Reviewerly/exHarmony huggingface dataset:
Multitask Learning by Multiwave Optical Diffractive Network

Recently, there has been tremendous research studies in optical neural networks that could complete comparatively complex computation by optical characteristic with much more fewer dissipation than electrical networks. Existed neural networks based on the optical circuit are structured with an optical grating platform with different diffractive phases at different diffractive points (Chen and Zhu, 2019 and Mo et al., 2018). In this study, it proposed a multiwave deep diffractive network with approximately 10 6 synapses, and it is easy to make hardware implementation of neuromorphic networks. In the optical architecture, it can utilize optical diffractive characteristic and different wavelengths to perform different tasks. Different wavelengths and different tasks inputs are independent of each other. Moreover, we can utilize the characteristic of them to inference several tasks, simultaneously. The results of experiments were demonstrated that the network could get a comparable performance to single-wavelength single-task. Compared to the multinetwork, single network can save the cost of fabrication with lithography. We train the network on MNIST and MNIST-FASHION which are two different datasets to perform classification of 32∗32 inputs with 10 classes. Our method achieves competitive results across both of them. In particular, on the complex task MNIST_FASION, our framework obtains an excellent accuracy improvement with 3.2%. In the meanwhile, MNSIT also has the improvement with 1.15%.
Narrative Navigation: Visualizing Story Order and Locations in Augmented Reality

Narrative Navigation connects mobile storytelling with geo-locations and Augmented Reality (AR). Narrative Navigation is designed to help users better understand a story. In this paper, we introduce our mobile app prototype that guides users to the physical locations of a story using AR flags. We indicate not just story locations and the user's distance to those story points, but also show the flow of a story through its geo-locations. The prototype thus provides users with a new way to immerse themselves in a location-based story.
High Accuracy Thyroid Tumor Image Recognition Based on Hybrid Multiple Models Optimization

With the development of computer vision recognition technology, more and more researchers apply this technology to the recognition of tumor images.But for cost reasons, many hospitals still use low-cost ultrasound and other cheap equipment, resulting in ambiguity, artifacts and many similar tumor noise areas images.Recent related studies have high precision in clear image recognition, such as face and number recognition in color images.However, they showed low accuracy and unstable results in ultrasonic image due to its fuzziness.The straight reason may be that many existing algorithms are not suitable for the fuzzy and noise image, and easily to misjudge the real objects and noise areas.In this paper, we proposed an approach based on R-CNN and RPN to distinguish the real objects from noise areas after data enhancement and morphological filtering with the optimization of a series of hyper-parameters and the combination of Color Doppler Flow Imaging (CDFI) blood flow signal model.We found that the key features of high-noise ultrasound images can be obtained quickly and accurately.From the experimental results, the accuracy and stability of our proposed method outperforms the state-of-the-art approaches on the real world thyroid tumor image data set.
Two‐tiered face verification with low‐memory footprint for mobile devices

Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario.
Hand Segmentation for Arabic Sign Language Alphabet Recognition

This research aims to separate the hands from the background of colored images representing the Arabic Sign language alphabet gestures.This hand segmentation task is one of the main challenges of image based Sign language recognition systems due to the issue of skin tones variations and the complexity of the background.For this purpose, an efficient system that segment the hand object and separate it from the rest of the image based on deep learning is investigated.More specifically, the DeepLab v3+ network architecture that is a combination of spatial pyramid pooling module and encode-decoder structure will be trained to learn the visual characteristics of the hand and segment it with detailed boundaries.The effectiveness of the proposed solution is investigated on a large dataset of size 12000 with an accuracy of 98%, an IoU of 93% of and BF score of 87%.
On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

Cooperation between Evolutionary Algorithms (EAs) and Constraint Handling Techniques (CHTs) plays an important role in Constrained Optimization Evolutionary Algorithms (COEAs). A constrained composite differential evolution (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> oDE) sets a good example on the cooperation. This paper tries to further study the inner mechanism, i.e., the effect of different methods on generating and selecting solutions. In the solution generating part, a new method, which adopts stochastic ranking in selecting the best individual in the DE mutation operators, is proposed. In the solution selecting part, a new constraint handling technique combination is added. The experiments on benchmark functions from IEEE CEC2006 verify the effect. During the experiment, it is found that a new cooperation manner performs better than C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> oDE, which reflects the importance of cooperation.
The Validity of the Pop-Up Book Media on Puberty Topics for Sixth Grade Elementary School

This research is based on the limitations of the material in student books and the lack of use of media in the learning process. This study aimed at developing a pop-up book media on the topic of puberty class VI in elementary school. The implementation of this research referred to the procedure of the development model which consisted of the Analyze, Design, Development, Implementation, and Evaluation stages. The subject of this research was the pop-up book media, while the object in this study was the validity of the pop-up book media. Data collection in this study used a questionnaire method. To determine the validity of the pop-up book media, a rating scale instrument was used in the form of an assessment sheet given to four experts. The validation data was then analyzed using the mean formula to find out the average score of the validity of the pop-up book media. The analysis showed the average score of validation of pop-up book media is 4.86 with very good qualifications. This media can be used in the learning process. The teacher will be helped in delivering the material and students can understand the material and take an active role in learning.
<i>Retracted on July 26, 2022</i> : Open set recognition through unsupervised and class-distance learning

We present a novel semi-supervised framework for training classifiers and simultaneously detecting out-of-distribution inputs. We do this by training on an closed classification dataset and an auxiliary simulated-open dataset, which consists of examples from outside the closed set. Through unsupervised learning and incorporating a class-distance value for each known class, we can identify out-of-distribution RF devices with state-of-the-art accuracy. We define metrics for quantifying robustness in terms of both classification and Open Set Recognition (OSR). Finally we discuss uncertainty estimation and calibrate our open set predictions so that they represent confidence.
Differentially Private Graph Publishing and Randomized Response for Collaborative Filtering

Several methods for providing edge and node-differential privacy for graphs have been devised.However, most of them publish graph statistics, not the edge-set of the randomized graph.We present a method for graph randomization that provides randomized response and allows for publishing differentially private graphs.We show that this method can be applied to sanitize data to train collaborative filtering algorithms for recommender systems.Our results afford plausible deniability to users in relation to their interests, with a controlled probability predefined by the user or the data controller.We show in an experiment with Facebook Likes data and psychodemographic profiles, that the accuracy of the profiling algorithms is preserved even when they are trained with differentially private data.Finally, we define privacy metrics to compare our method for different parameters of ε with a k-anonymization method on the MovieLens dataset for movie recommendations.
An Attention-Based Recognizer for Scene Text

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. Although STR method has been greatly developed, the existing methods still can't recognize any shape of text, such as very ric... | Find, read and cite all the research you need on Tech Science Press
Designing Sentic: Participatory Design with People Living with Dementia

The number of people living with dementia is increasing rapidly and puts pressure on our society. An increasing body of evidence suggests that listening to music and engaging with systems that allow you to do this can be beneficial in dementia care. Building on a growing interest in HCI for designing technology for dementia, this chapter will focus on how to provide people with dementia personal and direct access to a music system. This paper presents the design process of Sentic, a music player with an interface that can be tailored to fit its users’ needs. An interface can be selected that suitably matches with the capabilities of people living with dementia. The design is the result of using a participatory design approach. A strategy of adaptation was found to be the most appropriate in designing for people living with dementia. This chapter shows the potential of tailoring interactions so that they support and maintain autonomy and facilitate personal access to technology, for people with dementia, and offers considerations and opportunities for future design and research.
Simulating Dramatic Networks

Abstract “Yes, interesting,” said someone from the audience, the first time I presented on dramatic networks; “but, as a mathematician, I feel I only understand something if I know how to ‘make’ it. So: how do you make a dramatic network? What elements does it need, what rules, what stages?” At the time, I had no idea how to respond; in a discipline like ours, where the objects of study are emphatically given – passed on with care, and often with reverence, from generation to generation – the idea of “making” Hamlet sounded half absurd, half sacrilegious. But that’s exactly my object here: neither real plays, nor even the networks that can be extracted from them, but their “simulations” instead.
The Online Test application uses Telegram Bots Version 1.0

The development of the smartphones that rapidly increasing, and high smartphone Users as a tool to make a lot of information, make emergence of instant messaging applications with a variety of options for use. Telegram, as one of the instant messaging application that is fairly new, offering in its various features compared to other instant messaging applications, so it can grow rapidly and much in demand in only 2 years. One of the interesting features is the Bot telegram, a special account without number that can handle Command from the User and gives answers appropriate to Command functionality. The account serves as the interface of the running system that communicate via Telegram Bot API. This study seeks to build a Bot telegram with database to built The Online Test application uses Telegram Bots Version 1.0. In each lecture meeting, a Lecturer can make a short test in the form of multiple choices to review the material at the previous meeting and get information about what percentage of students understand the material that has been taught before. We can use the application to send realtime messages to facilitate the test. Of course, which supports the creation of bots easily to add features that did not yet exist. Of the many we chose Telegram to be an online test environment by utilizing the Bot creation facilities provided by Telegram. This article will be a guide that makes it easier for lecturers to give a simple test at the beginning of the meeting, so that they can attract students' daily grades. And in order to be more interactive there are features of student value progress reports that can also be accessed individually so that students can know the progress of their learning outcomes.
Membangun Budaya Literasi Berbasis Kearifan Lokal dalam Mata Kuliah Menulis Puisi Mahasiswa

Literacy is now beginning to become a discourse in various media, both print and social media. This happened since Anies Baswedan, when he was Minister of Education and Culture, launched the School Literacy Movement (GLS). Trends in global currents and the Indonesian economy are also entering the era of the creative industry revolution, an era of revolution that relies heavily on the products of the creative industries. Creative industries can be defined as industries based on expertise, talent and creativity; such as fashion, crafts, publishing (books and print media), music, film and advertising. Literary works and creative industries are both based on talent and creativity and are equally cultural. The difference is, the process of creating literary works is more oriented to the interests of the literary (literary), while the creative industry is more oriented to the interests of the market. Local Wisdom is considered so interesting to be the main topic raised in literacy development because through learning in the classroom, students will indirectly get to know the local wisdom of each region. This paper seeks to reveal the steps that can be taken so that the culture of literacy or GLS can still go hand in hand with the development of the existing creative industries. The method used is certainly descriptive and through a qualitative approach. In conclusion, the Indonesian literacy movement still needs to be promoted, and Indonesian language learning in tertiary institutions still needs to be improved, so that people become more proficient in Indonesian and continue to love Indonesian as a means of communication, cultural imaging, and strengthening the national identity. The low ability of students in the field of poetry indicates the need to empower poetry writing for students. This article aims to illustrate how to empower the literacy culture of students writing poetry in the face of the era of the creative industry revolution.
Using EdSurvey to Analyse PIAAC Data

This chapter describes the use of the R package EdSurvey and its use in analysing PIAAC data. The package allows users to download public use PIAAC data, explore the codebooks, explore data, read in and edit relevant variables, and run analyses such as regression, logistic regression, and gap analysis.
Linear semi-infinite programming approach for entanglement quantification

We explore the dual problem of the convex roof construction by identifying it as a linear semi-infinite programming (LSIP) problem. Using the LSIP theory, we show the absence of a duality gap between primal and dual problems, even if the entanglement quantifier is not continuous, and prove that the set of optimal solutions is nonempty and bounded. In addition, we implement a central cutting-plane algorithm for LSIP to quantify entanglement between three qubits. The algorithm has global convergence property and gives lower bounds on the entanglement measure for nonoptimal feasible points. As an application, we use the algorithm for calculating the convex roof of the three-tangle and $\ensuremath{\pi}$-tangle measures for families of states with low and high ranks. Since the $\ensuremath{\pi}$-tangle measure quantifies the entanglement of W states, we apply the values of the two quantifiers to distinguish between the two different types of genuine three-qubit entanglement.
A fully distributed motion coordination strategy for multi-robot systems with local information

This paper investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace. Based on the realistic assumptions that each robot is subject to both velocity and input constraints and can have only local view and local information, a fully distributed multi-robot motion coordination strategy is proposed. Building on top of a cell decomposition, a conflict detection algorithm is presented first. Then, a rule is proposed to assign dynamically a planning order to each pair of neighboring robots, which is deadlock-free. Finally, a two-step motion planning process that combines fixed-path planning and trajectory planning is designed. The effectiveness of the resulting solution is verified by a simulation example.
Application of College English Listening Online Examination Platform Based on Streaming Media Technology

As one of the key contents of the college English test, listening online examination has become one of the required questions on the online examination platform. Due to the large size of the listening file, how to ensure the smooth playback of the listening file has become a problem that must be solved in the online examination. This paper focuses on solving college English listening online examination by using streaming media technology. It gives an overview of streaming media technology, and proposes the use of streaming media technology to build the college English listening online examination platform. It can ensure the smooth reception and playback display of the terminal data stream of the listening online examination.
Research Overview of the Informative Teaching Approach under Cloud Computing Environment

Cloud computing technology plays an important role in informative teaching. Firstly, this paper summarizes the application of cloud computing technology in colleges and universities, then describes the existing teaching approaches in cloud computing environment in detail, finally summarizes the existing problems in the teaching mode and looks forward to the research direction of teaching mode in cloud environment.
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering

Commonly-used clustering algorithms usually find ellipsoidal, spherical or other regular-structured clusters, but are more challenged when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and other algorithms. Our approach estimates the cumulative distribution function of the normed residuals from an appropriately fit $k$-groups model and calculates the estimated nonparametric overlap between each pair of clusters. Groups with high pairwise overlap are merged as long as the estimated generalized overlap decreases. Our methodology is always a top performer in identifying groups with regular and irregular structures in several datasets and can be applied to datasets with scatter or incomplete records. The approach is also used to identify the distinct kinds of gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and the distinct kinds of activation in a functional Magnetic Resonance Imaging study.
Single-Image Super-Resolution Analysis in DCT Spectral Domain

Advances in deep learning techniques have lead to drastic changes in contemporary methods used for a variety of computer vision problems. Single-image super-resolution is one of these problems that has been significantly and positively influenced by these trends. The mainstream state-of-the-art methods for super-resolution learn a non-linear mapping from low-resolution images to high-resolution images in the spatial domain, parameterized through convolution and transposed-convolution layers. In this paper, we explore the use of spectral representations for deep learning based super-resolution. More specifically, we propose an approach that operates in the space of discrete cosine transform based spectral representations. Additionally, to reduce the artifacts resulting from spectral processing, we propose to use a noise reduction network as a post-processing step. Notably, our approach allows using a universal super-resolution model for a range of scaling factors. We evaluate our approach in detail through quantitative and qualitative results.
IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI

Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.