Reviewerly/exHarmony huggingface dataset:
Efficient Lattice-Based Zero-Knowledge Arguments with Standard Soundness: Construction and Applications.

We provide new zero-knowledge argument of knowledge systems that work directly for a wide class of language, namely, ones involving the satisfiability of matrix-vector relations and integer relations commonly found in constructions of lattice-based cryptography. Prior to this work, practical arguments for lattice-based relations either have a constant soundness error \((2/3)\), or consider a weaker form of soundness, namely, extraction only guarantees that the prover is in possession of a witness that “approximates” the actual witness. Our systems do not suffer from these limitations.
Learning to Create Cartoon Images from a Very Small Dataset

This paper proposes a framework to automatically create cartoon images with low computation resources and small training datasets. The system segments and reassembles regions according to the topologies learned from example images. Region relationship trees are constructed for training images with no requirement of manual labeling. An enhanced clustering mechanism with no prior knowledge of cluster number is designed to effectively group components into desired groups for image creation. Compared with methods based on Generative Adversarial Networks, the proposed framework which performs automatic reasoning, clustering and reassembling regions of cartoon images can create better images with a very small amount of training samples.
Efficient AES for Biometric Image Signal Processing

Abstract: Several computation schemes and architectural designs have been suggested during the last three decades for efficient hardware implementation of biometric applications. The biometric application of advanced encryption standard (AES) algorithm in high texture image signal processing. The proposed AES very large-scale Integration (VLSI) architecture has 16 % and 15 % less dynamic power consumption and LUT utilization then available best design available AES architecture in the presented literature survey. The chip of AES design is created by using cadence tool, whereas the efficient inner product. Keywords: AES, biometric, FPGA, image signal processing, VLSI Cite this Article: Vikas Tiwari, Abhijeet Anand. Efficient AES for Biometric Image Signal Processing. Current Trends in Signal Processing. 2019; 9(1): 7–9p.
Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the&#x0D; Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory.&#x0D; &#x0D; Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.
A Generic Construction for Revocable Identity-Based Encryption with Subset Difference Methods.

To deal with dynamically changing user’s credentials in identity-based encryption (IBE), providing an efficient key revocation method is a very important issue. Recently, Ma and Lin proposed a generic method of designing a revocable IBE (RIBE) scheme that uses the complete subtree (CS) method by combining IBE and hierarchical IBE (HIBE) schemes. In this paper, we propose a new generic method for designing an RIBE scheme that uses the subset difference (SD) method instead of using the CS method. In order to use the SD method, we generically design an RIBE scheme by combining IBE, identity-based revocation (IBR), and two-level HIBE schemes. If the underlying IBE, IBR, and HIBE schemes are adaptively (or selectively) secure, then our RIBE scheme is also adaptively (or selectively) secure. In addition, we show that the layered SD (LSD) method can be applied to our RIBE scheme and a chosen-ciphertext secure RIBE scheme also can be designed generically.
EFFECT OF INTELLIGENT TUTOR IN ENHANCING THINK-PAIR-SHARE IN LEARNING OF LIGHT EMITTING DIODE TELEVISION TROUBLESHOOTING

The study determined the effect of Intelligent Tutor (IT) in enhancing Think-Pair-Share (TPS) in the learning of LED TV troubleshooting. One research question and one null hypothesis tested at .05 level of significance guided the study. The study adopted the quasi-experimental design, involving pre-test, post-test, non-equivalent treatment groups A and B using a population of 329 electronic students. Multistage random sampling technique was used to select a sample of 166 electronic students from Technical Colleges offering Radio, Television, and Electronic Work trade (RTVE) at National Technical Certificate (NTC) level in North-west, Nigeria. The instruments for data collection is the LED TV Troubleshooting Skills Performance Test (TTSPT). The TTSPT and reters’ guide along with lesson plans, teacher training manual, and tables of specifications were validated by two electronic experts in the Department of Industrial and Technology Education, Federal University of Technology Minna, Niger state and one in the Department of Vocational and Technology Education, Abubakar Tafawa Balewa University Bauchi, Bauchi state. The inter-rater technique was used to find out the reliability of the TTSPT in which three raters used a scoring guide to rate 20 electronic students in the pilot tested class on their LED TV troubleshooting skills. The inter-rater reliability was calculated using Kendall’s Coefficient of Concordance and the size of the concordance was found to be .87 which shows that there was high agreement among the raters, and therefore, the instrument has score reliability. The internal consistency of the TTSPT was checked using Kuder-Richardson 20 (KR20) and was found to be 0.76. Data were collected by administering TTSPT on treatment groups A and B of TC II electronic students as pre-test and post-test. The mean gain of each group was computed to answer the research question while ANCOVA was used to test the null hypothesis at .05 level of significance. Findings that emerged from the investigation revealed that the effect of IT for enhancing TPS learning method on students’ LED TV troubleshooting skills performance is higher than the effect of Traditional Workshop Practice for enhancing TPS learning method. Based on this finding, it was concluded that the IT which was augmented with TPS learning method is a reliable opportunity for enhancing electronic students’ skills performance in LED TV troubleshooting. It was therefore recommended that State and Federal Ministries of Education should provide the needed support in terms of facilities and equipment for the planning, inclusion, and implementation of IT for enhancing TPS in learning of LED TV troubleshooting in Technical Collages in North-West, Nigeria.
A New Perspective on Pool-Based Active Classification and False-Discovery Control

In many scientific settings there is a need for adaptive experimental design to guide the process of identifying regions of the search space that contain as many true positives as possible subject to a low rate of false discoveries (i.e. false alarms). Such regions of the search space could differ drastically from a predicted set that minimizes 0/1 error and accurate identification could require very different sampling strategies. Like active learning for binary classification, this experimental design cannot be optimally chosen a priori, but rather the data must be taken sequentially and adaptively in a closed loop. However, unlike classification with 0/1 error, collecting data adaptively to find a set with high true positive rate and low false discovery rate (FDR) is not as well understood. In this paper, we provide the first provably sample efficient adaptive algorithm for this problem. Along the way, we highlight connections between classification, combinatorial bandits, and FDR control making contributions to each.
Toward a Characterization of Loss Functions for Distribution Learning

In this work we study loss functions for learning and evaluating probability distributions over large discrete domains. Unlike classification or regression where a wide variety of loss functions are used, in the distribution learning and density estimation literature, very few losses outside the dominant \emph{log loss} are applied. We aim to understand this fact, taking an axiomatic approach to the design of loss functions for distributions. We start by proposing a set of desirable criteria that any good loss function should satisfy. Intuitively, these criteria require that the loss function faithfully evaluates a candidate distribution, both in expectation and when estimated on a few samples. Interestingly, we observe that \emph{no loss function} possesses all of these criteria. However, one can circumvent this issue by introducing a natural restriction on the set of candidate distributions. Specifically, we require that candidates are \emph{calibrated} with respect to the target distribution, i.e., they may contain less information than the target but otherwise do not significantly distort the truth. We show that, after restricting to this set of distributions, the log loss and a large variety of other losses satisfy the desired criteria. These results pave the way for future investigations of distribution learning that look beyond the log loss, choosing a loss function based on application or domain need.
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

Nonlinear embedding manifold learning methods provide invaluable visual insights into a structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their embedding quality can be poor. We propose a natural extension to several manifold learning methods aimed at identifying pressured points, i.e. points stuck in the poor local minima and have poor embedding quality. We show that the objective function can be decreased by temporarily allowing these points to make use of an extra dimension in the embedding space. Our method is able to improve the objective function value of existing methods even after they get stuck in a poor local minimum.
Educational Data Mining in Chin State

Now a day Educational data mining is new emerging technique of data mining that can be applied on the data related to the field of education. In this proposed method si Based on the clustering methods such as centroid based, distribution based and density based clustering. Cluster includes groups in with small distance among the cluster members. The student’s school requirement level of optimization is formulated by using clustering. In centroid based clustering, clusters are represented by a central vector. The number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem. The clustering model most closely related to statistics is based on distribution model. Experiments attempts to improve the accuracy by using the method of data mining using R Tool.
Do I Know You? A Two-Stage Framework for Novelty Detection

In this work, we address the problem of novelty detection in the context of image classification, where the goal is to classify whether a query belongs to one of the classes seen during training or to a novel class. Given a network trained on a set of training classes, we utilize the activations from the final fully connected layer in a two-stage framework to determine whether a given query is seen or novel. In the first stage, for a given query, analyzing the top retrieved samples gives us a set of probable categories for that query. For the second stage, a comparator network is designed which can compare features from two samples and classify them into similar or dissimilar pairs. Prototype exemplars from each of the probable categories computed in the first stage are compared with the query feature to determine whether they come from the same or different category. The scores from both the stages are fused using a simple yet effective fuzzy aggregation operator to give a final decision on whether the given query is from a seen or novel class. Extensive experiments on three benchmark databases, MNIST, HASYv2, and Fashion-MNIST demonstrate the effectiveness of the proposed framework.
How a Reduction in Legal Working Hours Affects Employment in the Ocean and Fishery Industries

Jeong, S.B.; Chang, J.I.; Choe, S.B., and Choi, S., 2019. How a reduction in legal working hours affects employment in the ocean and fishery industries. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 201-205. Coconut Creek (Florida), ISSN 0749-0208.The Korean National Assembly passed amendments to the Labor Standard Act in February 2018. The amendments mainly pertain to reduction in legal working hours (from 68 to 52 hours), excluding some special cases of work hours and recess hours, thus extending mandatory rest time. The amendments will take effect gradually according to the size of enterprises from July 2018 to December 2022. Thus, this study examined the effects of reducing legal working hours on employment in the ocean and fishery industries based on the result of a survey of 1,155 enterprises in this industry. The survey consisted of three parts, the current state of working hours, the impact of reducing working hours on business operations, and the preferred supporting measures from the government. According to the survey results, the average weekly working time is 42.8 hours, and 8.5% of the respondents are expected to be affected by the reduction in working hours. However, only a few companies have developed countermeasures against it. And the smaller the size of a company, the more difficult to come up with countermeasures. In order to establish stable working time reduction, it is essential to improve labor productivity and establish various government support policies.
Benefits of Art Education in School Activities

The aim of this paper is to show how participation in school activities local and national festival brings benefits in teaching learning process. Participation in different fields give a students and opportunity to show their hidden talent and achieving their goal and future.
Counting the Optimal Solutions in Graphical Models

We introduce #opt, a new inference task for graphical models which calls for counting the number of optimal solutions of the model. We describe a novel variable elimination based approach for solving this task, as well as a depth-first branch and bound algorithm that traverses the AND/OR search space of the model. The key feature of the proposed algorithms is that their complexity is exponential in the induced width of the model only. It does not depend on the actual number of optimal solutions. Our empirical evaluation on various benchmarks demonstrates the effectiveness of the proposed algorithms compared with existing depth-first and best-first search based approaches that enumerate explicitly the optimal solutions.
Syn- and epigenetic nickel mineralization in the Kamchatka plutonic mafite-ultramafite formations

The paper presents data on known objects in the sulfide copper-nickel ore formation in Kamchatka, as well as other ore occurrences and points of nickel mineralization related to certain magmatic formations. The authors have revealed the empirical regularities in the distribution of the background contents of nickel, cobalt, copper, and sulfur in ore-free differences of the rocks from the regional mafite-ultramafite formations and their interrelation with ore mineralization of various genesis. Ore content in the intrusions of cortlandite-norite formation is caused by the concentration of nickel in the sulfides at the magmatic stage. The origin of the nickel ores in the dunite-harzburgite-gabbro formation of Kamchatka is mainly associated with its concen-tration caused by epigenetic processes. Possible identification of industrial nickel ore content can be associated with the copper-sulfides ores in the blocks of hyperbasites from the dunite-harzburgite-gabbro formation from the zones with the serpentinite melange in Eastern Kamchatka. In this case, nickel and cobalt can be considered only as a by-product in the processing of copper and zinc-copper ores.
Development of a Free Viewpoint Pedestrian Recognition System Using Deep Learning for Multipurpose Flying Drone

This paper describes the development of 3-D human recognition system of flying drone system for emergency rescue and investigation. In this system, deep neural network and its application for 3-D object recognition are key techniques for human detection from a free viewpoint. Some appearance based characteristics are captured as movie frames, and the system uses deep neural networks to automatically classify concerned object. The proposed system performs well that many kinds of views of personnels can be recognized from bird's-eye view. Experimental results show that the system can effectively recognize human objects with high accuracy.
The Labor Market Information System - Disseminating Data You Need for Planning and Decision-Making

This article provides an overview of the purpose of producing data for LMI dissemination, the supportive role of labor market data dissemination, and the LMI system infrastructure and innovative da...
INTRA-CLASS CONSTRAINED SPARSE REPRESENTATION WITH GRADIENT FEATURE FOR OCCLUDED FACE RECOGNITION

A novel occlusion face recognition method based on gradient feature and intra-class constrained sparse representation classification is proposed in this paper.We propose a second-order gradient feature to extract the texture and contour of images, which is more robust to occlusion than the original pixel of the face image.Intra-class constraint sparse representation is designed to make the test image close to the specific class by minimizing intra-class error.Furthermore, intra-class error is used as classification criteria.Experiments on the Extended Yale B and AR face databases prove the superiority and robustness of the proposed method for occluded face recognition, especially for the real world occlusion.
Environmental Risk Assessment Based on Crop Satellite Cloud Image in the Early Stage of Development—Taking New Development Areas as an Example

As the saying goes, “Look at the weather”, certain weather phenomena are always associated with certain clouds. Clouds also play a key role in climate change, primarily the impact of the Earth - atmosphere system’s energy budget. Understanding the radiation characteristics of clouds, determining the type of cloud and understanding the distribution of clouds are critical to the accuracy of weather forecasts, the effectiveness of climate monitoring, the scientific nature of climate models, and atmospheric and atmospheric remote sensing. Since the 20th century, 60 years developed a meteorological satellite is a major breakthrough in meteorological technology. The temporal and spatial continuity of the meteorological satellite cloud image data provided by it is unmatched by any previous detection method. The meteorological satellite cloud image plays an extremely important role in the generation of weather forecasts and the monitoring of the atmospheric environment. With the deepening of China’s climate risk assessment research and the development of economic construction, analyzing natural disasters from the perspective of risk has become a new perspective of disaster analysis. It helps decision makers to carry out natural climate warnings and develop disaster prevention measures and strategies. Targeted best tech policies to prevent problems. In this paper, the satellite image data and conventional detection data are used to analyze the climate of the newly built area in the early stage of development, and the crop suitability and probability distribution are combined. In the early stage of development, the risk model is constructed through the growth of crops, and the environmental conditions of the area are analyzed. Provide reference basis for the development and location of the new district.
Unsupervised Image Retrieval With Mask-Based Prominent Feature Accumulation

For unsupervised image retrieval, which features are chosen for final representation determines its performance. Nowadays, unsupervised methods can deal with most image retrieval tasks. However, it is still a challenging task to retrieve images with complex background. In this paper, we propose a new approach of mask-based prominent feature accumulation (MPFA), which utilizes MAX-Mask and SUM-Mask to retain significant features in each channel for all database images. Channels are then sorted by MPFA to select representative channels of feature maps extracted from pre-trained CNN. After that, the final image representation is generated by aggregating the selected channels. Experiments on the public datasets show improvement of our proposed approach compared to state-of-the-art methods, especially for images with complex background.
Task-Dependent Attention Allocation through Uncertainty Minimization in Deep Recurrent Generative Models

Allocating visual attention through saccadic eye movements is a key ability of intelligent agents. Attention is both influenced through bottom-up stimulus properties as well as top-down task demands. The interaction of these two attention mechanisms is not yet fully understood. A parsimonious reconciliation posits that both processes serve the minimization of predictive uncertainty. We propose a recurrent generative neural network model that predicts a visual scene based on foveated glimpses. The model shifts its attention in order to minimize the uncertainty in its predictions. We show that the proposed model produces naturalistic eye-movements focusing on salient stimulus regions. Introducing the additional task of classifying the stimulus, modulates the saccade patterns and enables effective image classification. Given otherwise equal conditions, we show that different task requirements cause the model to focus on distinct, task-relevant regions. The results provide evidence that uncertainty minimization could be a fundamental mechanisms for the allocation of visual attention.
Interactive Education Mobile Game Application: Petah Simpulan Bahasa

Abstract — Simpulan Bahasa which are commonly used amongst Malaysian people are a combination of two or three words that have a different meaning from the basic words. Thus, the importance of the Simpulan Bahasa in sustaining the Malaysian language cannot be denied and must be maintained and learned by the next generation. However, there are serious questions regarding the effectiveness and student's engagement of this conventional lecture approach because studying and memorizing the concepts from the teachers' note is considered as an ineffective way of teaching. (Aisyah Abdul Manap, 2013 [1]). Therefore, the purpose of this research is to develop an interactive education mobile game as an instrument for improving learning and mastery of the Simpulan Bahasa for the students.