Reviewerly/exHarmony huggingface dataset:
Pain: the impulse in the search for health by means of integrative and complementary practices

BACKGROUND AND OBJECTIVES: This study investigated the relations between the playful component and the process of rehabilitation, treatment, and promotion of health in the context of a group that treats the pain, located in Florianópolis (Brazil). METHODS: The research followed a qualitative approach, a descriptive-exploratory field research. A matrix-guided systematic observation was conducted for two months by the group leader, two volunteers and about 15 participants. A field diary was used to register complementary information. Besides two semi-structured interview guides were used, applied to four members and the person responsible for the group after the two-month observational period. The data were organized and analyzed in three topics: “Characterization of the investigated group and dynamics of the meetings,” “The group as healing potential” and “Lian Gong/Qi Gong as a possibility to look at the pain.” RESULTS: The participants pointed out that working on Lian Gong/Qi Gong, meditation and auriculotherapy contemplating the playful component the group becomes a place of recognition of each one’s pain subjectivity, by the individual that suffers and by the collective, which has fostered the recovery of specific pain and good sensations to those involved, such as happiness, enthusiasm and pleasure. CONCLUSION: The creation of the group and people’s engagement has decreased the number of specific requests for physiotherapy sessions and provided greater autonomy to the participants to handle their own pain.
Statistical Analysis and Security Evaluation of Chaotic RC5-CBC Symmetric Key Block Cipher Algorithm

In some previous research works, it has been theoretically proven that RC5-CBC encryption algorithm behaves as a Devaney topological chaos dynamical system.This unpredictable behavior has been experimentally illustrated through such sensitivity tests analyses encompassing the avalanche effect phenomenon evaluation.In this paper, which is an extension of our previous work, we aim to prove that RC5 algorithm can guarantee a much better level of security and randomness while behaving chaotically, namely when embedded with CBC mode of encryption.To do this, we have began by evaluating the quality of such images encrypted under chaotic RC5-CBC symmetric key encryption algorithm.Then, we have presented the synthesis results of an hardware architecture that implements this chaotic algorithm in FPGA circuits.
Hacking the Collection: Changing the Collection in Response to a Migration

In 2016, the Munday Library at St. Edward’s University migrated to a new discovery layer. The Library had been an EBSCO Discovery Services (EDS) customer for six years, and the migration to Ex Libris’s Alma-Primo negatively impacted content discovery and collection usage due to issues with EBSCO’s proprietary metadata and Primo’s OpenURL link resolver. This article discusses switching the library’s collection using collection data analysis to respond to the change in discovery layer. The data-informed changes made to the collection improved collection usability and access.
Encrypting Multiple Images using Stacked Autoencoders

Encryption of images has been carried out by chaos-based algorithms using a single sequence for a long time. The existing schemes are not secure enough for batch image encryption. A batch image encryption scheme using a stacked autoencoder network is proposed in this paper wherein two chaotic sequences are used. A shuffling matrix is produced by the first sequence which is used to shuffle image pixels on each input image, and the other sequence generates a series of independent sequences which are used to confuse the permuted images with the encrypted images. In this scheme, parallel computing of the stacked autoencoder reduces the run-time complexity and encryption effect is enhanced. The efficiency of the used scheme is evaluated by a series of analysis techniques. The results and analysis concluded that the proposed scheme is very efficient and is able to resist statistical attack and differential attack.
Aretousa: A Competitive Audio Streaming Software for Network Music Performance

Many existing open source systems provide support for Network Music Performance (NMP), with each one catering to a specific system and usage scenario. As our research in evaluating the Quality of Experience (QoE) of NMP systems as perceived by musicians involves widely different scenarios and requires extensive instrumentation of the platform, we built a new NMP system, Aretousa. Our system offers a large number of configuration and monitoring options, without sacrificing latency, the most critical factor for NMP. To show that Aretousa provides flexibility while being competitive with the state of the art in terms of latency, we present measurements comparing it against JackTrip in multiple setups over a high speed research network.
Autonomous multi-function floor cleaning robot with zig zag algorithm

&lt;span&gt;In this present era, people live a very busy life. People in cities have irregular and long working times. In such a situation a person will always find ways of saving time. Household chores are the ones that are most dreaded upon and cleaning a home tops the list. An autonomous floor cleaning robot is developed to help people to complete their cleaning task. This paper presents the development of an autonomous floor cleaning robot by using Arduino as a platform for processing and controlling the input and output. Autonomous vacuum cleaner robot is designed to make cleaning process become easier compared to using manual vacuum. The idea is basically by having the sensor to detect any object and send the input to Arduino that will control the robot movement. This robot can perform vacuum and wiping task with water spray function. It is also programmed to move in zigzag movement to ensure that the robot can clean all the corner of the house and avoid obstacle autonomously. This robot consists of three ultrasonic sensors, one infra-red (IR) sensor and two brushes in front of it to ensure effective cleaning. It is designed to run on flat surface. This autonomous multi-function floor cleaning robot has lower cost compared to existing vacuum robot on the shelves and is suitable for home and small premise usage.&lt;/span&gt;
Comparing Usability of User Interfaces to Collect Family Health History.

Family health history (FHx) is vital in early detection of genetic diseases. This research studied two different FHx collection interface and compared them based on the IBM CSUQ usability metrics. We found the conversational interface to be significantly better in terms of overall satisfaction, system usefulness, interface quality and information quality than the traditional interface.
Are Few-shot Learning Benchmarks Too Simple ?

We show that several popular few-shot learning benchmarks can be solved with varying degrees of success without using support set Labels at Test-time (LT). To this end, we introduce a new baseline called Centroid Networks, a modification of Prototypical Networks in which the support set labels are hidden from the method at test-time and have to be recovered through clustering. A benchmark that can be solved perfectly without LT does not require proper task adaptation and is therefore inadequate for evaluating few-shot methods. In practice, most benchmarks cannot be solved perfectly without LT, but running our baseline on any new combinations of architectures and datasets gives insights on the baseline performance to be expected from leveraging a good representation, before any adaptation to the test-time labels.
Augmented Reality in Museums and Cultural Heritage Settings

This chapter reviews augmented reality applications with emphasis on cultural heritage settings, particularly museums. It presents the history and the evolution of augmented reality programmes and applications in museums and cultural heritage institutions. Examples of good and poor practices are examined. In presenting these examples, we discuss issues related to the use of augmented reality in museum environments and within museum educational programmes. We conclude with thoughts on future research and development.
Architecture of Integrated Machine Learning in Low Latency Mobile VR Graphics Pipeline

In this paper, we discuss frameworks to execute machine learning algorithms in the mobile VR graphics pipeline to improve performance and rendered image quality in real time. We analyze and compare the benefits and costs of various possibilities. We illustrate the strength of using machine framework in graphics pipeline with an application of efficient spatial temporal super-resolution that amplifies GPU render power to achieve better image quality.
Proposed UX Model for Children Educational Mobile Application

The evolution of mobile learning is robustly popular among children these days since the engagement is portable and owns unique interactive specialties that tends to attract more users. User experience (UX) is one of the potential approaches in designing mobile applications to deliver a complete experience for utilizing a technology. However, this concept of UX has not been adopted while designing educational mobile application for children even when researchers have claimed that children’s experience differs from adults. Therefore, the inadequate experience of mobile learning leads in affecting children’s cognitive skills in learning perspective. The problem statement argued in this paper is children’s cognitive skills on solving educational critical thinking problems are diminishing due to lacking of adequate elements while designing educational mobile application. To support the problem this paper aims to develop EduMobile UX Model for children when designers are developing educational mobile application for them. This study conducted a literature review to develop EduMobile UX Model. This model will be referred by designers when designing children educational mobile application.
Segmentation Guided Regression Network for Breast Cancer Cellularity

Evaluation and diagnosis of breast cancer will be more and more vital in medical field. A general solution to breast cancer cellularity is to modify output of a state-of-the-art classification backbone to prediction a score between 0 and 1. However, this solution does not take clinical meaning of cancer cellularity which defined as proportion of cancer cells over image patches into consideration. In this paper, a segmentation guided regression network is proposed for breast cancer cellularity, adding more semantic detailed features for regression task. Consequently, the proposed method can not only take advantage of global context features from classification backbone, but also position feature and texture feature from segmentation network. A powerful segmentation network with 0.8438 mean Intersection-over-Union is obtained on extremely class imbalanced datasets. The proposed method with Resnet101 as regression backbone gets PK value of 0.9260 and L1 loss of 0.0719.
THE RISK ASSESSMENT: THE COMMON DENOMINATOR FOR PRIVACY AND SECURITY COMPLIANCE

In the words of Ferris Bueller, “life moves pretty fast. If you don't stop and look around once in a while, you could miss it.” Although this quote is from the iconic 1980s movie, Ferris Bueller’s Day Off, its application to risk assessments is apropos. A risk assessment enables a person to “stop and look around” on an annual basis to make sure that technical, administrative and physical safeguards are not missed. New NIST guidance underscores the importance of risk mitigation in relation to both privacy and security by highlighting the role of the risk assessment in relation to both legal requirements and best practices.
Development ripple tank practicum module to improve the collaborative problem-solving skills with adaptive technology

Practicum of physics in college used as a means to develop the competence of prospective physics teachers. In general, each practicum equipped with a module, where one of the characteristics of the module is adaptive. Adaptive here means the module can adjust to the development of science and technology and is flexible to use. A technology that can be adapted to modules is Augmented Reality (AR). In addition to adaptive, this practicum module is expected to be able to improve students’ collaborative problem-solving skills. This development research uses the stages of 4D, Define, Design, Develop, and Disseminate, where from this development a wave practicum module and AR application are obtained. All products made validated by material, media, and learning experts. The product was tested by five groups, three students in a group. The students are physics preserve teacher from the same class. After conducting a field test, it can conclude that the practicum module with adaptive technology can help students to improve collaborative problem-solving.
Residual stress measurement of glass based on Muller matrix Ellipsometer

In order to measure the residual stress in flat glasses accurately, a new method based on Muller matrix ellipsometer is proposed. Under the transmission mode of ellipsometer, the sample with residual stress can be equivalent to a planar stress model in the direction perpendicular to the incident light. According to the stress-optic law, the magnitude of residual stress can be obtained according to the phase delay induced by residual stress in the sample. First, the phase delay can be calculated from the Muller matrix elements. Then the stress birefringence can be obtained in line with the thickness and the measuring wavelength. Finally, the residual stress can be carried out by dividing the photoelastic coefficient. The residual stress of quartz glass with a diameter of 20mm and a thickness of 3mm is measured. The phase accuracy of the ellipsometer reaches 0.1&deg;. The measurement results of ellipsometer are in good agreement with those of dual-frequency laser interferometer, which has a higher phase accuracy than the ellipsometer. The experiment results indicate that Mueller matrix ellipsometer can be used as a new tool for high-precision, non-destructive measurement of residual stress.
Bat algorithm and k-means techniques for classification performance improvement

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.
Monash University’s Submissions to the WNGT 2019 Document Translation Task

We describe the work of Monash University for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019).We submitted systems for both directions of the English-German language pair.Our main focus is on employing an established document-level neural machine translation model for this task.We achieve a BLEU score of 39.83 (41.46 BLEU per WNGT evaluation) for En-De and 45.06 (47.39 BLEU per WNGT evaluation) for De-En translation directions on the Rotowire test set.All experiments conducted in the process are also described.
Iterative Peptide Modeling With Active Learning And Meta-Learning.

Often the development of novel materials is not amenable to high-throughput or purely computational screening methods. Instead, materials must be synthesized one at a time in a process that does not generate significant amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both material properties and predictive modeling accuracy. In this work, we study the effectiveness of active learning, which optimizes the order of experiments, and meta learning, which transfers knowledge from one context to another, to reduce the number of experiments necessary to build a predictive model. We present a novel multi-task benchmark database of peptides designed to advance active, few-shot, and meta-learning methods for experimental design. Each task is binary classification of peptides represented as a sequence string. We show results of standard active learning and meta-learning methods across these datasets to assess their ability to improve predictive models with the fewest number of experiments. We find the ensemble query by committee active learning method to be effective. The meta-learning method Reptile was found to improve accuracy. The robustness of these conclusions were tested across multiple model choices. We find that combining meta-learning with active learning methods offers inconsistent benefits.
Image Acquisition for High Quality Architectural Reconstruction

In this work we propose a simple optimization based technique to compute camera poses for drone assisted automated image acquisition. We use this technique to create highly detailed 3D models of buildings using multi-view reconstruction. Our reconstructed models are great for use in virtual reality (VR) environments since they exhibit good amount of detail that is useful for creating realistic virtual walkthroughs. Creating a good 3D reconstruction with a set of nadir images is difficult since the vertical surfaces of buildings are not captured very well and are therefore not reconstructed accurately. Acquisition of non-nadir images require avoiding obstacles around the structure. Our technique is based on mathematical optimization, and is capable of calculating camera positions and orientations to maximally cover horizontal as well as vertical surface patches while avoiding obstacles around the building. We present a complete pipeline for a mostly automated and robust approach via a camera mounted quadcopter drone. We also validate our approach via a graphics-based simulation.
Traffic Routing Optimization using Ant Colony Optimization

Traffic congestion is one of the main problems in traffic system and it occurs on most cities around the world.This project focuses on solving recurring congestion that occurs in area around Kuala Terengganu city.Traffic routing can be implemented to reduce traffic congestion by finding the best path for a vehicle.Ant Colony Optimization algorithm is applied to optimize traffic routing in this project.Ant Colony Optimization (ACO) takes inspiration from the foraging behavior of some ant species and these ants release pheromone on the ground in order to mark some favorable path that should be followed by other ants.Simulation of Urban Mobility (SUMO) is used for the simulation of traffic congestion and traffic routing.Traffic Control Interface (TraCI) gives the program that is written in Python to access the traffic while the simulation is running and it allows to retrieve values of road travel time and to re-route a vehicle based on the path that is found by ACO.The efficiency of ACO is compared with Dijkstra algorithm.It is found that average travel time for vehicle simulation in ACO perform 30% faster than Dijkstra.
SISTEM PENGARSIPAN ARSIP ELEKTRONIK

&lt;p&gt;In the current modern era, manual filing systems are less popular because they are less practical so it is necessary to implement in the form of paperless office or digitalization. The advantages of electronic files are easily visible. There will always be a need for hard-copy printed material, if only to ‘feel’ holding a physical copy of a journal, but despite the possible losses caused by technological changes there are good reasons (including space, searching, access and permanence) to consider the electronic version to be more flexible and useful products.Documents or archives in the form of paper that are stored in offices or government institutions should be managed properly so that they benefit the organization. To keep it easier and safer, some actions are taken, including by melting, sorting, indexing, placing it into a folder and putting it into a filing cabinet. This applies also to electronic archives, which require several methods to manage them so that they are easy to understand and easy to find if needed so that they are useful both when needed or in the future.&lt;/p&gt;&lt;p&gt;Keywords: Archives, filing systems, electronic archives.&lt;/p&gt;
AVE-WLS Method for Lossless Image Coding

In the paper currently the most efficient from the data compaction point of view image lossless coding method is presented. Being computationally complex the algorithm is still more time efficient than its main competitors. The method is based on weighted LS (WLS) technique idea with many improvements introduced, e.g. its main stage is followed by a two-step NLMS predictor. Prediction error is coded by a completely new highly efficient binary context arithmetic coder. Its performance is compared with that of other coders for a set of widely used benchmark images.
A Fusion Method for Word Vector Based on Fasttext-KdTree

Text categorization is an important part of the field of natural language processing, and it is also one of the current research hot issues. However, at present, text categorization technology still faces the problem of losing some semantic information caused by new words. For this reason, this paper proposes a fusion method for word vector based on fastText-kdTree. Firstly, the method trains word vectors by using fastText model and fills in unknown word vectors by combining n-gram model. Secondly, it uses the idea of kdTree nearest neighbor to find multiple word vectors similar to unknown words. Finally, it fuses the multiple word vectors to form a new representation of word vectors by a gate mechanism. The experimental results show that the proposed method can achieve 91.08% classification accuracy.
A Weakly Supervised Text Detection Based on Attention Mechanism

In this paper, we propose a new method for natural image text detection under a weakly supervised data set. Currently, most of the text detection models are based on bounding box label training data. However, the cost of the bounding box label training data is very high. In order to solve this problem, we propose an attention mechanism that can be trained on image-level labels data and roughly identifies text regions via an automatically learned attentional map based on a convolutional neural network. There are three main steps: firstly, a VGG model is trained using image-level labels data to score the likelihood that a text region exists in the picture; secondly, the region of interest is extracted by means of the attention mechanism and the extracted region is evaluated using the network trained in the first step to getting the text region and finally, the text line is extracted in the text region using the MSER algorithm. Trained with the weakly supervised data which is only with image-level labels, our model can generate bounding boxes for the text line in the image. The results of our model are very close to those of the models using bounding box label training data on the text detection benchmark sets of MSRA-TD500, ICDAR2013, and ICDAR2015.