reality may improve understanding of academic material or provide realistic training for high-risk fields. In transportation, virtual reality is a valuable method of data collection to study behaviours of transportation systems, which can lead to optimisations. Virtual reality technology also offers potential in the exploration field. Detailed simulations may facilitate exploration of historic or archaeological sites in environments that are otherwise unrealistic to visit; recreations of experiences that are normally unsafe, unattainable, or costly to visit are perfect use cases for virtual reality. More applications for virtual reality will be discovered once technological drawbacks are resolved.
Intelligent cloud assistance for healthcare sectors

E-healthcare systems have been measured by the drastically facilitating healthcare monitoring, earlier intervention and disease modelling and proof-based medical consultancy using medical image feature extraction and text mining. Due to certain resource constraints encountered in wearable devices, it is essential to outsource frequently generated healthcare information of individuals to cloud systems. Regrettably, handling both computation and storage to some un-trusted entity is a sequence of privacy and security crisis. The prevailing approaches significantly concentrate on finely tuning privacy preservation statistical medical text analysis and access, which is barely considered as a dynamic fluctuation of health condition and analysis of medical image. In this work, an effectual and secure privacy preservation dynamic medical modelling scheme has been anticipated as a healthcare service with cloud-based e-healthcare solution that offers automated functions to notify nearby hospitals in providing immediate proper treatment for medical emergencies.
Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modeling

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality of Assurance (SQA). Many existing research studies are based on software traditional metric sets and defect prediction models are built in machine language to detect the bug for limited source code line. Inspired by the above existing system. In this paper, defect prediction is focused on predicting defects in source code. The objective of this thesis is to improve the software quality for accurate defect prediction is source code for file level. So, that it helps the developer to find the bug and fix the issue, to make better use of a resource which reduces the test effort, minimize the cost and improve the quality of software. A new approach is introduced to improve the prediction performance of Bidirectional RNNLM in Deep Neural Network. To build the defect prediction model a defect learner framework is proposed and first it need to build a Neural Language Model. Using this Language Model it helps to learn to deep semantic features in source code and it train &amp; test the model. Based on language model it combined with software traditional metric sets to measure the code and find the defect. The probability of language model and metric set Cross-Entropy with Abstract Syntax Tree (CE-AST) metric is used to evaluate the defect proneness and set as a metric label. For classification the metric label K-NN classifier is used. BPTT algorithm for learning RNN will provide additional improvement, it improves the predictions performance to find the dynamic error.
An Art of Web Services using Web Mining and Semantic Web Techniques

Todays web browsers serve as an easy access to numerous sources of text and multimedia data. More than a billion pages are indexed by search engines, and finding the desired information is not an easiest task. Over the last decade, there is an explosive growth in the information available on the World Wide Web (WWW). The objective of this paper is to provide an outline of web mining, its various classifications, its subtasks, and to give a perspective to the research community about the potential of applying techniques to extract meaningful patterns. This paper also gives information in the area of web services, semantic web mining and comparison of traditional web applications and semantic web applications thereby providing the guidelines for future research in the area of web services using web mining and semantic web.
PROSPECTING BLOCKCHAIN TECHNOLOGY AND NON-FUNGIBLE TOKENS (NFTS) IN THE DIGITAL MARKETPLACES: MARKET TRENDS, TAXES, RIGHTS, AND FUTURE ASPECTS

The NFT marketplace is only three years old, yet it was already reshaping the worldwide market in 2021-2022. The Non-fungible tokens (NFTs) are a new and rapidly growing trend changing how digital assets are exchanged. NFTs are digital tokens representing unchangeable ownership of digital goods such as artwork and collectibles and are traded on blockchain-based marketplaces. NFTs generate unique ways to manage, utilize, transfer, design, and save digital data and have undergone a quick surge in numerous applications spanning artwork, entertainment, media, content sharing, and digital crypto commerce. This study aims to look into non-fungible tokens for marketplaces and how they affect global markets. Our main contribution is to prospecting NFTs for Marketplaces. In doing so, we highlight essential questions of NFTs for marketplaces and examine what the taxes and intellectual rights of properties of NFTs are. Further, the paper provides trending information such as total bitcoin price and the mean price per transaction transmitted to NFTs Networks, Most Popular NFT collections transaction volume, the proportion of web traffic to NFTs markets by area, number of active NFT collections on OpenSea.
Chameleon DNN Watermarking: Dynamically Public Model Ownership Verification

Deep neural network (DNN) has made unprecedented leaps in functionality and usefulness in the past few years, revolutionizing various promising fields such as image recognition and machine translation. The trainer’s high-performance DNNs are often considered intellectual property (IP) due to their expensive training costs. However, one pre-trained model may face various infringement problems when hacked by a malicious user, such as illegal copying or secondary selling. Digital watermarking is one of the effective methods currently used for model ownership verification. Nonetheless, limited by the ex-ante nature of the watermark embedding phase and the ex-post nature of the verification phase, previous research has only supported private verification or one-time public verification, failing to achieve multiple public verifications. In this paper, we introduce the definition of chameleon DNN watermarking and propose the first DNN watermarking scheme based on chameleon commitment, which allows multiple public verifications to declare the owner’s model ownership without exposing the core watermark information. We give a comprehensive security analysis of the verification scheme of chameleon DNN watermarking and prove by experiments that chameleon DNN watermarking can maintain the high-performance and robustness of the model.
Towards Evaluating the Security of Human Computable Passwords Using Neural Networks

Passwords are playing a major role for authentication in our daily life. However contemporary passwords are typically either difficult to remember or vulnerable to various attacks. In 2017, Blocki, Blum, Datta and Vempala introduced the concept of human computable passwords as a promising authentication method. The fundamental concerns for designing human computable passwords are their usability and security. So far, the security evaluation on human computable passwords authentication schemes is mainly based on complexity-theoretic analysis. In this paper, we initially investigate the security of human computable passwords against neural network-based adversarial attacks. Specifically, we employ the typical multilayer perceptron (MLP) model to attempt to attack the human computable passwords authentication scheme proposed by Blocki-Blum-Datta-Vempala. We present implementation results and the corresponding analysis as well. Our results imply that it is possible for an MLP to learn a simple function, but is difficult for an MLP to learn piecewise functions well.
Group Signatures with Designated Traceability over Openers’ Attributes in Bilinear Groups

Anonymity and traceability are two properties that are seemingly difficult to be compatible. “Group signatures with designated traceability” that was introduced at CANDAR 2021 is a group signature scheme in which a signer is capable of designating openers by specifying an opening access structure. In this paper, we give an instantiation of the scheme in the algebraic setting of bilinear groups.
Provably Secure Password-Authenticated Key Exchange Based on SIDH

Password-authenticated key exchange (PAKE) schemes are cryptographic schemes for securely establishing a shared session key between a client and a server communicating over an insecure channel by using a low-entropy password. In this paper, we propose a PAKE based on SIDH, where the password is used to derive a torsion points obfuscator independent of ephemeral keys. We analyze its security and prove that it is secure in the Bellare-Pointcheval-Rogaway (BPR) model, assuming the hardness of the supersingular isogeny computational Diffie-Hellman (SI-CDH) problem.
Collision-Resistant and Pseudorandom Hash Function Using Tweakable Block Cipher

This paper presents a method to construct a keyed Merkle-Damgård hash function satisfying collision resistance and the pseudorandom function property using a tweakable block cipher in the TWEAKEY framework. Its compression function adopts double-block construction to achieve sufficient level of collision resistance. Not only does the padding of the proposed keyed hash function not employ Merkle-Damgård strengthening, but it is also not injective. Due to the novel feature, the proposed keyed hash function achieves the minimum number of calls to its compression function for any message input. The proposed keyed hash function is shown to be optimally collision-resistant in the ideal cipher model. It is also shown to be a secure pseudorandom function if the underlying tweakable block cipher in the TWEAKEY framework is a secure tweakable pseudorandom permutation in two tweakey strategies.
ANALISIS PENYEBAB STAGNASI SKOR SAKIP PADA KEMENTERIAN PENDIDIKAN DAN KEBUDAYAAN

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Expression Time Dependent Schrödinger Equation from De Broglie Hypothesis and Hamiltonian function

The Schrödinger and de Broglie hypothesis are directly derived from classical force and blank hypothesis by the quantum of total energy relation () and de Broglie relation. New time dependent Schrödinger equation is obtained by using wave function and non – relativistic wave function beside plank hypothesis .Also obtained new equation from TDSE to on additional kinetic energy and potential energy in classical equation which the result is corresponding to TDSE in 3D by using de Broglie hypothesis.
Machine Learning-Based Model for Injury-Free Sports Training

An outdoor sport like basketball requires a lot of practice and physical endurance of the player.A small gesture error may also result in injury and loss in the game.The proposed machine learning-based gesture recognition and recommender model shall activate an injury-free sports training environment.The arenas with this implementation shall provide real-time feedback to the sportsperson under training.It would look in detail at the locomotive controls, dribble maneuvers, sharp turns, and gestures used and generate a simulation for better response by the trainee of basketball.