Reviewerly/exHarmony huggingface dataset:
Trilingual 3D Script Identification and Recognition using Leap Motion Sensor

Recently, the development of depth sensing technologies such as Leap motion and Microsoft Kinect sensors facilitate a touch-less environment to interact with computers and mobile devices. Several research have been carried out for the air-written text recognition with the help of these devices. However, there are several countries (like India) where multiple scripts are used to write official languages. Therefore, for the development of an effective text recognition system, the script of the text has to be identified first. The task becomes more challenging when it comes to 3D handwriting. Since, the 3D text written in air is consists of single stoke only. This paper presents a 3D script identification and recognition system written in three languages, namely, Hindi, English and Punjabi using Leap motion sensor. In the first stage, script identification was carried out in one of the three language. Next, Hidden Markov Model (HMM) was used to recognize the words. An accuracy of 96.4% was recorded in script identification whereas accuracies of 72.99%, 73.25% and 60.5% were recorded in script identification of Hindi, English and Punjabi scripts, respectively.
Thermal performance comparison of recycled and clay roof tiles due heat radiation by digital holographic interferometry

It is through the recycling process that disposed waste materials could regain a new purpose on their use, take for instance the tetrapak™ packaging. This material consists of thermally joint layers of paper, aluminum and polyethylene. Once recycled, the two latter materials form a new composite named poly-aluminum which is employed to manufacture roof tiles that serve as environmental protection against solar radiation and rain. This work reports an optical nondestructive comparison study of the surface deformation in poly-aluminum and clay roof tiles exposed to heat radiation. The optical system based on a sensitive out-of-plane interferometer configuration records several image holograms from a controlled heat source that raises the temperature on the tile’s surface. The retrieved optical phase renders the tiles surface’s deformation for a temperature range of 25 degrees on a Celsius scale. The results show a greater deformation profile from the poly-aluminum tile compared with the classic clay tile. However, its thermal insulation properties are better than those of the clay roof tile.
Enhancement strict avalanche criterion value in robust S-boxes construction using selected irreducible polynomial and affine matrixes

Abstract One indicator that a robust S-box is an ideal strict avalanche criterion value of 0.5. One method for applying the ideal strict avalanche criterion value is the selection of the precise irreducible polynomial and affine matrix. In this paper, we will discuss the robust S-boxes construction with the selected irreducible polynomial r ( x ) = x 8 + x 5 + x 3 + x 2 + 1 combined with the three selected affine matrixes i.e. t 1 , t 2 , and t 3 . Hence, the combination of selected irreducible polynomial and affine matrixes results in S-box 1 , S-box 2 , and S-box 3 . The test of strict avalanche criterion shows that S-box 2 and S-box 3 produce the same value of 0.49951. Nonlinearity and bit independence criterion tests are also conducted to complete the S-box testing.This result shows S-box 2 and S-box 3 are the best S-boxes compared to S-box 1 and S- boxes from previous studies.
Impact of Cloud Computing on Green Supply Chain Management

Almost every market in the world aims to go globally as wide as possible. It is important to understand the need for information and internet merged together in order to achieve such goal. Cloud systems are the new way of delivering and managing information to different parts in the world in a secure manner. However, not many organizations have moved to cloud. The rely of green supply chain management systems on cloud is not as popular as expected and not increasing fast enough. There is a trust issue raised against cloud and the adoption to it. Cloud can bring different benefits to green supply chain management systems such as the low cost of running, speed of dealing with information, and information availability which can have significant impact on energy savings. Organization contribution in the process of moving to cloud is important to attach any knowledge database to take competitive advantage.
Teaching Digital Image Processing Topics via Matlab Techniques

Utilizing visual aids in teaching have become extremely important in the modern age of information technology.Most of the conventional teaching techniques of image processing are focused on traditional means; teachers, textbooks and classrooms.These techniques are stood as a stumbling block in front of image technology development.Matlab can be considered as a matrix-oriented computing engine.Since image can be thought as a matrix, therefore, Matlab is the ideal program for image processing.This work proposes to deepen the perception the topics of digital image processing by utilizing Matlab.We believe that teaching the digital image processing with Matlab will lead to moving the enthusiasm of students and helping them to think creatively.Teaching digital image processing topics with Matlab will make these topics easier, that is due to using visual, interactive as well as experimental methods will attract students' attention more than traditional methods, which will lead to building deeper understanding and better memory for digital image processing topics.
Formation-aware Cloud Segmentation of Ground-based Images with Applications to PV Systems

Ground-based sky imaging has won popularity due to its higher temporal and spatial resolution when compared with satellite or air-borne sky imaging systems. Cloud identification and segmentation is the first step in several areas, such as climate research and lately photovoltaic power generation forecast. Cloud-sky segmentation involves several variables including sun position and type and altitude of clouds. We proposed a training-free cloud/sky segmentation based on a threshold that adapts to the cloud formation conditions. Experimental results show that the proposed method reaches higher detection accuracy against state-of-the-art algorithms; additionally, qualitative results over hemispherical high dynamic range (HDR) sky images are provided. The proposed cloud segmentation method can be applied to shading prediction for photovoltaic (PV) systems.
Enhance Multi-factor Authentication Model for Intelligence Community Access to Critical Surveillance Data

Protection of critical data is one of the greatest challenges in any organization around the globe, especially for the intelligence community. Managing data, assets and resources require strong security method such as the authentication process that can guarantee only designated person will be receiving the required information. Any breach of information and assets could risk in the nation’s sovereignty and give significant impacts in social, political, economy and diplomacy or even lives. Authentication method enables intelligence data to be transferred covertly and the access to the system by a legitimate user is guaranteed, hence the elements of confidentiality, integrity, and availability of the data is assured. This study analyzed various authentication methods used to secure multiple platforms of data and system. This study serves as theoretical analysis on multi-factor authentication model for intelligence community access to critical surveillance data. This study aims to propose the enhance model that could be used as a basis to build a framework of the secured authentication system to avoid common attack on authentication and access management.
Gated Relational Graph Neural Network for Semi-supervised Learning on Knowledge Graphs

Entity classification is an important task for knowledge graph (KG) completion and is also crucial in many upper-level applications. Traditional methods use unsupervised representation learning to embed entities and relations into a continuous low-dimensional space, and then use the embeddings in downstream tasks. Recent years, Graph Neural Networks (GNNs) have been gaining growing interest, among which Graph Convolutional Network (GCN) is widely used in semi-supervised tasks due to its excellent capability of aggregating neighborhood features. However, GCN lacks the ability to deal with edge features, which is essential in KGs. In this paper, we propose Gated Relational Graph Neural Network (GRGNN) targeted on entity classification problem in KGs. More specifically, we apply the idea of TransE to incorporate features of entities and relations, and introduce gate mechanism to leverage hidden states of current node and its neighbors. Our method achieves state-of-the-art performance compared with other methods in FB15K and DB10K datasets.
Use of flannel puzzle to improve the initial of counting ability

Abstract The initial of counting ability is essential for children to solve problems in everyday life. This study aims to describe the use of Flannel Puzzle in improving the initial of counting ability in Renggali one of the early childhood education institutions. This action research on 15 student who were the subject of research. Data are analysed by descriptive qualitative and quantitative. Stages of planning, action, observation, and reflection. The results of the study conclude that: flannel puzzles can improve initial of counting ability. By using the flannel puzzle the initial of counting ability of students can increase. Students’ abilities increase from 44.66% before action and increased in the first cycle around 49.25% and in the second cycle became 90.16%.
The Implementation of Backtracking Algorithm on Crossword Puzzle Games Based on Android

Abstract The development of smartphones at this time is improving very fast and rapidly can affects to the renewal of all applications in the smartphone. At this time we can do anything with a smartphone in hand. In other hand smartphone also can reduce the saturation and boredom of many games made for smartphones. One of example is the crossword game application. Filling or playing crossword puzzles turns out many benefits, in addition to increasing the freshness of the mind from routine activities, this game can also add to our insight in various things, such as vocabulary, various important terms and popular words we often find in filling out cross-breeding. The backtracking algorithm is used to determine the exact steps to be taken in completing the crossword puzzle game. By using the backtracking algorithm, it is can expected the completion of the crossword puzzle game so this game can be more easily to solved. The system is built using Java with the ADT IDE for Eclipse as a programming language. The result of this final project is an application of a crossword puzzle game about the term computer and informatics that can give correct answers automatically.
The pleasure of the hypertext

In this paper I examine the relation between textualism and eroticism and how this relation evolves in the digital age. The point of departure is Roland Barthes’ works on the notion of text and especially The pleasure of the text (1973), where we find enough evidence that the attitude of the French semiologist is of fetishistic character with explicit erotic connotations. Such attitude is quite representative for the whole epoch of both structuralism and post-structuralism. The age of the hypertext (and the internet in general) changes the textualists’ culture in new forms of intertextual exchange where the pleasure itself becomes object of communicative exchange. The eroticism of the hypertext is more explicit compared to the text and it is getting a myriad of forms, difficult to be put in a general model. The last chapter examines the consequences of the hypertextual reality for the educational institutions and their role in the digitalized societies.
An Online Developer Profiling Tool Based on Analysis of GitLab Repositories

There are more and more developers as software is playing increasingly important roles in today’s economic and social development. As a result, evaluating developers’ expertise scientifically has become an urgent need for both Internet companies and developers. However, it seems that there is no satisfactory method to meet this demand currently. In this paper, we propose a solution to profile developers by analyzing their source code. We conduct the analysis of developers in terms of code quantity, code quality, skills, contribution, personalized commit time, and projects they participated in based on the GitLab code repositories. And we comprehensively evaluate developers’ expertise from four perspectives, which are code quantity, code quality, contribution and score of projects they participated in. Compared with existing methods, our evaluation indicators are more comprehensive. We design and implement an online tool that can provide developer searching and profiling. Our tool has been used in Neusoft and Wonders Group to characterize the expertise and performance of their software developers.
Generative Networks for Synthesizing Human Videos in Text-Defined Outfits

Generating a video from a textual input is a challenging research topic that would have a variety of applications in industries such as retail, e-commerce, online entertainment, education etc. In this paper, we discuss the application of generating videos of a human subject in a desired outfit using an input video of the subject. We present a two stage solution, wherein at the first stage a generative model is learned such that, given the subject's image and a textual description of the outfit, a corresponding image of the subject in the described outfit is synthesized. At the second stage, all the frames of the subject's video are individually processed by the stage 1 model to generate corresponding frames and an optical flow based post processing step is performed to maintain visual coherence across the generated frames. Towards the stage-1 objective, multiple supervised and unsupervised convolutional neural network (CNN) based generative models have been proposed. A novel approach to inject an external masking layer that maintains the structural integrity of the generated images is also presented. We train and test the different methods on the publicly available multi-view clothing image data-set and the performance in videos is showcased on a set of real-world commercial videos. The experiments show the efficacy of our approach in generating images/videos in both low (64 × 64) and high (256 × 256) resolutions.
Use of Thermal Infrared Remote Sensing for Targeting Mineral Deposits

We review a collection of recent mineral discoveries made by major and junior mining companies in various geological environments and show how Aster thermal imagery could have been used to facilitate such discoveries. This process of hindsight exploration confirms that Aster thermal imagery is an effective tool for mineral exploration under cover and finds wide application in mineral exploration.
Deep Colorization by Variation

We propose an adversarial learning based model for image colorization in which we elaborately adapt image translation mechanism that are optimized according to the task. After developing approaches on improving the global and local quality of the image colorization by analyzing this processing made by network architecture and objective functions, we formulate a diverse map-ping from the gray scale images to colorful images by latent space variation within the model. At last, discussion on the theoretical framework for studying color information distribution and video colorization is given.
Application of deep learning for power control in the interference channel

In this letter, a transmit power control architecture based on the recurrent neural network (RNN) is proposed to solve the power allocation problem for the interference channel to maximize the weighted sum-rate. Compared with the conventional power control schemes, such as weighted minimization mean squared error (WMMSE), which require a considerable number of computations, the RNN-based scheme not only requires much lower computational complexity, but also can well learn and characterize inter-user relationship in the interference channel. The simulation results show that the proposed scheme achieves the sum-rate close to the WMMSE-based scheme with much lower complexity and obtains higher sum-rate than the deep neural network (DNN)-based scheme with much less model parameters.
Conditioned Generative Model via Latent Semantic Controlling for Learning Deep Representation of Data

Learning representations of data is an important issue in machine learning. Though generative adversarial network has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. Moreover, most of GAN's have a large size of manifold, resulting in poor scalability. In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data and learns a representation of the data efficiently. Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables, which follow the distribution, into the generative model. As the larger scale of latent space caused by deploying various distributions makes training unstable, we need to separate the process of defining the distributions explicitly and operation of generation. We prove that a variational auto-encoder is proper for the former and modify a loss function of VAE to map the data into the corresponding pre-defined latent space. The decoder, which generates the data from the associated latent space, is used as the generator of the LSC-GAN. Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method. Besides, our model achieves a high compression ratio that can hold about 24 pixels of information in each dimension of latent space.
Weighted Subset Selection for Fast SVM Training

We propose a data reduction method that improves the speed of training the support vector machine (SVM) algorithm. In particular, we study the problem of finding a weighted subset of training data to efficiently train an SVM while providing performance guarantees. Relying on approximate nearest neighborhood properties, the proposed method selects relevant points and employs the concept of maximal independent set to achieve desired coverage of the training dataset. Performance guarantees are provided, demonstrating that the proposed approach enables faster SVM training with minimal effect on the accuracy. Empirical results demonstrate that the proposed method outperforms existing weighted subset selection techniques for SVM training.
Research on Information Extraction Technology of Iron Oxide Based on Airborne Hyperspectral Data

Aiming at the problem of low accuracy in evaluating the content of hematite, goethite and limonite by hyper-spectral technology, this paper proposed a method for estimating iron oxide abundance in surface rocks by using airborne hyper-spectral data CASI. The research is started with accurate and reliable data collection. Then, the airborne hyper-spectral data were preprocessed, including geometric rectification, radiation correction, atmospheric correction and spectral reconstruction. Basing on that, the spatial distributing information of hematite, goethite and limonite had been extracted. Besides, according to the spectral absorption features, the iron relative abundance inversion model was built. Finally, combining with the geological setting of study area and previous study, the authors forecast other favorable target areas using alteration distribution map. The results reveal that extraction of anomalies agrees with the ore-bearing strata. Mineralization clues have been found in prediction areas, which show practical meanings.
An indoor video-based for single face identification among people

Abstract The purpose of this research is to build a system that can detect certain face among several people. The system works using face recognition in searching suspects from a database. 30 images and 9 videos are used for training and testing. In the training process carried out several stages: (1) pre-processing of face images, (2) extraction of facial image training features using the Histogram of Oriented Gradient (HOG) method, (3) face classification using the SVM method. There are several steps for testing process i.e.: (1) video frame extraction, (2) cascade classifier method with the Local Binary Pattern (LBP) feature descriptor, (3) HOG for extracting facial features detected in the frames, and (4) SVM as the face classifier. The processed video data is 1920x1080 pixel resolution which has been recorded using a CCTV camera which is mounted on a wall with height and slope angle of 2.5 meters and 60 degrees. There are two goals implemented in this research. The first goal is to find the highest accuracy from several testing of frame sampling by setting the best threshold value. The second goal is to find the lowest processing time.
Analysis of Fundamental Frequency, Jitter and Shimmer in Stuttered and Non-Stuttered Speech of Marathi Language

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Deep Learning in Textural Medical Image Analysis

One of the characteristics of medical image analysis is that several medical images are not in the structure domain like natural images but in the texture domain. This chapter introduces a new transfer learning method, called “two-stage feature transfer,” to analyze textural medical images by deep convolutional neural networks. In the process of the two-stage feature transfer learning, the models are successively pre-trained with both natural image dataset and textural image dataset to get a better feature representation which cannot be derived from either of these datasets. Experimental results show that the two-stage feature transfer improves the generalization performance of the convolutional neural network on a textural lung CT pattern classification. To explain the mechanism of a transfer learning on convolutional neural networks, this chapter also shows analysis results of the obtained feature representations by an activation visualization method, and by measuring the frequency response of trained neural networks, in both qualitative and quantitative ways, respectively. These results demonstrate that such successive transfer learning enables networks to grasp both structural and textural visual features and be helpful to extracting good features from the textural medical images.
Penjadwalan Pakan Dan Pengendalian Suhu Pada Kandang Babi Secara Otomatis Berbasis Arduino

he research was conducted on the pig farm owned by Mr. Santo, at the West Bandulan, Sukun Sub District of Malang in East Java province for 4 weeks (1 month). This research uses 4 pigs where 2 tails are fed automatically and the 2 tail still uses manual way. The purpose of this research is to provide farmers with information on the effects of feeding timely on the increase in pig weight. The method used is the experiment (experimental) consisting of 2 experiments that were first performed automatic feeding of 2 pigs that both feed manually against 2 pigs so as to find a comparison of The experiment. Looking at this, then the author devised a tool scheduling and automatic feeding of Arduino-based Mega 2560. With this tool, the author hopes to help pig farmers in increasing pig weights and relieve breeder's work. Thereby can improve the welfare of pig farmers. Results of the experiment showed the growth of pigs using the automated system better with the result 0.58 – 0.68 kg per day.