Reviewerly/exHarmony huggingface dataset:
APPLICATION OF DIGITAL ELEVATION METHOD ( DEM ) FOR FLOOD ESTIMATION ON UPSTREAM CILIWUNG RIVER, WEST JAVA, INDONESIA

Flood generally caused by the high speed of urban growth that is not balanced by efforts to avoid it. The decrease of vegetation closing and the disappearing of the natural reservoir can cause the lack of field ability to restrain rainfall and disturb surface stream flow that influences the balance of hydrology along the river basin. Hydrology models tended to be spatially distributed. By computer support with higher memory capability and faster speed, the increase of development and usage of spatially distributed models substitute lump parametric models or aggregate to spatially simple. Furthermore, there are many efforts of integration forms, either database or hydrology models with Geographic Information System (GIS). The approach of the integration of spatially distributed hydrology model and GIS is used to simulate the hydrology process interactively with the basis of spatiotemporal, with a variety of land use, rainfall, and detention reservoir volume changes (embuing/ small dam). The DEM which emerges land surface morphology information is used to represent hydrology process of surface runoff. Topography structure extraction algorithm will be the slope and the current direction is an important factor in describing drainage network. The elevation numbers and its descendants are depicted in grid cells in 50 m x 50 m measurement. The result shows such as in flood hydrograph which is varied base on land use, design rainfall and detention reservoir (embung/ small dam).
Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Aim: The aim of this study is to find relational connections (interdependence) between the two most general categorical aspects of a neuron, i.e., between the form (morphology) and its function, using as a model for this task dentate nucleus neurons. Furthermore, the configuration of the dentatostriate nucleotopic inter-cluster mapping of the dentatostriate neural network is investigated in order to determine mutual, inter-neuronal, neuromorphofunctional remote influence, i.e. the neuromorphofunctional relations at the level of a neural network.Materials and methods: (Semi) virtual dentate and neostriate adult human neuronal samples were used. Neuromorphological parameters of each neuron have been directly measured, i.e. experimentally determined, whereas the corresponding neurofunctional parameters have been theoretically obtained. The neuromorphological parameters determine the following properties of a neuron: neuron shape, compartmental length and size/ surface, dendritic branching, complexity and organization of neuronal morphology. The group of neurofunctional parameters determines functional aspects of action potential (AV/AP), as well as neurofunctional properties of the perikaryodendritic compartment of a neuron. Data analysis is performed using response surface (RSM) modeling, along with partial least-squares (PLSR) and principal component regression analysis (PCR), accompanied by canonical and Pearson correlation analysis. A stepwise algorithm formulates the complete data analysis.Results: Obtained RSM models represent response-predictor relations, where a neuromorphological/functional response parameter is expressed as a function in terms of parameters of other category (morphology/function). Additionally, RSM modeling is also used to decipher the symmetry of the dentatostriate inter-cluster neural network by the corresponding inter-cluster inter-nuclear mapping, using so-called integral parameters/variables, obtained on a computational, theoretical manner. The obtained network is a fully connected, symmetric, Hopfield neural network.Conclusion: Neuronal morphology and function are definitely interrelated and depend on each other. By intensity, however, this interconnectedness can be treated as mild to moderate. It is determined by elementary neuromorphofunctional relations, observed at the macroscopic, phenomenological level, i.e. only through measured parameters as their observable and explicit manifestation without considering the microscopic, molecular causality of them. These relations are the strongest when acting upon a single neuron and their mutual remote influence on each other weakens in neural circuits and networks up to 10% of deterministic relational interconnection strength observed at the level of single neuron relations.
Potential Application of Virtual Reality for Interface Customisation (and Pre-training) of Amputee Patients as Preparation for Prosthetic Use

Virtual Reality has been used to great effect in the field of retraining and strengthening neural pathways in victims of serious brain injury and stroke. Meanwhile, VR visualisation of missing limbs in amputees has been used to great effect not only in the treatment of “phantom limb syndrome” but in helping amputees restore muscle tone in remaining limb sections and torso prior to fitting these areas for prosthetics. The natural next step, combining elements of both approaches, is the potential application of virtual reality to actively train the patient for using these prostheses prior to them being fitted, and furthermore adjusting and customising the prosthetic itself to the emergent needs of the patient whilst using the VR training. This raises fascinating new applications not only for virtual reality itself, but for the numerous peripheral technologies which have risen around VR. These technologies include force feedback, “haptic” sensory simulation and monitoring of muscle strength, position and movement ranges. This chapter aims to assess the capabilities of these technologies, both now and in the future. By reviewing the work of two key studies in this area this chapter aims to bring together the necessary skills and establish the collaborative crossovers (and existing precedents) which would be required to develop this application of VR in the future.
Fake Product Review Monitoring &amp; Removal and Sentiment Analysis of Genuine Reviews

Any E-Commerce website gets bad reputation if they sell a product which has bad review, the user blames the e-Commerce website rather than manufacturers most of the times. In some review sites some great audits are included by the item organization individuals itself so as to make so as to deliver false positive item reviews. To eliminate these type of fake product review, we will create a system that finds out the fake reviews and eliminates all the fake reviews by using machine learning. We also remove the reviews that are flood by a marketing agency in order to boost up the ratings of a particular product. Finally Sentiment analysis is done for the genuine reviews to classify them into positive and negative. We will use Bag-of-words to label individual words according to their sentiment.
Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System

One of the most important points to improve the profits in an industrial process lies on the fact of achieving a good optimisation and applying a smart maintenance plan. Under this circumstances an early anomaly plays an important role. Then, the implementation of classifiers for anomaly detection is an important challenge. As many of the anomalies that can occur in a plant have an unknown behaviour, it is necessary to generate artificial outliers to check these classifiers. This work presents different one-class intelligent techniques to perform anomaly detection in an industrial facility, used to obtain the main material for wind generator blades production. Furthermore, artificial anomaly data are generated to check the performance of each technique. The final results achieved are successful in general terms.
Fully Asymmetric Remote Collaboration System

In this paper, we investigate the requirements for fully asymmetric remote collaboration and present a prototype system. Unlike previously studied asymmetric remote collaboration systems, fully asymmetric remote collaboration handles the cases, where a user of one side is a complete novice having difficulty in explaining his/her problems and unfamiliar with a collaboration system, and thus the remote expert of the other side must locate the local user's problem and teach the local user by having full control of the remote collaboration system. These cases can frequently occur when we collaborate with elderly people. For a fully asymmetric remote collaboration system, there are some requirements which are a little bit different from those of an asymmetric one. We itemize them in the three phases of collaboration: identifying a problem, finding a solution, and showing the solution to a local user. We also design and implement a prototype of a fully asymmetric collaboration system to meet the requirements. Our system consists of a robot with cameras and a projector for a local user and an HMD (head mounted display) with a controller for a remote expert. We also design an efficient HMD-based interaction method for fully asymmetric remote collaboration with our system. We show the efficiency of our prototype system in a fully asymmetric remote collaboration situation by user evaluation.
Shuffled Complex Evolution based Quantum Particle Swarm Optimization algorithm for mechanical design optimization problems

This paper presents a hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviate as SP-QPSO for solving mechanical constrained design optimization problems. Quantum Particle Swarm Optimization (QPSO) is a variant of standard Particle Swarm Optimization (PSO) algorithm and enhances the local search ability of the algorithm over feasible space. Although QPSO is easier to implement, more intelligent and efficient than the standard PSO, but due to its strategy, the efficiency of the QPSO algorithm's performance in solving complex and high-dimensional problems has decreased. Normalization of constraints is crucial for the efficient permanence of the algorithm. A growing number of researchers have proposed different strategies for handling constraints. For solving constrained optimization problems, Adaptive constraint-handling techniques are extremely promising for solving constrained optimization problems as they use population details during the search to adjust different penalty parameters for each constraint without tuning any type of user defined penalty parameter. In this paper, a novel parameter free adaptive penalty is employed to handle constraints in the search region. The efficiency of SP-QPSO algorithm is numerically investigated using five engineering design problems which have different natures of objective functions, constraints and decision variables with up to six variables and six constraints. The experimental results are analyzed in comparison with those reported in the literature. The computational results show that the proposed algorithm can provide better solutions in terms of efficiency and computational time than traditional PSO and other optimization algorithms.
Semi-supervised Clustering with Deep Metric Learning

Semi-supervised clustering has attracted lots of reserach interest due to its broad applications, and many methods have been presented. However there is still much space for improvement, (1) How to learn more discriminative feature representations to assist the traditional clustering methods; (2) How to make use of both the labeled and unlabelled data simultaneously and effectively during the process of clustering. To address these issues, we propose a novel semi-supervised clustering based on deep metric learning, namely SSCDML. By leveraging deep metric learning and semi-supervised learning effectively in a novel way, SSCDML dynamically update the unlabelled to labeled data through the limited labeled samples and obtain more meaningful data features, which make the classifier model more robust and the clustering results more accurate. Experimental results on Mnist, YaleB, and 20 Newsgroups databases demonstrate the high effectiveness of our proposed approach.
Super-Resolution of Face Images Using Weighted Elastic Net Constrained Sparse Representation

High-resolution (HR) face images are usually preferred in many computer vision tasks. However, low-resolution (LR) face images, which are often obtained in real scenarios, can be converted to a high resolution with the super-resolution techniques. In this paper, we propose the weighted elastic net constrained sparse representation (WENSR) super resolution method for face images. The method considers image gradient and weighted elastic net penalties. Due to the high similarity between human faces, it is not very suitable to only use ℓ1-norm in the sparse representation model. The elastic net has a grouping effect and is more suitable for real-world data. The gradient is very important information in the image, we also use image gradient to enhance the final output. The tests of our method on both synthetic data and real-world data, such as FEI, CAS-PEAL-R1, and CMU+MIT face image dataset suggest a competitive performance gain in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM).
The contribution of the reading habit, vocabulary mastery, and grammar to the experience writing skill of 5th grade students of state elementary schools

This study aims to find out the contribution of reading habit, vocabulary mastery, and grammar mastery to the experience writing skill of the 5th grade students of state elementary schools in Bambanglipuro Subdistrict, Bantul, Yogyakarta. This study is an ex-post facto using the correlational quantitative research approach. The population comprised all 5th grade students of state elementary schools in Bambanglipuro Subdistrict, Bantul, Yogyakarta totaling 300 students. A sample of 169 students was established using the proportional random sampling technique. The instruments consisted of a test and questionnaire. The test was used to measure the vocabulary mastery, grammar mastery, and experience writing skill. The questionnaire was used to measure reading habit. The data were analyzed using the multiple regression analysis. The results show that there is a significant and positive contribution among reading habit, vocabulary mastery, and grammar mastery to the experience writing skill.
New Challenges in Display-Saturated Environments

We live in a world in which our physical spaces are becoming increasingly enriched with computing technology. Pervasive displays have been at the forefront of this progression and are now commonplace. In this paper, we focus on the natural end-point of this trend and consider the case when displays become truly ubiquitous and saturate our physical environments. We use as motivation a state-of-the-art display deployment in which mobile users navigating the space are simultaneously exposed to many hundreds of displays within their field of view and we highlight a number of new research challenges.
Linking Health Records with Knowledge Sources Using OWL and RDF.

This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources.
Environmental Benefits of Enhanced Hecc- Elgamal Cryptosystem for Security in Cloud Data Storage Using Soft Computing Techniques

Cloud services is used by many organizations and it has captured a major segment of the competitive market today. The green, or eco-friendly, aspect of the cloud is one of the most multifaceted advantages of cloud computing. The environmental advantages of cloud services include: reducing a firm’s carbon footprint, data center efficiency, dematerialization, saving green, educed electricity use and so on. Even with its unprecedented growth, the question of security is also of paramount concern among the users of cloud services. There is a huge demand for new protocols and tools in order to enhance and assess the security strength of its service. Notwithstanding the present methods used for encrypting the files in cloud they are not highly efficient. Hence this enhanced technique is proposed to overcome all these challenges and improve the environmental benefits. In this method, initially the authentication of the user is verified. Once the authentication of the user is verified successfully dual encryption is performed on the cloud stored files using ElGamal cryptosystem and Hyper Elliptical Curve Cryptography (HECC). The aim for using the proposed system is for analysis of security which can be enhanced through the technique of sharing many keys amongst the two parties. Integer selection is an important attribute which defines the proper security to the cloud storage. For ensuring high security, this integer selection is performed by utilizing BAT algorithm. After the encryption, the suggested technique uses the HECC algorithm. In HECC, key generation is done by point addition and point doubling based elliptic curve cryptography. The dual encryption in this method provides efficient security to the cloud data. The proposed technique performance is evaluated in reference with environmental protection, storage cost, computation cost and execution time and is implemented in JAVA. The experimental results show the efficacy of the system as it utilizes only less time for both encryption and decryption of sensitive data.
A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.
Pelatihan Pembuatan Media Pembelajaran Matematika Berbasis Flash Menggunakan Adobe Animate bagi Guru SMP di Kabupaten Kuningan

TRAINING FOR MAKING FLASH-BASED MATHEMATICS LEARNING MEDIA USING ANIMATE ADOBE FOR SECONDARY SCHOOL TEACHERS IN KUNINGAN DISTRICT. The purpose of the community partnership program for MGMP teachers at the Kuningan Cluster and Luragung Clusters in Kuningan Regency is to improve teacher competency in creating, developing and implementing flash-based mathematics learning media using Adobe Animate. The problem faced by partners is in implementing mathematics learning in schools that are still dominated through the use of textbooks, so that the teacher’s ability to demonstrate mathematical concepts in learning is still lacking. This was allegedly due to the limited ability of teachers to use learning media, especially ICT-based media. Mathematical MGMP SMP Kuningan cluster and Luragung cluster in Kuningan Regency have a sufficient number of teacher members, but in terms of competence still needs improvement. Understanding of computer-based learning is still very minimal because mathematics teachers have not received specific training on ICT-based mathematics learning. To overcome this condition an activity was packaged in the form of training on the development of flash-based mathematics learning devices using adobe animate for the mathematics MGMP of the Brass Cluster and Luragung Group. This activity was carried out through the following stages: 1) the provision of flash-based mathematics learning and development workshops using adobe animate; 2) mentoring in making flash-based mathematics learning devices using adobe animate; 3) mentoring implementation of flash-based mathematics learning devices using adobe animate; and 4) evaluation and replication.
Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.
Emergent Escape-based Flocking behavior using Multi-Agent Reinforcement Learning

In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we propose SELFish (Swarm Emergent Learning Fish), an approach with multiple autonomous agents which can freely move in a continuous space with the objective to avoid being caught by a present predator. The predator has the property that it might get distracted by multiple possible preys in its vicinity. We show that this property in interaction with self-interested agents which are trained with reinforcement learning to solely survive as long as possible leads to flocking behavior similar to Boids, a common simulation for flocking behavior. Furthermore we present interesting insights in the swarming behavior and in the process of agents being caught in our modeled environment.
Design and Development of Real-Time E-Voting System with High Security Features

In this day and age of developing cutting edge innovations, the traditional voting technique can be changed to a more up to date and powerful approach termed as electronic voting system.The electronic voting system gives a helpful, simple and proficient approach to cast a ballot eliminating the shortcomings of traditional approach.The point of this research work is to exhibit an electronic voting system (E-Voting) to be connected to organization constituent body.A software application was developed utilizing web API's and the concept of Dynamic systems development method (DSDM) used with object-oriented methodology for the development of the application.