Reviewerly/exHarmony huggingface dataset:
PENERAPAN METODE WATERFALL PADA SISTEM INFORMASI MANAJEMEN BUKU PERPUSTAKAAN BERBASIS WEB

The development of increasingly advanced information technology can provide many benefits for completing work quickly and accurately. One example that requires the delivery of information quickly and accurately is the field of library, this is in accordance with the function of the library which is the heart of education.&#x0D; Most libraries are still many who adhere to a conventional system, of course this will result in disruption of the continuity of the process of managing books in the library. Therefore, the author takes the theme of this study regarding Library Book Management Information Systems Based on Websites by using the waterfall method on software development as well as methods of observation and literature on data collection.&#x0D; This Information System is the best solution for problem solving in managing library books. With the use of managed computer data technology becomes faster, reducing inefficient time and reducing the occurrence of errors in processing data.
Semantic Interaction with Human Motion Using Query-Based Recombinant Video Synthesis

The ability of a machine to understand the motion and behaviour of a particular actor is a very important task in machine vision. This problem has so many possible applications in domains such as motion retargeting, robot navigation, healthcare, psychology, augmented reality applications such as games etc. In this paper we demonstrate a human-robot interaction system based on a gestural query, where the computer response is a computer generated video of another human movement. This work differs from other recent video retargeting systems since it is not meant to modify the target video as such, but rather query a video database for the most responsive segment through gestural interpretation process. For this purpose we developed a generative video system capable of extracting the latent representation of free movements such as dance and expressive gesture, and querying and re-editing multiple found video segments in response to an input movement query. One of the main challenges in this approach is finding the "units" of continuous movement input so that both the style of the target video and the relevant aspect of the query video would be related in a meaningful way. In this paper we describe a gestural motif extraction system that combines deep feature learning with structural similarity analysis to allow such query based human-computer motion interaction.
Pedestrian Detection and Tracking: A Driver Assistance System

Detection and tracking of pedestrian is a challenging task due to variable appearances, wide range of poses, and irregular motion of pedestrian along with motion of tracking camera under complex outdoor environmental conditions. In this paper, we propose an algorithm for pedestrian detection and tracking using HOG descriptors and particle filtering technique. A robust algorithm for pedestrian detection is proposed which works under nonlinear motion and overcomes occlusions. The performance of the above algorithm is tested for outdoor environment using standard dataset. The particle filter has benefits of handling nonlinear motion and occlusions, and they concentrate consecutively on the higher density regions of the state space and it is simple to realize which provides a robust tracking environment. Performance comparison of particle with conventional Kalman is also presented for the above-said cases.
A New Technique for Extrinsic Text Summarization

Text summarization is the process where a text document is downsized, with a computer program, so as to create a summarized text and possesses the most essential points of the primary text. The present work is one new type of extrinsic technique of summarization, which finds an exemplary subset of the original sentence set, which possesses the precise erudition of the primary text.
Lacunae in Forensic Handwriting Examination: Scope for Exploitation

The process of forensic handwriting examination consists of two steps: the first step is the objective phase of the process and requires correct ocular observation to make a note of similarities and dissimilarities in the two samples of writing; the second step is subjective phase and depends on proper evaluation of what is observed based on expert’s belief, assumption and experience. The second phase, because of its subjective nature, can also be exploited by dishonest expert resulting in conflicting evidence. This scenario is obviously disruptive for justice, and generally weakens the credibility of this type of evidence.
HCI in China

Human Computer Interaction (HCI) is experiencing explosive growth in both Chinese industry and academia. We propose to organize an international workshop to coordinate and unite the ongoing efforts, and to facilitate collaboration between local community with the global HCI community. This extended abstract describes the background, goals, organizers, themes, and plans of the proposed workshop.
Strongly secure ramp secret sharing with more participants based on Reed-Solomon codes

The number of participants in the McEliece-Sarwate strongly secure ramp secret sharing scheme is at most q − L, where q is the size of each share and L is the number of symbols in the secret. We propose another construction of strongly secure ramp secret sharing that can support q participants also based on the Reed-Solomon codes.
Architecture-Based Dynamic Evolution Runtime Environment (ADERE) for Service-Based Systems

Continuous availability and flexible customization are key requirements in most of today's SaaS multi-tenant sys-tems. In these systems, shutting down and restarting for an up-date comes with a high risk and increased cost. Providing a runtime evolution-centric environment would help in providing highly available systems. However, it is crucial that this solution ensures the system conformance to its architectural constraints, when the runtime evolution occurs, in addition to appropriate state handling. The solution must also consider a system with multiple tenants allowing independent customization for each tenant application, without affecting participating tenants. In this paper, we present an architecture-aware runtime environ-ment (ADERE) for dynamic SaaS evolution. It builds on the advantages of existing approaches and aims at providing a mid-dleware layer amenable to highly available and highly customi-zable systems. ADERE comes with a light API that is easy to use within applications code.
Research on image denoising algorithm based on improved anisotropic diffusion synthetic aperture radar

The presence of speckle noise seriously affects the application of synthetic aperture radar (SAR) images in image fusion, so it is especially important to suppress speckle noise. According to the formation mechanism of speckle noise, this paper proposes a SAR image speckle noise removal algorithm based on improved anisotropic diffusion. The algorithm improves the diffusion coefficient c(x) based on the P-M equation diffusion filter algorithm, and adds the iterative termination condition, and obtains the filtering algorithm suitable for SAR images. This method can not only solve the problem that there are many isolated noise points in the traditional P-M model filtering, but also has a good effect on image edge preservation. The simulation results show that the improved P-M model can eliminate noise well and maintain the edge information of the image well.
Coverless information hiding method based on the keyword

Information hiding is an important technique to make secure the transmission of information. However, the existing information hiding method based on text is difficult to resist various steganography detection. Coverless text information hiding, a new information-hiding technique, is presented in this paper to ensure data security. Firstly the presented method selects the appropriate natural text as information carrier from the big data, and then generates the plaintext automatically by the natural text and the prior agreement. The method we propose can resist many types of existing steganalysis. The experimental results show that this method is effective.
Research on the Quantitative Method of Cognitive Loading in a Virtual Reality System

Aimed at the problem of how to objectively obtain the threshold of a user’s cognitive load in a virtual reality interactive system, a method for user cognitive load quantification based on an eye movement experiment is proposed. Eye movement data were collected in the virtual reality interaction process by using an eye movement instrument. Taking the number of fixation points, the average fixation duration, the average saccade length, and the number of the first mouse clicking fixation points as the independent variables, and the number of backward-looking times and the value of user cognitive load as the dependent variables, a cognitive load evaluation model was established based on the probabilistic neural network. The model was validated by using eye movement data and subjective cognitive load data. The results show that the absolute error and relative mean square error were 6.52%–16.01% and 6.64%–23.21%, respectively. Therefore, the model is feasible.
Optimization strategy of computer programming for mathematical algorithm of facial recognition model

Facial recognition is a technique for biometric identification, generally applied to finance, cybersecurity, public security, property management, and other important management departments. In this paper, the facial recognition system based on improved LBP and PCA mathematical algorithm is used to extract the relationship between the facial centre point and other phase domain points. After using PCA to reduce the dimension, the facial features are obtained. The facial recognition system designed by the thesis covers face image creation, face image pre-processing, face image recognition and result output module. Studies have shown that the system’s face recognition accuracy can be as high as 90%.
Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding

We present a new method, called hyperplane folding, that increases the margin in Support Vector Machines (SVMs). Based on the location of the support vectors, the method splits the dataset into two parts, rotates one part of the dataset and then merges the two parts again. This procedure increases the margin as long as the margin is smaller than half of the shortest distance between any pair of data points from the two different classes. We provide an algorithm for the general case with n-dimensional data points. A small experiment with three folding iterations on 3-dimensional data points with non-linear relations shows that the margin does indeed increase and that the accuracy improves with a larger margin. The method can use any standard SVM implementation plus some basic manipulation of the data points, i.e., splitting, rotating and merging. Hyperplane folding also increases the interpretability of the data.
Pruning Extreme Wavelets Learning Machine by Automatic Relevance Determination

Extreme learning machines are used for various contexts in artificial intelligence, such as for classifying patterns, performing time series prediction and regression problems, and being a more viable solution for training hidden layer weights to determine values of the learning model. However, the essence, the model determines that these weights should be determined randomly, and the Moore Penrose pseudoinverse will define only the weights that will act in the output layer. Random weights make this learning a black box because there is no relationship between the hidden layer weights and the problem data. This paper proposes the initialization of weights and bias in the hidden layer through the Wavelets transform that allows the two parameters, previously initialized at random, to be more representative about the problem domain, allowing the frequency range of the input patterns of the network to aid in the definition of weights of the ELM hidden layer. To assist in the representativeness of the data, a technique of selection of characteristics based on automatic relevance determination will be applied to the selection of the most characteristic dimensions of the problem. To compose the network structure, activation functions of the type rectified linear unit, also called ReLU, were used. The proposed model was submitted to the classification test of binary patterns in real classes, and the results show that the proposition of this work assists in bringing better accuracy to the classification results, and thus can be considered a feasible proposition to the training of neural networks that use extreme learning machine.
Perception-Optimized Encoding for Visually Lossy Image Compression

We propose a compression encoding method to perceptually optimize the image quality based on a novel quality metric, which emulates how the human visual system form opinion of a compressed image. Compared to the existing perceptual-optimized compression methods, which usually aim to minimize the detectability of compression artifacts and are sub-optimal in visually lossless regime, the proposed encoder aims to operate in the visually lossy regime. We implement the proposed encoder within the JPEG 2000 standard, and demonstrate its advantage over both detectability-based and conventional MSE encoders.
EP-1937 Distributed AUC algorithm: a privacypreserving approach to measure the performance of Cox models

Recent years have brought both a notable rise in the ability to efficiently harvest vast amounts of information, and a concurrent effort in preserving and actually enforcing the privacy of patients and their related data, as evidenced by the European GDPR. In these conditions, the Distributed Learning Ecosystem has shown great potential in allowing researchers to pool the huge amounts of sensitive data need to develop and validate prediction models in a privacy preserving way and with an eye towards personalized medicine. The aim of this abstract is to propose a privacy-preserving strategy for measuring the performance of Cox Proportional Hazard (PH) model.
Presenting a data imputation concept to support the continuous assessment of human vital data and activities

Data acquisition of mobile tracking devices often suffers from invalid and non-continuous input data streams. This issue especially occurs with current wearables tracking the user's activity and vital data. Typical reasons include the short battery life and the fact that the body-worn tracking device may be doffed. Other reasons, such as technical issues, can corrupt the data and render it unusable. In this paper, we introduce a data imputation concept which complements and thus fixes incomplete datasets by using a new merging approach that is particularly suitable for assessing activities and vital data. Our technique enables the dataset to become coherent and comprehensive so that it is ready for further analysis. In contrast to previous approaches, our technique enables the controlled creation of continuous data sets that also contain information on the level of uncertainty for possible reconversions, approximations, or later analysis.
Relating information complexity and training in deep neural networks

Deep Neural Networks may be costly to train, and if testing error is too large, retraining may be required, unless lifelong learning methods are applied. Crucial to addressing learning at the edge, without access to powerful cloud computing, is the notion of problem difficulty for non-standard data domains. While it is known that training is harder for classes that are more entangled, the complexity of data points was not previously studied as an important contributor to training dynamics. We analyze data points by their information complexity and relate the complexity of the data to the test error. We elucidate training dynamics of DNNs, demonstrating that high complexity datapoints contribute to the error of the network, and that training DNNs consist of two important aspects - (1) Minimization of error due to high complexity datapoints, and (2) Margin decrease where entanglement of classes occurs. Whereas data complexity may be ignored when training in a cloud, it must be considered as part of the setting when training at the edge.
An improvement to Chevalley's theorem with restricted variables

Recently Schauz and Brink independently extended Chevalley's theorem to polynomials with restricted variables. In this note we give an improvement to Schauz-Brink's theorem via the ground field method. The improvement is significant in the cases where the degree of the polynomial is large compared to the weight of the degree of the polynomial.
Scene Text Detection with Inception Text Proposal Generation Module

Most scene text detection methods based on deep learning are difficult to locate texts with multi-scale shapes. The challenges of scale robust text detection lie in two aspects: 1) scene text can be diverse and usually exists in various colors, fonts, orientations, languages, and scales in natural images. 2) Most existing detectors are difficult to locate text with large scale change. We propose a new Inception-Text module and adaptive scale scaling test mechanism for multi-oriented scene text detection. the proposed algorithm enhances performance significantly, while adding little computation. The proposed method can flexibly detect text in various scales, including horizontal, oriented and curved text. The proposed algorithm is evaluated on three recent standard public benchmarks, and show that our proposed method achieves the state-of-the-art performance on several benchmarks. Specifically, it achieves an F-measure of 93.3% on ICDAR2013, 90.47% on ICDAR2015 and 76.08%1 on ICDAR2017 MLT.
Integrating Measurement Principles into Formative Assessment

This chapter discusses the application of measurement principles to the theory and practice of classroom formative assessment. It describes assessment generally—and formative assessment particularly—from the perspective of evidentiary reasoning. The chapter explains how principles from educational measurement and the practice of formative assessment might be brought together. The development and application of evidentiary reasoning to assessment comes primarily from the work of Mislevy and colleagues on Evidence Centered Design. That model weights evidence according to its value, generating a score, a qualitative characterization, or both. That measurement model also provides an estimate of the uncertainty associated with that score or characterization. When formative assessment is embedded in educational software, formal measurement models can be employed because student responses can be automatically captured, the responses used in characterizing underlying proficiencies, and those characterizations acted upon to adjust instruction.
Unsupervised extra trees: a stochastic approach to compute similarities in heterogeneous data

In this paper, we present a method to compute similarities on unlabelled data, based on extremely randomised trees. The main idea of our method, unsupervised extremely randomised trees (UET), is to randomly split the data in an iterative fashion until a stopping criterion is met, and to compute a similarity based on the co-occurrence of instances in the leaves of each generated tree. We evaluate our method on synthetic and real-world datasets by comparing the mean similarities between instances with the same label and the mean similarities between instances with different labels. These metrics are analogous to intracluster and intercluster similarities, and they are used to assess the computed similarities instead of a clustering algorithm’s results. Our empirical study shows that the method effectively gives distinct similarity values between instances belonging to different clusters and gives indiscernible values when there is no cluster structure. We also assess some interesting properties such as invariance under monotonic transformations of variables and robustness to correlated variables and noise. Finally, we perform hierarchical agglomerative clustering on synthetic and both real-world homogeneous and heterogeneous datasets and compare the results obtained by using UET and by using standard similarity measures. Our experiments show that our approach based on UET outperforms existing methods in some cases and reduces the amount of preprocessing typically needed when dealing with real-world datasets.
Data-Loss resilience video steganography using frame reference and data ensemble reconstruction

In this paper, we propose a robust video steganographic method, which can efficiently hide confidential messages in video sequences, and ensure that these messages are perfectly reconstructed by recipient. To apply proposed scheme to video sequences, we must be faced with two nontrivial problems: (a) how to effectively minimize the total steganographic distortion for each video frame? (b) how to recover the hidden messages if some frames are lost or damaged? We tackle the first question by designing a new distortion function, which employs two continuous adjacent frames with the same scene as side-information. The second question is addressed by data sharing. In this mechanism, the original data is expanded and split into multiple shares by using multi-ary Vandermonde matrix. Since these shares contain a lot of data redundancy, the recipient can recover the hidden data even if some frames are damaged or lost during delivery. Extensive experiments show that proposed scheme outperforms the state-of-the-arts in terms of robustness and diverse attacks.
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference.

In this paper, we introduce Hierarchical Invertible Neural Transport (HINT), an algorithm that merges Invertible Neural Networks and optimal transport to sample from a posterior distribution in a Bayesian framework. This method exploits a hierarchical architecture to construct a Knothe-Rosenblatt transport map between an arbitrary density and the joint density of hidden variables and observations. After training the map, samples from the posterior can be immediately recovered for any contingent observation. Any underlying model evaluation can be performed fully offline from training without the need of a model-gradient. Furthermore, no analytical evaluation of the prior is necessary, which makes HINT an ideal candidate for sequential Bayesian inference. We demonstrate the efficacy of HINT on two numerical experiments.
Instrumentation of Learning Situation using Automated Speech Transcription: A Prototyping Approach

This paper presents the ongoing conception of a set of tools, based on live transcription of speech during lectures and designed to instrument traditional lectures as well as web conferences or hybrid learning situations. The toolset exploits speech and interactions taking place during courses, keeps track of them and facilitates their reuse both in students' studies and in future iterations of the course delivered by the teacher. Its goal is to help students stay focused on the teacher's explanations and offer them greater possibilities of interactions. The prototype was conceived with an approach based on the analysis of communicational and informational needs of the end users, especially in regard to the instrumentation possibilities offered by the innovative technologies considered in the project. In this paper, we detail the different tools produced in order to offer synchronous and asynchronous support to the learning activity. We describe a real-life test as well as changes brought to the device afterwards, and finally we describe the first experiment conducted with the device.