Reviewerly/exHarmony huggingface dataset:
Monthly Characterization of the Generation of Photovoltaic Arrays. Microgrid Case CEDER, Soria, Spain

The implicit difficulties (resources and intermittent generation) of renewable energies prevent their insertion with greater security and reliability in electricity networks. However, as the priority of favoring environmental issues and represent one of the main axes of the electricity sector, it is essential to make efforts that characterize the behavior of the effective production of photovoltaic systems in real applications. In this way, the objective of this article is to propose a methodology where the monthly profile of future facilities is estimated. To this end, the received solar radiation (based on the Gamma probability distribution) is modeled along with the generation of seven photovoltaic arrays of different technology, connected to the microgrid of the Center for the Development of Renewable Energies (CEDER), belonging to the Center of Energy, Environmental and Technological Research (CIEMAT), located in Soria, Spain. The measurement of the solar resource was made with a Baseline Surface Radiation Network, and the injection of photovoltaic power to the microgrid was acquired with the help of smart meters. The verification of the procedure was carried out with simulations in Matlab and the statistical analyzes were confirmed with the JMP software. The results may be helpful in sizing a backup model and will collaborate in the proper management of the case study energy.
Sleep Detection Using Physiological Signals from a Wearable Device

Internet of Things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytic. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of ambulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consist of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality.
Continuous Authentication Over Hand-Waving for Android Smartphones

In this paper we propose a method of continuous authentication based on micromovements of the hand of mobile user. It shows good accuracy: on average, 4 movements are enough to understand who is using the device: a legitimate user or a violator. Our method has been tested on various devices, and applicability in conditions of limited training sample and in absence of some sensors in the smartphone has been proved. The proposed method can be used as an independent method of continuous authentication, or as an additional method, in combination with other methods of user recognition like passwords or PINs.
Enhancing Feed-Forward Neural Network in Image Classification

In this paper. the researcher use Feed Forward Neural Network for image classification. The objective of this paper is to Enhancing the structure of FFNN by adding dropout method into the input layer and hidden layer to prevent over-fitting the network and then initialized by random uniform initializer for each layer then setting a bias of zeros, also adding the following activation function ReLu&Softmax to the network and the dataset that the researcher use was the MNIST-handwritten numbers. The dropout method is one of the easy and efficient regularization methods that avoids overfitting which stochastically drops out some neurons and trains the network at each weight update. After that, researcher determines the performance of the result by evaluating the FFNN with dropout on the basis of loss and accuracy to take the average of the difference between the standard FFNN and CNN. The result of FFNN with dropout method had an average of 99.86% accuracy and 0.47% of loss, the standard FFNN got 98.13% accuracy and loss of 9.15%, while the CNN had an average of 99.26% accuracy and 2.39% of loss. As the training process of neural network is an iterative process comprising forward propagation, speed improvement using dropout would also provide a significantly decreased training time.
Named Entity Recognition for Open Domain Data Based on Distant Supervision

Named Entity Recognition (NER) for open domain data is a critical task for the natural language process applications and attracts many research attention. However, the complexity of semantic dependencies and the sparsity of the context information make it difficult for identifying correct entities from the corpus. In addition, the lack of annotated training data makes impossible the prediction of fine-grained entity types for detected entities. To solve the above-mentioned problems in NER, we propose an extractor which takes both the near arguments and long dependencies of relations into consideration for the entities and relations mention discovery. We then employ distant-supervision methods to automatically label mention types of training data sets and a neural network model is proposed for learning the type classifier. Empirical studies on two real-world raw text corpus, NYT and YELP, demonstrate that our proposed NER approach outperforms the existing models.
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-Identification

In video surveillance, person re-identification is the task of searching person images in non-overlapping cameras. Though supervised methods for person re-identification have attained impressive performance, obtaining large scale cross-view labeled training data is very expensive. However, unlabelled data is available in abundance. In this paper, we propose a semi-supervised metric learning approach that can utilize information in unlabelled data with the help of a few labelled training samples. We also address the small sample size problem that inherently occurs due to the few labeled training data. Our method learns a discriminative space where within class samples collapse to singular points, achieving the least within class variance, and then use a maximum margin criterion over a high dimensional kernel space to maximally separate the distinct class samples. A maximum margin criterion with two levels of high dimensional mappings to kernel space is used to obtain better cross-view discrimination of the identities. Cross-view affinity learning with reciprocal nearest neighbor constraints is used to mine new pseudo-classes from the unlabelled data and update the distance metric iteratively. We attain state-of-the-art performance on four challenging datasets with a large margin.
Automatic detection of volcanic ash from Himawari – 8 satellite using artificial neural network

Volcanic ash is a significant phenomenon towards aviation safety and capacity of influencing climate change. Therefore, accurate information of the volcanic ash spatial distribution in the atmosphere possessed a fundamental role in the community. Polar type satellites such as Terra / Aqua equipped with MODIS sensors are capable of providing vivid imagery of the volcanic ash spatial distribution. However, the deficiency of this satellite is unable to perform optimal imagery for real-time monitoring due to its limitation require to be located above the volcanic ash site. Therefore, a geostationary satellite is a feasible solution to solve this issue hence its capability to observe specified fixed location continuously. Despite its capability, this type of satellite also performs designated weaknesses hence non-absolute perpendicularity observation angles on certain conditions towards the observed objects from the satellite fixed position. The purpose of this study is to create an automatic detection system using Himawari-8 satellite observations data by applying Artificial Neural Network (ANN) with training datasets and utilizing Terra / Aqua polar type satellite with MODIS sensors as validator. The input variation based on previous research references were using three bands, all bands, and four variations of satellite bands. The result of the study justifies the models established using all bands and four variation bands can produce good performances in training data, although less consistent if applied towards other cases. While the single-pixel model with three-band input well suited over Mt. Merapi volcanic eruption event on June 1st, 2018 with 93.71% accuracy
Optical Flow Prediction for Blind and Non-Blind Video Error Concealment Using Deep Neural Networks

A novel optical flow prediction model using an adaptable deep neural network architecture for blind and non-blind error concealment of videos degraded by transmission loss is presented. The two-stream network model is trained by separating the horizontal and vertical motion fields which are passed through two similar parallel pipelines that include traditional convolutional (Conv) and convolutional long short-term memory (ConvLSTM) layers. The ConvLSTM layers extract temporally correlated motion information while the Conv layers correlate motion spatially. The optical flows used as input to the two-pipeline prediction network are obtained through a flow generation network that can be easily interchanged, increasing the adaptability of the overall end-to-end architecture. The performance of the proposed model is evaluated using real-world packet loss scenarios. Standard video quality metrics are used to compare frames reconstructed using predicted optical flows with those reconstructed using “ground-truth” flows obtained directly from the generator.
From Iff to Is: Some New Thoughts on Identity in Relevant Logics

In this paper, I set out a semantics for identity in relevant logic that is based on an analogy between the biconditional and identity. This analogy supports the semantics that Priest has set out for identity in basic relevant logic and it motivates a version of the Routley–Meyer semantics in which identities can be viewed as constraints on the ternary relation that is used to treat implication.
Hybrid Feature Selection Algorithm to Support Health Data Warehousing

Large volumes of data are being generated each day in healthcare. In addition, these huge amounts of data from healthcare datasets cause the issue of proper knowledge discovery. Currently, data integration is an approach, which is increasingly utilized by healthcare data specialists for analyzing the information and data mining. "Which features or attributes should we use to integrate data for data warehouses"-is a difficult question to answer. It requires deep knowledge of the problem domain. Automatic feature selection is the process of selecting a subset of relevant features automatically for later use. In this paper, we proposed a method using four random forest based feature selection algorithm and domain knowledge. Experimental results show that our hybrid method can select a required number of features from a large set of attributes.
An attempt to develop an item management system for WEKO3

近年, 世界では論文や研究データを始めとした研究成果などに対してアクセスを容易にし, データの発信,共有, 再利用を促すことを目的とした「オープンサイエンス」と呼ばれる試みが活発になってきている. これに伴って, 国内では, リポジトリシステムであるWEKO を利用して多くの学術研究機関でリポジトリの構築が行われている. しかし, データリポジトリとして利用することを考えた場合, メタデータを画一化できないことから, ユーザが所望したデータにたどり着けないことがある. そこで本研究ではこれを解決できるより利便性を向上させるインターフェースの開発を目的としている.. 本インターフェースは, アイテムにタグを割り振ることで管理を行うシステムであり, 機能としてはタグ検索・編集, 外部用API などがある. これによってメタデータに欠けているものを後から補完し, ユーザがより容易にアイテムにたどり着けることを目的としている.
Resilient Distributed Estimation: Linear Time-Variant Observation Models

Owing to the attack from adversaries, attention is increasingly paid to security for wireless sensor networks. This paper considers resilient distributed estimation of an unknown linear variant vector parameter under measurement attacks. Each agent may not be locally observable. By fusing the local measurements and estimates of itself and its neighbors, a consensus estimate is obtained based on the proposed Saturating Adaptive Innovation Estimator (SAIE) for resilient distributed estimation against measurement attacks. Theoretical analysis demonstrates that the estimates tend almost surely to the true vector, so long as the proportion of adversarial agents is lower than a specific value and the network is globally observable. Simulation results illustrate the proposed SAIE can achieve an acceptable estimation performance.
Tangibility of Fuzzy Approach Risk Assessment in Distributed Software Development Projects

Software is a product or a service or a project whose requirements are captured; specification document is prepared by requirements analysis; in/out architecture is designed; related source codes, variables, methods, classes, modules and relationships among these are written; necessary test and integration operations are done; when needed, maintenance, repair and update operations are executed by software or computer engineers and also that has its own documentation jobs and user manual, and has numeric and textual data, and also multimedia tools in its memory. Software development projects have a large financial burden and need to invest in high volumes. When looked at costs based on the international tangible data on computer software; it was $150 billion in 1985, it was $2 trillion in 2010 and it passed over $5 trillion after 2015. Also, in the year of 2018, the giro of Apple Store is about $250 million. Despite the costs, expenses and investments that are exponentially increasing every year, the rate of successful development of the software projects is not very high. Based on the “CHAOS” report (international size) prepared in 2016 only 17% of the software projects developed were completed in a timely manner, in the allocated budget and in accordance with the requirements. 53% of the projects were completed over time and/or over budget and/or also without fulfilling the requirements exactly. 30% of the software projects cannot have been completed in the development phase and were cancelled. For that software development projects with such high expenses and low success rate can have a better quality structure, a risk assessment approach has to be determined for the risk assessment methodology. So, some problems which may form software risks can be recognized and determined on time before causing trouble and endangering for software development projects. In this paper, some software risk assessment approaches underlying software risk management were introduced and explained in detail in addition to “fuzzy approach”. In addition, that this fuzzy technique is more useful and more effective for software risk evaluation in comparison with the others was showed and expressed by giving linguistic rules in this article. This paper tried to explain the terms from the general to the specific.
Reinforcement Learning Approach for Navigation of Ground Robotic Platform in Statically and Dynamically Generated Environments

This paper considers robotic platform navigation in terms of logistics, movement and track routing within indoor environments. Smart navigation and platform routing using a neural network are investigated. The paper discusses environment modeling with Unity ML software suite in static (prefabricated) and dynamically generated environments. Along with reinforcement learning, a procedural generation approach and its possible industrial applications are considered. The proposed algorithm for environment generation is characterized by higher performance comparing to analogues and allows to avoid model overfitting.
A machine that learned to listen, speak, and listen while speaking

In this paper, we introduce our recent machine speech chain frameworks based on deep learning that learned, not only to listen or speak but also listen while speaking. To the best of our knowledge, this is the first deep learning model that integrates human speech perception and production behaviors. Our experimental results show that the proposed approach significantly improved the performance more than separate systems that were only trained with labeled data.
The Research of Image Mosaic Techniques Based on Optimized SIFT Algorithm

Image mosaic refers to the process of stitching multiple images those have overlapping areas of small perspective and low resolution into a panoramic image with high resolution and wide perspective through the corresponding image registration and fusion algorithm. In the mosaic of panoramic images, the traditional SIFT algorithm has large amount of calculation that leads to mismatching and unsatisfactory splicing effect in the process of generating feature vectors and performing feature matching. To this end, this paper proposes an optimized SIFT algorithm. The optimization algorithm, at the first time, introduces the Laplacian operator in order to sharpen the edges of the image. Then, based on the SIFT algorithm, matching the feature points by bidirectional matching algorithm. Finally, in the part of image fusion, an algorithm of luminance weight fusion in HSI color space is proposed. Experiments show that compared with the traditional SIFT algorithm, the proposed optimization algorithm can effectively reduce the error matching and improve the matching accuracy of feature points. In the image fusion part, the phenomenon of ghost image and the sudden change of luminance during image mosaic is effectively eliminated, besides the fusion effect is optimized, and ends with a good image mosaic result.
Requester-Centric CP-ABE Combining Removing Escrow and Outsourcing Decryption

Key escrow and low efficiency are two practical bottlenecks of attribute-based encryption. In this paper, a Requester-Centric CP-ABE (RC-CP-ABE) combining removing escrow and outsourcing decryption is proposed. By the bilinear map of composite order, we optimized the computational complexity of key transformation in the case of a large number of attributes. Without adding roles in the system, data requesters can undertake the key transformation which make them the center in the interactive process and the bridge linking removing escrow with outsourcing decryption. Security analysis has been made to prove our algorithm secure.
The Induction Problem: A Machine Learning Vindication Argument

The problem of induction is a central problem in philosophy of science and concerns whether it is sound or not to extract laws from observational data. Nowadays, this issue is more relevant than ever given the pervasive and growing role of the data discovery process in all sciences. If on one hand induction is routinely employed by automatic machine learning techniques, on the other most of the philosophical work criticises induction as if an alternative could exist. But is there indeed a reliable alternative to induction? Is it possible to discover or predict something in a non inductive manner? This paper formalises the question on the basis of statistical notions (bias, variance, mean squared error) borrowed from estimation theory and statistical machine learning. The result is a justification of induction as rational behaviour. In a decision-making process a behaviour is rational if it is based on making choices that result in the most optimal level of benefit or utility. If we measure utility in a prediction context in terms of expected accuracy, it follows that induction is the rational way of conduct.
Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data

With the increase in collection of temporal event data, especially electronic health record (EHR) data, numerous different visualization and analysis techniques have been developed to assist with the interpretation of such data. As datasets grow increasingly large in both number of event sequences and number of event types, two problems arise: how to group event types, and how to describe selection bias that can occur when selecting cohorts. This poster summarizes two papers, conditionally accepted to VAST, that introduce a dynamic and interactive algorithm for hierarchical event grouping, a scented scatter-plus-focus visualization that supports hierarchical exploration, a tree-based cohort provenance visualization, and a set of visualizations that provide per-dimension selection bias information for pairs of cohorts [2, 4]. These methods are integrated into the web-based interactive medical analysis tool Cadence.
Improvement for Traditional Genetic Algorithm to Use in Optimized Path Finding

Genetic algorithm tries to find the optimized solution with different process stages. All stages are inspired by the natural mechanisms with the genes as individuals. Modelling that natural loop in Computer systems to find the optimized populations which is various combinations of genes, provide a good method to find a solution for problems that can’t solve with any mathematical definition. Today, genetic algorithm is using for diverse fields like path finding, robotic, medical, network, big data and so more. In this work, genetic algorithm improved for path finding methods. All stages are examined and discussed to find possible improvements. A new step which is called as “Fate Decide Operator” is implemented and compared with traditional genetic algorithm. Fate decide algorithm’s tests shows that the fate decide operator has some advantages for path finding algorithms. Improved genetic algorithm can be used in various problems.
Analysis of Applied Problems in Modern Textbooks of Algebra and Geometry from Positions of the FSES BGE Requirements

The article discusses the concept of «mathematical literacy», which is important from positions of the PISA international studies requirements. There are three types of activity (formulation of the situation mathematically, application of mathematics, interpretation of the result), the possession of which is checked in the PISA study. The article presents the subject results formulated in the FSES of BGE, on the study of which the subject area «Mathematics» is aimed, reflecting the need for the formation of selected components of mathematical literacy in students. The article presents the results of the analysis of tasks presented in the most common textbooks on algebra and geometry of the main school. The purpose of the analysis was to identify the presence of practice-oriented tasks in textbooks that meet the requirements of the FSES of BGE and the requirements of diagnostic tools used in international PISA studies. During the study the following methods were used: comparison, analysis, theoretical. The study was based on the most frequently used in school practice textbooks of the following authors: L.S. Atanasyan, V.F. Butuzov, S.B. Kadomtsev, E.G. Poznyak, I.I. Yudina, L.S. Atanasyan, V.F. Butuzov, etc. Result: the article presents the results of the analysis of the problem material presented in the most common textbooks on algebra and geometry of the general school. On the basis of the analysis of three author's lines on the study of geometry in the general school, common problems are identified. An example of a practical problem, confirming the conclusions, formulated during the analysis is given. The authorcomes to the conclusion that there is still lack of practice-oriented tasks in modern textbooks on algebra, helping to motivate students to conscious assimilation of mathematical facts and statements, providing the formation of the ability to model on the basis of a real, life situation.