Reviewerly/exHarmony huggingface dataset:
Orthogonal object proposal and its application

In this study, the authors propose an object proposal algorithm that can accurately propose object candidate regions at each frame, despite noise in a video. Accordingly, they define three orthogonal planes, namely vertical–horizontal, temporal–vertical, and temporal–horizontal planes. As these planes are orthogonal, they are the most compact planes that can span the spatiotemporal space of a video. Their algorithm selects good object proposals for the vertical–horizontal plane with the help of the object proposal results of the other planes. Experimental results demonstrate that the proposed algorithm produces better object proposals than the baseline algorithm and other state‐of‐the‐art methods. In particular, their method provides more accurate object proposals in challenging environments with severe noise and background clutter. In addition, the object proposal results are utilised for visual tracking problems, and the experimental results show that their visual tracker outperforms recent deep‐learning‐based trackers.
Analysis of Variance of Graph-Clique Mining for Scalable Proof of Work

Recently, Bitcoin is becoming one of the most popular decentralized cryptographic currency technologies, and Bitcoin mining is a process of adding transaction records to Bitcoin’s public ledger of past transactions or blockchain. To obtain a bitcoin, the mining process involves compiling recent transactions into blocks and trying to solve a computationally difficult puzzle, e.g., proof of work puzzle. A proof of work allows miners the ability to quantify how much work a given proof contains. Basically, the required time for mining is decided in advance, but problems will occur if the value is large for dispersion. In this paper, we first accept that the required time between consecutive blocks follows the exponential distribution. That is, the variance is stable as long as the expected time is fixed. Then, we focus on the graph clique mining technique proposed by the literature, like Tromp (BITCOIN 2015) and Bag-Ruj-Sakurai (Inscrypt 2015), which is based on a computational difficulty problem of searching cliques of undirected graphs, where a clique is a subset of vertices. In particular, when the clique size is two, graph clique mining can be used to gain Bitcoins. The previous work also claimed that if the clique size is parameterized and increased, even if the expected time is fixed, the variance would not be stable. However, no qualitative or quantitative results were given to support their claim. Motivated by this issue, in this work, we propose a simple search algorithm for graph cliques mining, and perform a small scale evaluation on Bitcoin and Graph cliques’s solo mining to investigate the variance issue.
Mean–semivariance optimality for continuous-time Markov decision processes

In this paper we study the mean–semivariance problem for continuous-time Markov decision processes with Borel state and action spaces and unbounded cost and transition rates. The optimality criterion is to minimize the semivariance of the discounted total cost over the set of all policies satisfying the constraint that the mean of the discounted total cost is equal to a given function. Under reasonable conditions, we show that the semivariance optimal value function is a solution to the optimality equation of the mean–semivariance criterion by an iteration approach. Moreover, we obtain the existence of mean–semivariance optimal policies from the optimality equation. Furthermore, we give a value iteration algorithm to compute approximately an optimal policy and the optimal value, and analyze the convergence of the algorithm.
A Clustering-Based Method for Detecting Text Area in Videos Recorded with the Aid of a Smartphone

Text detection is a crucial task in image processing and computer vision applications. Several methods have been proposed, but little attention has been given to the detection of text area in the video frame recorded with the aid of a smartphone. To answer this gap, we propose in this work a new method for text detection in video acquired by a smartphone. The method is based on the use of the Line Segment Detector (LSD) algorithm and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to detect the line segments that belong to the text region. Subsequently, we carry out a set of filters to select the four segments that represent the four sides of the text area in each video frame. The experimental results on ICDAR2015 Smartphone Document Capture Dataset demonstrate that the proposed method provides better performance and achieves promising detection accuracy.
Significant Regional Detection Based on Precise Edge Learning

The traditional saliency detection obtains significant regions by using the region contrast of color information. In complex and multi target images, the method often performs poorly, and the significant region can not be detected completely. Therefore, a significant region detection method based on accurate edge learning is proposed in this paper. First, in order to achieve better image cutting effect, we use multiple WMF (weighted median filter) to process the source image and then use the method of graph cut to divide the image to get some regions with similar features, that is, the segmentation graph. At the same time, we use background prior method to get the preliminary salient map. The edge learning area is obtained by combining the initial saliency map and the bootstrap map. The area is the foreground and the other regions are the background, and the convolution neural network VGG16 model is used to complete the detection. The algorithm is verified on the open dataset ECSSD. The results show that the algorithm has better recall and precision.
Determination of the Requirements for the Neural Network for Recognition Algorithm UHRSI

The Earth remote sensing is becoming a new and quickly developing multiscience area of a practical importance and different business applications. The IT industry now in accordance with the academic sector is developing new competences in the area which includes the development of new algorithms and calibration of existing and already working program complexes and libraries. Investigation about using neural networks for detection geo-objects on the satellite images are held by different research organizations. In this paper we present the new way of determination of the initial data, the architecture type of the neural network, the accuracy characteristics of the recognized objects for the algorithm of recognition of soil use areas of open type based on the data of ultrahigh resolution space imagery (UHRSI) which was made according to the practice on the newest satellite images. The results could be used in the various application of remote data analysis including satellite application in different branches of military and economic needs.
A criterion of perfect balance for shift-composition of functions over a finite alphabet

Abstract We prove a criterion of perfect balance for sliding superposition of functions over an arbitrary finite alphabet. We also give examples of applying this result to the construction of perfectly balanced functions that are not permutations with respect to the first and to the last variable.
MEMBANGUN KARAKTER MELALUI PEMBELAJARAN SASTRA: PROBLEMATIKA PEMBINAAN KARAKTER

Character education is an ongoing process for transforming and building individual behavior into human beings who have good knowledge, attitude, and skills. Character building on the learner is needed because of the problematic in learning about dishonesty, indiscipline, and the violence done by the learner. Efforts to build individual characters can be implemented in learning materials and learning models. One effort to build the character of learners through literary learning. Literary works play an important role in the implementation of character education. The purpose of learning literature is learners are expected to develop honest behavior, discipline, responsibility, care, polite, environmentally friendly, mutual cooperation, cooperation, love peace, responsive and proactive by way of appreciating the literature to refine the character of learners. In the Kurikulum 2013, literary learning materials used as a means of building the character of learners are short story texts, fable, pantun (poem), and novel.
Big Data and Further Analysis

‘Big Data’ is a relative term used to describe a tremendously large data. The large data is inclusive of audio, video, unstructured text, social media information, and so much more. Its concept has gained wide publicity or attention in many disciplines. Interestingly, ‘Big data’ means different things to various disciplines. For example, in atmospheric study, ‘big data’ means volume of data as large as one terabytes and above. Meanwhile in particle physics, ‘big data’ is in petabytes and above. For communication outfit, ‘big data’ may mean zettabytes. Hence, there is the need for disciplinary and multi-disciplinary outfit or research institutes to embrace ‘big data’ technologies such as in-memory technologies, sensory (Internet of Things) equipment, Cloud Data Storage, magnetic storage, Big Data databases (e.g. MongoDB) etc.
Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car

The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.
Bio-sensor based on multiclass support vector machine with a reject option

In this work we explore the problem of multiclass classification where the classifier may abstain from classifying on some observation. We derivate a new surrogate loss function and a multiclass decision rule by using a reject threshold on posterior probabilities in the Bayes decision rule, known as Chow's rule. The goal of the decision rule is to minimize the value of given misprediction and rejection cost functions specified by the user. We suggest a general training algorithm by plug-in the surrogate loss in to Support Vector Machine (SVM) structure. We then test the algorithm on various real -life problem in the photonic medical sensing field where accuracy is critical. We present an example of a non-invasive way of detecting glucose level in blood to help patients with Diabetes mellitus diseases while the sensing is performed with speckle-based approach to analyze remote sensing of biomedical parameters. The results will show that the value of the reject threshold has importance in determining how many samples to reject and in the overall accuracy of prediction. As the threshold grow so does the number of samples rejected and overall accuracy, meaning that only samples with strong confidence are outputted in the classification process. A very important point in working with reject option is that there is a tradeoff between the number of samples being rejected and the accuracy of the labeled samples. High precision comes with high rejection rate, while low rate of rejection derogates from the general correctness of the output.
Salient Spin Images : an application to archaeozoology for bones recognition

Archaeozoology or zooarchaeology is the scientific field that aims to rebuild natural and cultural relations between human and animals. To reach that objective, archaeozoologists study faunal remains, such as bones. In this regard, a 3D representation is put into place. This would allow a better understanding of the initial spatial positions of the animals in the pits, and would make it possible to examine in a more rigorous manner the explanatory hypotheses by manipulating the 3D view of the pit and bones. This manipulation invokes the use of computer vision techniques, including 3D object recognition methods. In this work we present a novel approach to recognize 3D bones in occluded and cluttered scenes inspiring from a stat of the art method called spin images. We combine this method with saliency concept to improve the the robustness to occlusions and clutters significantly and also to decrease the complexity of the algorithm.
360-Degree Panoramic Image Stitching for Un-ordered Images Based on Harris Corner Detection

Objectives: This work aims to provide a system to arrange images automatically and produce the High-resolution panoramic images of a 360-degree wide range. Methods: This work consists of many stages which start by arranging the set of unordered input images. The Harris corner detector is used in the process of detecting key-points. Euclidean distance measure is used to compute the distance between points and determine matches. The stage of stitching consists of several steps which are detecting key-points, matching, estimating the shifting matrix between images by using Random Sample Consensus (RANSAC) algorithm, aligning, and blending. Findings: Experiments show that this method is able to arrange different sets of images, where samples taken are between 15–18 images. Depending on the threshold values in the verification process applied to the matched points, which range from (0.4 to 0.6), the number of extracted features and the degree of accuracy can be controlled. Also, the process of blending images based on the taking of a different percentage of the overlapping areas according to the proximity to the contact line gave a distinctive blending result. Application: Production of panoramic images are used in real estate photography, the creation of an attractive experience on viewing all location details, and lots of other uses. Keywords: Blending, Harris Corner Detector, Image Stitching, Panorama
An Extension to Traditional Qwerty Cryptographic Method

Cryptography comes from the Greek word kryptos which means hidden. The aim of our research is to provide secrecy for the data during transmission. In this we discuss about the qwerty cipher algorithm, its merits and demerits. The existing qwerty algorithm is based on the use of a keyboard which is not much secured. We have proposed an enhancement to the existing algorithm by using a diagonalized qwerty algorithm which is more secure and highly difficult for the intruder to understand or decrypt the cipher text, even if it is decrypted the result would be gibberish.
Deterministic polynomial factoring over finite fields: A uniform approach via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:mi mathvariant="script">P</mml:mi></mml:math>-schemes

We introduce a family of combinatorial objects called P-schemes, where P is a collection of subgroups of a finite group G. A P-scheme is a collection of partitions of right coset spaces H\G, indexed by H∈P, that satisfies a list of axioms. These objects generalize the classical notion of association schemes as well as m-schemes (Ivanyos et al., 2009). We apply the theory of P-schemes to deterministic polynomial factoring over finite fields: suppose f˜(X)∈Z[X] and a prime number p are given, such that f(X):=f˜(X)modp factorizes into n=deg⁡(f˜) distinct linear factors over the finite field Fp. We show that, assuming the generalized Riemann hypothesis (GRH), f(X) can be completely factorized in deterministic polynomial time if the Galois group G of f˜(X) is an almost simple primitive permutation group on the set of roots of f˜(X), and the socle of G is a subgroup of Sym(k) for k up to 2O(log⁡n). This is the first deterministic polynomial-time factoring algorithm for primitive Galois groups of superpolynomial order. We prove our result by developing a generic factoring algorithm and analyzing it using P-schemes. We also show that the main results achieved by known GRH-based deterministic polynomial factoring algorithms can be derived from our generic algorithm in a uniform way. Finally, we investigate the schemes conjecture in Ivanyos et al. (2009), and formulate analogous conjectures associated with various families of permutation groups. We show that these conjectures form a hierarchy of relaxations of the original schemes conjecture, and their positive resolutions would imply deterministic polynomial-time factoring algorithms for various families of Galois groups under GRH.
K-Means Optimization Algorithm Based on Tightness Mutation

The random initial clustering center is K - means algorithm to determine the center of traditional way, there will be a clustering result is not stable, optimized to determine the initial clustering center K - means algorithm requires some parameter values, artificial subjective makes the clustering results. Therefore, based on the compactness information of the sample distribution in space, this paper proposes a k-means algorithm to optimize the initial clustering center by using the compactness mutation. The algorithm using the sample space distribution information, through calculating the tightness of the spatial distribution of the sample whether mutations sample information intensity, and based on class sample tightness mutation segmentation principle of cluster center, puts forward the tightness mutation to optimize the initial clustering center of the K - Means algorithm, through the optimization of K - Means clustering algorithm in the UCI machine learning database data set Sentiment labelled sentences and Sentence experiments on Corpus show that the algorithm not only can get better clustering results, The clustering results have high stability.
A robust unscented transformation for uncertain moments

This paper proposes a robust version of the unscented transform (UT) for one-dimensional random variables. It is assumed that the moments are not exactly known, but are known to lie in intervals. In this scenario, the moment matching equations are reformulated as a system of polynomial equations and inequalities, and it is proposed to use the Chebychev center of the solution set as a robust UT. This method yields a parametrized polynomial optimization problem, which in spite of being NP-Hard, can be relaxed by some algorithms that are proposed in this paper.
Retorica e logica nella critica di Lorenzo Valla del quadrato delle opposizioni

In this paper I discuss Lorenzo Valla's criticism of the traditional Square of Opposition displayed in the second book of the Dialectica. I show that, according to Valla, the opposition rules of the propositions must take into account both common speeches and the correct use of Latin language, not the formal link occurring between the parts of propositions. In Valla's perspective, this theoretical change is carried out through rhetoric and philology, and it involves a reassessment of the arts of the trivium. As regards this topic, I argue that Valla aims niether to reduce dialectic to rhetoric nor to replace the former with the latter, but rather to establish some rhetorical principles as a better-suited way to set the opposition rules, because they take into account the linguistic context in which these rules apply.
Evaluating Reading Material Formats on Children Learning in Science Education

Children nowadays are exposed with a variety of reading formats, however their influence on different types has not yet fully explored.This study examines learners' learning achievement and motivation involving in three different reading formats, i.e., text books, pop-up books, and AR e-books, in the context of natural science instruction focusing on household plants.Study participants of the 4th graders included six classes in northern Taiwan and were paired to form a control group of 43 students, an experimental group A with 52 students, and an experimental group B with 53 students.The results showed that students who learned with the AR e-book demonstrated significant improved learning achievement over those who used the printed textbook.As for learning motivation, no significant difference was found between the printed textbook and pop-up book groups, both were significantly better than that of the AR e-book group.This research contributed to the evaluation of the three reading formats that inherit various degrees in interactivity and digitization.The results could be useful when deciding an appropriate reading format under the consideration of reading comprehension and reading motivation.Further study include the improvement of learning motivation in the AR e-book.
SKOS-Based Concept Expansion for LOD-Enabled Recommender Systems

This paper presents a concept expansion strategy for Linked Open Data-enabled recommender systems (LDRS). This strategy is based on annotations from Simple Knowledge Organization System (SKOS) vocabularies. To this date, the knowledge structures of SKOS graphs have not yet been thoroughly explored for item similarity calculation in content-based recommender systems (RS). While some researchers have already performed an unweighted concept expansion on skos:broader links, the quantification of the relatedness of concepts from SKOS graphs with quality issues, such as the DBpedia category system, should be further investigated to improve recommendation results. For this purpose, we apply our approach in conjunction with a suitable concept-to-concept similarity metric and test it on three different LDRS datasets from the multimedia domain (i.e., movie, music and book RS). The results showed that our approach has a diversifying effect on result lists, while at least providing the same level of accuracy as a system running in non-expansion mode.
Application a machine vision system in labelling machines

Machine Vision systems are becoming more and more significant recently. Set of cameras connected with a PC computer have a great potential in solving many problems that can occure in the industry. Labeling machnes are reasonable application example of these systems. Within this work the construction of labeling machine was presented and concept of using cameras to check label quality, provide help with pre-setting of the machine and monitor the whole labeling process was discussed. This paper describes the proposal of the solution, that will be universal and possibile to apply on every labeling machine with the similar construction to one described below. Moreover, this application could be considered as an inspiration to develope new vision system based structures with other kind of machines, by using same solutions as those shown in this paper.
Securing Sensitive Information Files Based on Session Keys using Numeric Encryption and Bit Scrambling

Objectives:The study of proposed method deeply focuses on encrypting and decrypting the file contents using session based keys.Methods/Statistical Analysis: Encrypt_transmission and decrypt_receiver algorithms are used.Findings: Transmission of information through the network in the highly technological era has become a routine process.Transmission of information includes personal details, credit card and debit card numbers, Account transaction and other sensitive issues.As a result securing this type of sensitive information from hackers is considered as a daunting task.Most of the security techniques are vulnerable these days.Taking into account all the above factors an efficient algorithm has been proposed based on the user login for encryption and decryption.The algorithm works uniquely for every user.Application/Improvements: This algorithm is also suitable for financial related transactions.