Reviewerly/exHarmony huggingface dataset:
Training Teachers and Learners to Use Corpora

Corpora have become common currency among professionals involved in foreign language syllabus and materials design, particularly in the case of English. However, it has frequently been observed that the key actors in the educational process – teachers and learners – are reluctant to engage in data-driven activities. This chapter attempts to pinpoint the reasons for such diffidence and discuss ways to promote corpus-based activities. The discussion starts with a presentation of teachers’ and students’ experience with data-driven learning (DDL) reported in several surveys. Next, the necessary skills for the application of corpora in language education and different models of teacher and learner training in DDL are examined. The chapter also recapitulates the results of several studies into the effectiveness of such training, which identify the obstacles in the use of data-driven activities in the process of language instruction and learning. The methodology used to collect such information is also reviewed. Finally, two selected examples of courses promoting DDL are discussed, followed by analyses of the effects of the instruction. Overall, the chapter argues for an adequate model of teacher and learner training which puts more emphasis on the integration of data-driven activities with other classroom procedures and tasks. This should raise teachers’ and learners’ awareness of the full potential of corpus-based techniques and encourage them to employ such activities as part and parcel of their standard teaching and learning practices.
Learner Corpora to Inform Testing and Assessment

Learner corpora have recently started to become very influential in informing the field of language testing and assessment. For example, there has been an increasing awareness among researchers and language testing scale developers of the benefits of including more specific linguistic descriptors or “criterial features”, which may emerge from the analysis of learner corpora. Insights gained from learner corpus analyses have, thus, recently begun to be of great use in the field of language testing and assessment, especially in order to provide quantitative descriptions of the way proficiency can be operationalized quantitatively and across L1s at different levels. The present chapter provides an in-depth overview of the developments and current research in the field and provides an overview of how learner corpora can be of use to inform language testing and assessment.
Coherence evolution of bipartite state under decoherent environment

Abstract We provide an analysis on the quantum coherence (QC) of a general two-qubit pure state interacting with a local decoherent environment. Our results show that the QC in the environment obeys a conservation relation, i.e. the coherence evolution is equal to the product of the coherence contained in the initial state and the environmental action on the maximum entangled state. This means that the coherence dynamics behaviors of the composite quantum-environment system can be observed by analyzing the environmental action. Moreover, within the Markovian environment, the QC damps monotonously, while it exhibits damped oscillations and suffers damped revivals within the non-Markovian environment. These phenomena of damped oscillations and revivals have been interpreted as memory effects of the non-Markovian environment.
Trend Summary by SCOPUS Literature Analysis on Machine Learning Assisted BIPV Research

With the accelerated development of science and technology across fields, publications in almost all fields have increased exponentially every year. According to the Scopus search, the number of papers related to buildings and solar energy that used machine learning has grown at an average annual rate of 16.1%, exceeding 3000 publications since 2019. Review papers have been published consistently since 2010, demonstrating that they are on a trajectory of initial growth. Because there is a limit to reading and analyzing large quantities of papers every year, we developed a methodology that reads, analyzes, and visualizes published literature information. In this method, we provide a query to the Scopus database to retrieve data and then visualize the number of publications by year, journal, and keyword along with other analyses results. The relationship and frequency analyses results can also be shown among words in titles, keywords, and abstracts. Analysis of research on building-integrated photovoltaics yielded the result that publishing on Energy and Buildings (impact factor [IF] 4.067, modified rank normalized impact factor [mrnIF] top 1.7%) and Solar Energy (IF 4.018, mrnIF top 1.7%) is active. Over the past 10 years, approximately 1–1.5% of machine learning-related research has been published, with a gradual increase.
Artificial Intelligence Innovation and Privacy Law: The Moderating Role of Culture

With the advent of artificial intelligence (AI) centred around a logic of commercial surveillance and data capitalism, scholars are increasingly being called upon to consider the implications of privacy law. We respond by exploring the relationship between privacy law and AI innovation and how that relationship between privacy law and AI innovation varies across countries with different culture. Employing a unique dataset of AI patents, we perform a difference-in-differences analysis by exploiting the introduction of General Data Protection Regulation (GDPR). Our results show that compared with the GDPR-unaffected countries, the GDPR-affected countries exhibited a lower AI innovation rate after the GDPR introduction. Further, we find this negative effect is weaker in GDPR-affected countries that have higher levels of individualism, masculinity, and power distance, but stronger with higher levels of uncertainty avoidance. The main contribution of our study is introducing the role of privacy law and its contingencies vis à vis national culture that constitute drivers of AI innovation.
Heterogeneous mixing of dynamic differential evolution variants in distributed frame work for global optimisation problems

Differential evolution (DE) is a real parameter optimisation algorithm added to the pool of algorithms under evolutionary computing field. DE is well known for simplicity and robustness. The dynamic differential evolution (DDE) was proposed in the literature as an extension to DE, to alleviate the static population update mechanism of DE. Since the island-based distributed models are the natural extension of DE to parallelise it with structured population, they can also be extended for DDE. This paper, initially, implements distributed versions for 14 variants of DDE and also proposes an algorithm heterogeneous mixing of dynamic differential evolution variants (hmDDEv) to mix different DDE variants in island-based distributed model. The proposed hmDDEv algorithm is implemented and validated against a well-defined benchmarking suite with 14 benchmarking functions, by comparing it with its constituent DDE variants. The efficacy of hmDDEv is also validated with two state-of-the-art distributed DE algorithms.
GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For the commercial adoption and massive roll-out of the deepfake detection technology, it is vital to evaluate and understand the fairness (the absence of any prejudice or favoritism) of deepfake detectors across demographic variations such as gender and race. As the performance differential of deepfake detectors between demographic subgroups would impact millions of people of the deprived sub-group. This paper aims to evaluate the fairness of the deepfake detectors across males and females. However, existing deepfake datasets are not annotated with demographic labels to facilitate fairness analysis. To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender. Our analysis on the gender-labeled version of the datasets suggests (a) current deepfake datasets have skewed distribution across gender, and (b) commonly adopted deepfake detectors obtain unequal performance across gender with mostly males outperforming females. Finally, we contributed a gender-balanced and annotated deepfake dataset, GBDF, to mitigate the performance differential and to promote research and development towards fairness-aware deep fake detectors. The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering

Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the approximation of high-dimensional scalar functions with low intrinsic dimensionality. By introducing a low dimensional bias we can fight the curse of dimensionality affecting these quantities of interest, especially for many-query applications. We seek a gradient-based reduction of the parameter space through linear active subspaces or a nonlinear transformation of the input space. Then we build a low-fidelity response surface based on such reduction, thus enabling nonlinear autoregressive multi-fidelity Gaussian process regression without the need of running new simulations with simplified physical models. This has a great potential in the data scarcity regime affecting many engineering applications. In this work we present a new multi-fidelity approach that involves active subspaces and the nonlinear level-set learning method, starting from the preliminary analysis previously conducted in Romor et al. 2020. The proposed framework is tested on two high-dimensional benchmark functions, and on a more complex car aerodynamics problem. We show how a low intrinsic dimensionality bias can increase the accuracy of Gaussian process response surfaces.
RWN: Robust Watermarking Network for Image Cropping Localization

Image cropping can be maliciously used to manipulate the layout of an image and alter the underlying meaning. Previous image cropping detection schemes only predict whether an image has been cropped, ignoring which part of the image is cropped. This paper presents a novel robust watermarking network for image cropping localization. We train an anti-cropping processor (ACP) that embeds a watermark into a target image. The visually indistinguishable protected image is then posted on the social network instead of the original image. At the recipient’s side, ACP extracts the watermark from the attacked image, and we conduct feature matching on the original and extracted watermark to locate the position of the cropping. We further extend our scheme to detect tampering attacks on the attacked image, and a simple yet efficient method (JPEG-Mixup) is proposed that noticeably improves the generalization of JPEG robustness. We demonstrate that our scheme is the first to provide high-accuracy and robust image cropping localization.
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design

Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text into training examples, which are contiguous text segments of sizes processable by the neural architecture. We highlight a bias introduced by this common practice: we prove that the pretrained NLM can model much stronger dependencies between text segments that appeared in the same training example, than it can between text segments that appeared in different training examples. This intuitive result has a twofold role. First, it formalizes the motivation behind a broad line of recent successful NLM training heuristics, proposed for the pretraining and fine-tuning stages, which do not necessarily appear related at first glance. Second, our result clearly indicates further improvements to be made in NLM pretraining for the benefit of Natural Language Understanding tasks. As an example, we propose "kNN-Pretraining": we show that including semantically related non-neighboring sentences in the same pretraining example yields improved sentence representations and open domain question answering abilities. This theoretically motivated degree of freedom for pretraining example design indicates new training schemes for self-improving representations.
VOCABULARY DEVELOPMENT WITH MOBILE DEVICES AND APPLICATIONS

One of the crucial aspects of language is vocabulary. When learning a new language, having a big vocabulary helps with receiving and producing messages, as well as supporting four basic skills of language. Vocabulary teaching approaches have altered far too much in the language acquisition process. In the language teaching process, many different strategies have been used, and new ones are being developed all the time. Words were previously taught alongside their translation or synonym, and the teaching strategy was Grammar-translation; however, as the teaching method has evolved and the Communicative Approach has gained traction, the vocabulary teaching method has evolved as well. With flashes and pictures, the kids were taught words in context. With technology improvements, vocabulary teaching practices have improved. First Vocabulary is taught via computer games and activities when computers have started to be used in the classrooms during Computer Assisted Language Learning (CALL) process. In the present period, many vocabulary-learning applications for cell phones, tablets, and laptops have been developed. It is a relatively recent approach to exploit mobile technology and internet-based vocabulary teaching programs to teach vocabulary. Mobile devices were started to be used for educational reasons which caused the birth of Mobile Assisted Language Learning (MALL). On the App Store and Google Play, there are thousands of vocabulary-building apps. Some of these programs are drill-based, while others are context-based. One of the most popular video-based methods for teaching vocabulary is YouTube. Teachers can use the Google Classroom application to teach vocabulary in virtual classrooms. Kahoot and wordwall are two recent popular programs for creating a variety of interesting vocabulary challenges. This research analyses the ideas of prep class students at Van Yüzüncü Yıl University for the usage of mobile applications in vocabulary development.
Passive Attention in Artificial Neural Networks Predicts Human Visual Selectivity

Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification and localization in artificial neural networks (ANNs). Are the same regions similarly informative to human observers? Using data from 79 new experiments and 7,810 participants, we show that passive attention techniques reveal a significant overlap with human visual selectivity estimates derived from 6 distinct behavioral tasks including visual discrimination, spatial localization, recognizability, free-viewing, cued-object search, and saliency search fixations. We find that input visualizations derived from relatively simple ANN architectures probed using guided backpropagation methods are the best predictors of a shared component in the joint variability of the human measures. We validate these correlational results with causal manipulations using recognition experiments. We show that images masked with ANN attention maps were easier for humans to classify than control masks in a speeded recognition experiment. Similarly, we find that recognition performance in the same ANN models was likewise influenced by masking input images using human visual selectivity maps. This work contributes a new approach to evaluating the biological and psychological validity of leading ANNs as models of human vision: by examining their similarities and differences in terms of their visual selectivity to the information contained in images.
Overcoming missing values using imputation methods in the classification of tuberculosis

Indonesia is one of the countries with the highest population density in the world with a very high number of Tuberculosis (TB). This TB disease is very serious because it is very easily transmitted through the air, namely, droplets that come from a TB patient who coughs or sneezes. In diagnosing a disease, missing data often occurs, resulting in researcher errors in the data collection process, so this study proposes the mean Imputation method to overcome missing data. For the classification of TB disease data in Bangkalan Regency, Indonesia, which consists of 886 data, the method used is Naive Bayes compared to Logistics Regression. For the distribution of training and testing data, this research uses multiple trains and tests K-Fold cross-validation with a total of k=10. Based on research trials using the mean imputation method is better than the one imputation method in filling in the missing data for this case with an average accuracy is 97.36% and the F1 score is 95.01% better than one imputation with an average accuracy is 97.35% and F1 score is 94.35 % on the Naive Bayes method. For TB classification, the Naive Bayes method produces an average accuracy is 97.36% and the F1 score is 95.01% better than the logistic regression method in classifying tuberculosis with an accuracy rate is 97.36% with an F1 score is 89.58%.
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case

6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account to apply the algorithms. While machine learning offers significant advantages for 6G, AI models' security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction use case. We have also presented the adversarial learning mitigation method's performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model and undefended model are very close.
Pseudo-3D Scene Modeling for Virtual Reality Using Stylized Novel View Synthesis

Stylized Novel View Synthesis is an emerging technique that combines style transfer and view synthesis. However, none of the existing works explore their applications in Virtual Reality (VR). This work devises a novel application for stylized novel view synthesis. We propose to replace actual 3D scene models or 360 images with stylized stereoscopic images for the areas outside the major play area but are still visible to the user. User study results reveal that users can feel 3D sense and tell them from plane texture. Codes and other materials are available at: kuan-wei-tseng.github.io/ArtNV
Symmetry and Quantum Query-to-Communication Simulation

Buhrman, Cleve and Wigderson (STOC'98) showed that for every Boolean function f : {-1,1}^n to {-1,1} and G in {AND_2, XOR_2}, the bounded-error quantum communication complexity of the composed function f o G equals O(Q(f) log n), where Q(f) denotes the bounded-error quantum query complexity of f. This is in contrast with the classical setting, where it is easy to show that R^{cc}(f o G) < 2 R(f), where R^{cc} and R denote bounded-error communication and query complexity, respectively. Chakraborty et al. (CCC'20) exhibited a total function for which the log n overhead in the BCW simulation is required. We improve upon their result in several ways. We show that the log n overhead is not required when f is symmetric, generalizing a result of Aaronson and Ambainis for the Set-Disjointness function (Theory of Computing'05). This upper bound assumes a shared entangled state, though for most symmetric functions the assumed number of entangled qubits is less than the communication and hence could be part of the communication. To prove this, we design an efficient distributed version of noisy amplitude amplification that allows us to prove the result when f is the OR function. In view of our first result, one may ask whether the log n overhead in the BCW simulation can be avoided even when f is transitive. We give a strong negative answer by showing that the log n overhead is still necessary for some transitive functions even when we allow the quantum communication protocol an error probability that can be arbitrarily close to 1/2. We also give, among other things, a general recipe to construct functions for which the log n overhead is required in the BCW simulation in the bounded-error communication model, even if the parties are allowed to share an arbitrary prior entangled state for free.
A question-answering system for aircraft pilots' documentation

The aerospace industry relies on massive collections of complex and technical documents covering system descriptions, manuals or procedures. This paper presents a question answering (QA) system that would help aircraft pilots access information in this documentation by naturally interacting with the system and asking questions in natural language. After describing each module of the dialog system, we present a multi-task based approach for the QA module which enables performance improvement on a Flight Crew Operating Manual (FCOM) dataset. A method to combine scores from the retriever and the QA modules is also presented.
Attitude stability control system of mobile robot mechanism based on nanosensor

Aiming at the poor stability of attitude control of mobile robot mechanism, a kind of attitude stability control system of mobile robot mechanism based on nanodisplacement sensor is designed. In the hardware part, a hydraulic drive is used to control the action posture of the mobile robot, a nanodisplacement sensor is used to collect the walking data of the robot, and serial communication of the upper computer is used to convert the data into electrical signals to realise the robot posture control. In the software part, the mathematical coordinate system of robot walking is constructed, and the rotating posture of the robot is controlled by the Euler angle. The experimental results show that the control performance of the designed system is stable and the control precision is high, which can realise the attitude stabilisation control of the mobile robot.
A QoE-driven Adaptive Transmission Scheme for Streaming Media Service

Ultra-High-Definition (UHD) streaming media services are quite sensitive to network fluctuations. To ensure smooth playback, adaptive streaming schemes, such as HLS and DASH, have been proposed. Those schemes are network-driven, thus may lead to service quality fluctuating frequently, impacting user's experience seriously. To overcome the above problem, a QoE-driven adaptive transmission scheme is proposed in this paper. Thanks to the well-designed scheduling module, our scheme adjusts the encoding and transmission bit rate based on estimated bandwidth, predicted service quality, and other terminal indicators. The performance of the proposed scheme has been evaluated against the alternative schemes. Simulation results show that our scheme can ensure the service perception quality at a higher level regardless network fluctuations.