Reviewerly/exHarmony huggingface dataset:
TransOS-ELM: A Short-term Photovoltaic Power Forecasting Method based on Transferred Knowledge from Similar Days

Photovoltaic (PV) power is an important part of renewable energy and has been distributed more and more widely. Due to the characteristics of intermittency and randomness, the accurate forecasting of short-term PV output will ensure secure operation and economic integration of PV power in smart grids. However, previous works on PV power forecasting show a strong dependency on weather conditions in common, that means there exists a sophisticated relationship which is hard to be described quantitatively between various influencing factors and PV power. What we have focused on is to leverage all kinds of dependencies to make up for the deficiency of traditional data-driven methods. In this paper, a hybrid learning model based on Online Sequential Extreme Learning Machine (OS-ELM) associated with transferred knowledge learned from similar days which share similar weather conditions with the target day, abbreviated as TransOS-ELM, is proposed and applied to short-term PV power forecasting problem. This model is able to optimize the prediction results according to the weather condition of the target day with the knowledge learned from samples of similar days and monitored in real time. This framework is made up of two stages. Firstly, the transfer learning part is designed to extract transferred knowledge from similar days' records, and this part of knowledge along with historical data will be put together to generate an output matrix. Secondly, the new generated data will be absorbed by the online learning model and used to modify the parameters of the first-step output. The most competitive characteristic of this model is that it can get transferred knowledge from the same PV station as well as the different ones, to tackle the problem of insufficient historical records. Both types show extraordinary performance across an open-source dataset and a real-world dataset monitored by ourselves.
Statistical properties of non-Gaussian quantum states generated via thermal state truncation

Quantum scissors devices proposed by Pegg are beneficial to obtain highly nonclassical quantum states and indispensable for meeting the requirements of quantum information and computation. In this paper, via inputting and detecting single photon states, quantum scissor operation is equivalent to a mixed superposition of three pure state projection operators, which means that the output states are always truncated for any input state. We theoretically prepare a class of non-Gaussian quantum states via thermal state truncation and investigate their statistical properties using average photon number, gain intensity and signal to noise ratio. It is shown that the intensity gain and signal to noise ratio greater than one can be achieved by modulating the thermal parameter and the transmissivity, which realizes the signal amplification and enhancement. Besides, quantum scissor operation can generate the highly non-classical quantum state by investigating the negativity volume of Wigner function. These results indicate that the usage of the new non-Gaussian states may have potential applications in certain quantum information processing.
When multi-view classification meets ensemble learning

With the coming of big data era, multi-view data represented by multiple features have been involved in many terms, such as machine learning, data mining and computer vision and so on. Due to the complex structure hidden in data, how to utilize the complementary and correlative information among multiple view features to improve classification performance is a challenging task. Moreover, the another challenging task is how to assign an appropriate weight for each classifier on the basis of its performance. To solve above problems, we proposed a supervised multi-view classification method based on Least Square Regression (LSR) and Ensemble Learning. To be specific, all samples for each view firstly can be classified by using Multi-class Support Vector Machine (MSVM); Then, to evaluate the classification results of different views for each sample, the optimal weight of each sample classification result is learned; Furthermore, considering the view difference with different classification quality, the view weights are assigned adaptively; Finally, we adopt the decision function values to determine the final classification results. Extensive experimental results show that the proposed method outperforms most state-of-the-art multi-view classification methods.
Least Significant Bit-Based Image Watermarking Mechanism

Now days, digital data security over network is a big issue. Digital data security and authentication is a big and challenge able research area in the field of computer science. A lot of security mechanisms are available for data authentication and privacy. For the purpose of data security and secure communication encryption mechanism are available, and also for data authentication several watermarking techniques are available. In this paper authors mainly focus research based on image watermarking approach by using least significant bit substitutions. Here authors represent comparative studies of different LSB based image watermarking approach.This paper mainly focuses on spatial domain based data hiding mechanism. The detail comparison is also given in the tabular form.
Recognition of Alphanumeric Patterns Using Backpropagation Algorithm for Design and Implementation With ANN

The artificial neural network has been called for its application as alphanumeric characters recognizing the network. The idea is to maintain the obsolete data available in hard copy form and to convert that data into digital form. Some specific bit patterns that correspond to the character are trained using the network. The numbers of input and output layer neurons are chosen. As there are many ways to do but in this the algorithm used for training the network is called the Backpropagation Algorithm using the delta rule. The testing and training patterns are provided for which weights are calculated in the program and patterns are recognized and analysis is done. The effect of variations in the hidden layers is also observed with pattern matrices.
Emotional analysis of safeness and risk perception of drones during the COVID-19 pandemic in Italy

The estimation of perceived safeness and risk by individuals is very significant for security and safety managing. Every person is based on the judgments of other persons to do a choosing and the Internet personifies the location where these judgments are mostly tracked, obtained, and assessed. From this point of view, social networks have a significant influence. For this reason, Opinion Mining and Sentiment Analysis have found remarkable utilizes in a multiplicity of circumstances and one of the most notable is represented by public safety and security. The purpose of the present work is to investigate the perception of safeness and risk of drones during COVID-19 pandemic in Italy. It was considered the period from Aprile 15 to December 10, 2020, properly divided into 4 significant phases, highlighting the emotional components using the semantic analysis of the textual contents present in Twitter.
Improvement of CluStream Algorithm Using Sliding Window for the Clustering of Data Streams

Today, data are produced in large amounts, mostly in form of data streams. A data stream is an unlimited stream of data that is produced in large amounts and with high speeds. Therefore, it can be defined as a sequence of data objects in specified time intervals. One of the most common processes performed on data streams is clustering which is aimed at dividing the data items into homogeneous groups. A well-known clustering algorithm is CluStream, an implemented version of which has been developed for the distributed environment of Apache Spark. This algorithm makes use of a tilted window. The present paper offers a modified version of the algorithm which utilizes a sliding window for clustering. In the proposed method, called CluStreamSW, only the latest data are used in updating the produced model and the old data are removed, which allows for a higher speed of execution and achieving more desirable results. The CluStreamSW was implemented in Apache Spark. The results of multiple executions of this algorithm on authentic data and comparing them with the CluStream algorithm based on tilted window indicate that our algorithm performs up to 30 percent in the CoverType dataset and up to 17 percent in the PowerSupply dataset better on average in terms of precision. In general, in some cases, more than 90 percent improvement in precision is achieved.
Face-the-Waste - Learning about Food Waste through a Serious Game

Consumer food waste in industrialised countries is becoming an increasing concern as its impact on greenhouse emissions is comparable to that of the aviation industry. In recent years we have accordingly seen a growing interest in HCI to support users getting into more sustainable consumption practices. As part of this movement, we present in this paper a serious game called Face-the-Waste that is meant to increase users food literacy and educate them about the impact and development of food waste. Our serious game comes in the form of a public installation that uses provocative design to engage the users. They had to answer multiple choice questions and if they answered wrongly real food would be disposed into a bin in front of the users eyes. The aim with this was to create a strong emotional response and increase the level of reflection on the topic. In our evaluation we not only found that the users often voiced very strong emotional reactions but also engaged and discussed the question and their content. Furthermore, we demonstrated that such provocations can add a new layer for the design of serious games.
Ultra-short-term Photovoltaic Power Prediction Based on Elman Neural Network and Satellite Cloud Images

Solar photovoltaic (PV) power generation has developed rapidly in recent years due to its clean and pollution-free characteristics. However, the variability of weather makes PV power generation very volatile and random, which brings challenges to the planning and operation of the existing power system. Therefore, accurate prediction of PV power generation is of great significance to improving the reliability of power system operation. This paper proposes a PV power prediction model based on Elman neural network and gray value extraction method of satellite cloud images, which effectively utilizes weather pattern and improves the accuracy of PV power prediction. Firstly, the satellite cloud image is grayed to get the gray value, and the gray value interval is clustered. Then the clustering result is used as the input of Elman neural network to build the PV power prediction model. Finally, the actual data are used to analyze the calculation example. The simulation results show that the optimization model after adding the gray value information of the satellite cloud image is more accurate than the original model. The relative coefficient of variation (RCV) is reduced from 5.42 % to 4.01 %, and the accuracy is increased by 1.41%. It provides a reference for using satellite cloud images to predict PV power generation.
Security and Privacy for Sharing Electronic Medical Records Based on Blockchain and Federated Learning

The sharing of Electronic Medical records (EMRs) has great positive significance for research of disease and doctors’ diagnosis. However, patients’ EMRs are usually distributed in the databases of multiple medical institutions. Due to the insecurity of the network environment and distrust of other parties, EMR owners worry about data insecurity and privacy leakage, which makes sharing with other parties difficult. Patients worry about the loose control of their health data as well. To solve this problem, we present a solution for the EMRs data sharing based on blockchain and federated learning, which will provide data security and patients’ privacy. Firstly, we propose a method for EMRs data retrieval records and sharing records as transaction records adding to the blockchain, and design the two algorithm processes, respectively. Secondly, federated learning is used to help EMRs data owners to build a model based on the original data. The data owner only shares the model instead of the original data. Finally, by security and privacy analytics, we analyzed the advantages and influence of the proposed model. Overall, the evaluation shows that the proposed solution is significantly superior to the previous models and achieves reasonable efficiency for sharing EMRs data.
Conversational User Interfaces to support Astronauts in Extraterrestrial Habitats

Long-term space missions are challenging and demanding for astronauts. Confined spaces and long-duration sensory deprivation may cause psychological problems for the astronauts. In this paper, we envision how extraterrestrial habitats (e.g., a habitat on the Moon or Mars) can maintain the well-being of the crews by augmenting the astronauts. In particular, we report on the design, implementation, and evaluation of conversational user interfaces (CUIs) for extraterrestrial habitats. The goal of such CUIs is to support scientists during their daily and scientific routines on their missions within the extraterrestrial habitat and provide emotional support. During a week-long so-called analog mission with four scientists using a Wizard of Oz prototype, we derived design guidelines for such CUIs. Successively, based on the derived guidelines, we present the implementation and evaluation of two CUIs named CASSIOPEIA and PEGASUS.
Uni-Messe: Unified Rule-Based Message Delivery Service for Efficient Context-Aware Service Integration

Rule-based systems, which are the typical technology used to realize context-aware services, have been independently implemented in various smart services. The challenges of these systems are the versatility of action, looseness, and the coding that is needed to describe the conditional branches. The purpose of this study was to support the realization of service coordination and smart services using context-aware technology by converting rule-based systems into services. In the proposed method, we designed and implemented the architecture of a new service: Unified Rule-Based Message Delivery Service (Uni-messe), which is an application-neutral rule management and evaluation service for rule-based systems. The core part of the Uni-messe proposal is the combination of a Pub/Sub and a rule-based system, and the proposal of a new event–condition–route (ECR) rule-based system. We applied Uni-messe to an audio information presentation system (ALPS) and indoor location sensing technology to construct concrete smart services, and then compared and evaluated the implementation to “if this then that” (IFTTT), which is a typical service coordination technology. Moreover, we analyzed the characteristics of other rule-based systems that have been serviced in previous studies and compared them to Uni-messe. This study shows that Uni-messe can provide services that simultaneously combine versatility, ease of conditional description, looseness, context independence, and user interface (UI), which cannot be achieved using conventional rule-based system services. By using Uni-messe, advanced heterogeneous distributed service coordination using rule-based systems and the construction of context-aware services can be performed easily.
Handwritten Mathematical Expression Recognition with Self-Attention

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN, the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. The experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of 51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. The improvements demonstrate the effectiveness of the self-attention module.
Ship-board determination of whole-rock (ultra-)trace element concentrations by laser ablation-inductively coupled plasma mass spectrometry analysis of pressed powder pellets aboard the D/V &amp;lt;i&amp;gt;Chikyu&amp;lt;/i&amp;gt;

Abstract. The Oman Drilling Project (OmanDP), performed under the International Continental Scientific Drilling Program (ICDP), is an international scientific research project that undertook drilling at a range of sites in the Semail ophiolite (Oman) to collect core samples spanning the stratigraphy of the ophiolite, from the upper oceanic crust down to the basal thrust. The cores were logged to International Ocean Discovery Program (IODP) standards aboard the D/V Chikyu. During ChikyuOman2018 Leg 3 (July–August 2018), participants described cores from the crust–mantle transition (CM) sites. The main rock types recovered at these sites were gabbros, dunites and harzburgites, rocks typically forming the base of the oceanic crust and the shallow mantle beneath present-day spreading centres. In addition to the core description, selected samples were analysed by X-ray fluorescence spectrometry (XRF) for their chemical compositions, including major, minor and some trace elements. To complement these standard procedures, we developed new approaches to measure ultra-trace element concentrations using a procedure adapted from previous works to prepare fine-grained pressed powder pellets coupled with laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) analysis using instrumentation aboard the D/V Chikyu. First, three (ultra)mafic reference materials were investigated to test and validate our procedure (BHVO-2, BIR-1a and JP-1), and then the procedure was applied to a selection of gabbro and dunite samples from the CM cores to explore the limitations of the method in its current stage of development. The obtained results are in good agreement with preferred values for the reference materials and with subsequent solution replicate analyses of the same samples performed in shore-based laboratories following Leg 3 for the CM samples. We describe this procedure for the determination of 37 minor and (ultra-)trace elements (transition elements and Ga, Li and Large-Ion Lithophile Elements (LILE), Rare Earth Elements (REE), High-Field-Strength Elements (HFSE), U, Th, and Pb) in mafic and ultramafic rocks. The presented method has the major advantage that it allows the determination at sea of the (ultra-)trace element concentrations in a “dry”, safe way, without using acid reagents. Our new approach could be extended for other elements of interest and/or be improved to be adapted to other rock materials during future ocean drilling operations aboard the D/V Chikyu and other platforms.
From data to physical artifact

research-article Share on From data to physical artifact: challenges and opportunities in designing physical data artifacts for everyday life Authors: Kim Sauvé Lancaster University Lancaster UniversityView Profile , Steven Houben Eindhoven University of Technology Eindhoven University of TechnologyView Profile Authors Info & Claims InteractionsVolume 29Issue 2March - April 2022 pp 40–45https://doi.org/10.1145/3511670Published:25 February 2022Publication History 0citation948DownloadsMetricsTotal Citations0Total Downloads948Last 12 Months948Last 6 weeks91 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Performance Evaluation of Federated Learning Over Wireless Mesh Networks with Low-Capacity Devices

Federated learning is a distributed learning technique in which a machine learning model is trained collaboratively among several nodes. While the privacy preservation of the training data is one of the important promises of federated learning, there is also an opportunity to use low capacity devices for machine learning model training by taking advantage of the fact that the training effort is divided among many nodes. In this paper, we conduct experiments with a federated learning network deployed on several low capacity devices connected to a wireless mesh network. The measurements show the hardware capacity and link bandwidth of the clients on the federated learning process. The results suggest that for heterogeneous networks the federated learning clients should be extended with more autonomous decision capacities according to the network and local conditions.
Legal Support of Digital Business: Competencies and Tools Training Future Lawyers

The challenge of mastering digital competencies in legal profession is determined by the changing digital trends affecting the traditional structure of the classical process of adducing evidence. Nowadays, judicial proceedings and legal activities are based on digital technologies, and digital images of evidence are being actively used. The digitalization of legal practice requires change in a lawyer’s professional skills and their educational qualifications due to the unpredictable development of smart technologies for fixing digital images of legal facts and assurances. The authors argue that the target competencies of legal education need to be modernized in line with the digital systems of modern law enforcement. The broad concept of electronic digital evidence provides the opportunity to train law students’ practical skills in forms, types, and evaluations of both written (alphabetic and graphic), and audio-visual digital evidence. The study focuses on identifying whether law students possess the digital competencies required in modern law enforcement realities at a high level (the skill of using basic IT technologies designed to optimize legal performance and legal decisions). The authors analyze digital competency development processes, the ability to work with information and analytical systems of legally relevant data, legal portals, and specialized expert legal decision-making systems, including the introduction of neural networks in optimal legal decisions.