Reviewerly/exHarmony huggingface dataset:
Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval

Cross-domain image retrieval aims at retrieving images across different domains to excavate cross-domain classificatory or correspondence relationships. This paper studies a less-touched problem of cross-domain image retrieval, i.e., unsupervised cross-domain image retrieval, considering the following practical assumptions: (i) no correspondence relationship, and (ii) no category annotations. It is challenging to align and bridge distinct domains without cross-domain correspondence. To tackle the challenge, we present a novel Correspondence-free Domain Alignment (CoDA) method to effectively eliminate the cross-domain gap through In-domain Self-matching Supervision (ISS) and Cross-domain Classifier Alignment (CCA). To be specific, ISS is presented to encapsulate discriminative information into the latent common space by elaborating a novel self-matching supervision mechanism. To alleviate the cross-domain discrepancy, CCA is proposed to align distinct domain-specific classifiers. Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval. To verify the effectiveness of the proposed method, extensive experiments are conducted on four benchmark datasets compared with six state-of-the-art methods.
An Ensemble Distillation Framework for Sentence Embeddings with Multilingual Round-Trip Translation

In this work, we propose a novel unsupervised contrastive learning framework to improve state-of-the-art sentence embeddings. First, we train a set of contrastive submodels which take multilingual round-trip translation(RTT) as data augmentation. The RTT naturally changes the length of the same sentence and replaces Synonyms simultaneously. Then we incorporate them into a single model through knowledge distillation. Specifically, it takes an input sentence and predicts the ensemble output of all submodels via a contrastive objective. Thus we preserve nearly the same semantic expressiveness as the ensemble model without increasing the test cost. We evaluate our framework on standard semantic textual similarity (STS) tasks. Experimental results show the advantage of our framework that we achieve an average of 79.27% Spearman's correlation, a 3.02% improvement compared to the previous best results using BERT-base.
Multiple Languages to Sign Language Using NLTK

Sign language involves visual gestures and signs that deaf and mute people adopt as their first conversation. People other than deaf or hard of hearing can also use sign language, like, people suffering from autism, apraxia of speech, cerebral palsy or down syndrome. It entails hand gestures, non-verbal communication or physical movement, and face emotions all at the same time. It may be used by communities who have difficulty to speak, those who can hear but cannot talk, and helps normal individuals to interact with hearing impaired people. There are several groups of hearing-impaired people throughout the globe, and each community's language is unique. American Sign Language (ASL) is utilized throughout United States; British Sign Language (BSL) is a form of communication in the United Kingdom; Australian Sign Language (AUSLAN) is used in Australia; and Indian Sign Language (ISL) is used in India. ISL signs are divided into three categories: one handed, two handed, and non-manual signs. One-handed and two-handed signs are sometimes known as manual signs. Changes in body position and facial emotions provide non-manual indicators. This translator converts English text to Sign language, allowing hearing-impaired persons in India to engage with others. This application takes multiple languages as input and gives signs as information.
A Solid-State Source of Single and Entangled Photons at Diamond SiV-Center Transitions Operating at 80K

Large-scale quantum networks require the implementation of long-lived quantum memories as stationary nodes interacting with qubits of light. Epitaxially grown quantum dots hold great potential for the on-demand generation of single and entangled photons with high purity and indistinguishability. Coupling these emitters to memories with long coherence times enables the development of hybrid nanophotonic devices that incorporate the advantages of both systems. Here we report the first GaAs/AlGaAs quantum dots grown by the droplet etching and nanohole infilling method, emitting single photons with a narrow wavelength distribution (736.2 ± 1.7 nm) close to the zero-phonon line of silicon-vacancy centers. Polarization entangled photons are generated via the biexciton-exciton cascade with a fidelity of (0.73 ± 0.09). High single photon purity is maintained from 4 K (g(2)(0) = 0.07 ± 0.02) up to 80 K (g(2)(0) = 0.11 ± 0.01), therefore making this hybrid system technologically attractive for real-world quantum photonic applications.
Tendencies of the Development of Higher Professional Education

The article describes a number of global tendencies of the development of modern higher professional education which are determined by the world transformations. The tendencies are under the influence of the world social problems, and are considered to be the most effective ones in the context of practical activities for achievement of educational goals. It has been revealed in the course of detailed analysis of scientific and pedagogical literature that the most popular tendencies of the development of higher professional education are the follows: online learning, distance learning, social media learning platforms, integrated problem-based learning, augmented and virtual reality learning, Internet resources (mass open online courses), blockchain technology, digitization of education, health-preserving environment of educational organization, social education management, students’ encouragement to self-education, students’ involvement to management of higher professional education institution, life-long learning, development of coaching, mentoring, tutorship, constant growth and improvement of teachers’ skills, gamification of education. Studying the educational tendencies of the development of higher professional education have allowed to find out that none of the educational tendencies is universal, although they are actual and problematic. The learning combines various methods that allows the most comprehensive solution of modern educational problems. The conclusions are drawn to the point that the only methodology that combines modern educational innovations and innovative teaching experience is considered to be the most progressive for further development of higher professional education and requires application into teaching practice.
Supplemental Material: Evolution of Miocene normal and dextral faulting in the lower Colorado River region near Blythe, California, USA

&lt;p&gt;Datasets S1–S4: Four Microsoft Excel files including tabulated structural data from stereoplots, calcite U-Pb analytical data, Ar/Ar analytical data, and cumulative dextral shear estimates. Item S1: One Microsoft Word document that describes U-Pb and Ar/Ar methodology. &lt;/p&gt;
Language learners’ motivation towards English learning through the VAK model

It has been proven that motivation is an essential factor in the learning process. In the case of learning a new language, this factor becomes even more significant, since it can determine the state of students' readiness to learn and therefore directly influences the success of language acquisition. For this reason, different teaching models have been studied that allow motivating language learners to achieve meaningful learning. One of these is the VAK model. This article presents the results of a study implemented in the Degree in Languages at the Universidad Juárez Autónoma de Tabasco with the objective of determining the effectiveness of the VAK model to improve student motivation and therefore the learning of the English language as L2.
Anomaly detection of bicep curl using pose estimation

Resistance training exercises can cause adverse effects and even injuries if not executed correctly. The latest pose estimation technologies in computer vision could help provide real-time analysis on exercising motion using on-device cameras. However, to identify whether an individual is performing an exercise correctly, postural deviations or anomalies from the correct patterns must be identified. In this study, a versatile solution is formulated to detect and analyze a specific resistance training exercise – bicep curl using BlazePose and binary tree algorithms in machine learning based on specific pose features. Ten decision tree models are developed to identify ten target pose anomalies including deviated trunk angles and misplaced elbows and wrists. The model sensitivity ranges from 73.7% (external rotated shoulders) to 97.4% (over-flexed trunk). These predicted results would be very useful in giving specific postural advises to learners of fitness exercises. Our research outputs could be extended to other exercises, and be implemented in mobile applications for various purposes such as exergames and sports analysis.
Portability of Deep-Learning Side-Channel Attacks against Software Discrepancies

Deep-learning side-channel attacks can reveal encryption keys on a device by analyzing power consumption with neural networks. However, the portability of deep-learning side-channel attacks can be affected when training data (from the training device) and test data (from the test device) are discrepant. Recent studies have examined the portability of deep-learning side-channel attacks against hardware discrepancies between two devices. In this paper, we investigate the portability of deep-learning side-channel attacks against software discrepancies between the training device and test device. Specifically, we examine four factors that can lead to software discrepancies, including random delays, instruction rewriting, optimization levels, and code obfuscation. Our experimental results show that software discrepancies caused by each factor can significantly downgrade the attack performance of deep-learning side-channel attacks, and even prevent an attacker from recovering keys. To mitigate the impacts of software discrepancies, we investigate three mitigation methods, including adjusting Points of Interest, domain adaptation, and multi-domain training, from the perspective of an attacker. Our results indicate that multi-domain training is the most effective approach among the three, but it can be difficult to scale given the diversity of software discrepancies.
An Intuitive Guide to Relevance-Based Prediction

Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets: 1) <italic>relevance</italic>, which measures the importance of an observation to a prediction; 2) <italic>fit</italic>, which measures the reliability of each individual prediction task; and 3) <italic>codependence</italic>, which holds that the choice of observations and predictive variables should be determined jointly for each individual prediction task.
Transfer Learning-based Forensic Analysis and Classification of E-Mail Content

research-article Free Access Share on Transfer Learning-based Forensic Analysis and Classification of E-Mail ContentJust Accepted Authors: Farkhund Iqbal College of Technological Innovation Zayed University, UAE College of Technological Innovation Zayed University, UAEView Profile , Abdul Rehman Javed Department of Electrical and Computer Engineering Lebanese American University, Lebanon Department of Electrical and Computer Engineering Lebanese American University, LebanonView Profile , Rutvij H. Jhaveri Department of Computer Science and Engineering, School of Technology Pandit Deendayal Energy University, India Department of Computer Science and Engineering, School of Technology Pandit Deendayal Energy University, IndiaView Profile , Ahmad Almadhor Department of Computer Engineering and Networks, College of Computer and Information Sciences Jouf University, Saudi Arabia Department of Computer Engineering and Networks, College of Computer and Information Sciences Jouf University, Saudi ArabiaView Profile , Umar Farooq Department of Computer Science National University of Computer and Emerging Sciences, Pakistan Department of Computer Science National University of Computer and Emerging Sciences, PakistanView Profile Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingAccepted on June 2023https://doi.org/10.1145/3604592Published:28 June 2023Publication History 0citation78DownloadsMetricsTotal Citations0Total Downloads78Last 12 Months78Last 6 weeks78 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
"Building" knowledge by creating manipulatives with the 3D printer: A course for mathematics student teachers

3D modelling and printing (3DMP) is a suitable context for education. With it, own manipulatives for mathematics education can be created, which was not possible easily before. We develop a seminar at Goethe University, in which use 3DMP to teach mathematics student teachers about mathematics, pedagogy, and technology. In this paper, we present the general 3DMP process, and how we integrate it in the seminar. The structure and contents of the seminar, as well as results from the students are laid out. The hardware and software used are shown and explained, as well as our approach to 3D modelling with the help of programming. The individual iterations of the seminar and the conclusions show that students mainly benefit from being able to realize their ideas very early and having the freedom to choose their own topic.
An Evaluation of Four Solutions to the Garden of Forking Paths Problem

In this paper (Rubin, 2017), I consider Gelman and Loken’s (2013, 2014) garden of forking paths problem. Forking paths occur when researchers decide which analyses to perform based on information from their sample. For example, a researcher may decide whether or not to drop an item from a scale based on how it affects the scale’s Cronbach alpha coefficient within the current sample. This sample-contingent decision rule causes a forking path in the analysis protocol because, during the course...
Peran Guru dan Gerakan Literasi Sekolah dalam Meningkatkan Literasi Sejarah Siswa Kelas XI di SMA Negeri 3 Pematangsiantar

Teachers play a crucial role as stakeholders in ensuring the continuity of literacy movements in schools. They undertake various efforts to sustain and develop school-based literacy programs, particularly in history. To this end, teachers can take several steps to promote and maintain the school's history literacy movement, such as planning and implementing the school's literacy history movement process. The research aims to determine the literacy skills of class XI students in lesson planning at SMAN 3 Pematangsiantar and to comprehend the role of teachers in fostering student literacy history in the learning process. A descriptive qualitative method was used to analyze descriptive data by describing the research results. The study utilized observation, interviews, and documentation to collect data from the principal, homeroom teacher, and class XI students. The study's findings suggest that teachers at SMAN 3 Pematangsiantar foster literacy movements through planning and processes implemented in the stages of habituation, development, and learning. Teachers can encourage students to participate in literacy programs by incorporating reading programs into the curriculum, such as a one-hour reading program or a reading lesson before class, and using various media, such as bulletin boards, to promote literacy movements. In addition, teachers and parents play essential roles in managing and supervising students since students learn not only at school but also actively learn at home.
An Easy Partition Approach for Joint Entity and Relation Extraction

The triplet extraction (TE) task aims to identify the entities and relations mentioned in a given text. TE consists of two tasks: named entity recognition (NER) and relation classification (RC). Previous work has either treated TE as two separate tasks with independent encoders, or as a single task with a unified encoder. However, both approaches have limitations in capturing the interaction and independence of the features for different subtasks. In this paper, we propose a simple and direct feature selection and interaction scheme. Specifically, we use a pretraining language model (e.g., BERT) to extract various features, including entity recognition, shared, and relation classification features. To capture the interaction, shared features consist of the common semantic information used by the two tasks simultaneously. We use a gate module to obtain the task-specific features. Experimental results on various public benchmarks show that our proposed method can achieve competitive performance, and the calculation speed of our model is seven times faster than CasRel, and two times faster than PFN.
Simulating Sparse and Shallow Gaussian Boson Sampling

Gaussian Boson Sampling (GBS) is one of the most popular quantum supremacy protocols as it does not require universal control over the quantum system, which favors current photonic experimental platforms and there is strong theoretical evidence for its computational hardness. However, over the years, several algorithms have been proposed trying to increase the performance of classically simulating GBS assuming certain constraints, e.g., a low number of photons or shallow interferometers. Most existing improvements of the classical simulation of GBS provide a performance increase regarding the probability calculation, leaving the sampling algorithm itself untouched. This paper provides an asymptotically better sampling algorithm in the case of low squeezing and shallow circuits.
Weakly-Guided Self-Supervised Pretraining for Temporal Activity Detection

Temporal Activity Detection aims to predict activity classes per frame, in contrast to video-level predictions in Activity Classification (i.e., Activity Recognition). Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. Thus, commonly, previous work on temporal activity detection resorts to fine-tuning a classification model pretrained on large-scale classification datasets (e.g., Kinetics-400). However, such pretrained models are not ideal for downstream detection, due to the disparity between the pretraining and the downstream fine-tuning tasks. In this work, we propose a novel weakly-guided self-supervised pretraining method for detection. We leverage weak labels (classification) to introduce a self-supervised pretext task (detection) by generating frame-level pseudo labels, multi-action frames, and action segments. Simply put, we design a detection task similar to downstream, on large-scale classification data, without extra annotations. We show that the models pretrained with the proposed weakly-guided self-supervised detection task outperform prior work on multiple challenging activity detection benchmarks, including Charades and MultiTHUMOS. Our extensive ablations further provide insights on when and how to use the proposed models for activity detection. Code is available at github.com/kkahatapitiya/SSDet.
Two Heads Are Better than One: Image-Point Cloud Network for Depth-Based 3D Hand Pose Estimation

Depth images and point clouds are the two most commonly used data representations for depth-based 3D hand pose estimation. Benefiting from the structuring of image data and the inherent inductive biases of the 2D Convolutional Neural Network (CNN), image-based methods are highly efficient and effective. However, treating the depth data as a 2D image inevitably ignores the 3D nature of depth data. Point cloud-based methods can better mine the 3D geometric structure of depth data. However, these methods suffer from the disorder and non-structure of point cloud data, which is computationally inefficient. In this paper, we propose an Image-Point cloud Network (IPNet) for accurate and robust 3D hand pose estimation. IPNet utilizes 2D CNN to extract visual representations in 2D image space and performs iterative correction in 3D point cloud space to exploit the 3D geometry information of depth data. In particular, we propose a sparse anchor-based "aggregation-interaction-propagation'' paradigm to enhance point cloud features and refine the hand pose, which reduces irregular data access. Furthermore, we introduce a 3D hand model to the iterative correction process, which significantly improves the robustness of IPNet to occlusion and depth holes. Experiments show that IPNet outperforms state-of-the-art methods on three challenging hand datasets.
Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.
AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-Realistic Style Transfer

Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods are too expensive to run in real-time. Meanwhile, bilateral grid based methods are much faster but still contain artifacts like overexposure. In this work, we propose the Adaptive ColorMLP (AdaCM), an effective and efficient framework for universal photo-realistic style transfer. First, we find the complex non-linear color mapping between input and target domain can be efficiently modeled by a small multi-layer perceptron (ColorMLP) model. Then, in AdaCM, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair. Experimental results demonstrate that AdaCM can generate vivid and high-quality stylization results. Meanwhile, our AdaCM is ultrafast and can process a 4K resolution image in 6ms on one V100 GPU.