Reviewerly/exHarmony huggingface dataset:
An Effective Tile Caching Mechanism of UAV Remote Sensing Map Based on Hilbert Coding Index

The Unmanned Aerial Vehicle (UAV) remote sensing system has become one of the most important tools for the acquisition of modern remote sensing data. The map generated by UAV remote sensing images is expected to be widely used in resource monitoring, post-disaster reconstruction and so on. Tile cache is an important way to speed up real-time loading of tile map. In order to accelerate the tile loading of UAV map, we take advantage of data locality principle and user behavior to build Hilbert coding index for each level in the tile pyramid. Then, we propose a neighborhood prefetching cache strategy, which is analyzed from the two dimensions of width and depth on the basis of Hilbert coding index. Compared with a simple strategy taking single tile as cache granularity, this caching strategy improves cache hit ratio and reduces average response time of tiles significantly. Meanwhile, Redis as the cache database, which not only supports the persistence of memory data, but also has the advantages of high development efficiency and strong scalability.
Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means

Maintain stock of goods so that there are no empty items, including one way to maintain customer satisfaction. To fulfill this, the seller must be able to analyze which data items are selling and which are not selling from the sales report data, this is not easy if the store is a retail store that has hundreds or even thousands of sales data every month. These problems can be solved by using one of the techniques in data mining namely algotihma K-MeansClustering.This research is intended to help Genta Corp which is a retail store in the city of Bogor that sells outdoor equipment, to make a grouping of sales data in order to maximize stock management.Variables used are item code, item data, item data and item stock.The data is processed by manual calculation using the K-Means algorithm and using the Rapid Miner Software so that the final result is in the form of three clusters where there are 2 types of bestselling goods, 8 types of goods which are quite in demand and 18 types of goods that are not in demand.This result can be utilized by the management of Genta Corp to improve stock management and sales strategies.
Comparison of Wavelet, Stationary Wavelet and Wavelet Packet Methods for De-noising of Partial Discharge on Power Cable System

The failure of Power Cable systems is mainly attributed to Partial Discharge activity in power Cable insulation and accessories such as terminations and joints. Different types of sensors are available for online and offline monitoring of Partial discharge activity. However the main difficulty involved in the Partial discharge measurement of Power Cable system at site is to eliminate the interference from other external and internal high frequency noisy signals. In this paper, de noising the PD signals by the wavelet transforms, stationary wavelet and wavelet packets techniques are compared for its merits and demerits. The method used for selection of mother wavelet is based on minimum entropy criteria. The two types of mother wavelet debauchees and symlet are used. The effectiveness of level dependent hard thresholding method in retrieving the original partial discharge signals over the other conventional thresholding methods is also analyzed. The laboratory measured Partial discharge signals are mixed with white Gaussian noise and discrete spectral interference (DSI) signals in such a way that the partial discharge signals are completely immersed in the simulated noises. The partial discharge signals are measured for power cables system with Improper Stress control tubing termination.
Peningkatan Hasil Dan Keaktifan Belajar Siswa Melalui NHT

Inappropriate learning processes can lead to low learning results and student inactivity. This research (PTK) aims to improve learning outcomes and activeness of students in VIIH class of SMP Negeri 6 Salatiga students in rectangular and triangular material through Numbered Heads Together (NHT) learning application model. The model used in this research is Kemmis &amp; Mc Taggart spiral model. It shows that the average result in the class is 64,4 (have not reach the minimum requirement) with percentage in passing the requirement of 46.40%. In cycle 2, the result increased with passing the average grade of 81.2 (fulfilling KKM) with percentage of 75% for passing grade requirement. Student activeness is increasing along with the learning outcomes. However, average category or high category on the 1st cycle is at 50,14%, while on the 2nd cycle, it can reach out to 80% or more. The two results concluded that implementing NHT could improve learning outcomes and activeness of students in VIIH class of SMPN 6 Salatiga.
Algebraic Degree Estimation of ACORN v3 Using Numeric Mapping

ACORN v3 is a lightweight authenticated encryption cipher, which was selected as one of the seven finalists of CAESAR competition in March 2018. It is intended for lightweight applications (resource-constrained environments). By using the technique numeric mapping proposed at CRYPTO 2017, an efficient algorithm for algebraic degree estimation of ACORN v3 is proposed. As a result, new distinguishing attacks on 647, 649, 670, 704, and 721 initialization rounds of ACORN v3 are obtained, respectively. So far, as we know, all of our distinguishing attacks on ACORN v3 are the best. The effectiveness and accuracy of our algorithm is confirmed by the experimental results.
On the Use of Fisher Vector Encoding for Voice Spoofing Detection

Recently, the vulnerability of automatic speaker recognition systems to spoofing attacks has received significant interest among researchers. A robust speaker recognition system demands not only high recognition accuracy but also robustness to spoofing attacks. Several spoofing and countermeasure challenges have been organized to draw attention to this problem among the speaker recognition communities. Low-level descriptors designed to detect artifacts in spoofed speech are found to be the most effective countermeasures against spoofing attacks. In this work, we used Fisher vector encoding of low-level descriptors extracted from speech signals. The idea behind Fisher vector encoding is to determine the amount of change induced by the descriptors of the signal on a background probability model which is typically a Gaussian mixture model. The Fisher vector encodes the amount of change of the model parameters to optimally fit the new-&nbsp;coming data. For performance evaluation of the proposed approach we carried out spoofing detection experiments on the 2015 edition of automatic speaker verification spoofing and countermeasure challenge (ASVspoof2015) and report results on the evaluation set. As baseline systems, we used the standard Gaussian mixture model and i-vector/PLDA paradigms. For a fair comparison, in all systems, Constant Q cepstral coefficient (CQCC) features were used as low-level descriptors. With the Fisher vector-based approach, we achieved an equal error rate (EER) of 0.1145% on the known attacks, 1.223% on the unknown attacks, and 0.668% on the average. Moreover, with a single decision threshold this approach yielded an EER of 1.05% on the evaluation set.
An Embedded Inference Framework for Convolutional Neural Network Applications

With the rapid development of deep convolutional neural networks, more and more computer vision tasks have been well resolved. These convolutional neural network solutions rely heavily on the performance of the hardware. However, due to privacy issues or the network instability, we need to run convolutional neural networks on embedded platforms. Critical challenges will be raised by limited hardware resources on the embedded platform. In this paper, we design and implement an embedded inference framework to accelerate the inference of the convolutional neural network on the embedded platform. For this, we first analyzed the time-consuming layers in the inference process of the network, and then we design optimization methods for these layers. Also, we design a memory pool specifically for neural networks. Our experimental results show that our embedded inference framework can run a classification model MobileNet in 80ms and a detection model MobileNet-SSD in 155ms on Firefly-RK3399 development board.
Histogram-based image watermarking algorithm using visual perception characteristics

Watermarking algorithms based on the geometric invariance of image histogram are effective and can resist various common attacks. However, all existing histogram-based image watermarking algorithms are constructed from all the pixels of the entire image; thus the embedded watermark energy is randomly distributed throughout the image, causing visual quality degradation in the smooth areas. In this paper, an improved algorithm using human visual perception characteristics is proposed. Firstly, we calculate the JND threshold mapping of the carrier image and select a portion of the pixels with the largest threshold as samples of the statistical histogram. Secondly, we calculate the mean of the selected pixel set, determine the embedding region and divide it into several groups. Finally, by adjusting the number of pixels in three bins per group, 2 bits of the watermark are embedded. According to the geometric invariance of the histogram and the different sensitivity of human eyes to the smooth and textured areas, we embed the watermark in the positions which are not easily perceived by human eyes. Experiments show that the proposed algorithm significantly improves the visual quality of the smooth areas in the watermarked image, but it has weaker robustness to signal processing attacks.
The Analysis of Students' Difficulties In Speaking English at the Tenth Grade of SMK N 2 Purworejo

The aims of the study are mainly to know the students’ difficulties in speaking, and to know the strategy used by teacher to overcome the speaking problems.The participants of the study were 36 students of the tenth grade of SMK N 2 Purworejo.The instruments used in this research were questionnaire, interview and documentation. After getting the data, the writer analyzed them to determine category of students’ speaking skill, students’ speaking difficulties and teacher strategies.The result of the study shows that the mean score of students skill in speaking is 64.8. It belongs to sufficient category. The students’ speaking difficulties covered linguistic and non linguistics problems. The linguistic problems include lack of vocabulary, pronunciation, grammar knowledge, while non linguistic problems, namely inhibition, nothing to say, uneven participant, mother tounge interference,anxiety, shyness, lack of self confident and low motivation. To solve the problems, the teacher has strategies such as drilling, brainstorming, role playing and giving the students motivation.
BUDDHIST STELE OF SWAT VALLEY: POINT CLOUD ANALYSIS AND INTERPRETATION

Abstract. With recent advancements on 3D sensors and cloud computing, high-speed, high-accuracy 3D measurement at micrometer level have been increase by scientists community and digital humanities researchers.The methodology proposed in this project aims to test some of the algorithms used in remote-sensing to the Buddhist sculptures from Swat Valley (Pakistan); these algorithms use high-resolution topographic data to identify, from DEMs, specific features like valleys, ridges, peaks, pits or surface anomalies.In the carved stone, the surface is analysed like a landscape, where carved areas are valleys bordered by slopes and crests. One of the simplest tools, the commonly used analytical hill-shading, which simulates artificial illumination on the DEM surface, is based on the same principle as the use of an oblique light source to highlight incisions in classic photography. Other families of algorithms that can be divided into three main groups (Slope and Curvature, Local Relief Model and Sky View Factor, Positive and Negative Openness and Geomorphons) are tested here.
Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning

Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems, such as quadcopters.This belief that Model-based strategies that involve the use of welltrained neural networks for making such high-level decisions always give better performance can be dispelled by making use of Model-free policy search methods.This paper proposes the use of a model-free random searching strategy, called Augmented Random Search (ARS), which is a better and faster approach of linear policy training for continuous control tasks like controlling a Quadcopter's flight.The method achieves state-of-the-art accuracy by eliminating the use of too much data for the training of neural networks that are present in the previous approaches to the task of Quadcopter control.The paper also highlights the performance results of the searching strategy used for this task in a strategically designed task environment with the help of simulations.Reward collection performance over 1000 episodes and agent's behavior in flight for augmented random search is compared with that of the behavior for reinforcement learning state-of-the-art algorithm, called Deep Deterministic policy gradient(DDPG) Our simulations and results manifest that a high variability in performance is observed in commonly used strategies for sample efficiency of such tasks but the built policy network of ARS-Quad can react relatively accurately to step response providing a better performing alternative to reinforcement learning strategies.
Using wireless and mobile technologies to enhance teaching and learning strategies

The tremendous development and sensational popularity of smart mobile devices that offer remarkable availability of seamless and uninterrupted communication services at anytime, anywhere, to any content as well as any prospects represent a new cohort of technological tools that could be utilized for inculcating creative skills in young children. Now a days many companies are manufacturing lot of apps that target children education and entertainment. Both the Parents and educators find a huge responsibility in evaluating and selecting such devices and decide which of these offer potential educational benefits for their children. This decision is critical to them since they restricted tools to evaluate the applications available in the market. Hence the development of an interactive and multisensory component based mobile application environment with integrated physical and virtual reality scenario could solve the issue related to the children education and entertainment. This is not just limited to education and the entertainment domain but will explore essential features of virtual reality environment and interactivity. Children with special education needs find it difficult to understand new knowledge and concept but by the use of virtual reality worlds produced for children could enhance their level of understanding. Therefore this paper dscusses the development of adaptable, customizable and virtual reality based compliant applications for children. These applications would be useful to enhance learning essentials and improve diverse sensory and cognitive impairments in even children with special needs by resolving mobility issues. This application could be made available on i-pads, tablets, etc and will be intelligent to perform switch between the different intellectual ranges of children accessing it. In other words it will present self-adaptability feature. This research study also focuses on the activities that are suitable for learning purposes with students with impairments.
Exploring Inter-Instance Relationships within the Query Set for Robust Image Set Matching

Image set matching (ISM) has attracted increasing attention in the field of computer vision and pattern recognition. Some studies attempt to model query and gallery sets under a joint or collaborative representation framework, achieving impressive performance. However, existing models consider only the competition and collaboration among gallery sets, neglecting the inter-instance relationships within the query set which are also regarded as one important clue for ISM. In this paper, inter-instance relationships within the query set are explored for robust image set matching. Specifically, we propose to represent the query set instances jointly via a combined dictionary learned from the gallery sets. To explore the commonality and variations within the query set simultaneously to benefit the matching, both low rank and class-level sparsity constraints are imposed on the representation coefficients. Then, to deal with nonlinear data in real scenarios, the`kernelized version is also proposed. Moreover, to tackle the gross corruptions mixed in the query set, the proposed model is extended for robust ISM. The optimization problems are solved efficiently by employing singular value thresholding and block soft thresholding operators in an alternating direction manner. Experiments on five public datasets demonstrate the effectiveness of the proposed method, comparing favorably with state-of-the-art methods.
A service-oriented cloud modeling method and process

The transition of software development from web to cloud has been accelerated. The development of cloud services requires a modeling method that reflects the characteristics of cloud including personalized service, resource sharing service, grouped and distributed services, and cross-platform operability. This study aimed to suggest a method of developing UML-based cloud services suitable for the characteristics of cloud services. A cloud service metamodel was defined using cloud applications’ characteristic modeling elements, and after that, how these cloud modeling elements are expressed into UML modeling elements was defined with an integrated metamodel between cloud and UML. By applying this hierarchical cloud metamodel, an MDA and MVC-based service-oriented cloud modeling process was established. By doing so, it will be possible to easily design services (applications) and solutions that are suitable for cloud computing environments, and in particular, to create hierarchical reuse models by the level of the abstraction of model-driven development.
HISDOM

In the information explosion era, mapping multiple ontologies in different knowledge bases could provide a common layer from which several ontologies could be accessed and exchanged in semantically sound manners. However, most ontology mapping approaches rely heavily on human annotation and are restricted to limited domains. Moreover, most prior work is limited to combining a few mapping factors with moderate mapping accuracy, and the ontology mapping weigh calculation process is not adaptive and dynamic. Based on the current generalization of ontology mapping methods, we propose a novel ontology mapping system called HISDOM to improve the generalization performance of ontology mapping. HISDOM uses comprehensive factors like concept names, attributes, instances, and structural similarities to determine the similarity of ontology. A key novelty of HISDOM is leveraging CNN to calculate the comment similarity to assist the mapping process of concepts in the ontology. Then, HISDOM dynamically derives the weight of different factors in the overall ontology similarity proportional to the amount of information of each factor in the ontology. Finally, based on the overall similarity, HISDOM determines whether the two concepts in the ontology can be mapped. We study the performance of HISDOM through extensive experiments. The results show that HISDOM outperforms several baselines in ontology mapping tasks, the dynamic and adaptive weighting mechanism is effective, and all the mapping factors of HISDOM are positive towards improving the ontology mapping accuracy.
Concept of Blockchain Technology

Blockchain, Bitcoin's core technology, and spinal cord have received enthusiastic attention since the last couple of decades. The Blockchain serves as a paradigm for distributed and unchangeable computations for bitcoins and cryptocurrencies. The key features behind this technology are to create a reliable, secure, transparent, decentralized, and reliable autonomous ecosystem. It is useful for a variety of applications, especially for legacy devices, resources, and infrastructure. In this article, we presented a technical overview, its application, and the challenges associated with blockchain technology and cryptocurrencies. This study aims to provide a ground-breaking overview and future research direction and promising importance of Blockchain.
Some Investigations on Similarity Measures Based on Absent Words

In this paper we investigate similarity measures based on minimal absent words, introduced by Chairungsee and Crochemore in [1]. They make use of a length-weighted index on a sample set corresponding to the symmetric difference M(x)ΔM(y) of the minimal absent words M(x) and M(y) of two sequences x and y, respectively. We first propose a variant of this measure by choosing as a sample set a proper subset 𝒟(x, y) of M(x)ΔM(y), which appears to be more appropriate for distinguishing x and y. From the algebraic point of view, we prove that 𝒟(x, y) is the base of the ideal generated by M(x)ΔM(y). We then remark that such measures are able to recognize whether the sequences x and y share a common structure, but they are not able to detect the difference on the number of occurrences of such a structure in the two sequences. In order to take into account such a multiplicity, we introduce the notion of multifactor, and define a new measure that uses both absent words and multifactors. Surprisingly, we prove that this similarity measure coincides with a distance on sequences introduced by Ehrenfeucht and Haussler in [2], in the context of block-moves strategies. In this way, our result creates a non trivial bridge between similarity measures based on absent words and those based on the block-moves approach.
Spherical formulation of moving object geometric constraints for monocular fisheye cameras

In this paper, we introduce a moving object detection algorithm for fisheye cameras used in autonomous driving. We reformulate the three commonly used constraints in rectilinear images (epipolar, positive depth and positive height constraints) to spherical coordinates which is invariant to specific camera configuration once the calibration is known. One of the main challenging use case in autonomous driving is to detect parallel moving objects which suffer from motion-parallax ambiguity. To alleviate this, we formulate an additional fourth constraint, called the anti-parallel constraint, which aids the detection of objects with motion that mirrors the ego-vehicle possible. We analyze the proposed algorithm in different scenarios and demonstrate that it works effectively operating directly on fisheye images.