Reviewerly/exHarmony huggingface dataset:
E-Worksheet for Science Processing Skills Using Kvisoft Flipbook

This paper presents research on laboratory-based e-worksheets using Kvisoft for science process skills. E-worksheets are made with the stages of development, implementation, and evaluation adopted from branches using 90 samples obtained from purposive sampling techniques. The aim is to look at students' science process skills after using e-worksheets on laboratory-based science process skills using the kvisoft application. The findings of this study are that e-worksheets using Kvisoft Flipbook can improve students' science process skills.
Online Non-Cooperative Radar Emitter Classification From Evolving and Imbalanced Pulse Streams

Recent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained for training the classifiers. However, such overly ideal assumptions have made it difficult to fit real-world REC problems into such restricted models. In this paper, to achieve online REC in a more realistic way, we convert the online REC problem into dynamically performing subspace clustering on pulse streams. Meanwhile, the pulse streams have evolving and imbalanced properties which are mainly caused by the existence of the non-cooperative emitters. Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online clustering. First, to achieve subspace clustering on subspace-imbalanced data, a static clustering approach called the improved ESC algorithm (I-ESC) is proposed. Second, based on the subspace clustering results obtained, DI-ESC can process the pulse stream in real-time and can further detect the emitter evolution by the proposed evolution detection strategy. The typically dynamic behavior of emitters such as appearing, disappearing and recurring can be detected and adapted by the DI-ESC. Extinct experiments on real-world emitter data show the sensitivity, effectiveness, and superiority of the proposed I-ESC and DI-ESC algorithms.
Acoustic impedance interpretation of cross-sectional human skin by using time and frequency domain deconvolution

In the human cheek skin assessment using an ultrasound microscope, deconvolution is the process to estimate the reflection coefficient that comes only from the skin without the interference of any other components. The conventional deconvolution method which is performed only in the frequency domain will lead to the instability of the reconstructed signal, especially on the low-frequency components of the signal, causing the generation of artifacts when the result is converted into the acoustic impedance distribution. In order to deal with this, this paper presents a deconvolution technique in both the time and frequency domain. The essential low-frequency components are determined by the time domain deconvolution, which is then combined with the high-frequency components from the conventional frequency domain deconvolution method, and the cross-sectional acoustic impedance profile along the depth direction has been successfully observed.
Channel Pruning for Deep Neural Networks Via a Relaxed Groupwise Splitting Method

A relaxed groupwise splitting method (RGSM) is developed and evaluated for channel pruning of deep neural networks. Experiments with VGG-16 and ResNet-18 architectures on CIFAR-10/100 image data show that RGSM can achieve much higher channel sparsity than group Lasso method, while keeping comparable accuracy.
Risk Assessment for Scientific Data

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.
Complete characterization of the directly implementable quantum gates used in the IBM quantum processors

Quantum process tomography of each directly implementable quantum gate used in the IBM quantum processors is performed to compute gate error in order to check viability of complex quantum operations in the superconductivity-based quantum computers introduced by IBM and to compare the quality of these gates with the corresponding gates implemented using other technologies. Quantum process tomography (QPT) of C-NOT gates have been performed for three configurations available in IBM QX4 processor. For all the other allowed gates QPT have been performed for every allowed position (i.e., by placing the gates in different qubit lines) for IBM QX4 architecture, and thus, gate fidelities are obtained for both single-qubit and 2-qubit gates. Gate fidelities are observed to be lower than the corresponding values obtained in the other technologies, like NMR. Further, gate fidelities for all the single-qubit gates are obtained for IBM QX2 architecture by placing the gates in the third qubit line ($q[2]$). It's observed that the IBM QX4 architecture yields better gate fidelity compared to IBM QX2 in all cases except the case of $\operatorname{Y}$ gate as far as the gate fidelity corresponding to the third qubit line is concerned. In general, the analysis performed here leads to a conclusion that a considerable technological improvement would be inevitable to achieve the desired scalability required for the realization of complex quantum operations.
Attributes-based person re-identification via CNNs with coupled clusters loss

Person re-identification (re-id) involves matching a per- son across nonoverlapping views, with different poses, illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework, the improved convolutional neural network (CNN) model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover, an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework, which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.
Robust Video Object Segmentation via Propagating Seams and Matching Superpixels

Video object segmentation aims at separating foreground object from background, and it is far from well solved for different challenges such as deformation, occlusion and motion blurs.This paper proposes a robust video object segmentation method by propagating patch seams and matching superpixels.First, we predict the initial object contour based on pixel-level target labels calculated by patch seam propagation and rough sets.By a patch seam, we map a current patch to its most similar patch from last frame and obtain its labels based on the labels of mapped patch.Second, we utilize superpixels as middle level cues to optimize predicted object contour.The bidirectional distance based on three brightness channels is provided to match superpixels between adjacent frames.Using the boundaries of matched results and initialized object contour, many candidates of object contours are constructed.Third, we define an energy function based on multi-features to measure contour candidates, and the contour with minimum energy is the final segmented result of current frame.Finally, by propagating patch seams and matching superpixels, we compute video object segmentation results frame by frame.Fourteen videos of SegTrack-v2 data are used to evaluate our method.The quantitative and qualitative evaluations show that our method performs better than most present methods especially in dealing with occlusion, deformation and motion blurs.
PrivateBus

Recently, the ubiquity of mobile devices leads to an increasing demand of public network services, e.g., WiFi hot spots. As a part of this trend, modern transportation systems are equipped with public WiFi devices to provide Internet access for passengers as people spend a large amount of time on public transportation in their daily life. However, one of the key issues in public WiFi spots is the privacy concern due to its open access nature. Existing works either studied location privacy risk in human traces or privacy leakage in private networks such as cellular networks based on the data from cellular carriers. To the best of our knowledge, none of these work has been focused on bus WiFi privacy based on large-scale real-world data. In this paper, to explore the privacy risk in bus WiFi systems, we focus on two key questions how likely bus WiFi users can be uniquely re-identified if partial usage information is leaked and how we can protect users from the leaked information. To understand the above questions, we conduct a case study in a large-scale bus WiFi system, which contains 20 million connection records and 78 million location records from 770 thousand bus WiFi users during a two-month period. Technically, we design two models for our uniqueness analyses and protection, i.e., a PB-FIND model to identify the probability a user can be uniquely re-identified from leaked information; a PB-HIDE model to protect users from potentially leaked information. Specifically, we systematically measure the user uniqueness on users' finger traces (i.e., connection URL and domain), foot traces (i.e., locations), and hybrid traces (i.e., both finger and foot traces). Our measurement results reveal (i) 97.8% users can be uniquely re-identified by 4 random domain records of their finger traces and 96.2% users can be uniquely re-identified by 5 random locations on buses; (ii) 98.1% users can be uniquely re-identified by only 2 random records if both their connection records and locations are leaked to attackers. Moreover, the evaluation results show our PB-HIDE algorithm protects more than 95% users from the potentially leaked information by inserting only 1.5% synthetic records in the original dataset to preserve their data utility.
Efficient Backbone Search for Scene Text Recognition.

Scene text recognition (STR) is very challenging due to the diversity of text instances and the complexity of scenes. The community has paid increasing attention to boost the performance by improving the pre-processing image module, like rectification and deblurring, or the sequence translator. However, another critical module, i.e., the feature sequence extractor, has not been extensively explored. In this work, inspired by the success of neural architecture search (NAS), which can identify better architectures than human-designed ones, we propose automated STR (AutoSTR) to search data-dependent backbones to boost text recognition performance. First, we design a domain-specific search space for STR, which contains both choices on operations and constraints on the downsampling path. Then, we propose a two-step search algorithm, which decouples operations and downsampling path, for an efficient search in the given space. Experiments demonstrate that, by searching data-dependent backbones, AutoSTR can outperform the state-of-the-art approaches on standard benchmarks with much fewer FLOPS and model parameters.
An accountable privacy-preserving scheme for public information sharing systems

Due to the emergence of data externalization technologies, as cloud and fog computing, setting up public information-sharing applications has become much easier. Yet, many concerns related to information security need to be addressed. While sharing information, privacy is without any doubt one of the major concerns for all users. Several proposals in the literature treated privacy issues using existing anonymization techniques, but few of them considered accountability service. Whereas, when security systems do not adopt accountability mechanisms, full anonymity may encourage users to act maliciously. In this paper, we propose a novel accountable privacy-preserving solution for public information sharing in data externalization platforms. Based on signatures, our scheme allows externalization servers to authenticate any user in the system without violating its privacy. In case of misbehavior, our solution allows to trace malicious users thanks to an authority. Moreover, our solution ensures privacy-preserving and accountability services in a completely distributed manner, without a permanent resort to the authority. Finally, we show through experimentation that our solution outperforms existing accountable privacy-preserving solutions.
Understanding the Number Concepts through Learning Connected Mathematics (CM): A Local Cultural Approach

Mathematics becomes an important subject in elementary school.Numbers are one part of school mathematics.Learning that links between concepts and what is close to students' minds makes it easy for students in the process of reaching concepts.This is CM based on local culture.This research used a 2x2 factorial design.The population is all elementary school students from the "Integrated Islamic Foundation" in Bengkulu City.Samples were taken by using an intact group technique.The total sample is 60 students.With the local culture-based CM learning for experimental class and conventional learning for control class, the research instrument is a test of the ability to understand the concept of numbers.It is a valid and reliable instrument.The data was analyzed using the ANCOVA statistical test.The results of this study are Fo (A) = 9.7289 with, df = (1, 57) and p-value = 0.090 <0.05, which means Ho is rejected.It is showed that there is a linear effect of the covariate (initial ability) to understand the concept between the students who follow the learning by using CM with a local cultural approach.Fo (B) = 4.8096, df (1, 57) and p-value = 0.016 <0.05.In this case, Ho was also rejected.Thus, it can be inferred that there is the influence of CM learning with a local cultural approach on the ability to understand number concepts.It can be concluded that the average score and comprehension of the number concept for students who were taught by using CM learning was higher than the students who were taught with conventional learning models.Finally, the mean score for students' ability to understand the number concepts among the students who were given local culture-based materials was higher than the students who were given the materials which are not based on local culture.
No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distribution, and fitting parameters of asymmetrical generalized Gaussian distribution are extracted. Thirdly, the features extracted in the dual-domain are fused to form the quality-aware feature vector. Finally, quality regression process by random forest is conducted to build the relationship between image features and quality score, yielding a measure of image quality. The resulting algorithm is tested on the LIVE database and compared with competing IQA models. Experimental results on the LIVE database indicate that the proposed DFF-IQA method is more consistent with the human visual system than other competing IQA methods.
Experimental Observation of Coherent-Information Superadditivity in a Dephrasure Channel

We present an experimental approach to construct a dephrasure channel, which contains both dephasing and erasure noises, and can be used as an efficient tool to study the superadditivity of coherent information. By using a three-fold dephrasure channel, the superadditivity of coherent information is observed, and a substantial gap is found between the zero single-letter coherent information and zero quantum capacity. Particularly, we find that when the coherent information of n channel uses is zero, in the case of larger number of channel uses, it will become positive. These phenomena exhibit a more obvious superadditivity of coherent information than previous works, and demonstrate a higher threshold for non-zero quantum capacity. Such novel channels built in our experiment also can provide a useful platform to study the non-additive properties of coherent information and quantum channel capacity.
Optimization of multiplexed single-photon sources operated with photon-number-resolving detectors

Detectors inherently capable of resolving photon numbers have undergone a significant development recently, and this is expected to affect multiplexed periodic single-photon sources where such detectors can find their applications. We analyze various spatially and time-multiplexed periodic single-photon source arrangements with photon-number-resolving detectors, partly to identify the cases when they outperform those with threshold detectors. We develop a full statistical description of these arrangements in order to optimize such systems with respect to maximal single-photon probability, taking into account all relevant loss mechanisms. The model is suitable for the description of all spatial and time multiplexing schemes. Our detailed analysis of symmetric spatial multiplexing identifies a particular range of loss parameters in which the use of the new type of detectors leads to an improvement. Photon number resolution opens an additional possibility for optimizing the system in that the heralding strategy can be defined in terms of actual detected photon numbers. Our results show that this kind of optimization opens an additional parameter range of improved efficiency. Moreover, this higher efficiency can be achieved by using less multiplexed units, i.e., smaller system size as compared to threshold-detector schemes. We also extend our investigation to certain time-multiplexed schemes of actual experimental relevance. We find that the highest single-photon probability is 0.907 that can be achieved by binary bulk time multiplexers using photon-number-resolving detectors.
An Interval Type-2 Fuzzy Controller Based on Data-Driven Parameters Extraction for Cement Calciner Process

The stable control of the calciner plant is vital for the clinker quality and energy consumption in the cement calcination process. However, traditional calciner control strategies cannot efficiently deal with complicated characteristics, such as the changes in the material component, process disturbances and uncertainty, and meanwhile cannot obtain knowledge from data. For these challenges, an intelligent control strategy based on an interval type-2 fuzzy logic controller (IT2FLC) is proposed by making full use of process data in this work. The feedback IT2FLC combines with feedforward IT2FLC, taking into account various disturbances. An improved interval type-2 fuzzy C-means (IT2FCM) clustering algorithm is used to extract membership functions and rules, taking into account process uncertainty. Finally, the proposed control strategy is applied to control a calciner simulation process with practical data. The results show that the proposed strategy has better performance than the type-1 fuzzy logic controller (T1FLC) and can better meet the demand for real-life applications.
A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation

Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each language and develops into a language-agnostic meaning representation that can be efficiently used for transfer learning. We systematically study the impact of the size of the attention bridge and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that there is no conflict between translation performance and the use of sentence representations in downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. Nevertheless, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. Similarly, we show that trainable downstream tasks benefit from multilingual models, whereas additional language signals do not improve performance in non-trainable benchmarks. This is an important insight that helps to properly design models for specific applications. Finally, we also include an in-depth analysis of the proposed attention bridge and its ability to encode linguistic properties. We carefully analyze the information that is captured by individual attention heads and identify interesting patterns that explain the performance of specific settings in linguistic probing tasks.
Augmented reality: effect on conceptual change of scientific

In recent years, Augmented Reality (AR) has received increasing emphasis and researchers gradually promote it Over the worlds. With the unique abilities to generate virtual objects over the real-world environment, it can enhance user perception. Although AR recognised for their enormous positive impacts, there are still a ton of matters waiting to be discovered. Research studies on AR toward conceptual change, specifically in scientific concept, are particularly limited. Therefore, this research aims to investigate the effect of integrating AR on conceptual change in scientific concepts. Thirty-four primary school students participated in the study. A pre-test and post-test were used to assess participants’ understanding of the scientific concepts before and after learning through AR. The findings demonstrated that 82% among them had misconceptions about the scientific concepts before learning through AR. However, most of them (around 88%) able to correct their misconceptions and shifted to have a scientific conceptual understanding after learning through AR. These findings indicate that AR was effective to be integrated into education to facilitate conceptual change.