Reviewerly/exHarmony huggingface dataset:
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints

Lexically constrained neural machine translation (NMT) draws much industrial attention for its practical usage in specific domains.However, current autoregressive approaches suffer from high latency.In this paper, we focus on non-autoregressive translation (NAT) for this problem for its efficiency advantage.We identify that current constrained NAT models, which are based on iterative editing, do not handle low-frequency constraints well.To this end, we propose a plug-in algorithm for this line of work, i.e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints.Experiments on the general and domain datasets show that our model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.
Development of Affine Transformation Method in the Reconstruction of Songket Motif

Songket is one of the clothes with a high artistic value besides Batik originating from Indonesia.Songket is widely used at weddings and other traditional formal events.Songket is the result of a traditional craft consisting of the main ingredients of cloth made of cotton and silk threads by inserting the feed (basic material) transversely on the Lunsing (yeast cloth).The quality of Songket art is found in the motifs on the Songket.This research has a conceptual outline in terms of improving the quality of Songket cloth and making it easier for craftsmen and artists to spare their art for the reconstruction process of the Silungkang Songket motif.This research aims to get a new Songket motif with a more modern and aesthetic motif but still has its original characteristics and identity.Based on these objectives, the researchers focused and motivated to develop the motif of one of the types of Songket in Indonesia, namely Silungkang Songket, in the form of a more modernized motif.This research develops a digital image transformation method named Affine Segmentation Point (ASP) to produce modernized Songket motifs.The basis of the developed ASP method is the Affine transformation.Songket image input was used as data and created a new motif in this study: a Songket image with the Rangkiang Lumbung Padi Besar motif.The results of this study can create excellent Songket motifs and modernization.The new motifs produced have been tested by Songket experts, Songket observers, Songket craftsmen, and Songket sellers with an interest rate of 97.5%.The method developed is very precise and can be used to develop other high-value artistic motifs on traditional cloth.
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network

Graph neural networks (GNNs) have been intensively studied in various real-world tasks. However, the homophily assumption of GNNs' aggregation function limits their representation learning ability in heterophily graphs. In this paper, we shed light on the path level patterns in graphs that can explicitly reflect rich semantic and structural information. We therefore propose a novel Structure-aware Path Aggregation Graph Neural Network (PathNet) aiming to generalize GNNs for both homophily and heterophily graphs. Specifically, we first introduce a maximal entropy path sampler, which helps us sample a number of paths containing structural context. Then, we introduce a structure-aware recurrent cell consisting of order-preserving and distance-aware components to learn the semantic information of neighborhoods. Finally, we model the preference of different paths to target node after path encoding. Experimental results demonstrate that our model achieves superior performance in node classification on both heterophily and homophily graphs.
Low Latency UHD Adaptive Video Bitrate Streaming Based on HEVC Encoder Configurations and Http2 Protocol

Applying 4K, (Ultra HD) Real-time video streaming via the internet network, with low bitrate and low latency, is the challenge this paper addresses. Compression technology and transfer links are the important elements that influence video quality. So, to deliver video over the internet or another fixed capacity medium, it is essential to compress the video to more controllable bitrates (customarily in the 1-20 Mbps range). In this study, the video quality is examined using the H.265/HEVC compression standard, and the relationship between quality of video and bitrate flow is investigated using various constant rate factors, GOP patterns, quantization parameters, RC-lookahead, and other types of video motion sequences. The ultra-high-definition video source is used, down sampled and encoded at multiple resolutions of (3480x2160), (1920x1080), (1280x720), (704x576), (352x288), and (176x144). To determine the best H265 feature configuration for each resolution experiments were conducted that resulted in a PSNR of 36 dB at the specified bitrate. The resolution is selected by delivery (encoder resource) based on the end-user application. While video streaming adapted to the available bandwidth is achieved via embedding a controller with MPEG DASH protocol at the client-side. Video streaming Adaptation methods allow the delivery of content that is encoded at different representations of video quality and bitrate and then dividing each representation into chunks of time. Through this paper, we propose to utilize HTTP/2 as a protocol to achieve low latency video streaming focusing on live streaming video avoiding the problem of HTTP/1.
Privacy-Preserving Image Template Sharing Using Contrastive Learning

With the recent developments of Machine Learning as a Service (MLaaS), various privacy concerns have been raised. Having access to the user’s data, an adversary can design attacks with different objectives, namely, reconstruction or attribute inference attacks. In this paper, we propose two different training frameworks for an image classification task while preserving user data privacy against the two aforementioned attacks. In both frameworks, an encoder is trained with contrastive loss, providing a superior utility-privacy trade-off. In the reconstruction attack scenario, a supervised contrastive loss was employed to provide maximal discrimination for the targeted classification task. The encoded features are further perturbed using the obfuscator module to remove all redundant information. Moreover, the obfuscator module is jointly trained with a classifier to minimize the correlation between private feature representation and original data while retaining the model utility for the classification. For the attribute inference attack, we aim to provide a representation of data that is independent of the sensitive attribute. Therefore, the encoder is trained with supervised and private contrastive loss. Furthermore, an obfuscator module is trained in an adversarial manner to preserve the privacy of sensitive attributes while maintaining the classification performance on the target attribute. The reported results on the CelebA dataset validate the effectiveness of the proposed frameworks.
A novel sensor-based architecture using 5G and Blockchain for remote and continuous health monitoring

The healthcare sector strives to improve the quality of care delivered to patients, especially considering new digital technologies, such as sensor-based networks (SBN). The application of SBN can help to provide remote and continuous monitoring for patients that need continuous treatment and management. In this context, this research proposed a novel sensor-based architecture for healthcare monitoring including 5G as a recent technology for low latency data communication. Blockchain is another component that is integrated to prevent forgery, tampering and enable more secure transmission. Furthermore, this study proposed a unique data architecture that is based on a new protocol that aims to transmit gathered sensor data over existing network architecture (for example, routers) without the need for intermediate devices such as mobile phones, PDAs or tablets. The proposed data architecture is discussed for its application in healthcare through two case scenarios, continuous monitoring of chronic diseases and back pain.
Active preference-based optimization for human-in-the-loop feature selection

In various classification problems characterized by a large number of features, feature selection (FS) is essential to guarantee generalization capabilities. The FS problem is often ill-posed due to significant correlations among features, which may lead to several different feature subsets with comparable scores in terms of classification performance. However, not all these subsets are equivalent from a domain-oriented point of view due to known relationships among features and their different acquisition costs in production to deploy the trained classifier. In this paper, we consider the potential benefits of including the domain expert’s preferences in the FS task, thus integrating both objective elements (e.g., classification accuracy) and subjective (often not quantifiable) considerations in the selection process. This goes in the direction of increasing the interpretability and the trustworthiness of the machine learning model, which is an often desired property in many application domains such as in medicine. The proposed method consists of an iterative procedure. At each iteration, the expert is asked to express a “human” preference on pairs of classifiers, each one trained from a different subset of features. The expressed preferences are used algorithmically to update a suitable surrogate function that mimics the latent subjective expert’s objective function, and then to propose a new classifier for testing and comparison. The proposed method has been tested on academic and experimental FS problems, and notably, on a COVID’19 patients record. The preliminary experimental results are promising, in that a parsimonious and accurate solution is obtained after a relatively short number of iterations.
Feature Extraction and Dimensionality Reduction Models for Printed Numerals Recognition

Feature extraction is an important aspect in the field of pattern recognition and machine learning. Processing an image directly by a software tool is not possible due to the size of the image which contains a large amount of data to process. A perfectly designed feature extraction method increases the overall classification process either in machine learning or pattern recognition. Some kind of statistical or geometrical features from images are extracted and are inputted to machine learning algorithms for effective processing. The work presented here uses a novel feature extraction method for dimensionality reduction for classification of Odia numerals. The proposed method converts input images into equivalent matrices; row wise decimal values and produces the feature vector of the input image as a one dimensional array of numerical equivalent of each OCR Odia numeral. The primary attributes of this method is that not only it reduces the dimensionality of the input pattern but also supports image reconstruction. The effectiveness of the method has been experimented for the printed Odia numerals. The recognition rate achieved through the experiment is 81.9&#x0025;.
THE IMPACT OF THE SHIFT TO ONLINE EDUCATION DURING THE COVID-19 PANDEMIC ON EFL LEARNERS’ AUTONOMY

Saudi EFL learners have been traditionally described as non-autonomous. During the coronavirus pandemic, Saudi universities shifted to online education. Scholars maintain that online education by its very nature enhances learner autonomy. The current study, that was conducted after students received three semesters of online education, aimed to explore the effect of the shift to online education on Saudi college EFL learners’ autonomy. It also investigated gender differences in learner autonomy. Both quantitative data by means of a self-report questionnaire (N=306) and qualitative data by means of a semi-structured online interview (N=20) were collected and analyzed. Quantitative data revealed that Saudi college EFL learners are autonomous (with a total mean of 4.02 out of 5). This mean is much higher than the means obtained in studies conducted before the shift to online education. Females outperformed males in just one dimension of learner autonomy, autonomous learning enjoyment. No gender differences were found in the other dimensions of learner autonomy or total learner autonomy. Qualitative data analysis indicated that students attributed to online education improvement in five important aspects of their learner autonomy, namely, goal setting and organization of study time, the use of the Internet and social media as learning resources, the use of performance feedback, self-assessment and computer skills. An aspect that witnessed some improvement but still has room for much improvement is reflection on learning.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0926/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education

E-learning is an educational system that uses information technology in the learning process. One of the platforms that can be used in developing e-learning is Moodle. This research investigates the implementation of e-learning in the learning process in the Computer Education Department. The purpose of this research is to examine the use of E-learning in supporting the learning process. The respondents of this research are 130 active students of the Computer Education Department from the 2016 to 2018 Batches. The method used is a research questionnaire whose data are analyzed using the SPSS Statistics 25 program. This research shows no significant effect of student expectation on e-learning in supporting the learning process. Meanwhile, ease of e-learning, e-learning performance, and social influence for the benefit of e-learning have a significant effect on supporting the learning process and improve learning independence.
To Trust or Not To Trust Prediction Scores for Membership Inference Attacks

Membership inference attacks (MIAs) aim to determine whether a specific sample was used to train a predictive model. Knowing this may indeed lead to a privacy breach. Most MIAs, however, make use of the model's prediction scores - the probability of each output given some input - following the intuition that the trained model tends to behave differently on its training data. We argue that this is a fallacy for many modern deep network architectures. Consequently, MIAs will miserably fail since overconfidence leads to high false-positive rates not only on known domains but also on out-of-distribution data and implicitly acts as a defense against MIAs. Specifically, using generative adversarial networks, we are able to produce a potentially infinite number of samples falsely classified as part of the training data. In other words, the threat of MIAs is overestimated, and less information is leaked than previously assumed. Moreover, there is actually a trade-off between the overconfidence of models and their susceptibility to MIAs: the more classifiers know when they do not know, making low confidence predictions, the more they reveal the training data.
Using Machine Learning to Profile Asymmetry between Spiral Galaxies with Opposite Spin Directions

Spiral galaxies can spin clockwise or counterclockwise, and the spin direction of a spiral galaxy is a clear visual characteristic. Since in a sufficiently large universe the Universe is expected to be symmetric, the spin direction of a galaxy is merely the perception of the observer, and therefore, galaxies that spin clockwise are expected to have the same characteristics of galaxies spinning counterclockwise. Here, machine learning is applied to study the possible morphological differences between galaxies that spin in opposite directions. The dataset used in this study is a dataset of 77,840 spiral galaxies classified by their spin direction, as well as a smaller dataset of galaxies classified manually. A machine learning algorithm was applied to classify between images of clockwise galaxies and counterclockwise galaxies. The results show that the classifier was able to predict the spin direction of the galaxy by its image in accuracy higher than mere chance, even when the images in one of the classes were mirrored to create a dataset with consistent spin directions. That suggests that galaxies that seem to spin clockwise to an Earth-based observer are not necessarily fully symmetric to galaxies that spin counterclockwise; while further research is required, these results are aligned with previous observations of differences between galaxies based on their spin directions.
Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications

In this paper, we propose a novel hybrid discriminative generative model by integrating a modified version of hidden Markov model (HMM), multivariate Beta-based HMM with support vector machine (SVM). We apply Fisher Kernel to define decision boundary and separate classes. In this model, we assume that HMM emission probabilities follow a Beta mixture model as generalizing the assumption of Gaussianity may not be practical in modeling real-world applications. HMM as a generative model needs less amount of data however, its accuracy is less than discriminative models such as SVM. Moreover, in some applications, data may have various feature-length. We tackle this problem with Fisher Kernel. We apply our proposed model to medical applications, lung cancer detection, colonoscopy image, and colon tissue analysis. The results indicate that our proposed model could be a promising alternative.
Sophisticated and modernized library running system with OCR algorithm using IoT

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.
Neural text generation for query expansion in information retrieval

Expanding users’ query is a well-known way to improve the performance of document retrieval systems. Several approaches have been proposed in the literature, and some of them are considered as yielding state-of-the-art results in Information Retrieval. In this paper, we explore the use of text generation to automatically expand the queries. We rely on a well-known neural generative model, OpenAI’s GPT-2, that comes with pre-trained models for English but can also be fine-tuned on specific corpora. Through different experiments and several datasets, we show that text generation is a very effective way to improve the performance of an IR system, with a large margin (+10 %MAP gains), and that it outperforms strong baselines also relying on query expansion (RM3). This conceptually simple approach can easily be implemented on any IR system thanks to the availability of GPT code and models.
Translating Metainferences Into Formulae

In this paper, we present a way to translate the metainferences of a mixed metainferential system into formulae of an extended-language system, called its associated σ-system. To do this, the σ-system will contain new operators (one for each standard), called the σ operators, which represent the notions of "belonging to a (given) standard". We first prove, in a model-theoretic way, that these translations preserve (in)validity. That is, that a metainference is valid in the base system if and only if its translation is a tautology of its corresponding σ-system. We then use these results to obtain other key advantages. Most interestingly, we provide a recipe for building unlabeled sequent calculi for σ-systems. We then exemplify this with a σ-system useful for logics of the ST family, and prove soundness and completeness for it, which indirectly gives us a calculus for the metainferences of all those mixed systems. Finally, we respond to some possible objections and show how our σ-framework can shed light on the “obeying” discussion within mixed metainferential contexts
Autonomous Navigation and Control System for Capturing A Moving Drone

This paper describes an autonomous navigation and control system for capturing the maneuvering drones. A vision-based navigation method seeks and detects the intruding drone, then, the target trajectory is predicted by fusing onboard vision and inertial-measurement resources. The target’s relative position, velocity and acceleration are also obtained at the same time. Then, we present a modified proportional-derivative (PD) algorithm based on the estimated target states. In addition, the boundary constraints of the protected area are considered to avoid a collision. The proposed capture navigation and control system has demonstrated its efficiency both in simulation, flight experiments, and MBZIRC 2020, where our team won the Challenge I competition.
Effect of UI/UX Designer on Front End

In this digital era, product quality cannot be separated from aesthetics, those who are responsible for the beauty of product appearance are UI/UX Design and FrontEnd. Therefore, this study aims to find out what distinguishes UI/UX from FrontEnd and the correlation between UI/UX to FrontEnd. The method that this research uses is descriptive by presenting a complete picture of a particular situation based on facts, theories, and accurate reference sources with previous research. The results show that the presence of a UI/UX Designer can affect FrontEnd in the design/development of information systems. The main influence it has is in terms of appearance being more interactive as well as attractive. Even so, the position of FrontEnd cannot be replaced by a UI/UX Designer, because it is FrontEnd here who will implement the design and ensure that the aesthetics and functionality of the system run well.
Mask wearing detection in public places based on YOLOv5

The new type of coronary pneumonia has not completely ended. Wearing a mask in public is not only the most simple and effective means of epidemic prevention, but also an important epidemic prevention policy. In order to efficiently, accurately and robustly detect whether people wear masks in public scenes, firstly, the mask data set is established, and the self-made crowd image data set is trained based on YOLOv5s model in Google Colaboratory, so as to obtain the optimal detection model. Finally, the verification experiment of the detection model is carried out in the actual scene. The experimental results show that the detection accuracy of personnel wearing masks based on YOLOv5s detection model is 87%, and it is easy to deploy in low-performance mobile devices.