Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF CANVA-BASED INTERACTIVE E-BOOK AND BOOK CREATOR USING THE RADEC LEARNING MODEL TO SUPPORT CREATIVE THINKING SKILLS

The involvement of technology in 21st century learning is indispensable at every level of education. Teachers can involve digital learning media to support the process of teaching and learning activities. Digital-based learning media such as interactive e-books can be used as learning media that can be used in 21st century learning. In addition to digital-based learning media, the learning model that can be applied is an innovative learning model. The RADEC learning model is one of the learning models that can be applied in current learning. This study aims to develop an interactive e-book through the RADEC learning model, and determine the feasibility of an interactive e-book through the RADEC learning model. This research is a research and development or Research and Development ADDIE model. This study obtained the feasibility results from the validation of material experts, validation of linguists, and validation of media experts. The results of the validation of material experts, linguists, and media experts get scores in the very valid category for use. The results of student responses regarding product trials get scores in the very interesting category for students to use in the learning process. The results of the group creativity assessment are very interesting and can increase the creativity of students. Based on these results, it can be concluded that interactive e-books through the RADEC learning model are very valid and feasible to be used in learning and can increase students' creativity.
Rasch measurement and strategies of Science Teacher's Technological, Pedagogical, and Content Knowledge in Augmented Reality

The use of Augmented Reality in the Teachers’ Technological, Pedagogical, and Content Knowledge (TPACK) framework is one of the systematic approaches to enriching students' experience in a science classroom. A study was conducted to explore teachers’ TPACK in Augmented Reality abilities. This article aims to identify science teachers’ level of TPACK in Augmented Reality using a questionnaire. A group of teachers (n=100) was trained to determine science teachers' TPACK at AR levels. Selected Science teachers then responded to a survey and analyzed using the Rasch Model. Male teachers (1.77 logits) outperformed female teachers (-0.55 logit) in TPACK abilities. In addition, teachers 36-40 years of age with 6-10 years of teaching experience had higher ability in TPACK in AR. Findings also indicated the Pedagogical Knowledge (PK) scoring is at +1.23logit and TPACK +0.29 logit suggesting both PK and TPACK components were of higher difficulty level compared to other components.The findings suggest that the training on TPACK in AR especially in the areas of PK and TPACK is necessary and should be based on the teacher’s demographic profile, teaching experiences, and other components of the TPACK framework.
Asset and Inventory Data Monitoring Application Design PT. Transmarco

The use of IT asset and inventory management information systems can greatly benefit businesses in managing their IT assets. The objective of this study is to develop an information system application that simplifies the management of IT assets by monitoring inventory data, tracking requests for repair, borrowing, purchase, and inventory recording of IT assets. The study was conducted at PT. Transmarco and the development of the information system application involved layout design, diagram design (including an activity diagram), and database design processes. Modular programming languages such as PHP, Sublime Text, MySQL, and CSS were used in the development process. The IT Asset and Inventory Data Monitoring Application provides information on incoming goods data, outgoing goods data, stock goods, and usage history. By implementing this application, it is expected that managing asset data and IT inventory at PT. Transmarco will become much simpler, including managing item data, item history, and stock of goods. This system will enable firm personnel to efficiently manage IT assets according to the desired outcomes. Overall, the development of the IT asset and inventory data monitoring application at PT. Transmarco showcases the potential benefits of IT asset and inventory management information systems for businesses. The streamlining of IT asset management processes can result in improved efficiency, reduced costs, and better decision-making regarding IT asset investments.
Limits of Model Selection under Transfer Learning

Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task. Now, in addition to the usual tradeoffs on approximation vs estimation errors involved in model selection, this problem brings in a new complexity term, namely, the transfer distance between source and target distributions, which is known to vary with the choice of hypothesis class. We present a first study of this problem, focusing on classification; in particular, the analysis reveals some remarkable phenomena: adaptive rates, i.e., those achievable with no distributional information, can be arbitrarily slower than oracle rates, i.e., when given knowledge on distances.
Advanced Version of Voice for Deaf and Dumb Persons

Abstract: Normal and dumb individuals can naturally communicate with one another using sign language, but people hasn’t comprehend their sign language, they frequently struggle to do so. The major goal of this initiative is to lessen the communication gap between deaf-dumb persons and other people. The suggested device transforms their hand motions into voice that a layperson may comprehend in order to lessen this barrier. Gloves, accelerometer and flex sensors, a microprocessor, a voice module, and a 16x16 LCD display make up this device. To put it simply, the voice module will translate the gestures into real-time speech output, and the display will provide text foreach corresponding gesture. So, this device offers both deaf - dumb and sighted people an effective means of communication.
Enhancing multilingual speech recognition in air traffic control by sentence-level language identification

Automatic speech recognition (ASR) technique is becoming increasingly popular to improve the efficiency and safety of air traffic control (ATC) operations. However, the conversation between ATC controllers and pilots using multilingual speech brings a great challenge to building high-accuracy ASR systems. In this work, we present a two-stage multilingual ASR framework. The first stage is to train a language identifier (LID), that based on a recurrent neural network (RNN) to obtain sentence language identification in the form of one-hot encoding. The second stage aims to train an RNN-based end-to-end multilingual recognition model that utilizes sentence language features generated by LID to enhance input features. In this work, We introduce Featurewise Linear Modulation (FiLM) to improve the performance of multilingual ASR by utilizing sentence language identification. Furthermore, we introduce a new sentence language identification learning module called SLIL, which consists of a FiLM layer and a Squeeze-and-Excitation Networks layer. Extensive experiments on the ATCSpeech dataset show that our proposed method outperforms the baseline model. Compared to the vanilla FiLMed backbone model, the proposed multilingual ASR model obtains about 7.50% character error rate relative performance improvement.
Just Noticeable Difference-aware Per-Scene Bitrate-laddering for Adaptive Video Streaming

In video streaming applications, a fixed set of bitrate-resolution pairs (known as a bitrate ladder) is typically used during the entire streaming session. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience. This paper introduces a Just Noticeable Difference (JND)-aware per-scene bitrate ladder prediction scheme (JASLA) for adaptive video-on-demand streaming applications. JASLA predicts jointly optimized resolutions and corresponding constant rate factors (CRFs) using spatial and temporal complexity features for a given set of target bitrates for every scene, which yields an efficient constrained Variable Bitrate encoding. Moreover, bitrate-resolution pairs that yield distortion lower than one JND are eliminated. Experimental results show that, on average, JASLA yields bitrate savings of 34.42% and 42.67% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder Constant Bitrate encoding using x265 HEVC encoder, where the maximum resolution of streaming is Full HD (1080p). Moreover, a 54.34% average cumulative decrease in storage space is observed.
Data-Driven Subgroup Identification for Linear Regression

Medical studies frequently require to extract the relationship between each covariate and the outcome with statistical confidence measures. To do this, simple parametric models are frequently used (e.g. coefficients of linear regression) but usually fitted on the whole dataset. However, it is common that the covariates may not have a uniform effect over the whole population and thus a unified simple model can miss the heterogeneous signal. For example, a linear model may be able to explain a subset of the data but fail on the rest due to the nonlinearity and heterogeneity in the data. In this paper, we propose DDGroup (data-driven group discovery), a data-driven method to effectively identify subgroups in the data with a uniform linear relationship between the features and the label. DDGroup outputs an interpretable region in which the linear model is expected to hold. It is simple to implement and computationally tractable for use. We show theoretically that, given a large enough sample, DDGroup recovers a region where a single linear model with low variance is well-specified (if one exists), and experiments on real-world medical datasets confirm that it can discover regions where a local linear model has improved performance. Our experiments also show that DDGroup can uncover subgroups with qualitatively different relationships which are missed by simply applying parametric approaches to the whole dataset.
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning

We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularly, we consider a continuum of constraints instead of a finite number of constraints as in the case of ordinary CMDPs. We also devise two reinforcement learning algorithms for SICMDPs that we call SI-CRL and SI-CPO. SI-CRL is a model-based reinforcement learning algorithm. Given an estimate of the transition model, we first transform the reinforcement learning problem into a linear semi-infinitely programming (LSIP) problem and then use the dual exchange method in the LSIP literature to solve it. SI-CPO is a policy optimization algorithm. Borrowing the ideas from the cooperative stochastic approximation approach, we make alternative updates to the policy parameters to maximize the reward or minimize the cost. To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems. We present theoretical analysis for SI-CRL and SI-CPO, identifying their iteration complexity and sample complexity. We also conduct extensive numerical examples to illustrate the SICMDP model and demonstrate that our proposed algorithms are able to solve complex sequential decision-making tasks leveraging modern deep reinforcement learning techniques.
The Effectiveness of Applying Different Strategies on Recognition and Recall Textual Password

Using English words as passwords have been a popular topic in the last few years. The following article discusses a study to compare self-selection of the system-generated words for recognition and self-generated words for recall for nouns and mixture words. The results revealed no significant difference between recognition and recall of password nouns. The average memorability rate of noun recognition was 75.72%, slightly higher than noun recall 74.23% in long-term memory. Also, there was no significant difference between recognition and recall mixture word passwords. The average memorability rate of mixture word recognition was 95.23%, and recall was 84.14% in long-term memory. The authors concluded that the recognition and recall of mixed word passwords had a higher memorability rate than nouns.
A Coupled Flow Approach to Imitation Learning

In reinforcement learning and imitation learning, an object of central importance is the state distribution induced by the policy. It plays a crucial role in the policy gradient theorem, and references to it--along with the related state-action distribution--can be found all across the literature. Despite its importance, the state distribution is mostly discussed indirectly and theoretically, rather than being modeled explicitly. The reason being an absence of appropriate density estimation tools. In this work, we investigate applications of a normalizing flow-based model for the aforementioned distributions. In particular, we use a pair of flows coupled through the optimality point of the Donsker-Varadhan representation of the Kullback-Leibler (KL) divergence, for distribution matching based imitation learning. Our algorithm, Coupled Flow Imitation Learning (CFIL), achieves state-of-the-art performance on benchmark tasks with a single expert trajectory and extends naturally to a variety of other settings, including the subsampled and state-only regimes.
Improved Complexity Analysis of Quasi-Polynomial Algorithms Solving Parity Games

We improve the complexity of solving parity games (with priorities in vertices) for $d={\omega}(\log n)$ by a factor of ${\theta}(d^2)$: the best complexity known to date was $O(mdn^{1.45+\log_2(d/\log_2(n))})$, while we obtain $O(mn^{1.45+\log_2(d/\log_2(n))}/d)$, where $n$ is the number of vertices, $m$ is the number of edges, and $d$ is the number of priorities. We base our work on existing algorithms using universal trees, and we improve their complexity. We present two independent improvements. First, an improvement by a factor of ${\theta}(d)$ comes from a more careful analysis of the width of universal trees. Second, we perform (or rather recall) a finer analysis of requirements for a universal tree: while for solving games with priorities on edges one needs an $n$-universal tree, in the case of games with priorities in vertices it is enough to use an $n/2$-universal tree. This way, we allow to solve games of size $2n$ in the time needed previously to solve games of size $n$; such a change divides the quasi-polynomial complexity again by a factor of ${\theta}(d)$.
Environmental sound synthesis from vocal imitations and sound event labels

One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effectively express the features of environmental sounds, such as rhythm and pitch, using vocal imitations, which cannot be expressed by conventional input information, such as sound event labels, images, or texts, in an environmental sound synthesis model. In this paper, we propose a framework for environmental sound synthesis from vocal imitations and sound event labels based on a framework of a vector quantized encoder and the Tacotron2 decoder. Using vocal imitations is expected to control the pitch and rhythm of the synthesized sound, which only sound event labels cannot control. Our objective and subjective experimental results show that vocal imitations effectively control the pitch and rhythm of synthesized sounds.
MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer

With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic. MHD aims to localize all moments and predict clip-wise saliency scores simultaneously. Despite progress made by existing DETR-based methods, we observe that these methods coarsely fuse features from different modalities, which weakens the temporal intra-modal context and results in insufficient cross-modal interaction. To address this issue, we propose MH-DETR (Moment and Highlight Detection Transformer) tailored for MHD. Specifically, we introduce a simple yet efficient pooling operator within the uni-modal encoder to capture global intra-modal context. Moreover, to obtain temporally aligned cross-modal features, we design a plug-and-play cross-modal interaction module between the encoder and decoder, seamlessly integrating visual and textual features. Comprehensive experiments on QVHighlights, Charades-STA, Activity-Net, and TVSum datasets show that MH-DETR outperforms existing state-of-the-art methods, demonstrating its effectiveness and superiority. Our code is available at https://github.com/YoucanBaby/MH-DETR.
Computational Quantum Secret Sharing

Quantum secret sharing (QSS) allows a dealer to distribute a secret quantum state among a set of parties so that certain subsets can reconstruct the secret, while unauthorized subsets obtain no information. While QSS was introduced over twenty years ago, previous works focused only on existence of perfectly secure schemes, and the share size of the known schemes is exponential even for access structures computed by polynomial size monotone circuits. This stands in contrast to the classical case, where efficient computationally-secure schemes have been long known for all access structures in $\mathsf{monotone~P}$, and one can even obtain shares which are much shorter than the secret which is impossible with perfect security. In this work, we initiate the study of computationally-secure QSS and show that computational assumptions help significantly in building QSS schemes. We present a simple compiler and use it to obtain a large variety results: We construct polynomial-time QSS schemes under standard assumptions for a rich class of access structures. This includes many access structures for which previous results in QSS required exponential share size. We also construct QSS schemes for which the size of the shares is significantly smaller than the size of the secret. As in the classical case, this is impossible with perfect security. We also use our compiler to obtain results beyond computational QSS. In the information-theoretic setting, we improve the share size of perfect QSS schemes for a large class of access structures to $1.5^{n+o(n)}$, improving upon best known schemes and matching the best known result for general access structures in the classical case. Finally, we show construct efficient schemes for all access structures in $\mathsf{P}$ and $\mathsf{NP}$ when the quantum secret sharing scheme is given multiple of copies of the secret.
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning

Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of data from past tasks. While most rehearsal-based approaches study how to effectively exploit the knowledge from the buffered past data, little attention is paid to the inter-task relationships with the critical task-specific and task-invariant knowledge. By appropriately leveraging inter-task relationships, we propose a novel CL method named DualHSIC to boost the performance of existing rehearsal-based methods in a simple yet effective way. DualHSIC consists of two complementary components that stem from the so-called Hilbert Schmidt independence criterion (HSIC): HSIC-Bottleneck for Rehearsal (HBR) lessens the inter-task interference and HSIC Alignment (HA) promotes task-invariant knowledge sharing. Extensive experiments show that DualHSIC can be seamlessly plugged into existing rehearsal-based methods for consistent performance improvements, and also outperforms recent state-of-the-art regularization-enhanced rehearsal methods. Source code will be released.
Control Variate Sliced Wasserstein Estimators

The sliced Wasserstein (SW) distances between two probability measures are defined as the expectation of the Wasserstein distance between two one-dimensional projections of the two measures. The randomness comes from a projecting direction that is used to project the two input measures to one dimension. Due to the intractability of the expectation, Monte Carlo integration is performed to estimate the value of the SW distance. Despite having various variants, there has been no prior work that improves the Monte Carlo estimation scheme for the SW distance in terms of controlling its variance. To bridge the literature on variance reduction and the literature on the SW distance, we propose computationally efficient control variates to reduce the variance of the empirical estimation of the SW distance. The key idea is to first find Gaussian approximations of projected one-dimensional measures, then we utilize the closed-form of the Wasserstein-2 distance between two Gaussian distributions to design the control variates. In particular, we propose using a lower bound and an upper bound of the Wasserstein-2 distance between two fitted Gaussians as two computationally efficient control variates. We empirically show that the proposed control variate estimators can help to reduce the variance considerably when comparing measures over images and point-clouds. Finally, we demonstrate the favorable performance of the proposed control variate estimators in gradient flows to interpolate between two point-clouds and in deep generative modeling on standard image datasets, such as CIFAR10 and CelebA.
Drinfeld modules in SageMath

We present the first implementation of Drinfeld modules fully integrated in the SageMath ecosystem. First features will be released with SageMath 10.0.
Building a Non-native Speech Corpus Featuring Chinese-English Bilingual Children: Compilation and Rationale

This paper introduces a non-native speech corpus consisting of narratives from fifty 5- to 6-year-old Chinese-English children. Transcripts totaling 6.5 hours of children taking a narrative comprehension test in English (L2) are presented, along with human-rated scores and annotations of grammatical and pronunciation errors. The children also completed the parallel MAIN tests in Chinese (L1) for reference purposes. For all tests we recorded audio and video with our innovative self-developed remote collection methods. The video recordings serve to mitigate the challenge of low intelligibility in L2 narratives produced by young children during the transcription process. This corpus offers valuable resources for second language teaching and has the potential to enhance the overall performance of automatic speech recognition (ASR).