Reviewerly/exHarmony huggingface dataset:
COMPARISON OF AUGMENTED REALITY AND CONVENTIONAL TEACHING ON SPECIAL NEEDS STUDENTS' ATTITUDES TOWARDS SCIENCE AND THEIR LEARNING OUTCOMES

This study examined the impact of the Augmented reality (AR) use on Jordanian 6th grade students' attitudes towards science and their learning outcomes. A quantitative quasi-experimental study is preceded with the Pre-test and Post-test control group design model, where 24 students who identified to have special needs participated in this study and were randomly divided into two groups. Two groups, control group 12 students were taught conventionally, and 12 students were designated as the experimental group, they used the AR technology for four weeks. The two scales used in this research were reliable and validated. The results show significant results for the AR technology in enhancing student learning outcomes. Additionally, results supported that AR technology has the potential to enhance students with learning disabilities positive attitudes. The result shows that AR technology helped students in promoting positive attitudes towards students and enhance students learning outcomes. Keywords: augmented reality, attitudes towards science, learning disabilities, learning outcomes
A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters

Abstract This article proposes a new parallel performance model for different workloads of Spark Big Data applications running on Hadoop clusters. The proposed model can predict the runtime for generic workloads as a function of the number of executors, without necessarily knowing how the algorithms were implemented. For a certain problem size, it is shown that a model based on serial boundaries for a 2D arrangement of executors can fit the empirical data for various workloads. The empirical data was obtained from a real Hadoop cluster, using Spark and HiBench. The workloads used in this work were included WordCount, SVM, Kmeans, PageRank and Graph (Nweight). A particular runtime pattern emerged when adding more executors to run a job. For some workloads, the runtime was longer with more executors added. This phenomenon is predicted with the new model of parallelisation. The resulting equation from the model explains certain performance patterns that do not fit Amdahl’s law predictions, nor Gustafson’s equation. The results show that the proposed model achieved the best fit with all workloads and most of the data sizes, using the R-squared metric for the accuracy of the fitting of empirical data. The proposed model has advantages over machine learning models due to its simplicity, requiring a smaller number of experiments to fit the data. This is very useful to practitioners in the area of Big Data because they can predict runtime of specific applications by analysing the logs. In this work, the model is limited to changes in the number of executors for a fixed problem size.
Robust semi-device-independent certification of all pure bipartite maximally entangled states via quantum steering

The idea of self-testing is to render guarantees concerning the inner workings of a device based on the measurement statistics. It is one of the most formidable quantum certification and benchmarking schemes. Recently it was shown [A. Coladangelo, K. T. Goh, and V. Scarani, Nat. Commun. 8, 15485 (2017)] that all pure bipartite entangled states can be self-tested in the device-independent scenario by employing subspace methods introduced by Yang and Navascu\'es [Phys. Rev. A 87, 050102(R) (2013)]. Here, we have adapted their method to show that any bipartite pure entangled state can be certified in the semi-device-independent scenario through quantum steering. Analogous to the tilted Clauser-Horne-Shimony-Holt inequality, we use a steering inequality called the tilted steering inequality for certifying any pure two-qubit entangled state. Furthermore, we use this inequality to certify any bipartite pure entangled state by certifying two-dimensional subspaces of the qudit state by observing the structure of the set of assemblages obtained on the trusted side after measurements are made on the untrusted side. As a feature of quantum state certification via steering, we use the notion of assemblage-based robust state certification to provide robustness bounds for the certification result in the case of pure maximally entangled states of any local dimension.
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density

Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achieving high accuracy and avoiding negative transfer. Supervised hyper-parameter validation is not possible without labeled target data, which raises the question: How can we validate unsupervised adaptation techniques in a realistic way? We first empirically analyze existing criteria and demonstrate that they are not very effective for tuning hyper-parameters. Intuitively, a well-trained source classifier should embed target samples of the same class nearby, forming dense neighborhoods in feature space. Based on this assumption, we propose a novel unsupervised validation criterion that measures the density of soft neighborhoods by computing the entropy of the similarity distribution between points. Our criterion is simpler than competing validation methods, yet more effective; it can tune hyper-parameters and the number of training iterations in both image classification and semantic segmentation models. The code used for the paper will be available at https://github.com/VisionLearningGroup/SND.
Investigations on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1110" altimg="si206.svg"><mml:mi>c</mml:mi></mml:math>-boomerang uniformity and perfect nonlinearity

We defined in Ellingsen et al. (2020) a new multiplicative c-differential, and the corresponding c-differential uniformity and we characterized the known perfect nonlinear functions with respect to this new concept, as well as the inverse function in any characteristic. Here, we extend, via this differential, the boomerang uniformity concept, as introduced at Eurocrypt '18 by Cid et al. (2018), as a differential distinguisher of S-boxes of block ciphers. and investigate it in the context of perfect nonlinearity and related functions. We first characterize the new concept via Walsh transforms. We further investigate it in the context of perfect nonlinearity and related functions and describe this uniformity for the inverse function in even, respectively, odd characteristic. The methods are combinatorial and number theoretical in nature.
Benchmarking quantum tomography completeness and fidelity with machine learning

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly.
An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.
Digital Literacy and its Relevance to Comparative Education Researchers: Outcomes of SciVal Analytics

Digitisation is a multidimensional phenomenon having direct and indirect impact on all aspects of human activity. The sphere of science and research, especially comparative education research, is being inevitably affected. The dizzying pace of socio-economic changes complicated by COVID-19 pandemic made it obvious that we are dealing with the digitisation of shock, rather than phased, character. The article states the lack of serious scientific reflection on the currently witnessed "shock digitisation" of science, complicated with growing digital illiteracy of researchers. The latter is demonstrated through rigorous literature review and SciVal Scopus analytics. The article is concluded with the idea that the field of comparative education research requires future profound rethinking of assumptions and agenda priorities in several aspects. They include general qualification requirements for modern comparative education researcher and comparative research procedures, functional and digital literacy of comparativists, changes in their research career potentials and prospects.
Squeezing-enhanced communication without a phase reference

We study the problem of transmitting classical information using quantum Gaussian states on a family of phase-noise channels with a finite decoherence time, such that the phase-reference is lost after $m$ consecutive uses of the transmission line. This problem is relevant for long-distance communication in free space and optical fiber, where phase noise is typically considered as a limiting factor. The Holevo capacity of these channels is always attained with photon-number encodings, challenging with current technology. Hence for coherent-state encodings the optimal rate depends only on the total-energy distribution and we provide upper and lower bounds for all $m$, the latter attainable at low energies with on/off modulation and photodetection. We generalize this lower bound to squeezed-coherent encodings, exhibiting for the first time to our knowledge an unconditional advantage with respect to any coherent encoding for $m=1$ and a considerable advantage with respect to its direct coherent counterpart for $m>1$. This advantage is robust with respect to moderate attenuation, and persists in a regime where Fock encodings with up to two-photon states are also suboptimal. Finally, we show that the use of part of the energy to establish a reference frame is sub-optimal even at large energies. Our results represent a key departure from the case of phase-covariant Gaussian channels and constitute a proof-of-principle of the advantages of using non-classical, squeezed light in a motivated communication setting.
Reflections on research questions in mobile assisted language learning

Abstract Research questions are central to mobile assisted language learning (MALL) projects and studies, yet they have received little attention to date. Taking research questions as its central focus, this paper offers some reflections on the complexity of the broader field of mobile learning, on different kinds of research, on salient themes and challenges in mobile learning and MALL, and it suggests some research directions for the future. Since MALL research is interdisciplinary, and since research questions are an object of study in other fields of knowledge, the paper refers to sources from multiple disciplines to support a more comprehensive consideration of current and future research questions in MALL. The paper is fundamentally an invitation to a global conversation about research questions in MALL.
Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination

Measurement of qubits plays a key role in quantum computation. In superconducting multiqubit quantum processors, a multiplexed readout scheme is widely used. In such a scheme, measurement of a qubit state may be influenced by the state of neighboring qubits, due to various crosstalk effects, which will degrade the readout fidelity. To reduce the impact of crosstalk, we model the digital signal processing system used in measurements as a shallow neural network and train it to become a state discriminator. Applying our method to a six-qubit superconducting quantum chip, we see an overall improved readout performance compared with a contemporary qubit-state discriminator. The readout crosstalk is decreased by more than $80\mathrm{%}$. The training and optimization process of the neural network consumes only about 10 s.
Chatbot Development for Educational Institute

A chatbot is a software that is used to develop interaction between a user/human and a computer/system in a natural language like human chats. Chatbots chat with the client in a discussion according to the input of a human and answer to the client. It makes the user think that it is chatting with a human being where as they're chatting with the computer. The chat bot application helps the student to know about the admission process of the college from anywhere with internet connection and receive fast replies. This chatbot system reduces the work of admission process department by providing the required information to the students or parents and also reduces the workload of the department to keep on answering all the queries of the students.
A Novel Approach to Detect Phishing Attacks using Binary Visualisation and Machine Learning

Protecting and preventing sensitive data from being used inappropriately has become a challenging task. Even a small mistake in securing data can be exploited by phishing attacks to release private information such as passwords or financial information to a malicious actor. Phishing has now proven so successful, it is the number one attack vector. Many approaches have been proposed to protect against this type of cyber-attack, from additional staff training, enriched spam filters to large collaborative databases of known threats such as PhishTank and OpenPhish. However, they mostly rely upon a user falling victim to an attack and manually adding this new threat to the shared pool, which presents a constant disadvantage in the fight back against phishing. In this paper, we propose a novel approach to protect against phishing attacks using binary visualisation and machine learning. Unlike previous work in this field, our approach uses an automated detection process and requires no further user interaction, which allows faster and more accurate detection process. The experiment results show that our approach has high detection rate.
A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems

A chaotic system refers to a deterministic system with seemingly random irregular motion, and its behavior is uncertain, unrepeatable, and unpredictable. In recent years, researchers have proposed various image encryption schemes based on a single low-dimensional or high-dimensional chaotic system, but many algorithms have problems such as low security. Therefore, designing a good chaotic system and encryption scheme is very important for encryption algorithms. This paper constructs a new double chaotic system based on tent mapping and logistic mapping. In order to verify the practicability and feasibility of the new chaotic system, a displacement image encryption algorithm based on the new chaotic system was subsequently proposed. This paper proposes a displacement image encryption algorithm based on the new chaotic system. The algorithm uses an improved new nonlinear feedback function to generate two random sequences, one of which is used to generate the index sequence, the other is used to generate the encryption matrix, and the index sequence is used to control the generation of the encryption matrix required for encryption. Then, the encryption matrix and the scrambling matrix are XORed to obtain the first encryption image. Finally, a bit-shift encryption method is adopted to prevent the harm caused by key leakage and to improve the security of the algorithm. Numerical experiments show that the key space of the algorithm is not only large, but also the key sensitivity is relatively high, and it has good resistance to various attacks. The analysis shows that this algorithm has certain competitive advantages compared with other encryption algorithms.
A Payload Optimization Method for Federated Recommender Systems

In this study, we introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and users depends on the number of items to recommend. The model payload grows when there is an increasing number of items. This becomes challenging for FRS if it is running in production mode. To tackle the payload challenge, we formulated a multi-arm bandit solution that selected part of the global model and transmitted it to all users. The selection process was guided by a novel reward function suitable for FL systems. So far as we are aware, this is the first optimization method that seeks to address item dependent payloads. The method was evaluated using three benchmark recommendation datasets. The empirical validation confirmed that the proposed method outperforms the simpler methods that do not benefit from the bandits for the purpose of item selection. In addition, we have demonstrated the usefulness of our proposed method by rigorously evaluating the effects of a payload reduction on the recommendation performance degradation. Our method achieved up to a 90% reduction in model payload, yielding only a ∼ 4% - 8% loss in the recommendation performance for highly sparse datasets.
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

There remain many open questions pertaining to the scaling behaviour of Transformer architectures. These scaling decisions and findings can be critical, as training runs often come with an associated computational cost which have both financial and/or environmental impact. The goal of this paper is to present scaling insights from pretraining and finetuning Transformers. While Kaplan et al. presents a comprehensive study of the scaling behaviour of Transformer language models, the scope is only on the upstream (pretraining) loss. Therefore, it is still unclear if these set of findings transfer to downstream task within the context of the pretrain-finetune paradigm. The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient. To this end, we present improved scaling protocols whereby our redesigned models achieve similar downstream fine-tuning quality while having 50\% fewer parameters and training 40\% faster compared to the widely adopted T5-base model. We publicly release over 100 pretrained checkpoints of different T5 configurations to facilitate future research and analysis.
Elastic Tracker: A Spatio-temporal Trajectory Planner for Flexible Aerial Tracking

This paper proposes Elastic Tracker, a flexible trajectory planning framework that can deal with challenging tracking tasks with guaranteed safety and visibility. Firstly, an object detection and intension-free motion prediction method is designed. Then an occlusion-aware path finding method is proposed to provide a proper topology. A smart safe flight corridor generation strategy is designed with the guiding path. An analytical occlusion cost is evaluated. Finally, an effective trajectory optimization approach enables to generate a spatio-temporal optimal trajectory within the resultant flight corridor. Particular formulations are designed to guarantee both safety and visibility, with all the above requirements optimized jointly. The experimental results show that our method works more robustly but with less computation than the existing methods, even in some challenging tracking tasks.
SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators

As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation. The generation of realistic-looking hand-written text is important because it can be used for data augmentation in handwritten text recognition (HTR) systems or human-computer interaction. We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods by augmenting the training feedback with a tailored solution to mitigate pen-level artifacts. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system and the separate characters of the word. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
Distinguishability and “which pathway” information in multidimensional interferometric spectroscopy with a single entangled photon-pair

Correlated photons inspire abundance of metrology-related platforms, which benefit from quantum (anti-) correlations and outperform their classical counterparts. While these mainly focus on entanglement, the role of photon exchange phase and degree of distinguishability has not been widely used in quantum applications. Using an interferometric setup, we theoretically show that, when a two-photon wave function is coupled to matter, it is encoded with “which pathway?” information even at low-degree of entanglement. An interferometric protocol, which enables phase-sensitive discrimination between microscopic interaction histories (pathways), is developed. We find that quantum light interferometry facilitates utterly different set of time delay variables, which are unbound by uncertainty to the inverse bandwidth of the wave packet. We illustrate our findings on an exciton model system and demonstrate how to probe intraband dephasing in the time domain without temporally resolved detection. The unusual scaling of multiphoton coincidence signals with the applied pump intensity is discussed.