Reviewerly/exHarmony huggingface dataset:
Denoising after Entropy-Based Debiasing a Robust Training Method for Dataset Bias with Noisy Labels

Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias). Recent debiasing techniques have successfully achieved generalization performance by underestimating easy-to-learn samples (i.e., bias-aligned samples) and highlighting difficult-to-learn samples (i.e., bias-conflicting samples). However, these techniques may fail owing to noisy labels, because the trained model recognizes noisy labels as difficult-to-learn and thus highlights them. In this study, we find that earlier approaches that used the provided labels to quantify difficulty could be affected by the small proportion of noisy labels. Furthermore, we find that running denoising algorithms before debiasing is ineffective because denoising algorithms reduce the impact of difficult-to-learn samples, including valuable bias-conflicting samples. Therefore, we propose an approach called denoising after entropy-based debiasing, i.e., DENEB, which has three main stages. (1) The prejudice model is trained by emphasizing (bias-aligned, clean) samples, which are selected using a Gaussian Mixture Model. (2) Using the per-sample entropy from the output of the prejudice model, the sampling probability of each sample that is proportional to the entropy is computed. (3) The final model is trained using existing denoising algorithms with the mini-batches constructed by following the computed sampling probability. Compared to existing debiasing and denoising algorithms, our method achieves better debiasing performance on multiple benchmarks.
Global Dilated Attention and Target Focusing Network for Robust Tracking

Self Attention has shown the excellent performance in tracking due to its global modeling capability. However, it brings two challenges: First, its global receptive field has less attention on local structure and inter-channel associations, which limits the semantics to distinguish objects and backgrounds; Second, its feature fusion with linear process cannot avoid the interference of non-target semantic objects. To solve the above issues, this paper proposes a robust tracking method named GdaTFT by defining the Global Dilated Attention (GDA) and Target Focusing Network (TFN). The GDA provides a new global semantics modeling approach to enhance the semantic objects while eliminating the background. It is defined via the local focusing module, dilated attention and channel adaption module. Thus, it promotes semantics by focusing local key information, building long-range dependencies and enhancing the semantics of channels. Subsequently, to distinguish the target and non-target objects both with rich semantics, the TFN is proposed to accurately focus the target region. Different from the present feature fusion, it uses the template as the query to build a point-to-point correlation between the template and search region, and finally achieves part-level augmentation of target feature in the search region. Thus, the TFN efficiently augments the target embedding while weakening the non-target objects. Experiments on challenging benchmarks (LaSOT, TrackingNet, GOT-10k, OTB-100) demonstrate that the GdaTFT outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.
Dampak Penggunaan Quantum Teaching Terhadap Pembelajaran Pada Mata Pelajaran IPS

Education is one of the most important parts in an effort to advance the life of the nation. Because of the need for special attention in the use of learning models to support the course of learning. Quantum teaching is one of the suitable learning models to be applied to the course of learning. In this article, we will discuss the impact given by the use of quantum teaching on social studies subjects in junior high schools. Writing articles using the article review method by presenting the contents of various previous studies to find out how the impact given by the use of quantum teaching is. Quantum teaching has a positive impact on the learning system that affects several things. starting from learning outcomes, how students can be motivated to learn, and also the emergence of various kinds of potential that are given to the implementation of more interesting learning styles to be given to students
Many bounded versions of undecidable problems are NP-hard

Several physically inspired problems have been proven undecidable; examples are the spectral gap problem and the membership problem for quantum correlations. Most of these results rely on reductions from a handful of undecidable problems, such as the halting problem, the tiling problem, the Post correspondence problem or the matrix mortality problem. All these problems have a common property: they have an NP-hard bounded version. This work establishes a relation between undecidable unbounded problems and their bounded NP-hard versions. Specifically, we show that NP-hardness of a bounded version follows easily from the reduction of the unbounded problems. This leads to new and simpler proofs of the NP-hardness of bounded version of the Post correspondence problem, the matrix mortality problem, the positivity of matrix product operators, the reachability problem, the tiling problem, and the ground state energy problem. This work sheds light on the intractability of problems in theoretical physics and on the computational consequences of bounding a parameter.
Perbedaan Hasil Belajar Matematika Antara Penggunaan Media Barang Bekas Dengan Media Gambar Pada Siswa Kelas II SD Gugus 5 Kecamatan Panarukan Kabupaten Situbondo

This study aims to determine the results of learning mathematics between students who take lessons using used media and learning using media images in class II students, and to find out differences in learning outcomes in mathematics between students who take lessons using used media and media images. This research was conducted in class II SD Cluster 5 Panarukan District with 34 students consisting of 21 students in class II SDN 1 Gelung and 13 students in class II SDN 2 Gelung. The study determined the division of class II SDN 1 Gelung using used media while class II SDN 2 Gelung used picture media. This type of research is quantitative research. The data collection method was carried out from 2 to 9 September 2021. Meanwhile, the data analysis used was the Normality Test, Homogeneity Test and Hypothesis Test. Based on the results of data analysis, it can be concluded that the average learning outcomes of class II students who use used media are better when compared to media images. This shows that the application of second-hand media is more effective and can significantly improve class II student learning outcomes compared to using media images.
Niel's Chess -- The Battle of the Quantum Age

In this paper, a quantum variant of chess is introduced, which can be played on a traditional board without the need of using computers or other electronic devices. The rules of the game arise naturally by combining the rules of conventional chess with key quantum-physical effects such as superposition and entanglement. Niel's Chess is recommended for ages 10 and above, to everyone who wishes to play a creative game with historical roots and at the same time gain intuition about the foundational quantum effects that power cutting-edge technologies like quantum computing and quantum communication, which are poised to revolutionise our society in the coming decades.
2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made. The field of Explainable AI (XAI) has emerged in response. However, it faces a significant challenge known as the disagreement problem, where multiple explanations are possible for the same AI decision or prediction. While the existence of the disagreement problem is acknowledged, the potential implications associated with this problem have not yet been widely studied. First, we provide an overview of the different strategies explanation providers could deploy to adapt the returned explanation to their benefit. We make a distinction between strategies that attack the machine learning model or underlying data to influence the explanations, and strategies that leverage the explanation phase directly. Next, we analyse several objectives and concrete scenarios the providers could have to engage in this behavior, and the potential dangerous consequences this manipulative behavior could have on society. We emphasize that it is crucial to investigate this issue now, before these methods are widely implemented, and propose some mitigation strategies.
Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation

In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language models and empirical prompt engineering. This approach demonstrates a considerable improvement over the baseline, highlighting the effectiveness of data-centric techniques. Our prompt engineering strategy further improves performance by producing superior synthetic translation examples.
COMPREHENSION AMELIORATION USING READING EXERCISES (CARE): AN ANIMATED READING MATERIAL IN ENHANCING READING COMPREHENSION

This study determined the significant effect of utilizing CARE (Comprehension Amelioration using Reading Exercises) in improving the reading comprehension of grade 8 students of Banca-Banca Integrated National High School S.Y. 2022-2023.It specifically sought to determine the acceptability level of the components of the animated reading materials with regards to activities, text, sounds, and animation; the acceptability level of the contents of the animated reading materials with regards to noting details, sequencing events, getting the main idea and predicting outcomes; the level of students' reading comprehension in using the animated reading materials in terms of pre-test and post-test and the significant difference in the reading comprehension of the students after utilizing the animated reading materials as supplementary materials in the pre-test and posttest.The mean and standard deviation were used to measure the acceptability level of the components and contents of the animated reading material.Mean and Standard deviation together with percentage and frequency were used to determine the level of students' reading comprehension in using the animated reading materials in terms of pre-test and post-test.Furthermore, T-test was used as the statistical treatment to determine the significant difference in the reading comprehension of the students after utilizing the animated reading materials as supplementary materials in the pre-test and posttest.Findings revealed the following: (1) The acceptability level of the components of the animated reading materials with regards to activities, text, sounds, and animation was very evident as evaluated by the respondents of the study.(2) The acceptability level of the contents of the animated reading materials with regards to noting details, sequencing events, getting the main idea and predicting outcomes was very evident as evaluated by the respondents of the study.(3) The the level of students' reading comprehension in using the animated reading materials in terms of pre-test and posttest of the respondents resulted from Fair to Very Satisfactory after they used the animated reading material.(4) The test of the significant difference in the pre-test and posttest of the students resulted that there is a significant difference in the reading comprehension of the students after utilizing the animated reading materials as supplementary materials.
Blockchain technology research and application: a systematic literature review and future trends

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain.
Backpropagation scaling in parameterised quantum circuits

The discovery of the backpropagation algorithm ranks among one of the most important moments in the history of machine learning, and has made possible the training of large-scale neural networks through its ability to compute gradients at roughly the same computational cost as model evaluation. Despite its importance, a similar backpropagation-like scaling for gradient evaluation of parameterised quantum circuits has remained elusive. Currently, the most popular method requires sampling from a number of circuits that scales with the number of circuit parameters, making training of large-scale quantum circuits prohibitively expensive in practice. Here we address this problem by introducing a class of structured circuits that are not known to be classically simulable and admit gradient estimation with significantly fewer circuits. In the simplest case -- for which the parameters feed into commuting quantum gates -- these circuits allow for fast estimation of the gradient, higher order partial derivatives and the Fisher information matrix. Moreover, specific families of parameterised circuits exist for which the scaling of gradient estimation is in line with classical backpropagation, and can thus be trained at scale. In a toy classification problem on 16 qubits, such circuits show competitive performance with other methods, while reducing the training cost by about two orders of magnitude.
Knowledge Attitude and Practice of Students Towards Online Communication in EFL

Online learning refers to education that occurs through the Internet using technological tools such as tablets, smartphones, laptops, and PCs. This study intends to determine how structural equation modeling (SEM) can be used to evaluate the adequacy of the variables included in the knowledge, attitude, and practices (KAP) model concerning online communication in English as a foreign language (EFL). An exploratory study was conducted with 112 female undergraduate English language program (ELP) students at the King Khalid University of Saudi Arabia. A KAP questionnaire was distributed to those students using a Google form. The data analysis was carried out using SPSS version 20. The SEM analysis was conducted using Analysis of Moment Structures (AMOS) software version 5.0. The results showed that the mean score of KAP toward online communication in EFL was 3.78, 3.90, and 3.70, respectively. The mean practice score is lower than the mean knowledge and attitude score. The SEM analysis demonstrated that the variables used in the KAP model are positively related (p&lt;0.05). Variables used in the KAP questionnaire are adequately fit to evaluate the ELP students’ KAP toward online communication in EFL. Hence, policymakers can utilize these variables to assess the students’ KAP toward online communication in EFL at HEIs. The findings of KAP can aid universities in framing and implementing appropriate strategies to motivate online communication and enhance the student’s English language skills.
Commuting SYK: a pseudo-holographic model

In this work, we study a type of commuting SYK model in which all terms in the Hamiltonian are commutative to each other. Because of the commutativity, this model has a large number of conserved charges and is integrable. After the ensemble average of random couplings, we can solve this model exactly in any $N$. Though this integral model is not holographic, we do find that it has some holography-like features, especially the near-perfect size winding in high temperatures. Therefore, we would like to call it pseudo-holographic. We also find that the size winding of this model has a narrowly peaked size distribution, which is different from the ordinary SYK model. We apply the traversable wormhole teleportation protocol in the commuting SYK model and find that the teleportation has a few features similar to the semiclassical traversable wormhole but in different parameter regimes. We show that the underlying physics is not entirely determined by the size-winding mechanism but involves the peaked-size mechanism and thermalization. Lastly, we comment on the recent simulation of the dynamics of traversable wormholes on Google's quantum processor.
Students’ perception of social presence in the online EFL speaking class

Some students claim to suffer from anxiety, which lowers their self-esteem and causes them to feel anxious and uneasy while studying a foreign language. Establishing a social connection between the lecturer and the students is one of the possible solutions to this issue. The purpose of this study was to examine students' perceptions of social presence in an online speaking class and how social presence affects students' confidence in speaking class. This study employed quantitative research as its method of inquiry. The researchers distributed questionnaires to 159 students to collect data, but only 51 students responded. In addition, the researchers conducted interviews with a subset of participants to gain additional insight into the data required for the study. The research findings on students' perception of social presence in the online speaking class and the effect of social presence on students' confidence in speaking class revealed that students had a favorable perception of social presence in the online speaking class. The first finding's mean score was seventy-two point nine (x ̅ = 72.9). The findings demonstrated that social presence influences students' self-confidence, encourages them to learn more, and assists students in improving their speaking ability in speaking class.
Robust and Space-Efficient Dual Adversary Quantum Query Algorithms

The general adversary dual is a powerful tool in quantum computing because it gives a query-optimal bounded-error quantum algorithm for deciding any Boolean function. Unfortunately, the algorithm uses linear qubits in the worst case, and only works if the constraints of the general adversary dual are exactly satisfied. The challenge of improving the algorithm is that it is brittle to arbitrarily small errors since it relies on a reflection over a span of vectors. We overcome this challenge and build a robust dual adversary algorithm that can handle approximately satisfied constraints. As one application of our robust algorithm, we prove that for any Boolean function with polynomially many 1-valued inputs (or in fact a slightly weaker condition) there is a query-optimal algorithm that uses logarithmic qubits. As another application, we prove that numerically derived, approximate solutions to the general adversary dual give a bounded-error quantum algorithm under certain conditions. Further, we show that these conditions empirically hold with reasonable iterations for Boolean functions with small domains. We also develop several tools that may be of independent interest, including a robust approximate spectral gap lemma, a method to compress a general adversary dual solution using the Johnson-Lindenstrauss lemma, and open-source code to find solutions to the general adversary dual.
Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals.
Hybrid noise protection of logical qubits for universal quantum computation

Quantum computers now show the promise of surpassing any possible classical machine. However, errors limit this ability and current machines do not have the ability to implement error correcting codes due to the limited number of qubits and limited control. Therefore, dynamical decoupling (DD) and encodings that limit noise with fewer qubits are more promising. For these reasons, we put forth a model of universal quantum computation that has many advantages over strategies that require a large overhead such as the standard quantum error correcting codes. First, we separate collective noise from individual noises on physical qubits and use a decoherence-free subspace (DFS) that uses just two qubits for its encoding to eliminate collective noise. Second, our bath model is very general as it uses a spin-boson type bath but without any Markovian assumption. Third, we are able to either use a steady global magnetic field or to devise a set of DD pulses that remove much of the remaining noise and commute with the logical operations on the encoded qubit. This allows removal of noise while implementing gate operations. Numerical support is given for this hybrid protection strategy which provides an efficient approach to deal with the decoherence problems in quantum computation and is experimentally viable for several current quantum computing systems. This is emphasized by a recent experiment on superconducting qubits which shows promise for increasing the number of gates that can be implemented reliably with some realistic parameter assumptions.