Reviewerly/exHarmony huggingface dataset:
Analysis of Solar Radiation Data from Satellite and Ground Station for PV System Applications in Al-Diwaniyah Location

Abstract Solar radiation power is the fuel for all solar energy production purposes. All sets of activities such as chemical, physical and biological revolving near the ground or in the atmosphere layer. The solar radiation data at ground level and in the atmosphere are an important characteristic in solar energy applications such as photovoltaic (PV) systems. Various meteorological databases are accessible with data acquired from ground measurements, satellite data, or unification of both. In this paper, the comparison between satellite data derived from ground station installed at Al-Diwaniyah location and PVGIS website so as to design the PV system for maximum competence under different climatic conditions.The monthly data for one year (2015) was analyzed and assessed at zero and tilt planes. The results showed that the difference and bias between acquired satellite and ground station data. The annual rate value of solar radiation for selected location is 1423.5 (Kwh/m2/year) for ground data and 1825 (Kwh/m2/year) for PVGIS data, so, this rate is suitable for PV solar system applications.
A Multi-Modal Gaze Tracking Algorithm

Gaze tracking is an assistant system of human-computer interaction. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. Firstly, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block data set. For 12 classified data, the lowest recognition error rate is 5.34%.
Run-Length Encoding in a Finite Universe

Text compression schemes and compact data structures usually combine sophisticated probability models with basic coding methods whose average codeword length closely match the entropy of known distributions. In the frequent case where basic coding represents run-lengths of outcomes that have probability p, i.e. the geometric distribution $$\Pr (i)=p^i(1-p)$$ , a Golomb code is an optimal instantaneous code, which has the additional advantage that codewords can be computed using only an integer parameter calculated from p, without need for a large or sophisticated data structure. Golomb coding does not, however, gracefully handle the case where run-lengths are bounded by a known integer n. In this case, codewords allocated for the case $$i>n$$ are wasted. While negligible for large n, this makes Golomb coding unattractive in situations where n is recurrently small, e.g., when representing many short lists of integers drawn from limited ranges, or when the range of n is narrowed down by a recursive algorithm. We address the problem of choosing a code for this case, considering efficiency from both information-theoretic and computational perspectives, and arrive at a simple code that allows computing a codeword using only O(1) simple computer operations and O(1) machine words. We demonstrate experimentally that the resulting representation length is very close (equal in a majority of tested cases) to the optimal Huffman code, to the extent that the expected difference is practically negligible. We describe efficient branch-free implementation of encoding and decoding.
Efficient Sparse Processing in Smart Home Applications

In recent years, smart home technology has become prevalant and important for various applications. A typical smart home system consists of sensing nodes sending raw data to a cloud server which performs inference using a Machine Learning (ML) model trained offline. This approach suffers from high energy and communication costs and raises privacy concerns. To address these issues researchers proposed hierarchy aware models which distributes the inference computations across the sensor network with each node processing a part of the inference. While hierarchical models reduce these overheads significantly they are computationally intensive to run on resource constrained devices which are typical to smart home deployments. In this work we present a novel approach combining Hierarchy aware Neural Networks (HNN) with variational dropout technique to generate sparse models which have low computational overhead allowing them to be run on edge devices with limited resources. We evaluate our approach using an extensive real-world smart home deployment consisting of several edge devices. Measurements across different devices show that without significant loss of accuracy, energy consumption can be reduced by up to 35% over state-of-the-art.
Completing right-censored data in time-series modelling

This paper focuses on nonparametric regression modelling of time series observations with data irregularities, such as censoring due to a cutoff value. In general, researchers do not prefer to put up with censored cases in time series analyses because their results are generally biased. In this paper, we present two imputation algorithms for handling auto-correlated censored data: artificial neural network based imputation and Gaussian imputation. These algorithms provide an estimation of the censored data points and replace them with their estimates. After the imputation procedure, the right-censored time series data is modelled and the performance of imputation methods are monitored. Thus, the effect of two imputation methods is evaluated for both modelling and completing censored observations. The purpose of this study is to prepare the censored data set for analysis without manipulating the observed part of the data such as synthetic data transformation or Kaplan-Meier weights. In this paper, algorithms for two methods are given and imputation processes are expressed.
Optical Character Recognition for Coptic fonts

In this paper, we show that the OCR engine Ocropy can be trained for fonts used in rather complex and varied Coptic typeset. For each of the three fonts presented in this paper, we used a number of texts from scholarly editions with different philological and editorial standards and texts from two different dialects of Coptic (Bohairic and Sahidic). Despite the complexity of the training data, we observed accuracy rates of 97.5%, for one font even up to 99%.
A Simulation Approach to Multi-station Solar Irradiance Data Considering Temporal Correlations

Solar energy is one of important renewable energy sources and simulation of solar irradiance can be used as input for simulation of photovoltaic (PV) generation. This paper proposes a simulation algorithm of multi-station solar irradiance data considering temporal correlations. First of all, we group all the days of the observed data to k clusters for each station based on their daily features of solar irradiance and the daily states constitute Markov chain of days. Then, we reduce state permutations of different stations before getting Markov Transition Probability Matrix (MTPM). In terms of the observed data and MTPM, the simulation approach is proposed. Finally, we test our approach by applying to solar irradiance data of three stations and show that the properties of simulated data match those of the observed data.
&lt;b&gt;A Study of Feature Clustering Analysis based on the Hidden Layer Representation of an Autoencoder&lt;/b&gt;

Analysis of customer purchase behavior is an active research area. Several recent studies in this area have focused on detecting groups based on similar features from the available business data. One of the efficient approaches used for dividing all data into several groups with similar features is called clustering. Application of clustering to customer or item data can generate results that can help a company develop efficient marketing strategies. However, it is a non-trivial task to apply a general clustering method to a dataset with high dimensionality and sparsity owing to the difficulty of defining an appropriate similarity metric between the data samples. In this situation, one should reduce the dimensionality of data in advance. The autoencoder is a well-known model based on neural network, which can convert high-dimensional nonlinear data to low-dimensional expressions. In this research, we propose a method for clustering high-dimensional sparse data efficiently with deep learning method. The first step of our proposed method is to adopt SDAE for dimensionality reduction. The second step is to conduct a clustering analysis using the hidden layer expressions of SADE. The effectiveness and the usability of the proposed method are clarified through its application on real purchase history data.
Computational Simulation on Facial Expressions Using Finite Element Analysis

Abstract The purposes of this research are to identify mechanical properties of silicone rubber used as artificial skin and perform computational simulation on facial expressions using Finite Element Analysis (FEA). The tensile test was carried out to find mechanical properties of silicone rubber. Calculations on facial expressions were carried out using the mechanical properties of artificial skin of silicone rubber identified by the tensile test.
Opinion Analysis of Software Developers Working Onsite on Public Sector Software Projects: An Exploratory Study

Among critical stakeholders of software projects developed in public institutions are software developers and project managers affiliated to sub-contractor companies, who are working onsite at the public institutions. The aim of this study is to determine the effect of working onsite in public software projects for software developers and managers with respect to selected factors. The research is designed as an exploratory and qualitative study. The study participants were 10 software developers, one technical team leader and four project managers, all having long experiences in working on public software projects on client premises. Semi-structured interview technique is employed as the data collection method and “ analysis” was applied on the obtained participant responses. Following this analysis, “key ” approach was used to identify the issues to be further considered. It has been determined that the productivity of software developers working onsite at public institutions is low due to intense requests, shortened time to fulfill these requests, and long duration of authorization processes. Moreover, a finding of the study is that the quality of the developed projects and the motivation of the developers are considered to be low for the same aforementioned reasons. Among the foremost causes for these results are the fact that employees work under two different hierarchical structures one in the public institution and one in the software company, more than one managers assign work and there is an authority confusion between organizational units and people in the public institutions.
In Search of a Workable Auxiliary Condition for Authority Arguments

OneAuxiliary condition of the methods of informal logic is the method of argument schemesArgument scheme / argumentative scheme and one of the most studied of the schemes is the one for Argument from Authority, also called Appeal to Expertise.
Dimensionality Reduction for Insect Bites Pattern Recognition

Dimensionality reductionKhan, Abdul Rehman has been widely developed for machine learning and if we consider features extracted from images, approach with which the data needs to be processed should be unambiguous. In this paper we will beRakesh, Nitin discussing some of the dimensionality reductionMatam, Rakesh techniques on dataset, which could be used for training any classification model to recognize which class the image belongs and provide mathematical acumen behind them.
The Mechanics of Fear. Re-envisioning Anxiety through Emerging Technologies of Affect/Funktionsweisen der Furcht. Neue Konzepte von Angst im Kontext der emotionalen Robotik

While there has been ample research in the fields of psychology and human- robot interaction on humans’ fear toward robots, advancements in artificial emotional intelligence and emotional robotics suggest the need to investigate not only the mechanisms by which humans in different social contexts de- velop anxiety toward robots but also the mechanics by which robots might develop anxiety themselves. Based on a year of ethnographic research among engineers in Japan modeling emotion in robots, this article contends that exploring how robots are both designed and imagined to sense anxiety reveals productive feedback loops between entertainment robotics, artificial intelligence research, and an evolving psychology of human-robot relations in Japan. By outlining some of the social, scientific, and political implications of these feedback loops, the essay aims to help answer the question of how recent technological strategies to realize robots that not only read, perform, but also in a philosophical and social sense have emotion are reshaping the designs of psychological research on fear and anxiety in contemporary Japan.
Distributed Training for Multilingual Combined Tokenizer using Deep Learning Model and Simple Communication Protocol

In the big data era, text processing tends to be harder as the data increase. There is also the growth of deep learning model for solving natural language processing tasks without a need for hand-crafted rules. In this research, we provide two big solutions in the area of text preprocessing and distributed training for any neural-based model. We try to solve the most common text preprocessing which are word and sentence tokenization. Our proposed combined tokenizer is compared by using a single language model and multilanguage model. We also provide a simple communication using MQTT protocol to help the training distribution.
A Framework for Distributed Data Processing

Nowadays, the data generated in the telecommunications networks tend to grow exponentially leading to a Big Data challenges, which makes it necessary to discover different ways to safely process this data. The reported strategies aim to provide reliable and flexible services for asynchronous data exchange. The parallel and distributed processing of large volumes of data plays a fundamental role in scenarios that require a response as soon as possible, such as detecting fraud in telecommunications services or carrying out security controls. In this paper, we present a strategy that allows to distribute data and manage several instances of the same application, which are executed in a distributed way. An aspect to be highlighted is that heterogeneity is not required in the computational units, that is, both conventional PCs and blade clusters can participate. Another important advantages of this tool are its flexibility and its adaptability. The data are distributed depending on the workload of the different application instances. Finally, a case study is presented for the distributed processing of the Windows Operating System logs.
How User Ratings Change with Time: Theoretical Explanation of an Empirical Formula

In many application areas, it is important to predict the user’s reaction to new products. In general, this reaction changes with time. Empirical analysis of this dependence has shown that it can be reasonably accurately described by a power law. In this paper, we provide a theoretical explanation for this empirical formula.
Comparative analysis of results of the pedagogical experiment on creative imagination development in speech impaired preschool children

The relevance of the research is due to the fact that one of the most important mental processes in preschool children is creative imagination. Thanks to creative imagination, the child can find new ways to solve arising problems. The article reflects results of the ascertaining and control experiments and presents statistical data.
Clustering Methodology in Mixed Data Sets

One of the most challenging tasks of data analysis is finding clusters in mixed data sets, as they have numerical and categorical variables, and lack a labeled variable to serve as a guide. These clusters could serve to summarize all the variables of a data set into one and be able to find information more easily than generating summarizations for each variable. In this research thesis, a methodology of clustering on mixed data sets is proposed, which yields better results than the methods applied in the state of the art.
On the 29th Annual Meeting of the Japanese Society for Environmental Education (Tokyo):

The 29th Annual Meeting of the Japanese Society for Environmental Education was held from August 24-26, 2018 at Tokyo Gakugei University. The year 2018 was two years before commemorating the 30th anniversary of the founding of the Society.
Comparative Evaluation of Training Schemes for the Locally Recurrent Probabilistic Neural Network

In the present study we evaluate the performance of various training schemes for the locally recurrent probabilistic neural network and seek for advantageous tradeoffs between required training time and classification accuracy. Specifically, we consider training schemes which make use of a simple incremental procedure for adjusting sigma, as well as methods based on particle swarm optimization or differential evolution in different configurations. The experimental evaluation was carried out in common experimental protocol based on the Parkinson speech dataset. The experimental results show that with a proper training configuration a high accuracy can be achieved even with limited training data.
Risk Management of Flood Disaster (A Case Study of Kendal River)

Abstract Kendal River, which runs through the city of Kendal, often overflows causing flood for the immediate areas. This study uses the concept of risk management aimed at relevant stakeholders. Data analysis of this study applied the Risk Breakdown Structure (RBS) method to describe each classification of risk sources to risk sub-classification. The results depicted that there were 20 (twenty) risks of causing flooding in Kendal City, with 5 (five) risks being the main cause. According to the Central Java Provincial Government, there was a 35% risk at the extreme level, 60% at the high level, and 5% at the middle level. On the scale of risk acceptance, there were 35% classified as unacceptable, and 65% classified as unexpected. According to the Kendal District Government, there was a 60% risk at extreme levels and 40% at high levels. On the risk acceptance scale, there were 60% classified as unacceptable and 40% classified as unexpected. The results of this study expected to provide a guideline for stakeholders in risk response according to their role both at the Provincial Government and District Government levels based on priority scales.
Answering Why-Questions Using Probabilistic Logic Programming

We present a novel architecture of a closed domain question answering system that learns to answer why-questions from a small number of example interpretations. We use a probabilistic logic programming framework that can learn probabilities for rules from positive and negative example interpretations. These rules are then used by a meta-interpreter to generate an explanation in the form of a proof for a why-question. The explanation is displayed as an answer to the question together with a probability. In certain contexts, follow-up questions can be asked that conditionally depend on these why-questions and have an effect on the probability of the subsequent answer. The presented approach is a contribution to explainable artificial intelligence that aims to take machine learning out of the black-box.
A Note on "Enhancing cloud storage security against a new replay attack with an efficient public auditing scheme"

With the rapid development of cloud computing, more and more people choose to store their data on the cloud. To solve the problem of cloud data integrity, a wide variety of auditing schemes have been put forward. However, all of the above auditing schemes cannot be against the new replay attack. In order to solve this problem, Fan et al. proposed an efficient public auditing scheme to enhance cloud storage security against a new replay attack. In this note, we prove that the auditing scheme in the above-mentioned paper is not secure. We give the analysis process to show the malicious cloud can pass the auditor's verification even if it has deleted or modified user's data.
Analysis of Combination Knowledge Acquisition of Haar Training for Object Detection on the Viola Jones Method

Viola Jones method uses the file classifier to object detection. The training process to create object classifier file requires very high computer resources and time which is directly proportional to the amount of training data. The amount of training data determines the accuracy of object detection. The long training process is caused because the computer has low specifications and the distribution of Haartraining files will speed up the process of vector file formation, minimize errors when cutting Haar features on positive objects and also minimize errors that occur during the training process. The problems that arise next are how to overcome this so that a better knowledge is obtained. This study provides analysis results of the process of merging knowledge acquisition and its effect on the accuracy of object detection using the Viola-Jones method with the final result undetected object decrease 52.62% and object detected increase 23.78 %
Machine Interpretable Mathematical Model Descriptions

Slides from my presentation at: The 2007 Tohoku University Global COE Program Global Nano-Biomedical Engineering Education and Research Network Centre, the Department of Bioengineering, National University of Singapore
Camshift Tracking Method Based on Correlation Probability Graph for Model Pig

The identification and tracking for model pigs, as a vital research content for studying the habits of model pigs, drawed more and more considerable attention. To fulfill people requirements for effectiveness of the non-significant model pig tracking in breeding environment, a Camshift tracking approach based on correlation probability graph, i.e., CamTracor-PG, is proposed in this paper, in which the correlation probability graph is introduced to achieve target positioning and tracking. Technically, according to the circular arrangement of pixels in the inverse probability projection graph, multiplying the inverse projection probability value of a pixel by its surrounding pixels, which could obtain the weighted sum. Then, the target projection grayscale graph is established by utilizing the correlation probability value for positioning, identification and tracking of model pigs. Finally, extensive experiments are conducted to validate reliability and efficiency of our approach.