Reviewerly/exHarmony huggingface dataset:
Spatial Aggregation of Small-Scale Photovoltaic Generation Using Voronoi Decomposition

In this article, a methodology based in Voronoi decomposition is proposed to spatially aggregate small-scale solar generation. The locations of relevant electrical infrastructure are used to manage the uncertainty on locating solar photovoltaic installations. The known coordinates of step-down high- to medium-voltage electrical substations (i.e. bulk supply points) are used to divide the territory and find multiple representative locations where solar resource can be assessed. Modelling solar photovoltaic generation from global solar radiation observations permits the estimation of power output and degradation factor due to age for the entire small-scale photovoltaic fleet. The results are validated against multiple solar installations across the region of study (Northern Ireland, UK) and show a relatively low root mean square error with monthly values ranging from 0.036 to 0.123 kW/kWp. The proposed method is scalable to larger and smaller geographical areas and transferable to other categories of solar photovoltaic installations. This methodology can serve as a basis for multiple applications, such as solar generation forecasting. System operators could utilise this method to improve knowledge of when, where and in what amount additional resources would be required to manage solar penetration in favour of a robust, low-carbon and efficient power network.
Representation of Words Over Vectors in Recurrent Convolutional Attention Architecture for Sentiment Analysis

Subjectivity in the text concerns to sentiment analysis, which entails Natural language processing (NLP) techniques to detain the casual way of communication. Many NLP undertakings used to capture the sentimental contextual information through distributed representations. Neural networks (NNs) like recurrent and convolutional neural networks have productively attained the remarkable outcomes in text classification. However, few of architectures learning through vector representation of words in the recurrent network for capturing long-term dependencies while in the convolutional for feature extraction along with pooling layer; a max-pooling task for dimensional reduction has been proposed, still, unable to seizure enough syntactic and semantic regularities for sentiment analysis. This paper empowers sentiment analysis by exploring unsupervised learning of vectors representation such as Word2Vec and GloVe, then acquired word vectors is input to the proposed architecture which is composed of RNN and CNN engaging with attention-mechanism referred to Recurrent Convolutional Attention Architecture “RCAA.” Experimentations show that through unsupervised learning of representation of words into qualified vectors in interpreting the similarities related to syntactic and semantic context and the sentiments at adequate computational cost along with the combination neural architecture by succeeding accuracy on word2vec by 83.62%, GloVe by 85.72% as compared with Random initialization by 79.97% on rotten tomatoes test dataset respectively.
CNN-Based Crowd Counting Through IoT: Application For Saudi Public Places

Crowd counting in specific places has recently been considered as a significant contribution in many applications in terms of security and economic values. Recently, the Kingdom of Saudi Arabia has considered new ways and methods to diversify sources of income, where many non-traditional establishments in several fields have been initiated and put in place. However, controlling the number of visitors and participants to events and exhibitions has always been a challenge, as it has always been considered as an important success factor to any event. Smart public places approach is one of the inevitable directions of development in Saudi Arabia, where security, comfort, and safety of crowds is to be controlled and managed using machine learning techniques, more specifically, IoT-based crowd counting techniques. Such a technology will not only help in resolving security and safety problems, but also will play a significant role in reducing waiting time for visitors, by giving indicators, projections and advices on crowded places. In this paper, a mobile-based model is proposed for counting people in high and low crowded public places in Saudi Arabia under various scene conditions with no prior knowledge. The proposed model is built based on pre-trained convolutional neural network (CNN) called VGG-16 with some modifications on the last layer of the CNN to increase the efficiency of the training model. In addition to the improvement of efficiency, the proposed method accepts images of arbitrary sizes/scales as inputs. The applicability of the proposed method has been evaluated by incorporating IoT architecture, where surveillance cameras to be connected to the Internet to capture live pictures of different public places. To achieve this goal, New and special Saudi people dataset as well as some other existing dataset, have been produced and used to train the network. The result shows a significant improvement to the efficiency of the DCNN over the existing counting networks.
A flexible 3D point reconstruction with homologous laser point array and monocular vision

A 3D recovery approach is achieved by the monocular vision sensor and the homologous laser point array with the arbitrary relative pose to the 2D reference. In the modeling process, firstly, the 3D texture board is moved to different positions in the view field of the vision sensor. Therefore, the laser rays intersect with the cubic board and generate laser points on it. Then, the Plücker matrices of the laser rays are determined by the laser points. The algebraic solution of the joint point of laser rays, the optic center of the projector, is contributed by the singular value decomposition. Thirdly, the laser rays are refined by the algebraic solution of the joint point and represented in the 2D-reference coordinate frame. Finally, the reconstruction is achieved by the calibrated laser rays of the projector. The reconstruction method is experimentally conducted to verify the performance and precision in various measurement conditions. The reconstruction error averages are 0.942 mm, 1.055 mm, 1.374 mm, 2.063 mm, under the 700 mm–1000 mm measuring distances between the vision sensor and the optic center. As there is no strict requirement for the relative installation position and orientation between the laser-ray projector and the 2D reference, the approach prompts the flexibility for the on-site active-vision measurement.
Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections

In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection G of t sequences Si of length n each, we can represent G succinctly in 2nHk(T) + O(n' loglogn) + o(q n') + o(tn) bits using O(t n2 + q n') time, where Hk(T) is the kth-order empirical entropy of the sequence T ∈ G that is used as the reference sequence, n' is the total number of variations between T and the sequences in G, and q is a small fixed constant. We can restore any length len substring S[ sp, ..., sp + len-1] of S ∈ G in O(ns' + len(logn)2 / loglogn) time and report all positions where P occurs in G in O(m ·t + occ ·t ·(logn)2/loglogn ) time. In addition, we propose a dynamic programming method to find the variations between T and the sequences in G in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.
Prediction of Bitcoin Transactions Included in the Next Block

This paper proposes a method to predict transactions that are likely to be included in the next block from the mempool of unconfirmed transactions in the Bitcoin network. To implement the proposed method, we applied machine learning to the transactions data collected from the Bitcoin network and divided our implementation into the following three objects: Data Collector; Data Preprocessor; and Analyzer. We used the random forest classifier algorithm because the problem of predicting the likelihood of a transaction to be included in the next block is a binary classification problem. We evaluated the performance of our model by comparing transactions in the mempool against transaction published in the next two blocks mined at the time of our experiments. For both blocks, our model has a prediction accuracy of more than 80% and a minimal false negative error. The analysis of transaction inclusion in the next block is fundamental as it could drive the price of Bitcoin or signify the properties of a given transaction such as illegal or legal.
Creating Special Issues Automatically for Papers Accepted in Journals

Significant increases have been observed in the number of scientific publications in recent years. Every year thousands of researchers participate in academic life. Researchers share their studies and results with other researchers. This sharing is very important for academic progress. One of the most important platforms where publications reach other researchers is journals. Journals periodically publish articles. Some journals also publish special issues. These issues can be about a current topic, event or important person. In our study, a method for creating a special issue from the articles that are not published in the regular issues is proposed. In some cases, too many articles are sent to journals and these articles accumulate because all of them cannot be published in regular issues. The aim of this study is to determine the ones on similar topics and to create a special issue. The similarity of the articles to each other, i.e. the probability of being on a common topic, is found by the Doc2Vec method which is one of the deep learning methods. A special issue is created by selecting a certain number of articles with high similarity. The title of the special issue is determined from the common topic of the articles. In this way, both the authors' waiting time for publication of their articles is reduced and a special issue is created from the available articles in popular topic. The articles in this issue, belong to a current topic published by many researchers, will get more attention and be highly cited from other researchers.
Measurement of Cholesterol Levels through Eye based on Co-Occurrence Matrix on Android

Cholesterol is the most important component of cell membranes that contain fat with most of it being produced in the liver and other parts of food. In general, cholesterol can be known through blood samples carried out in the laboratory. But scientifically, cholesterol can be determined through cholesterol in the iris or called Arcus Senilis. The iris part of a person is very unique so that the patterns and structures are different so that it becomes a feature of identification on someone. Cholesterol can be determined through the thickness of the white value of the cholesterol ring so that it can produce training characteristic values. Linear regression analysis was used to obtain linear regression modeling and then from the results of the linear regression modeling that will be used to determine the value of cholesterol levels. This paper designed an Android-based application for measuring cholesterol levels from eye images. The extraction method used is Gray-Level Co-Occurrence Matrix and calculation of cholesterol levels using Linear Regression. In this research, the process of taking eye images through a smartphone camera and cholesterol consisted to the several classes including Normal Cholesterol, Risk Cholesterol, and High Cholesterol.
Security Anlysis of Certificateless Aggregate Signature Scheme in VANETs

Certificateless aggregate signature (CLASS) scheme which combines on certificateless signature and aggregation signature solves the identity-based (ID) public key infrastructure (PKI)'s key escrow problem, the PK problem of traditional PKI. So, CLASS schemes can be applied in many fields to solve the privacy problem and security problem, for example in the information network and system of medicine and biology. Also there are many CLASS schemes to be proposed for these fields. In this manuscript, we analyze the CLASS scheme for VANETs proposed in 2018 which is more efficient than other similar schemes. We find which the CLASS scheme cannot satisfy the security the following two properties, namely unforgeability and traceability as they claimed. That is to say that the attacker may forge a correct signature and it may pass the signature verification but the attacker unknows the secret key. So, the CLASS scheme is not suitable for applying in any system. As an improving, after analyzing original scheme, it is found that the key problems for being insecure and give one simple method to solve the existed drawbacks.
Digital human face detection in video sequences via a physiological signal analysis

Imagine a future in which computer-generated (CG) human characters become almost indistinguishable from real humans. It is not that far away. Real-time rendering of photorealistic facial animation is just around the corner. To prepare for this world in which CG content will be intermingled with the natural, there is a fundamental need to understand how human beings can preserve the awareness of what is real and what is not. We present a methodology for distinguishing real and CG human faces within the context of multimedia forensics, by exploiting the heart rate as the discriminating feature. The underlying idea is that humans always present a pulse signal that can be automatically extracted from a video sequence depicting their face, while this is not the case for virtual humans. Experimental validation shows the effectiveness of the proposed solution.
Open Innovation Using Satellite Imagery for Initial Site Assessment of Solar Photovoltaic Projects

One of the responses to the fight against climate change by the developing world has been the large-scale adoption of solar energy. The adoption of solar energy in countries like India is propagating mainly through the development of energy producing photovoltaic farms. The realization of solar energy producing sites involves complex decisions and processes in the selection of sites whose knowhow may not rest with all the stakeholders supporting (e.g., banks financing the project) the industry value chain. In this article, we use the region of Bangalore in India as the case study to present how open innovation using satellite imagery can provide the necessary granularity to specifically aid in an independent initial assessment of the solar photovoltaic sites. We utilize the established analytical hierarchy process over the information extracted from open satellite data to calculate an overall site suitability index. The index takes into account the topographical, climatic, and environmental factors. Our results explain how the intervention of satellite imagery-based big data analytics can help in buying the confidence of investors in the solar industry value chain. Our study also demonstrates that open innovation using satellites can act as a platform for social product development.
Applying Learning Analytics to Assess Learning Effect by Using Mobile Learning Support System in U-Learning Environment

This research proposed a mobile learning support system based on U-learning environment and aimed to design an effective Learning Analytics(LA) model and the essential analytics indicators, so as to apply this LA model to assess learning effect by using mobile learning support system and Augment Reality(AR) learning APP in U-learning environment. The research team launched the first experimental learning for a training program for a total of 408 teachers, who signed up for the learning on the mobile learning support system and AR learning APP in U-learning environment; the paper obtained multi-dimensional data from the mobile learning support system and carried out in-depth learning analytics for the behavioral data of learners in three dimensions of access behavior, interactive behavior and learning effect. From the data analysis and evaluation, the designed learning analytics model and analytics indicators can represent the behavioral characteristics of learners in U-learning environment and provide a relatively systematic and comprehensive data analytics; and the developed mobile learning support system is recognized to be effective for efficient ubiquitous learning.
Semi-automatic construction of word-formation networks

Abstract The article presents a semi-automatic method for the construction of word-formation networks focusing particularly on derivation. The proposed approach applies a sequential pattern mining technique to construct useful morphological features in an unsupervised manner. The features take the form of regular expressions and later they are used to feed a machine-learned ranking model. The network is constructed by applying the learned model to sort the lists of possible base words and selecting the most probable ones. This approach, besides relatively small training set and a lexicon, does not require any additional language resources such as a list of vowel and consonant alternations, part-of-speech tags etc. The proposed approach is evaluated on lexeme sets of four languages, namely Polish, Spanish, Czech, and French. The conducted experiments demonstrate the ability of the proposed method to construct linguistically adequate word-formation networks from small training sets. Furthermore, the performed feasibility study shows that the method can further benefit from the interaction with a human language expert within the active learning framework.
The archived-based genetic programming for optimal design of linear/non-linear controllers

Evaluation of control signal function is one of the critical subjects in the optimal control problems. The optimal control is usually obtained by optimizing a performance index that is a weighted combination of control effort and state trajectories in the quadratic form, typically known as quadratic performance index (QPI). For the simple case of linear time-invariant (LTI) systems, problems are commonly solved using the well-established governing Riccati equation; however, obtaining the analytical solutions for linear time-variant (LTV) and nonlinear systems has always been highly debated in the optimal control problems. In this study, a newly developed type of Genetic Programming called the archived-based genetic programming (AGP) is presented. Using this algorithm, the analytical solutions for any type of optimal control problems can be obtained faster and more efficiently than the ordinary GPs. Subsequently, due to the inefficiency of QPI in capturing the general behavior of signals, a new performance index named the absolute performance index (API) is proposed in this study. Since the developed AGP algorithm could find the analytical solutions irrespective of the conventional mathematical calculations, it can be effectively implemented to solve the introduced API measures. According to the analytical results, it is observed that in a given problem, the solutions of API are more compatible with the design goals compared with QPI. Furthermore, it is shown that some new forms of the control signals such as impulse solutions, which may not be obtained using QPI, can only be estimated using API in defining the optimal control problems.
Hiper Parametre Optimizasyonu Hyper Parameter Optimization

The optimization of hyper parameters, which are the parameters that must be entered when designing machine learning models, has a positive effect on the performance of machine learning models as it reduces the operating cost. There are four methods (grid search, random search, Bayesian, and evolutionary algorithms) often used in the literature in this field. Within the scope of the study, these methods were discussed and their advantages and disadvantages were determined. When we look at the studies, it is noted that a hyper-parameter Analysis section, in which hyper-parameter values are selected at certain intervals and the connection between these values is analyzed, should be in all studies.
Deep Learning of Biomechanical Dynamics in Mobile Daily Activity and Fall Risk Monitoring

Smart health is paving a promising way for modern health management. Daily activity and fall risk monitoring is one important application that urges smart technologies, resulting from the fact that there are 29 million falls and 7 million fall injuries per year, and also the fact that appropriate exercise can lower the risk of death by up to 20 to 70%. However, it is very challenging to accurately identify an activity due to the diversity of the human biomechanical dynamics. Main reasons include: even a same person usually has different motion characteristics when performing a same activity; there are many different activities in our daily lives; and the sensor wearing habit may be different. In this paper, focusing on these challenges, a new intelligent computational approach is proposed for robust activity detection, leveraging biomechanical dynamics enhancement and deep learning technologies. It can unveil deep hidden biomechanical patterns from the mobile phone-sensed motion data, and robustly detect 17 types of daily and fall activities performed by 30 people. The detection accuracy of 11,770 activities is as high as 93.9%, indicating the effectiveness of the proposed approach. This research is expected to greatly advance mobile daily activity and fall risk monitoring in smart health era.
Intention Classification Based on Transfer Learning: A Case Study on Insurance Data

With the rapid development of Artificial Intelligence and Big Data technology, intelligent chatbot in insurance industry has become the major technical means to reduce labor costs and improve the quality of service. The core technology of this application is to understand and classify the users' intentions accurately. However, insurance as a product with complex knowledge system and long service cycle, users' intentions and the corresponding corpus is rather scattered. The initial corpus is especially scarce at the early stage of new business. So it is very important to classify the customers' intentions accurately based on the rare corpus. This paper offers an empirical case study on intention classification of insurance data by using transfer learning model BERT. The experimental comparative analysis result shows that method based on BERT model can better reduce the error rate than other existing model methods (TextCNN, HAN, ELMo).