Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Big Data Management and Decision-Making in Smart Grid 1

Smart grid (SG) has been emerged as the most ingenious idea worldwide towards solving the contemporary pressing power demand problems. SG provides a two-way power and communication flow between customers and utilities to improve grid controllability and reliability (Cagri Gungor et al., 2013). SG is supported by many data sources such as smart metres, sensors, detectors, and measurement units; therefore, a huge amount of data is being exchanged between the elements of SG. The biggest challenge facing SG is to collect and deal in real time with massive and important amount of data. Example is the data acquired by intelligent electronic devices, operating and maintenance data for electrical devices and equipment, and very large datasets used in decisionmaking, such as metrological service data and geospatial information system. These data are updated always through the fast technology growth. There is a consensus about the concept of the three V’s characterizing Big Data: volume, variety, and velocity (Ward and Barker, 2013). SG data achieve the three properties of the Big Data: volume, variety, and velocity. Big Data in SG are classified into two broad categories: structured and unstructured data (Thakur and Mann, 2014). “Structured data” can be ordered in columns, rows, or binary. These data can be collected from various sources such as phasor measurement data, sensors, and smart metres. “Unstructured data”, which are not organized, can be collected from photos, customer comfort level, social media, emails, etc. All generated massive amounts of data can provide better understanding of customer segmentation and can help improve the efficiency of electrical generation and scheduling. New forms of data processing are required to Power Electronics and Drives • AOP Volume 4(39), 2019 DOI: 10.2478/pead-2019-0011
Personalization through User Attributes for Transformer-based Sequential Recommendation

Sequential recommendation models are able to learn user preferences based solely on the sequences of past user - item interactions. However, there usually exist additional features about the items as well as the user that can be exploited to improve the model performance. Various models that successfully exploit additional item information have been developed. In contrast, the integration of user attributes into sequential item recommendation models has received far less attention, although user attributes can alleviate the cold-start problem to some degree through providing general preferences of the user at the start of a session. To address this, we propose a way to fuse user attributes directly at the item representation layer. To test our approach, we adapt the well known model BERT4Rec to use these fused user-item representations as input. We also experiment with a unidirectional transformer model inspired by SASRec. We evaluate both models on a real-world dataset from a fashion e-commerce shop as well as on the public Movielens-1m dataset. Our results show that the integration of user attributes improves the recommendation results for BERT4Rec for next item prediction and can also leverage the user information for recommendations in case of a cold-start setting. layers = 2, dropout = 0 . 1 for both models. For U-BERT4Rec we use a learning rate of 0 . 003, a hidden size of 18 and train for 200 epochs. For U-SASRec 𝑐𝑟𝑜𝑠𝑠 we end up with a learning rate of 0 . 001, a hidden size of 20 and train for 100 epochs. We run an additional hyperparameter study for U-BERT4Rec to tune the model and report the best model as U-BERT4Rec 𝑡𝑢𝑛𝑒𝑑 . The hidden size is increased to 48 for this model. Due to time limits we could not conclude further parameter studies.
The Aitutaki trochus fishery: a case study

1 Department of Primary Industries and Fisheries, Crayfish Point, Taroona, Tasmania, Australia 2 South Pacific Commission, Boite Postale D5, Noumea, New Caledonia 3 Ministry of Marine Resources, Rarotonga, Cook Islands 4 Ministry of Marine Resources, Araura Research Station, Aitutaki, Cook Islands 5 Department of Fisheries, Port Vila, Vanuatu 6 Fisheries Division, Ministry of Natural Resources, Honiara, Solomon Islands
A Survey of the Research on Power Management Techniques for High Performance Systems

This paper surveys the research on power management techniques for high performance systems. These include both commercial high performance clusters and scientific high performance computing (HPC) systems. Power consumption has rapidly risen to an intolerable scale. This results in both high operating costs and high failure rates so it is now a major cause for concern. It is imposed new challenges to the development of high performance systems. In this paper, we first review the basic mechanisms that underlie power management techniques. Then we survey two fundamental techniques for power management: metrics and profiling. After that, we review the research for the two major types of high performance systems: commercial clusters and supercomputers. Based on this, we discuss the new opportunities and problems presented by the recent adoption of virtualization techniques, and again we present the most recent research on this. Finally, we summarise and discuss future research directions.
Pattern recognition

Unsupervised feature selection (UFS) aims to reduce the time complexity and storage burden, improve the generalization ability of learning machines by removing the redundant, irrelevant and noisy features. Due to the lack of training labels, most existing UFS methods generate the pseudo labels by spectral clustering, matrix factorization or dictionary learning, and convert UFS to a supervised problem. The learned clustering labels re ﬂ ect the data distribution with respect to classes and therefore are vital to the UFS performance. In this paper, we proposed a novel subspace clustering guided unsupervised feature selection (SCUFS) method. The clustering labels of the training samples are learned by representation based subspace clustering, and features that can well preserve the cluster labels are selected. SCUFS can well learn the data distribution in that it uncovers the underlying multi-subspace structure of the data and iteratively learns the similarity matrix and clustering labels. Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods.
Overview of the Patent Mining Task at the NTCIR-8 Workshop

This paper introduces the Patent Mining Task at the Eighth NTCIR Workshop and the test collections produced in this task. The purpose of the Patent Mining Task is to create technical trend maps from a set of research papers and patents. We performed two subtasks: (1) the subtask of research papers classification and (2) the subtask of technical trend map creation. For the subtask of research papers classification, six participant groups submitted 101 runs. For the subtask of technical trend map creation, nine participant groups submitted 40 runs. In this paper, we also report on the evaluation results of the task.
in Computer

— The main objective of sentiment analysis or opinion mining is to choose whether the mode of a reviewer with some topic or the overall related dissection of a document. Due to cheap availability of internet , the utilization of internet has been increased. People sell any buy product on merchant websites and give opinion about the product on website. In this paper we present a sentiment classification of mobile reviews using supervised method. Dataset are taken from amazon.com. for experiment we use weka tools. our results shows that very effective over existing method.
Learning Generic Prior Models for Visual ComputationSong

Many generic prior models have been widely used in computer vision ranging from image and surface reconstruction to motion analysis, and these models presume that surfaces of objects be smooth, and adjacent pixels in images have similar intensity values. However, there is little rigorous theory to guide the construction and selection of prior models for a given application. Furthermore, images are often observed at arbitrary scales, but none of the existing prior models are scale-invariant. Motivated by these problems, this article chooses general natural images as a domain of application, and proposes a theory for learning prior models from a set of observed natural images. Our theory is based on a maximum entropy principle, and the learned prior models are of Gibbs distributions. A novel information criterion is proposed for model selection by minimizing a Kullback-Leibler information distance. We also investigate scale invariance in the statistics of natural images and study a prior model which has scale invariant property. In this paper, in contrast with all existing prior models, negative potentials in Gibbs distribution are rst reported. The learned prior models are veriied in two ways. Firstly images are sampled from the prior distributions to demonstrate what typical images they stand for. Secondly they are compared with existing prior models in experiments of image restoration.
ORIGIN AND EFFECTS OF ANTHROPOGENIC FLASH FLOODS ON RIVERS OF HOLY CROSS MTS. REGION (POLAND) IN 20th c.

In the 20th c. , after the disappearance of the system of small retention (number of artificial ponds, channels etc.) due to economic changes, on the rivers (Kamionka, Czarna Konecka) appeared events previously unknown during the whole Holocene. There are catastrophic flash floods caused by hydrotechnical damage. Geomorphological effects of these floods exceed many times the effects of secular processes.
Universal Consistency of Decision Trees in High Dimensions

: This paper shows that decision trees constructed with Classiﬁca-tion and Regression Trees (CART) methodology are universally consistent in an additive model context, even when the number of predictor variables scales exponentially with the sample size, under certain 1 -norm sparsity constraints. The consistency is universal in the sense that there are no a priori assumptions on the distribution of the predictor variables. Amazingly, this adaptivity to (approximate or exact) sparsity is achieved with a single tree, as opposed to what might be expected for an ensemble. Finally, we show that these qualitative properties of individual trees are inherited by Breiman’s random forests. Another surprise is that consistency holds even when the “mtry” tuning parameter vanishes as a fraction of the number of predictor variables, thus speeding up computation of the forest. A key step in the analysis is the establishment of an oracle inequality, which precisely characterizes the goodness-of-ﬁt and complexity tradeoﬀ for a misspeciﬁed model.
Effect of stress on the diffusion kinetics of methane during gas desorption in coal matrix under different equilibrium pressures

Stress has a significant influence on gas diffusion, which is a key factor for methane recovery in coal mines. In this study, a series of experiments were performed to investigate effect of stress on the gas diffusivity during desorption in tectonic coal. Additionally, the desorbed data were modeled using the unipore and bidisperse models. The results show that the bidisperse model better describes the diffusion kinetics than the unipore model in this study. Additionally, the modeling results using the bidisperse approach suggest that the stress impact on the macropore diffusivity is greater than the stress on the micropore diffusivity. Under the same equilibrium pressure, the diffusivity varies with stress according to a four-stage function, which shows an 'M-shape'. As the equilibrium gas pressure increased from 0.6 to 1.7 MPa, the critical point between stage 2 and stage 3 and between stage 3 and stage 4 transferred to a low stress. This difference is attributed to the gas pressure effects on the physical and mechanical properties of coal. These observations indicate that both the stress and gas pressure can significantly impact gas diffusion and may have significant implications on methane recovery in coal mines.
Simulation of the Finite Difference Time Domain in Two Dimension

The finite-difference time-domain (FDTD) method is one of the most widely used computational methods i n electromagnetic. This paper describes the design o f two-dimensional (2D) FDTD simulation software for transverse magnet ic (TM) polarization using Berenger's split-field perfectly matched layer (PML) formulation. The software is developed using Matlab programming language. Numerical examples validate t he software. Keywords—Finite difference time domain (FDTD) method, perfectly matched layer (PML), split-filed formulat ion, transverse magnetic (TM) polarization.
SPARK2: Top-k Keyword Query in Relational Databases

—With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.
Ontogenetic stages of ceratopsian dinosaur Psittacosaurus
in bone histology

The early ceratopsians Psittacosaurus and Protoceratops have provided important information on dinosaurian development because of abundant specimens of adults, subadults, juveniles, and even hatchlings. Here we present new data and methods for identifying key growth stages from bone histology. Previous studies on Psittacosaurus lujiatunensis from the Early Cretaceous Jehol Biota of China did not present in-depth analysis of growth patterns. Based on a histological study of 43 thin sections from 17 individuals of this species, we recognize four histological ontogenetic stages, i.e., hatchling, juvenile, sub-adult, and adult, but no fully-grown stage. We estimate life history and longevity from diaphyseal growth line counts and other features of histology. We show that P. lujiatunensis grew fast in early stages (hatchling, juvenile, and subadult), according to the density of vascular canals and the different type of bone tissue; the deposition of parallel fibred bone tissue in the outer cortex of the subadult stage indicates that growth rate was slowing down. We introduce a new graphical method to estimate the occurrence and volumes of vascular canals from thin sections more accurately than current two-dimensional approaches.
Supervised Machine Learning Method for Ontology-based financial decisions in Stock Market

semantics, ontological and information presentation, well as computational linguistics for Asian social networks are one of the most essential platforms for offering enhanced and real-time data mapping, as well as The designed technique predicts negative, positive, and neutral sentiment using stock news data taken from the Stock twists data-base. Therefore, findings in the study has improved the research, which is critical for stock market index prediction, and have led to a better knowledge of the stock market's behavior in response to numerous happenings. The study's scope was limited owing to the scarcity of financial market statistics; as a result, it focused on a single stock market; nonetheless, the suggested model would be useful for other financial markets for a long time. Further studies in this topic might take into account country-specific causes such as economic and political issues, as well as time-specific determinants such as policy changes and implementation timelines.
speaq 2.0: a wavelet-based workflow for high-throughput 1D NMR spectra processing and quantification

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. This has the effect that its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to
PERFORMANCE ANALYSIS OF THE AURORA LARGE VOCABULARY BASELINE SYSTEM 1

In this paper, we present the design and analysis of the baseline recognition system used for ETSI Aurora large vocabulary (ALV) evaluation. The experimental paradigm is presented along with the results from a number of experiments designed to minimize the computational requirements for the system. The ALV baseline system achieved a WER of 14.0% on the standard 5K Wall Street Journal task, and required 4 xRT for training and 15 xRT for decoding (on an 800 MHz Pentium processor). It is shown that increasing the sampling frequency from 8 kHz to 16 kHz improves performance significantly only for the noisy test conditions. Utterance detection resulted in significant improvements only on the noisy conditions for the mismatched training conditions. Use of the DSR standard VQ-based compression algorithm did not result in a significant degradation. The model mismatch and microphone mismatch resulted in a relative increase in WER by 300% and 200%, respectively.
Exploiting User Experience from Online Customer Reviews in Product Design

Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data.
SESSION XII TOOLS AND TECHNIQUES FOR CONNECTIONIST MODELING

This article presents a simulation-based tutorial system for exploring parallel distributed process ing (PDP) models of information processing. The system consists of software and an accompany ing handbook. The intent of the package is to make the ideas underlying PDP accessible and to disseminate someofthe main simulation programs that we have developed. This article presents excerpts from the handbook that describe the approach taken, the organization ofthe handbook, and the software that comes with it. An example is given that illustrates the approach we have taken to teaching PDP, which involves presentation of relevant mathematical background, together with tutorial exercises that make use of the simulation programs. Parallel distributed processing provides a new way of thinking about perception, memory,learning, andthink ing, and about basic computational mechanisms for in telligent information processing in general. These new ways of thinking have all been captured in simulation models. Our own understanding of parallel distributed processing (PDP) hascomeabout largely through hands on experimentation withthese models. And, in teaching PDP toothers, we havediscovered that their understand ingis enhanced through thesamekind of hands-on simu lation experience.
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A SURVEY ON LDA APPROACH IN PREDICTING LINK BEHAVIOR IN A SOCIAL NETWORK

Social network sites (SNS) in recent times are focusing mainly on user interactions. These SNS are attracting the attention of academic and industry researchers who are intrigued by their accordance and reach rapidly. Mainly data mining techniques have been very effective in using the content and graph structure that was available to solve various problems such as friendship link prediction, estimating the percentage of their friendship.etc. Topic models are one among the most effective approaches to discover latent topic analysis and text data mining. One desirable feature of a social network is to be capable to suggest potential friends to its existing users and the approach must be proved to be effective in improving the predictions. Topic modeling approach provides an easy way to analyze large volume of data and the topic modeling techniques like Latent Dirichlet Allocation (LDA) to uncover latent structure in user interests which have to be explored is going to be implemented. By using LDA, the users are predicting their friends and with the how much amount of percentage ratio they are becoming friends. In this review, it has been identified that LDA has a limitation of topic correlation modeling which can be overcome by using CTM (correlated topic model) and it can work better than Arm (Association Rule Mining) for list of 4 or more communities while the tagging can be effectively done when both LDA and association rules are used together.