Reviewerly/exHarmony huggingface dataset:
Blockchain Based Renewable Energy P2P Trading in Smart Grid

the dramatic increase in demand of global electricity utilization has introduced various environmental and sustainability concerns for Conventional Energy Systems (CES) based on fossil fuels, which driving the global energy manufacturers to adapt new efficient and resilient energy systems. Presently the focus of the scientist and researchers is on the Renewable Energy based and Smart Grid system, which has potential to address the contemporary problems found in conventional energy systems. On the other side , Smart is a new technology, which allows the integration of clean energy sources such as wind, solar, geothermal, hydro, biomass, and tidal energy systems, ensuring energy availability at all times. The quintessential purpose of renewable technologies is to contrive a sustained community. Nevertheless, it has become a challenging concern for centralized energy systems or Smart Grid to integrate and coordinate enormous consignment of expanding users and connections. Consequently, blockchain has emerged as a resilient technology in various industries offering data integrity, trust between parties, data immutability, and secure communication, making it an auspicious technology for decentralized SG and RE criterion. In this research article, a blockchain based energy trade based modal on Peer-to-Peer transactions management presented. The main goal of the research work is to describe energy-trading model based on crypto wallets as a digital currency using smart grid and renewable energies. Moreover, electrical grid technologies, which implemented in blockchain energy based systems, will be analyze. Furthermore, contemporary challenges of industrial and commercial communities employing Renewable Energy found in conventional and centralized energy systems will be address.
Evaluating the Return Volatility of Cryptocurrency Market: An Econometrics Modelling Method

Cryptocurrency is the blockchain financial technology used for transactions in financial institutions and exchanges.Bitcoin has attracted much coverage from investors and commentators as it represents the maximum market capitalization on a crypto-currency exchange.The study aims to determine the correlation between the daily log-returns and to understand the tendencies in the cryptocurrency market instability of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin.The correlation among the selected cryptocurrencies exists in the study.The analysis is focused primarily upon reference information from the preserved servers of cryptocurrency websites and finance.yahoo.com.This research assesses regular details on the Logarithmic return of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin for a timeframe spanning from October 01st, 2014, to April 30th, 2020.From 131 cryptocurrencies, we considered only 10 Cryptocurrencies due to the availability of data after October 2014.Where there was insufficient information, there were average results determined from preceding and succeeding data.Findings demonstrate that there is GARCH modelling of cryptocurrencies against Bitcoin.Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin; variability values throughout the duration had a significant effect on the updates from Bitcoin returns.We believe that it helps create information and resources that are valuable to practitioners and scholars who research and form cryptocurrency markets in the future.
Preserving Privacy and Security in Federated Learning

Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both. However, integrating these two lines of research remains a crucial challenge since they often conflict with one another with respect to the threat model. In this work, we develop a principle framework that offers both privacy guarantees for users and detection against poisoning attacks from them. With a new threat model that includes both an honest-but-curious server and malicious users, we first propose a secure aggregation protocol using homomorphic encryption for the server to combine local model updates in a private manner. Then, a zero-knowledge proof protocol is leveraged to shift the task of detecting attacks in the local models from the server to the users. The key observation here is that the server no longer needs access to the local models for attack detection. Therefore, our framework enables the central server to identify poisoned model updates without violating the privacy guarantees of secure aggregation.
Delineation of the Captive Limestone Mine Boundaries Using Multispectral Satellite Images Through the Use of NDVI and Google Earth Image Template Matching

Limestone mining sites usually have a spatiotemporal impact on land cover, which has a long-term impact on the environment and climate. Limestone is a vital resource in the cement and construction sector. The objective of this article is to map, identifying the volume of captive limestone mine land transformation in the Yerraguntla cement industrial region in YSR Kadapa district, Andhra Pradesh, India by classifying Multispectral images using Remote Sensing and Geographical Information System (RS&GIS) and assessing results with industry reports. Landsat OLI/ETM, Sentinel MSI, high resolution (~1m/pixels) Google earth images in 2006 and 2019, accompanied with industry reports were utilized for mapping and exploiting Limestone mine Change in the study area. In this research work, the Normalized differential vegetation index (NDVI) image was resampled using the Nearest Neighbor algorithm on the QGIS tool which produces mine area 469.92ha with Sentinel_2A and 555.39ha with Landsat_8 which were compared with Google Earth image (486.47ha) from Google Earth Pro (freeware) and industry reports (487.10ha) in 2019. In the year 2019, the results for the resampled sentinel_2A NDVI image achieved Limestone mine (class 2) Producer accuracy (PA) 98.82 %, User accuracy (UA) 92.79 %, and kappa coefficient 0.92 %, and for the Landsat_7 NDVI image PA 96.74 %, UA 85.17 %, and Kappa coefficient 0.82 percent in 2006. The Pearson's correlation coefficient between landsat_8 and seninel_2A NDVI images in 2019 was obtained as R2 =0.84. Finally, we suggest that the mining sector adopt an environmentally friendly environmental management plan to limit environmental deterioration during operations and to rehabilitate degraded lands after the mine is closed.
Design of automatic speech recognition in noisy environments enhancement and modification

Recurrent neural networks (RNN) and feed-forward multi-layer perceptron's have been proposed for determining the absence and presence of speech in continuous voice signals when there is a variety of background noise levels present.The Aurora2 and Aurora3 were used to conduct detailed performance evaluations on vocal activity detection.When a Recurrent neural network feeds on automatic speech recognition particular features and acoustic features, the best outcomes can be achieved, according to this study.Aurora2 and the French, Romanian and Norway portions of the Aurora3 corpus is also proposed for detailed studies of ASR.When noise presence probability is utilized to change for encoding speech, phone subsequent probabilities are employed; the WER is reduced by 10.3 percent.
Towards the FAIRification of Meteorological Data: A Meteorological Semantic Model

Meteorological institutions produce a valuable amount of data as a direct or side product of their activities, which can be potentially explored in diverse applications. However, making this data fully reusable requires considerable efforts in order to guarantee compliance to the FAIR principles. While most efforts in data FAIRification are limited to describing data with semantic metadata, such a description is not enough to fully address interoperability and reusability. We tackle this weakness by proposing a rich ontological model to represent both metadata and data schema of meteorological data. We apply the proposed model on a largely used meteorological dataset, the "SYNOP" dataset of Météo-France and show how the proposed model improves FAIRness.
PSDoodle

Keyword-based mobile screen search does not account for screen content and fails to operate as a universal tool for all levels of users. Visual searching (e.g., image, sketch) is structured and easy to adopt. Current visual search approaches count on a complete screen and are therefore slow and tedious. PSDoodle employs a deep neural network to recognize partial screen element drawings instantly on a digital drawing interface and shows results in real-time. PSDoodle is the first tool that utilizes partial sketches and searches for screens in an interactive iterative way. PSDoodle supports different drawing styles and retrieves search results that are relevant to the user's sketch query. A short video demonstration is available online at: https://youtu.be/3cVLHFm5pY4
Multi-Certificate Attacks against Proof-of-Elapsed-Time and Their Countermeasures

Proof-of-Elapsed-Time (POET) is a blockchain consensus protocol in which each participating node is required to wait for the passage of a specified time duration before it can participate in the block leader election in each round.It relies on trusted execution environments, such as Intel SGX, to ensure its security, and has been implemented in Hyperledger Sawtooth and used in many real-world settings.This paper examines the security issues including fairness guarantees of the Sawtooth's POET design and implementation, and discovers a new category of security attacks against POET, dubbed Multi-Certificate Attacks, which allows a malicious node to unfairly create multiple Certificates in each round of block leader election and select the one that maximizes her probability of winning.We have systematically analyzed the root causes of these attacks and assisted the Sawtooth community to fix several vulnerabilities in the latest version of POET.To further mitigate the identified threats, we propose a new design of POET in this paper, which we call POETA, that can be used to address the remaining vulnerabilities we have discovered.We have implemented POETA and evaluated its security and performance.
Adaptive photovoltaic solar module based on internet of things and web-based monitoring system

&lt;span&gt;This paper presents an intelligent of single axis automatic adaptive photovoltaic solar module. A static solar panel has an issue of efficiency on shading effects, irradiance of sunlight absorbed, and less power generates. This aims to design an effective algorithm tracking system and a prototype automatic adaptive solar photovoltaic (PV) module connected through &lt;/span&gt;&lt;span&gt;internet of things (IoT). The system has successfully designated on solving efficiency optimization. A tracking system by using active method orientation and allows more power and energy are captured. The solar rotation angle facing aligned to the light-dependent resistor (LDR) voltage captured and high solar panel voltage measured by using Arduino microcontroller. Real-time data is collected from the dynamic solar panel, published on Node-Red webpage, and running interactive via android device. The system has significantly reduced time. Data captured by the solar panel then analyzed based on irradiance, voltage, current, power generated and efficiency. Successful results present a live data analytic platform with active tracking system that achieved larger power generated and efficiency of solar panel compared to a fixed mounted array. This research is significant that can help the user to monitor parameters collected by the solar panel thus able to increase 51.82% efficiency of the PV module.&lt;/span&gt;
Research on the Influence of Camera Velocity on Image Blur and a Method to Improve Object Detection Precision

The relative motion between camera and target objects will inevitably result in image blurring, which will cause poor performance of visual perception algorithms. The existing methods focus on coarse-grained classification of random camera jitter to study the degradation of object detection performance. However, there are few studies on blur caused by camera with high-speed movement. In this paper, a novel idea that focuses on the relationship between the performance of the camera's visual perception algorithm (taking object detection as an example) and the forward speed of the camera is proposed. A four-wheeled experimental platform that can move in a straight line at a uniform speed of up to 10km/h was designed. Some sensors such as cameras and speed meters were mounted to take motion blurred pictures at different driving speeds. Then, object detection method YOLO-v5 was performed on the above image data to get the confidence of detection frames at different motion speeds, thus a precise expression of relationship between the confidence and speed could be obtain using polynomial regression method. Further, a preliminarily practice that to improve the object detection performance of this type of motion blur image was conducted. DeblurGAN-v2, a deblurring algorithm based on the generative adversarial networks, is used to deblur the original blurred pictures. By comparing with the result of object detected before and after deblurring, the conclusion that the object detection performance is improved as a whole after deblurring was drawn. These results may help expand the range of image perception (blurred images due to high speed) and generate some potential applications.
A framework for self-supervised federated domain adaptation

Abstract Unsupervised federated domain adaptation uses the knowledge from several distributed unlabelled source domains to complete the learning on the unlabelled target domain. Some of the existing methods have limited effectiveness and involve frequent communication. This paper proposes a framework to solve the distributed multi-source domain adaptation problem, referred as self-supervised federated domain adaptation (SFDA). Specifically, a multi-domain model generalization balance is proposed to aggregate the models from multiple source domains in each round of communication. A weighted strategy based on centroid similarity is also designed for SFDA. SFDA conducts self-supervised training on the target domain to tackle domain shift. Compared with the classical federated adversarial domain adaptation algorithm, SFDA is not only strong in communication cost and privacy protection but also improves in the accuracy of the model.
Optimal classical and quantum real and complex dimension witness

We find the minimal number of independent preparations and measurements certifying the dimension of a classical or quantum system limited to $d$ states, optionally reduced to the real subspace. As a dimension certificate, we use the linear independence tested by a determinant. We find the sets of preparations and measurements that maximize the chance to detect larger space if the extra contribution is very small. We discuss the practical application of the test to certify the space logical operations on a quantum computer.
Evaluating Top-N Recommendations Using Ranked Error Approach: An Empirical Analysis

Evaluation metrics or measures are necessary tools for evaluating and choosing the most appropriate modeling approaches within recommender systems. However, evaluation measures can sometimes fall short when evaluating recommendation lists that best match users&#x2019; top preferences. A possible reason for this shortcoming is that most measures mainly focus on the list-wise performance of the recommendations and generally do not consider the item-wise performance. As a result, a recommender system might apply a weak or less accurate modeling approach instead of the best one. To address these challenges, we propose a new evaluation measure that incorporates the rank order of a prediction list with an error-based metric to make it more powerful and discriminative and thus more suited for top-N recommendations. The main goal of the proposed metric is to provide recommender systems, developers and researchers an even better tool, which enables them to choose the best modeling approach possible, and hence maximizing the quality of top-N recommendations. To evaluate the proposed metric and compare its general properties against existing metrics, we perform extensive experiments with detailed empirical analysis. Our experiments and the analysis show the usefulness, effectiveness and feasibility of the new metric.
firefly technique with courtship training optimized for load balancing independent parallel computer task scheduling in cloud computing

Load Balancing strategies is essential provisioning towards cloud environments is a hard challenge due of the situation dynamic nature and varying additional actions selves. Load balancing approaches are spoken at a data center levels in just this investigation. A much more effective source management technique for virtualizing data centers that raises the quantity of computers to satisfy the necessities of changing assignments requiring relocation is suggested in this study. The anticipated method, termed First-ahead load balancing VM Allocation (FALB), incorporates the optimization unbiased, which constructs a accurate formula of UPMSP with sequential setup weeks back basic feasible. In particular, an enhanced firefly algorithm along with engagement learning is planned. Ultimately, in order to offer numerical solutions inside an appropriate timescale, the suggested algorithm is utilized to calculate the UPMSP with set of images process time. FALB is contrasted the with best solution availability of Cloud centered on VM migration dubbed Local Regression-Minimum Migration Time (LR-MMT) (LR-MMT).
The NOW of Time and How It Impacts Physics

This paper introduces the present moment, the NOW as it is called, into physics.Einstein thought the NOW could not be part of physics.Newton's definition of time, which he called mathematical time has defined a measure of time, but not time.It is used in all of physics, as well as all sciences, and civilization in general.Newton recognized that time appears to flow and thought the flow of time to be a universal constant independent of external influences.The common perception of the NOW of time does flow.Most conscious beings recognize time and space are different properties of Nature.So using this common belief as a clue, we introduce a new space called time-space, which is dependent upon external effects and controls the flow of time.Time-space is a two-dimensional space with only one unit, the second, but the physics of time-space is only one-dimensional and involves a periodic internal moving object, satisfying the philosophical notion that time and motion are intimately connected.Newton's flow of time is a universal constant, but Einstein's relativity, by assuming the speed of light to be constant, showed that the rate of time does depend on external influences when compared between different moving reference frames as well as different gravitational intensities.The NOW introduced here is thought to be an abstract quantity, consistent with Einstein's vision of I-time, as well as fields in general, having one property that is measurable, the period of the abstract moving object.The rate is constant within the solar system.Therefore, no changes are necessary to any of the physical theories.However, when applied to astrophysics and cosmology the altered rate has effects.This is demonstrated by applying the NOW rate to the rotation of the Andromeda galaxy and shows the rotation to be consistent with gravitation theories.Therefore, we conclude that galaxy rotation, which is the primary evidence for dark matter, dark matter does not exist.A broader conclusion is that the foundation of physics, as defined by Newton, is not correct.
Maximal activation weighted memory for aspect based sentiment analysis

The vast diffusion of social networks has made an unprecedented amount of user-generated data available, increasing the importance of Aspect Based Sentiment Analysis(ABSA) when extracting sentiment polarity. Although recent research efforts favor the use of self attention networks to solve the ABSA task, they still face difficulty in extracting long distance relations between non-adjacent words, especially when a sentence has more than one aspect. We propose the BERT-MAM model which approaches the ABSA task as a memory activation process regulated by memory decay and word similarity, implying that the importance of a word decays over time until it is reactivated by a similarity boost. We base experiments on the less commonly used Bidirectional Encoder Representations from Transformers (BERT), to achieve competitive results in the Laptop and Restaurant datasets. • Aspect Based Sentiment Analysis. • Activation weighted memory. • Bidirectional Encoder Representations from Transformers. • Memory decay.
ADAPTIVE EMPIRICISM

This paper presents a sketch of a moderately anti-realist position in philosophy of science that is a modification of Van Fraassenâ€™s constructive empiricism and that I call â€˜adaptive empiricismâ€™. This modification is motivated by the intuition that assessing what is or is not observable should be an important element of theory choice for an empiricist. (I use cases of underdetermination as examples.) Thus I argue that Van Fraassenâ€™s distinction between what is observable and what is unobservable should be adapted to changing theoretical and experimental contexts. I close with some ideas as to how to develop this position more fully.
On the Transferability of Neural Models of Morphological Analogies

Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). In this paper, we focus on morphological tasks and we propose a deep learning approach to detect morphological analogies. We present an empirical study to see how our framework transfers across languages, and that highlights interesting similarities and differences between these languages. In view of these results, we also discuss the possibility of building a multilingual morphological model.
Mining Software Repositories for Software Metrics (MSR-SM): Conceptual Framework

A number of software metrics estimate the complexity of software program by a couple of substantial software attributes and trends. Metric for measuring the reliability is extraordinary among the presently available complexity metrics by contemplating a non-physical attributes i.e. readability. Readability may be a key quality attribute for managing software source codes. Readability of the source code is mainly concerned with code maintainability which is a significant characteristic of software quality, mainly from the developers’ perspective. As the code is readable, the easier it is to modify, lesser errors, copious maintainable, easy to reuse and even more reliable. Readability is employed to enhance source codes for subsequent maintenance and extensibility. But code readability is not simply computable while dealing with open source software as contributors access the code and modify its structure according to his/her style of coding. This nature of development begins problems for the new contributors to understand the code structure. To enhance code readability, In this paper, we proposed a conceptual model of mining software repositories for software metrics in which we proposed a set of metrics for readability of the code that is easier to use and helpful to understand the code structure. We also mention a mechanism to validate the proposed metrics by the data extracted from the mining software repositories and comparing it with a survey conducting from experts working in industries.