Reviewerly/exHarmony huggingface dataset:
Supervised Deep Kriging for Single-Image Super-Resolution

We propose a novel single-image super-resolution approach based on the geostatistical method of kriging. Kriging is a zero-bias minimum-variance estimator that performs spatial interpolation based on a weighted average of known observations. Rather than solving for the kriging weights via the traditional method of inverting covariance matrices, we propose a supervised form in which we learn a deep network to generate said weights. We combine the kriging weight generation and kriging process into a joint network that can be learned end-to-end. Our network achieves competitive super-resolution results as other state-of-the-art methods. In addition, since the super-resolution process follows a known statistical framework, we are able to estimate bias and variance, something which is rarely possible for other deep networks.
An Exponential Tail Bound for the Deleted Estimate

There is an accumulating evidence in the literature that stability of learning algorithms is a key characteristic that permits a learning algorithm to generalize. Despite various insightful results in this direction, there seems to be an overlooked dichotomy in the type of stability-based generalization bounds we have in the literature. On one hand, the literature seems to suggest that exponential generalization bounds for the estimated risk, which are optimal, can be only obtained through stringent, distribution independent and computationally intractable notions of stability such as uniform stability. On the other hand, it seems that weaker notions of stability such as hypothesis stability, although it is distribution dependent and more amenable to computation, can only yield polynomial generalization bounds for the estimated risk, which are suboptimal. In this paper, we address the gap between these two regimes of results. In particular, the main question we address here is whether it is possible to derive exponential generalization bounds for the estimated risk using a notion of stability that is computationally tractable and distribution dependent, but weaker than uniform stability. Using recent advances in concentration inequalities, and using a notion of stability that is weaker than uniform stability but distribution dependent and amenable to computation, we derive an exponential tail bound for the concentration of the estimated risk of a hypothesis returned by a general learning rule, where the estimated risk is expressed in terms of the deleted estimate. Interestingly, we note that our final bound has similarities to previous exponential generalization bounds for the deleted estimate, in particular, the result of Bousquet and Elisseeff (2002) for the regression case.
Using Social Interactive Writing for English Language Learners (SWELL) with Fairy Tales Videos in Teaching Writing

This research aims to investigate whether the use of SWELL method with fairy tale videos improves the students’ writing ability and to find out which writing components mostly affected by the use of SWELL method. This research was conducted using a quasi-experimental design. The population of the research was the second grade students of SMPN 5 Marioriawa in academic year 2015/2016 which consisted of two classes (56 students). The sample was drawn using cluster random technique. The number of students in the class was 28 students. The research instrument used was writing test. The data obtained through the test were analyzed quantitatively by using 21.0 version of SPSS program. Based on the data analysis, the research findings reveals that there is a significant differentce of the students’ writing skill after being taught by using SWELL method. It is proved by the data that had been processed through SPSS program. The result showed that the mean score of students in experimental group was greater than the mean score in control group (85.03&gt;67.180, with t-test value is lower than the probability value (0.000&lt;0.05). It indicates that there is a significant improvement of students’ writing ability after the treatment. The writing skill of the second grade students of SMPN 5 Maroriawa can be improved significantly by using SWELL method with fairy tale videos. For the writing components that mostly affected by SWELL method. For different aspects of writing, content, organization, vocabulary, language use and mechanic, vocabulary is the most affected by the use SWELL method. The highest score in term of vocabulary was 18 with the percentage 90 with the highest converted score 87.55 categorized as very good categorization.
Learning (from) Deep Hierarchical Structure among Features

Data features usually can be organized in a hierarchical structure to reflect the relations among them. Most of previous studies that utilize the hierarchical structure to help improve the performance of supervised learning tasks can only handle the structure of a limited height such as 2. In this paper, we propose a Deep Hierarchical Structure (DHS) method to handle the hierarchical structure of an arbitrary height with a convex objective function. The DHS method relies on the exponents of the edge weights in the hierarchical structure but the exponents need to be given by users or set to be identical by default, which may be suboptimal. Based on the DHS method, we propose a variant to learn the exponents from data. Moreover, we consider a case where even the hierarchical structure is not available. Based on the DHS method, we propose a Learning Deep Hierarchical Structure (LDHS) method which can learn the hierarchical structure via a generalized fused-Lasso regularizer and a proposed sequential constraint. All the optimization problems are solved by proximal methods where each subproblem has an efficient solution. Experiments on synthetic and real-world datasets show the effectiveness of the proposed methods.
Classification of breast cancer grades using physical parameters and K-nearest neighbor method

Breast cancer is a health problem in the world. To overcome this problem requires early detection of breast cancer. The purpose of this study is to classify early breast cancer grades. Combination of physical parameters with k-nearest neighbor Method is proposed to detect early breast cancer grades. The experiments were performed on 87 mammograms consisting of 12 mammograms of grade 1,41 mammograms of grade 2 and 34 mammogram of grade 3. The proposed method was effective to classify the grades of breast cancer by an accuracy of 64.36%, 50% sensitivity and 73,5% specitifity. Physical parameters can be used to classify grades of breast cancer. The results of this study can be used to complement the diagnosis of breast mammography examination.
Automated Verification of Social Laws for Continuous Time Multi-Robot Systems

Designing multi-agent systems, where several agents work in a shared environment, requires coordinating between the agents so they do not interfere with each other. One of the canonical approaches to coordinating agents is enacting a social law, which applies restrictions on agents’ available actions. A good social law prevents the agents from interfering with each other, while still allowing all of them to achieve their goals. Recent work took the first step towards reasoning about social laws using automated planning and showed how to verify if a given social law is robust, that is, allows all agents to achieve their goals regardless of what the other agents do. This work relied on a classical planning formalism, which assumed actions are instantaneous and some external scheduler chooses which agent acts next. However, this work is not directly applicable to multi-robot systems, because in the real world actions take time and the agents can act concurrently. In this paper, we show how the robustness of a social law in a continuous time setting can be verified through compilation to temporal planning. We demonstrate our work both theoretically and on real robots.
Efektivitas Layanan Penguasaan Konten Menggunakan Model Pembelajaran Kooperatif Tipe Think Pair Share untuk Meningkatkan Keterampilan Bertanya Siswa dalam Belajar

Student with low asking skills tend to experience obstader in the learning process. Actually, the effort given by the counseling teacher hasn’t reached the taarget sa expected. This study aims to see the effectiveness of content mastery services using cooperative learning model type think pair share, to improve student questioning skills. This study uses a quantitative approch with design quasy experiment type the non equivalent control group in SHS N 1 Sungai Limau Kab. Padang Pariaman. The subjects are XI IPS 1 &amp; 3 class. Data collection techniques used questionnaire, than analyzed by different test technique wilcoxon and t-test. Research result show content mastery services used cooperative learning model type think pair share can improved student questioning skill and more effectiv than content mastery service without used learning model. Counseling teachers are expected to improve student’s questioning skill throught countent mastery services used cooperative learning proccess type think pair share.
Measurement Maximizing Adaptive Sampling with Risk Bounding Functions

In autonomous exploration a mobile agent must adapt to new measurements to seek high reward, but disturbances cause a probability of collision that must be traded off against expected reward. This paper considers an autonomous agent tasked with maximizing measurements from a Gaussian Process while subject to unbounded disturbances. We seek an adaptive policy in which the maximum allowed probability of failure is constrained as a function of the expected reward. The policy is found using an extension to Monte Carlo Tree Search (MCTS) which bounds probability of failure. We apply MCTS to a sequence of approximating problems, which allows constraint satisfying actions to be found in an anytime manner. Our innovation lies in defining the approximating problems and replanning strategy such that the probability of failure constraint is guaranteed to be satisfied over the true policy. The approach does not need to plan for all measurements explicitly or constrain planning based only on the measurements that were observed. To the best of our knowledge, our approach is the first to enforce probability of failure constraints in adaptive sampling. Through experiments on real bathymetric data and simulated measurements, we show our algorithm allows an agent to take dangerous actions only when the reward justifies the risk. We then verify through Monte Carlo simulations that failure bounds are satisfied.
Efficient Online Learning for Mapping Kernels on Linguistic Structures

Kernel methods are popular and effective techniques for learning on structured data, such as trees and graphs. One of their major drawbacks is the computational cost related to making a prediction on an example, which manifests in the classification phase for batch kernel methods, and especially in online learning algorithms. In this paper, we analyze how to speed up the prediction when the kernel function is an instance of the Mapping Kernels, a general framework for specifying kernels for structured data which extends the popular convolution kernel framework. We theoretically study the general model, derive various optimization strategies and show how to apply them to popular kernels for structured data. Additionally, we derive a reliable empirical evidence on semantic role labeling task, which is a natural language classification task, highly dependent on syntactic trees. The results show that our faster approach can clearly improve on standard kernel-based SVMs, which cannot run on very large datasets.
The weight distribution of a class of &lt;i&gt;p&lt;/i&gt;-ary cyclic codes and their applications

Cyclic codes over finite field have been studied for decades due to their wide applications in communication and storage systems. However their weight distributions are known only in a few cases. In this paper, we investigate a class of $ p$-ary cyclic codes whose duals have three zeros, where $ p$ is an odd prime. The weight distributions of the class of cyclic codes for all distinct cases are determined explicitly. The results indicate that these codes contain five-weight codes, seven-weight codes and eleven-weight codes. Some of these codes are optimal. Moreover, the covering structures of the class of codes are considered and being used to construct secret sharing schemes.
Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially

Semantic feature learning for natural language and programming language is a preliminary step in addressing many software mining tasks. Many existing methods leverage information in lexicon and syntax to learn features for textual data. However, such information is inadequate to represent the entire semantics in either text sentence or code snippet. This motivates us to propose a new approach to learn semantic features for both languages, through extracting three levels of information, namely global, local and sequential information, from textual data. For tasks involving both modalities, we project the data of both types into a uniform feature space so that the complementary knowledge in between can be utilized in their representation. In this paper, we build a novel and general-purpose feature learning framework called UniEmbed, to uniformly learn comprehensive semantic representation for both natural language and programming language. Experimental results on three real-world software mining tasks show that UniEmbed outperforms state-of-the-art models in feature learning and prove the capacity and effectiveness of our model.
Mining traces between source code and textual documents

Currently, researchers in computer science are dealing with a major challenge to link the source codes with the software documents. Writing informal documents by using natural and unstructured language causes this problem. In this paper, we present a model for recovery of traceable links between the source code and requirement documents. The proposed method in this paper is executed in four interconnected sections. The first section goes through extracting the features from the documents, which is followed by extracting the features from the source code. During the third section, abbreviations will be completed, using similarity measure as a feature. Finally, data mining algorithms will be implemented to find the hidden links between source code and software documentation. The most outstanding advantage of using this method is to be independent from the language. Also the preliminary results show that the proposed method has a good performance.
2FYSH: two-factor authentication you should have for password replacement

Password has been the most used authentication system these days. However, strong passwords are hard to remember and unique to every account. Unfortunately, even with the strongest passwords, password authentication system can still be breached by some kind of attacks. 2FYSH is two tokens-based authentication protocol designed to replace the password authentication entirely. The two tokens are a mobile phone and an NFC card. By utilizing mobile phones as one of the tokens, 2FYSH is offering third layer of security for users that lock their phone with some kind of security. 2FYSH is secure since it uses public and private key along with challenge-response protocol. 2FYSH protects the user from usual password attacks such as man-in-the-middle attack, phishing, eavesdropping, brute forcing, shoulder surfing, key logging, and verifier leaking. The secure design of 2FYSH has made 90% of the usability test participants to prefer 2FYSH for securing their sensitive information. This fact makes 2FYSH best applied to secure sensitive data needs such as bank accounts and corporate secrets.
Fusion Based Automatic Human Age Estimation Using Local Descriptors

This paper introduces a new age estimation method based on the fusion of local features extracted using histogram-based local texture descriptors.In this study, the age estimation performances of well-known powerful texture descriptor with improved modification Local Binary Patterns (mLBP), and Local Phase Quantization (LPQ), which have not been analysed in depth for age estimation, investigated.In addition, multi-scale and spatial texture analysis performed for all descriptors.In the spatial texture analysis, a new approach using Overlapped Blocks obtained by discrete blocks proposed to reduce the overlapped age groups between the neighbour age group.Then feature fusion performed to investigate the age estimation accuracies of different combinations of local texture descriptors.Multiple Linear Regression (MLR) used to estimate the specific age at the end of this proposed system.The results show that the age estimation accuracy of the proposed method is better when compared to previous methods on FG-NET, MORPH databases.
Enhancing the Anonymity in Information Diffusion Based on Obfuscated Coded Data

Linear network coding (LNC) is a promising approach to facilitate anonymity in information diffusion because each packet is generated by linearly combining multiple incoming packets. Since the coefficients used in the linear combination would reveal the correlation between incoming and outgoing packets at a node, most existing studies on anonymous LNC design focus on encrypting these coefficients. Despite the importance of these studies, the correlation of coded content can still be analyzed and the potential of un-encrypted LNC has not been fully exploited. In this paper, we tackle these issues and we propose a novel ALNCode scheme that can enhance anonymity by generating outgoing packets that are correlated to incoming coded packets of multiple flows. With solid theoretical analysis, we first prove the probability that incoming coded packets from different flows are correlated. Then, we prove that, if such correlation exists, we can design deterministic LNC to obfuscate the correlation of packets. With the same condition, we also prove the probability that a randomly generated coded packet is correlated to coded packets in other flows. Besides the theoretical study, we conduct extensive numerical experiments to understand the impacts of various coding parameters and the performance of ALNCode in real scenarios.
Mineral chemistry and thermometry of chlorites in mineralization zones and metamorphic rocks from Golgohar iron ore deposit (No. 1), Sirjan, Kerman

Mineral chemistry and thermometry of chlorites in mineralization zones and metamorphic rocks from Golgohar iron ore deposit (No. 1), Sirjan, Kerman
Correction to: Granular computing in mosaicing of images from capsule endoscopy

In the original publication, Acknowledgments was published incorrectly. The correct Acknowledgments is provided in this correction.
The Design and Simulation of Rose Harvesting Robot

The agriculture sector is backbone for several country economies; the economic status depends on the agriculture of those countries. If we increase the ease in agriculture process, with latest technologies through automation in processes such as cultivating, harvesting, spraying pesticides etc., it increases the growth of the economic status of the country by raising its GDP. Rose flower is one, having high demand in the society but the problem with this flower is its harvesting. Rose flower cultivation is one of the difficult methods, as to harvest the thorns on their stems cause's injuries and a training is required for the labours to learn harvesting. Decreasing the labour population day by day due to various problems increases the demand of labour and their cost. Due to the training and high labour costs in rose flower cultivation tends to increase in its production cost. To solve all these problems a rose harvesting machine which is fully automated is designed and it works with machine learning algorithms and image processing tool designed in Matlab.
Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems

Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation of candidate itemsets based on frequent itemsets found at the previous iteration, and pruning of clearly infrequent itemsets. The Dynamic Itemset Counting (DIC) algorithm is a variation of Apriori, which tries to reduce the number of passes made over a transactional database while keeping the number of itemsets counted in a pass relatively low. In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi many-core system for the case when the transactional database fits in main memory. Intel Xeon Phi provides a large number of small compute cores with vector processing units. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based internal layout for transactions and itemsets. This technique reduces the memory space for storing the transactional database, simplifies the support count via logical bitwise operation, and allows for vectorization of such a step. Experimental evaluation on the platforms of the Intel Xeon CPU and the Intel Xeon Phi coprocessor with large synthetic and real databases showed good performance and scalability of the proposed algorithm.
Analisis Kebutuhan Sumur Resapan Sebagai Salah Satu Upaya Dalam Mereduksi Banjir Genangan

Impermeable land in a region can not absorb rain water, so that rain water becomes run off and cause flood inundation. Absorption wells function to accommodate and absorb rain water into the soil. The purpose of this study to know the value of soil permeability, determine the dimensions and number of absorption wells in reducing flood. The study was conducted at Elementary School 1 Pulo Ie Kabupaten Nagan Raya with area of 1500 m2 and the number of buildings as many as 4 units. The absorption well planning refers to regulation of the Minister of Public Works 11/PRT/M/2014. The data used in the planning form average daily rainfall, flood volume, soil permeability value, runoff coefficient value, depth of ground water level and wide field of cistern. The result of analysis obtained maximum daily rainfall average maximum (R) equal to 36,25 mm/day, volumes of flood (Vab) 11,552 m3, cistern area 497 m2, runoff coefficient value of 0.75 and soil permeability value (K) 0.009725 cm/sec or 8.40 m/day. Rectangular Absorption wells with depth of well (H) 1.60 meters and base well (L) width 1.20 meters. The number of absorption wells required 5 units. Thus, the flood of inundation at the Elementary School 1 Pulo Ie can be accommodated by the absorption wells, so that the flood of inundation can be overcome.
Optimization study of fuzzy parametric uncertain system

This paper deals with the analysis and design of the optimal robust controller for the fuzzy parametric uncertain system. An LTI system in which coefficients depends on parameters described by a fuzzy function is called as fuzzy parametric uncertain system. By optimal control design, we get control law and feedback gain matrix which can stabilize the system. The robust controller design is a difficult task so we go for the optimal control approach. The system can be converted into state space controllable canonical form with the α-cut property fuzzy. For optimal control design, we find control law and get the feedback gain matrix which can stabilize the system and optimizes the cost function. Stability analysis is done by using the Kharitonov theorem and Lyapunov-Popov method. The proposed method applied to a response of Continuous Stirred Tank Reactor (CSTR).
Machine-Learning Billboard Collection [Humor]

Various puzzles, quizzes, games, humorous definitions, or mathematical that should engage the interest of readers.