Reviewerly/exHarmony huggingface dataset:
Perancangan Media Interaktif Gerak Tari Topeng Patih pada Wayang Topeng Malangan Menggunakan Sensor Kinect

Tari Topeng Patih is a part of the traditional dance performance art “Wayang Topeng Malangan” with a religious background which grew up around people in Malang City, East Java. Presently, art gallery of Tari Topeng Malangan has been difficult to be found. In addition to the difficulty of finding this art gallery even in the regions of origin, the problems is also about the interest and enthusiastic public, especially teenagers in Malang to learn this dance is still very rare. As a result, this art is slowly deteriorated and increasingly falling by the presence of various modern performances. In regarded to learn Tari Topeng Patih, the conventional learning activities requiring a special place such as a dance studio that is generally located far away from downtown Malang. Thus to overcome this problem is requires an instructional media that can be used as an alternative solution to learn and obtain information about Tari Topeng Patih, one way may be able to use the simulation method by utilizing the technology and education. The results of the design is the interactive media learning and simulation for basic motion of Tari Topeng Patih by using Kinect media.
Exemplar VAEs for Exemplar based Generation and Data Augmentation

We introduce Exemplar VAEs, a family of generative models that bridge the gap between parametric and non-parametric, exemplar based generative models. Exemplar VAE is a variant of VAE with a non-parametric prior in the latent space based on a Parzen window estimator. To sample from it, one first draws a random exemplar from a training set, then stochastically transforms that exemplar into a latent code and a new observation. We propose retrieval augmented training (RAT) as a way to speed up Exemplar VAE training by using approximate nearest neighbor search in the latent space to define a lower bound on log marginal likelihood. To enhance generalization, model parameters are learned using exemplar leave-one-out and subsampling. Experiments demonstrate the effectiveness of Exemplar VAEs on density estimation and representation learning. Importantly, generative data augmentation using Exemplar VAEs on permutation invariant MNIST and Fashion MNIST reduces classification error from 1.17% to 0.69% and from 8.56% to 8.16%.
Image encryption algorithm based on gradient decomposition and improved logistic map

We propose an image encryption algorithm based on gradient decomposition and an improved logistic map. The original image is first decomposed into three subimages with different gradient sizes using the gradient decomposition. The pixel position of the three subimages is then changed by applying Arnold transformation, and the random sequence generated by the improved logistic map is used to perform the XOR operation on the three subimages. Subsequently, the row pixel values of the three subimages are converted into binary sequences, and then the binary sequences are shuffled with the random sequences generated by the improved logistic map. Finally, the three subimages are obtained as the R, G, and B components of the color image to generate the final encrypted image. For image decryption, the original image can be simply restored by joining the three subimages. The experimental results show that the improved logistic map has a larger key space and better randomness compared with the classical logistic map. Furthermore, the proposed encryption scheme exhibits better robustness against various attacks than several existing encryption algorithms.
Collision-Free Navigation Using Laser Scanner and Tablet Computer for an Omni-Directional Mobile Robot System with Active Casters

A novel mobile system for effective object transportation was developed in this paper. By installing the proposed multiple double-wheel-type active casters to an object, the system itself becomes an omni-directional mobile robot that can be controlled with a tablet computer in a teleoperation manner. Each active caster was designed as an independent module having a micro-computer to control motors of both wheels; a Bluetooth communication component to connect with a tablet computer; a battery as a power source and so on. The operator's command for the desired motion of the main platform is transformed into an appropriate velocity command for each wheel based on the kinematic relationship between the object and the wheel coordinates. Collision avoidance navigation algorithm with a laser scanner attached to the object and a user interface with a tablet computer are also proposed for safe usage in real fields. The developed system and the collision avoidance algorithm were demonstrated through experiments.
Challenges for the Representation of Morphology in Ontology Lexicons

Recent years have experienced a growing trend in the publication of language resources as Linguistic Linked Data (LLD) to enhance their discovery, reuse and the interoperability of tools that consume language data. To this aim, the OntoLex-lemon model has emerged as a de facto standard to represent lexical data on the Web. However, traditional dictionaries contain a considerable amount of morphological information which is not straightforwardly representable as LLD within the current model. In order to fill this gap a new Morphology Module of OntoLex-lemon is currently being developed. This paper presents the results of this model as on-going work as well as the underlying challenges that emerged during the module development. Based on the MMoOn Core ontology, it aims to account for a wide range of morphological information, ranging from endings to derive whole paradigms to the decomposition and generation of lexical entries which is in compliance to other OntoLex-lemon modules and facilitates the encoding of complex morphological data in ontology lexicons.
Local Turn-Boundedness: A Curvature Control for Continuous Curves with Application to Digitization

This article focuses on the classical problem of the control of information loss during the digitization step. The properties proposed in the literature rely on smoothness hypotheses that are not satisfied by the curves including angular points. The notion of turn introduced by Milnor in the article On the Total Curvature of Knots generalizes the notion of integral curvature to continuous curves. Thanks to the turn, we are able to define the local turn-boundedness. This promising property of curves does not require smoothness hypotheses and shares several properties with the par(r)-regularity, in particular well-composed digitizations. Besides, the local turn-boundedness enables to constrain spatially the continuous curve as a function of its digitization.
Phylotastic: Improving Access to Tree-of-Life Knowledge With Flexible, on-the-Fly Delivery of Trees

A comprehensive phylogeny of species, i.e., a tree of life, has potential uses in a variety of contexts, including research, education, and public policy. Yet, accessing the tree of life typically requires special knowledge, complex software, or long periods of training. The Phylotastic project aims make it as easy to get a phylogeny of species as it is to get driving directions from mapping software. In prior work, we presented a design for an open system to validate and manage taxon names, find phylogeny resources, extract subtrees matching a user’s taxon list, scale trees to time, and integrate related resources such as species images. Here, we report the implementation of a set of tools that together represent a robust, accessible system for on-the-fly delivery of phylogenetic knowledge. This set of tools includes a web portal to execute several customizable workflows to obtain species phylogenies (scaled by geologic time and decorated with thumbnail images); more than 30 underlying web services (accessible via a common registry); and code toolkits in R and Python (allowing others to develop custom applications using Phylotastic services). The Phylotastic system, accessible via http://www.phylotastic.org , provides a unique resource to access the current state of phylogenetic knowledge, useful for a variety of cases in which a tree extracted quickly from online resources (as distinct from a tree custom-made from character data) is sufficient, as it is for many casual uses of trees identified here.
Synthetic Minority Over-Sampling for Improving Imbalanced Data in Educational Web Usage Mining

Educational data mining is the method for extracting and discovering new knowledge from education data. As education data is often complex and imbalanced, it requires a data preprocessing step or learning algorithms in order to obtain accurate analysis and interpretation. Many studies emphasize on classification and clustering methods in order to get insight and comprehensive knowledge from education data. However, a small number of previous works exclusively focused on the preprocessing of education data, particularly on the topic of the imbalanced dataset. Therefore, this research objective is to enhance the accuracy of data classification in educational web usage data. Our study involves the application of synthetic minority over-sampling techniques (SMOTE) to preprocess the raw dataset from web usage data. The minority class is a group of the students who failed the examination and the majority class is the students who passed the examination. In our experiments, four synthetic minority over-sampling methods are applied, SMOTE, and its variants: Borderline-SMOTE1, Borderline-SMOTE2, and SVM-SMOTE, in order to balance the number of samples in the minority class. The experiments are evaluated by comparing the results from well-known classification methods that are Naive Bayesian, decision tree, and k-nearest neighbors. The study experiments with real-world datasets from education data. The results present that synthetic minority over-sampling methods are capable of improving the detection of the minority class and achieve improving classification performance on precision, recall, and F1-value.&#x0D; Ed
Web Crawler of Power Grid Based on Selenium

Power grid companies have a massive amount of structured data that possesses potential appliance value. To protect information safety, power grid companies isolate their intranet from internet and require strong authentication to access databases. We developed a web crawler system to collect data from the intranet web pages, and utilize selenium, an automatic testing tool, to simulate human actions to avoid authentication. We proposed a depth-first search strategy to fulfill the need of topical data acquisition. The system provides a way to collect power data efficiently and it can help researchers and engineers build smart power grid.
Can e-Authentication Raise the Confidence of Both Students and Teachers in Qualifications Granted Through the e-Assessment Process?

The EU-funded TeSLA project - Adaptive Trust-based e-Assessment System for Learning (http://tesla-project.eu) has developed a suite of instruments for e-Authentication. These include face recognition, voice recognition, keystroke dynamics, forensic analysis and plagiarism detection were designed for integration within a university's virtual learning environment. These tools were trialed across the seven partner institutions: 4,058 participating students, including 330 Students with special educational needs and disabilities (SEND); 54 teaching staff.&#x0D; This paper describes the findings of this large-scale study where over 50% of students gave a positive response to the use of these tools. In addition, over 70% agreed that these tools were 'to ensure that my examination results are trusted' and 'to prove that my essay is my own original work'. Teaching staff also reported positive experiences of TeSLA: the figure reaching 100% in one institution. We show there is evidence that a suite of e-authentication tools such as TeSLA can potentially be acceptable to students and staff and be used to increase trust in online assessment. Also, that whilst not yet perfected for SEND students it can still enrich their experience of assessment. We find that care is needed when introducing such technologies to ensure the building of the layers of trust required for their successful adoption.
Iterative Truncated Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.
Payback of Image Encryption Techniques: A Quantitative Investigation

Recently, it is one of the concerns to protect multimedia applications with storage of images being an important security issue. The process used to encrypt data and protect these multimedia applications is encryption. Present cryptography provides crucial techniques in order to secure information and these encryption techniques are utilized for protection of private data from the unauthorized user. In this paper, literature reviews based mostly upon numerous secret writing techniques are explained and also the poll taker are going to be ready to get a plan for competent technique to be used.
Synthesizing Imperative Code from Answer Set Programming Specifications

We consider the problem of obtaining an implementation of an algorithm from its specification. We assume that these specifications are written in answer set programming (ASP). ASP is an ideal formalism for writing specifications due to its highly declarative and expressive nature. To obtain an implementation from its specification, we utilize the operational semantics of ASP implemented in the s(ASP) system. This operational semantics is used to transform the declarative specification written in ASP to obtain an equivalent efficient program that uses imperative control features. This work is inspired by our overarching goal of automatically deriving efficient concurrent algorithms from declarative specifications. This paper reports our first step towards achieving that goal where we restrict ourselves to simple sequential algorithms. We illustrate our ideas through several examples. Our work opens up a new approach to logic-based program synthesis not explored before.
Risk Management and Financial Performance of Listed Deposit Money Banks in Nigeria

The process of financial intermediation by Deposit Money Banks (DMBs) whereby funds are channeled from the surplus to deficit sectors of the economy involves borrowing on short-term and lending on long-term bases.This phenomenon, according to Adegbite (2017) promotes savings-investment process, capital formation and real growth of the economy.However, the short-term versus long-term dichotomy has created a risky adventure that throws at the DMBs, liquidity, profitability and going concern challenges.The longer tenured loans make credit payment period longer than the debt collection period of the depositors who can withdraw their deposits on demand.Frank Knight (1921), the most famous scholar to formalize definition of risk and make a distinction between risk and uncertainty (Holton, 2004) defines risk as situations where the outcome of a given action is unknown but the odds are measurable.Uncertainty on the other hand refers to a situation where all needed information to set the odds in the course of a given action are unknown.Recent global economic crisis has revealed that, it is difficult for institutions to accurately capture the riskiness of their activities.In managing risks, rather than trying to hedge against risk, there is a strategic step to determine the risks to ignore, the ones to reduce or eliminate and those ones to exploit in order to take advantage of opportunities to achieve the objectives of the organisation.Increasing exposures to some risk is an integral part of business success, and any entity that wants large rewards must be ready to take considerable amount of risk (Damodaran, 2017).For any business venture of which the DMBs are part, Audu (2014) submitted that it is difficult to evade risk altogether because without taking some level of risk, the returns from operations will no doubt be compromised and therefore Audu advocated avoidance of risks as much as it is feasible saying that the rational approach to risk, is at the very least to restrict exposure to it.According to Harle, Havas, Kremer, Rona and Samandari (2015), the analysis of stress has come up as a major supervisory instrument for financial stability, and banks have strengthened their risk lifestyles by always involving members of the board in major risk decisions.Nigerian economy was not shielded from the global financial market crises of 2007/2008 despite initial government posture of immunity.NDIC (2009) reported that the major weaknesses in the Nigerian banking sector include weak corporate governance, ineffective risk management strategies, heavy deteriorating facilities, sudden capital mop up and extreme illiquidity.
Efficient identity-based public-key quantum signature scheme

Based on the Hadamard operator and Bell states, an identity-based public-key quantum signature scheme is proposed. In our scheme, the signer’s identity is used as her public key. Her private keys are generated by the trusted private key generator (PKG). Given a quantum signature, the verifier can verify the validity of the quantum signature by the signer’s identity information without any public-key certificate. So, our quantum signature has the advantages of the identity-based signature system. On the other hand, in our scheme, it need not use the long-term quantum memory. The verifier need not perform any quantum swap test or state comparison algorithm. What is more, our quantum signature is secure against PKG’s forgery attack. And the disputation of losing quantum signature can be arbitrated by PKG. Therefore, our scheme is relatively more practical and efficient than the similar schemes.
An attribute-based encryption scheme based on unrecognizable trapdoors

Attribute-Based Encryption (ABE) has been widely used for ciphertext retrieval in the cloud environment. However, bi-flexible attribute control and privacy keywords are difficult problems that have yet to be solved. In this paper, we introduce the denial of access policy and the mutual matching algorithm of a dataset used to realize bidirectional control of attributes in the cloud server. To solve the problem of keyword privacy, we construct a security trapdoor by adding random numbers that effectively resist keyword guessing attacks from cloud servers and external attackers. System security is reduced to the Deterministic Bilinear Diffie-Hellman (DBDH) hypothesis problem. We validate our scheme through theoretical security analysis and experimental verification. Experiments are conducted on a real dataset, and results show that the scheme has higher security and retrieval efficiency than previous methods.
Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis

Purpose of this paper is to present a method for visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endocytoscopic images. Endocytoscope enables us to perform direct observation of cells and their nuclei on the colon wall at maximum 500-times ultramagnification. For this new modality, computer-aided pathological diagnosis system is strongly required for the support of non-expert physicians. To develop a CAD system, we adopt convolutional neural network (CNN) as the classifier of endocytoscopic images. In addition to this classification function, based on CNN weights analysis, we develop a filter function that visualises decision-reasoning regions on classified images. This visualisation function helps novice endocytoscopists to develop their understanding of pathological pattern on endocytoscopic images for accurate endocytoscopic diagnosis. In numerical experiment, our CNN model achieved 90 % classification accuracy. Furthermore, experimental results show that decision-reasoning regions suggested by our filter function contain characteristic pit patterns in real endocytoscopic diagnosis.
Computing Solution for the Recognition of Basic Actions of Violence in Real Time, from the use of Convolutional Neural Networks, Video Sequences and High Performance Computing

Among the most important social problems that Peru is currently facing, the high rates of domestic violence (violence against women and children) and criminal acts (assaults and robberies) frequently carried out on the streets stand out. In this work, a real-time tool for the detection of two types of violent actions is proposed: kick and punch.This research proposes to use the CNN called YOLO (You Only Look Once). The methodology involves Supervised Learning and Transfer Learning techniques since there is a small batch of data for training. In addition, a database of 1000 images (Frames) has been generated from 90 video sequences showing violence, which were obtained from the internet (YouTube) and by own elaboration (video recording). Taking into account that conventional computers have many limitations and that this type of work requires a large computational capacity, the processing was carried out in the IIAP "Manati" Supercomputer, in this way the tool can be run in real time.This computer solution achieved 84% accuracy, to detect two main acts of violence: punch and kick; which shows an appropriate result for the use and application of the tool. The results are promising and show that the proposed strategy is adequate to reach a solution.
Hybrid Big Bang-Big Crunch Algorithm for Cluster Analysis

Data clustering is an exploratory technique that organizes the data objects into different clusters in a competent way. There are number of techniques reported in clustering field. Several shortcomings associated with these techniques have been identified and resolved such as initial cluster center selection, number of clusters, slow convergence rate, local optima etc. In present work, a hybrid version of the big bang-big crunch (BB-BC) algorithm is developed to optimize clustering problems. The proposed algorithm work in two stages, initialization and optimization. The K-means algorithm act as initiation arbitrator to generate the initial population. While the big bang-big crunch algorithm acts as an optimizer to obtain the best solution. Here, the cluster centers generated from K-means are treated as preliminary population in BB-BC algorithm. The performance of proposed hybrid BB-BC algorithm is examined over seven benchmark datasets and compared with BB-BC, ACO, GA, PSO and K-means clustering algorithms. From the experimental results, it is clarified that proposed algorithm gives better clustering solution than rest of algorithms.