Reviewerly/exHarmony huggingface dataset:
Broadband chiral silicon photonic circuits based on fork-type inversely tapered nanowire waveguides

Optical spin or circular polarization provides a new degree of freedom to control light–matter interaction in the fundamentals and applications of light. To broaden the bandwidth of chiral (spin-controlled) coupling in photonic integrated circuits, we propose fork-type inversely tapered nanowire waveguides to compensate for the out-of-step phase evolution of adiabatic coupling between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">T</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> </mml:mrow> <mml:mn>0</mml:mn> </mml:msub> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">T</mml:mi> <mml:mi mathvariant="normal">M</mml:mi> </mml:mrow> <mml:mn>0</mml:mn> </mml:msub> </mml:mrow> </mml:math> ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">T</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> </mml:mrow> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> ) modes excited from the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>x</mml:mi> </mml:math> - and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>y</mml:mi> </mml:math> -polarization components of spin polarized light in free space, respectively. We design and simulate two kinds of devices based on air and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">i</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> <mml:mn>4</mml:mn> </mml:msub> </mml:mrow> </mml:math> up-claddings to show the feasibility of broadening the bandwidth of chiral silicon photonic circuits by using fork-type inverse tapers. Numerical results show that the bandwidth can approach 70 nm under high directionality of above 0.90. This broadband chiral coupling via the new phase synchronizing technique with fork-type inverse tapers may pave the way to develop on-chip spin photonics or polarization-based photonic integrated devices.
Prediction of Key Parameters of Coal Gasification Process Based on Time Delay Mining Fuzzy Time Cognitive Maps

Although China is a country short of oil and gas, its coal resources are relatively abundant. At present, the development of coal chemical industry has become a concern, and coal chemical industry cannot develop without the preparation of synthesis gas, coal gasification is the necessary means to prepare synthesis gas. Due to the time delay between process variables, the prediction model cannot reflect the real causality of each variable in the system, which leads to the problems of poor interpretation and low accuracy of prediction results. It is difficult to deal with this kind of multi-variable time series data by traditional model method. For this reason, this paper presents a time-delay mining fuzzy time-cognitive maps (TM-FTCM), which uses cross-correlation function (CCF) to mine the time-delay information from the data. It effectively solves the problem of inaccurate prediction model due to delay between industrial process variables. The validity of the proposed method is verified by the experimental analysis of the coal gasification process data.
Effective CNN-based Image Dehazing for UAV Deep Visual Odometry

With advancement in Unmanned Aerial Vehicle systems (UAVs), UAVs can perform navigation for aerial photography and environmental inspections in areas deemed unreachable or dangerous for humans. Compared to inertial-based or GPS-based navigation, visual-based navigation via visual odometry has become a popular alternative as the former incurs drift errors while the latter GPS-signal is not available in some locations. However, feature extraction algorithms in odometry are heavily dependent on visibility of the features and it does not work well in foggy or hazy environments. To tackle this problem, we approached image dehazing algorithms to increase the visibility before implementing odometry. In this work, we introduce MLCA-DehazeVO, where dehazing is performed using a network inspired from light convolutional autoencoder (Pavan et al, 2020). The network comprises of an encoder to extract the latent representation of the hazy images, and a decoder that reconstruct the dehazed image to closely match the clear images. For odometry procedures, feature is extracted using Convolutional Neural Network (CNN) and Long-Short Term Memory is deployed for sequential feature relations analysis. We experimented our methods using the Montefiore Institute Dataset of Aerial Images and Records (MIDAIR) on 5 different trajectories that provides both clear and foggy paired synthetic sequential images with ground truth trajectories and pose data for comparison. Our approach is evaluated and shown to outperform prior-based dehazing methods (e.g. Dark Channel Prior) and performed favourably against other learning-based dehazing methods (e.g. DehazeNet) (PSNR(prior)<60.00, SSIM(prior)<0.400, PSNR(learning)>60.00, SSIM(learning)>0.400 in general, with highest PSNR(MLCA) = 67.50 and highest SSIM(MLCA) = 0.747), with the deep VO approach outperforming traditional VO methods with respect to lower relative translational error t_rel and rotational error r_rel. Our model improved the algorithmic architecture in dehazing and VO with high accuracy, thus shedding light on autonomous navigation under adverse environment and atmosphere.
Augmented Reality Applications in Education: Teachers Opinion

A general outcome of many studies stated that Augmented Reality (AR) technologies can improve the way of learning, efficacy, and motivation of learning. Although the findings are promising, more work is required. Augmented Reality(AR) acts as a bridge between digital teaching and learning. Teachers are the central factor of any educational system, and they play an important role in incorporating and embracing technology into education. This study aims to discuss the use of AR technology and teachers' opinions about the technology, requirement of training, integrating AR with a classroom setting, and the availability of resources and skills. Qualitative research has been conducted in suburban and rural areas on teachers with different specialties and the output showed that the development process of AR if feasible, and is easy to adapt in the classroom settings.
Experimental demonstration of underwater decoy-state quantum key distribution with all-optical transmission

We demonstrate the underwater quantum key distribution (UWQKD) over a 10.4-meter Jerlov type III seawater channel by building a complete UWQKD system with all-optical transmission of quantum signals, synchronization signal and classical communication signal. The wavelength division multiplexing and the space-time-wavelength filtering technology are applied to ensure that the optical signals do not interfere with each other. The system is controlled by FPGA, and can be easily integrated into watertight cabins to perform field experiment. By using the decoy-state BB84 protocol with polarization encoding, we obtain a secure key rate of 1.82Kbps and an error rate of 1.55% at the attenuation of 13.26dB. We prove that the system can tolerate the channel loss up to 23.7dB, therefore may be used in the 300-meter-long Jerlov type I clean seawater channel.
Zero-Shot Federated Learning with New Classes for Audio Classification

Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users. However, new classes with completely unseen data distributions can stream across any device in a federated learning setting, whose data cannot be accessed by the global server or other users. To this end, we propose a unified zero-shot framework to handle these aforementioned challenges during federated learning. We simulate two scenarios here -- 1) when the new class labels are not reported by the user, the traditional FL setting is used; 2) when new class labels are reported by the user, we synthesize Anonymized Data Impressions by calculating class similarity matrices corresponding to each device's new classes followed by unsupervised clustering to distinguish between new classes across different users. Moreover, our proposed framework can also handle statistical heterogeneities in both labels and models across the participating users. We empirically evaluate our framework on-device across different communication rounds (FL iterations) with new classes in both local and global updates, along with heterogeneous labels and models, on two widely used audio classification applications -- keyword spotting and urban sound classification, and observe an average deterministic accuracy increase of ~4.041% and ~4.258% respectively.
Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data.
Meta-Reinforcement Learning With Dynamic Adaptiveness Distillation

Deep reinforcement learning is confronted with problems of sampling inefficiency and poor task migration capability. Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks. However, meta-RL methods lack enough queries toward the relationship between task-agnostic exploitation of data and task-related knowledge introduced by latent context, limiting their effectiveness and generalization ability. In this article, we develop an algorithm for off-policy meta-RL that can provide the meta-learners with self-oriented cognition toward how they adapt to the family of tasks. In our approach, we perform dynamic task-adaptiveness distillation to describe how the meta-learners adjust the exploration strategy in the meta-training process. Our approach also enables the meta-learners to balance the influence of task-agnostic self-oriented adaption and task-related information through latent context reorganization. In our experiments, our method achieves 10%–20% higher asymptotic reward than probabilistic embeddings for actor–critic RL (PEARL).
Learning Without Forgetting for 3D Point Cloud Objects

When we fine-tune a well-trained deep learning model for a new set of classes, the network learns new concepts but gradually forgets the knowledge of old training. In some real-life applications, we may be interested in learning new classes without forgetting the capability of previous experience. Such learning without forgetting problem is often investigated using 2D image recognition tasks. In this paper, considering the growth of depth camera technology, we address the same problem for the 3D point cloud object data. This problem becomes more challenging in the 3D domain than 2D because of the unavailability of large datasets and powerful pretrained backbone models. We investigate knowledge distillation techniques on 3D data to reduce catastrophic forgetting of the previous training. Moreover, we improve the distillation process by using semantic word vectors of object classes. We observe that exploring the interrelation of old and new knowledge during training helps to learn new concepts without forgetting old ones. Experimenting on three 3D point cloud recognition backbones (PointNet, DGCNN, and PointConv) and synthetic (ModelNet40, ModelNet10) and real scanned (ScanObjectNN) datasets, we establish new baseline results on learning without forgetting for 3D data. This research will instigate many future works in this area.
An extensible and reusable pipeline for automated utterance paraphrases

In this demonstration paper we showcase an extensible and reusable pipeline for automatic paraphrase generation , i.e., reformulating sentences using different words. Capturing the nuances of human language is fundamental to the effectiveness of Conversational AI systems, as it allows them to deal with the different ways users can utter their requests in natural language. Traditional approaches to utterance paraphrasing acquisition, such as hiring experts or crowd-sourcing, involve processes that are often costly or time consuming, and with their own trade-offs in terms of quality. Automatic paraphrasing is emerging as an attractive alternative that promises a fast, scalable and cost-effective process. In this paper we showcase how our extensible and reusable pipeline for automated utterance paraphrasing can support the development of Conversational AI systems by integrating and extending existing techniques under an unified and configurable framework.
Improved YOLOv3 model based on ResNeXt for target detection

In order to solve the problem of deep learning models that are difficult to balance accuracy and speed, this paper selects the YOLOv3 model with higher detection speed but lower accuracy to improve it, and intends to improve the accuracy of YOLOv3 to construct a high-speed and high-precision target detection model. First, we used the ResNeXt residual block to construct a backbone network with few parameters and high accuracy. Then we used the K-means++ clustering algorithm to obtain anchor boxes that is closer to the real box to help the model perform regression detection. The experimental results on the VOC data set show that the accuracy of the improved model is significantly improved, and achieved high-precision and high-speed detection.
Role of Mathematics in Machine Learning

Context: Machine learning is forming an algorithm that proposes a model that can learn from data and do predictions. It is used to know why something works and why one model is better than another. Machine learning is a built-in mathematical prerequisite. Machine learning is powered by a combination of statistics, calculus, linear algebra, and probability.Objective: This paper aims to examine the mathematics behind machine learning.Method: Different digital repositories are searched for research articles. Studies that are most relevant to the objective of the paper are selected for analysis. Result: Selected studies show that machine learning models are designed to perform calculation operations like matrix manipulation of the data for which linear algebra is used. The concept of probability is the dominant framework for dealing with uncertainty. Learning algorithms use the concept of probability to create an analysis on a given training dataset for creative prediction. Machine learning is intrinsically data-driven. The initial data acquired from different sources is in the form of junks, to search out the right data, statistical analysis is performed. Calculus helps to find the direction of change. In which direction should the unknown variable be changed such that the prediction is more optimal with minimum error?Conclusion: Findings of this paper are that the mathematical rules of designing models of machine learning are based on concepts of linear algebra, calculus, statistics, and probability. Statistics is widely used for the estimation of the value of a population parameter. Calculus tells us how to learn and optimize models. Linear algebra ensures the feasibility of algorithms to run on massive data sets. Probability predicts the likelihood of the occurrence of an event.
Usability Testing Website Dengan Menggunakan Metode System Usability Scale (Sus)s

In ensuring the effectiveness, efficiency and user satisfaction of the website, it is necessary to evaluate and evaluate the website. This study aims to analyze the Usability Testing Website Using the System Usability Scale (SUS) Method. The method used in this study is the SUS (System Usability Scale). Data collection techniques in this study were interview techniques, observation techniques or questionnaire techniques. The instrument used in this study was a questionnaire. The subjects in this study consisted of 30 respondents. The data analysis technique used in this research is descriptive qualitative analysis. The results of the evaluation in this study, namely the results of the assessment of the respondents, obtained a total value of the SUS score of 2012.50 with the resulting average value of 67.08, this shows that the total SUS score on the STIKI Indonesia website is 67.08, which means the level of user Acceptability Range is Marginal High, Grade Scale level is category D, user Adjective Rating level is OK category and SUS Score Percentile Rank is grade D. The website still needs to be evaluated and developed further so that its use can be more optimal.
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.
Towards Tokenization and Part-of-Speech Tagging for Khmer: Data and Discussion

As a highly analytic language, Khmer has considerable ambiguities in tokenization and part-of-speech (POS) tagging processing. This topic is investigated in this study. Specifically, a 20,000-sentence Khmer corpus with manual tokenization and POS-tagging annotation is released after a series of work over the last 4 years. This is the largest morphologically annotated Khmer dataset as of 2020, when this article was prepared. Based on the annotated data, experiments were conducted to establish a comprehensive benchmark on the automatic processing of tokenization and POS-tagging for Khmer. Specifically, a support vector machine, a conditional random field (CRF) , a long short-term memory (LSTM) -based recurrent neural network, and an integrated LSTM-CRF model have been investigated and discussed. As a primary conclusion, processing at morpheme-level is satisfactory for the provided data. However, it is intrinsically difficult to identify further grammatical constituents of compounds or phrases because of the complex analytic features of the language. Syntactic annotation and automatic parsing for Khmer will be scheduled in the near future.
Multipartite Verifiable Secret Sharing Based on CRT

Subrahmanyam, Rolla Rukma Rekha, N. Subba Rao, Y. V.In (t, n) threshold secret sharing scheme, the dealer distributes secret among a group of n participants, and any t threshold number of participants can reconstruct the secret. However, $$t-1$$ or lesser number of participants can not retrieve the secret. In verifiable secret sharing schemes (VSSS), participants can verify their share after receiving shares from the dealer to ensure that a dealer is not malicious. In multipartite secret sharing based on CRT scheme, a set of participants is divided into disjoint partitions, and whatever action performed at a single level is repeated at all other partitions. However, till date there is no mechanism to verify if dealer is malicious or not in multipartite secret sharing based on CRT. Two schemes are proposed for verification of a dealer, namely multipartite verifiable secret sharing based on CRT by using Iften, and multipartite verifiable secret sharing-based CRT by using kameer Kaya. Both proposed schemes are perfectly secure, and the security of both the schemes dependent on discrete logarithm problem.
Co-Evolutionary Niching Differential Evolution Algorithm for Global Optimization

Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms.In this paper, we present a co-evolutionary niching strategy (CoEN) to dynamically evolve appropriate niching methods and incorporate it into differential evolution (DE) to preserve the population diversity.The proposed CoEN strategy is achieved by optimizing a criterion, which involves both fitness improvement and population diversity resulting from employing the niching methods during evolution of DE.To verify the performance of proposed method, an extensive test on benchmark functions taken from CEC2019 and CEC2014 has been carried out.The results show the significance of the proposed CoEN and, by incorporating the CoEN, the resulting DE is able to achieve a better or competitive performance than related EA algorithms.