Reviewerly/exHarmony huggingface dataset:
Educational Augmented Reality Technology for Language Learning and Teaching: A Comprehensive Review

The purpose of this paper is to present a review of Augmented Reality (AR) technology as an educational tool for foreign language education. Following a short yet comprehensive literature review, the paper reviews educational AR technology in terms of learning theories, learning pedagogies, teachers, students, culture, infrastructure, and sustainability based on the framework developed by Osterweil et al. (2016) for evaluating the appropriateness of educational technology use in global development programs. The review showed that AR technology provides several benefits for language learning; however, it is not ready for total integration into language classes. The paper also provides practical suggestions for activities enriched with AR in four language skills and recommended applications. This review offers various implications for teachers, teacher educators, researchers and coursebook developers.
Best-Buddy GANs for Highly Detailed Image Super-resolution

We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Recently, generative adversarial networks (GANs) become popular to hallucinate details. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the ill-posed SISR task. Also, GAN-generated fake details may often undermine the realism of the whole image. We address these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the rigid one-to-one constraint, we allow the estimated patches to dynamically seek trustworthy surrogates of supervision during training, which is beneficial to producing more reasonable details. Besides, we propose a region-aware adversarial learning strategy that directs our model to focus on generating details for textured areas adaptively. Extensive experiments justify the effectiveness of our method. An ultra-high-resolution 4K dataset is also constructed to facilitate future super-resolution research.
SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions.To boost the models' performance, various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text.However, the inherent disparities between the two modalities necessitate a mutual analysis.In this paper, we propose a novel semisupervised learning framework, SPLAT, to jointly pre-train the speech and language modules.Besides conducting a self-supervised masked language modeling task on the two individual modules using unpaired speech and text, SPLAT aligns representations from the two modules in a shared latent space using a small amount of paired speech and text.Thus, during fine-tuning, the speech module alone can produce representations carrying both acoustic information and contextual semantic knowledge of an input acoustic signal.Experimental results verify the effectiveness of our approach on various SLU tasks.For example, SPLAT improves the previous stateof-the-art performance on the Spoken SQuAD dataset by more than 10%.
Human Signature Identification Using IoT Technology and Gait Recognition

This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones.
General Expressions for the Quantum Fisher Information Matrix with Applications to Discrete Quantum Imaging

The quantum Fisher information matrix is a central object in multiparameter quantum estimation theory. It is usually challenging to obtain analytical expressions for it because most calculation methods rely on the diagonalization of the density matrix. In this paper, we derive general expressions for the quantum Fisher information matrix that bypass matrix diagonalization and do not require the expansion of operators on an orthonormal set of states. Additionally, we can tackle density matrices of arbitrary rank. The methods presented here simplify analytical calculations considerably when, for example, the density matrix is more naturally expressed in terms of nonorthogonal states, such as coherent states. Our derivation relies on two matrix inverses that, in principle, can be evaluated analytically even when the density matrix is not diagonalizable in closed form. We demonstrate the power of our approach by deriving novel results in the timely field of discrete quantum imaging: the estimation of positions and intensities of incoherent point sources. We find analytical expressions for the full estimation problem of two point sources with different intensities and for specific examples with three point sources. We expect that our method will become standard in quantum metrology.Received 19 December 2020Accepted 1 April 2021Corrected 2 December 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020308Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum Information
Quantum circuit cutting with maximum-likelihood tomography

Abstract We introduce maximum-likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running clustered quantum circuits on quantum devices with a limited number of qubits. In addition to minimizing the classical computing overhead of circuit cutting methods, MLFT finds the most likely probability distribution for the output of a quantum circuit, given the measurement data obtained from the circuit’s fragments. We demonstrate the benefits of MLFT for accurately estimating the output of a fragmented quantum circuit with numerical experiments on random unitary circuits. Finally, we show that circuit cutting can estimate the output of a clustered circuit with higher fidelity than full circuit execution, thereby motivating the use of circuit cutting as a standard tool for running clustered circuits on quantum hardware.
Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

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Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

There is great interest in artificial intelligence (AI) in health care. As with many innovations in medicine, there is a fine line between potential benefits and harms with the use of AI. This is especially important for preventive services such as cancer screening, where the trade-off between benefits and harms is delicate and involves millions of individuals. This Commentary clarifies clinical challenges of AI in cancer screening programs and explores solutions that can enable its optimal implementation.
Manipulation by algorithms. Exploring the triangle of unfair commercial practice, data protection, and privacy law

Abstract The optimisation of sales practices in consumer markets through machine learning not only harbours the potential to better match consumer preferences with products, but also risks to facilitate the exploitation of consumer weaknesses discovered via data analysis. More specifically, recent technological advances have brought us to the edge of mind‐reading technologies, which automatically analyse mental states and adapt offers accordingly, in potentially manipulative ways. This article shows that, in market contexts, the challenges of manipulation by algorithm necessitate an integrated understanding of unfair commercial practice, data protection, and privacy law. It maps the interactions between these contiguous yet distinct fields of law, and draws on economics and computer science to develop a novel framework to deal with algorithmic influence. Furthermore, it critically discusses the Commission proposals for the Digital Services Act and the Artificial Intelligence Act, and suggests to complement them with more broadly applicable measures to mitigate algorithmic manipulation.
Tele-mentoring using augmented reality technology in healthcare: A systematic review

This systematic review aimed to identify how tele-mentoring systems that incorporate augmented reality (AR) technology are being used in healthcare environments. A total of 12 electronic bibliographic databases were searched using the terms “augmented reality”, “tele-mentoring” and “health”. The PRISMA checklist was used as a guide for reporting. The mixed method appraisal tool was used to assess the quality of the included experiments. The data were then analysed using a concept-centric approach and categorised primarily with regards to system performance and task performance measures. A total of 11 randomised controlled trials and 14 non-randomised designs were included for review. Both mentees and mentors assessed the system and task performance according to 25 categories. The feedback of mentees using AR tele-mentoring systems was generally positive. The majority of experiments revealed that the AR system was an effective tele-mentoring device overall and resulted in the effective performance of a procedure. Benefits included improvements in trainees’ confidence, task completion time and reductions in task errors and shifts in focus. However, the systems had limitations, including heaviness of the equipment, inconvenience, discomfort and distraction of wearing devices, limited battery life, the latency of video and audio signals and limited field of view.&#x0D; &#x0D; Implications for practice or policy:&#x0D; &#x0D; Health practitioners can apply AR technology to receive and follow real-time annotated instructions verbally and visually from remote experts.&#x0D; Technical developers may consider improving AR devices in terms of lighter weight, larger field of view, more ergonomic design, more stable network connection and longer battery life.&#x0D; Further AR-related experiments may need to explore AR tele-mentoring systems’ utility across healthcare environments with larger samples, real patient populations in remote settings, cost-benefit analysis and impacts on short- and long-term patient outcomes.&#x0D;
Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow

Current text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated "workflow" to conduct abstract screening for systematic reviews and other knowledge synthesis methods.We adopt widely recommended text-mining and machine-learning methods to (1) process title-abstracts into numerical training data; and (2) train a classification model to predict eligible abstracts. The predicted abstracts are screened by human reviewers for ("true") eligibility, and the newly eligible abstracts are used to identify similar abstracts, using near-neighbor methods, which are also screened. These abstracts, as well as their eligibility results, are used to update the classification model, and the above steps are iterated until no new eligible abstracts are identified. The workflow was implemented in R and evaluated using a systematic review of insulin formulations for type-1 diabetes (14,314 abstracts) and a scoping review of knowledge-synthesis methods (17,200 abstracts). Workflow performance was evaluated against the recommended practice of screening abstracts by 2 reviewers, independently. Standard measures were examined: sensitivity (inclusion of all truly eligible abstracts), specificity (exclusion of all truly ineligible abstracts), precision (inclusion of all truly eligible abstracts among all abstracts screened as eligible), F1-score (harmonic average of sensitivity and precision), and accuracy (correctly predicted eligible or ineligible abstracts). Workload reduction was measured as the hours the workflow saved, given only a subset of abstracts needed human screening.With respect to the systematic and scoping reviews respectively, the workflow attained 88%/89% sensitivity, 99%/99% specificity, 71%/72% precision, an F1-score of 79%/79%, 98%/97% accuracy, 63%/55% workload reduction, with 12%/11% fewer abstracts for full-text retrieval and screening, and 0%/1.5% missed studies in the completed reviews.The workflow was a sensitive, precise, and efficient alternative to the recommended practice of screening abstracts with 2 reviewers. All eligible studies were identified in the first case, while 6 studies (1.5%) were missed in the second that would likely not impact the review's conclusions. We have described the workflow in language accessible to reviewers with limited exposure to natural language processing and machine learning, and have made the code available to reviewers.
Mitigating Cybersickness in Virtual Reality Systems through Foveated Depth-of-Field Blur

Cybersickness is one of the major roadblocks in the widespread adoption of mixed reality devices. Prolonged exposure to these devices, especially virtual reality devices, can cause users to feel discomfort and nausea, spoiling the immersive experience. Incorporating spatial blur in stereoscopic 3D stimuli has shown to reduce cybersickness. In this paper, we develop a technique to incorporate spatial blur in VR systems inspired by the human physiological system. The technique makes use of concepts from foveated imaging and depth-of-field. The developed technique can be applied to any eye tracker equipped VR system as a post-processing step to provide an artifact-free scene. We verify the usefulness of the proposed system by conducting a user study on cybersickness evaluation. We used a custom-built rollercoaster VR environment developed in Unity and an HTC Vive Pro Eye headset to interact with the user. A Simulator Sickness Questionnaire was used to measure the induced sickness while gaze and heart rate data were recorded for quantitative analysis. The experimental analysis highlighted the aptness of our foveated depth-of-field effect in reducing cybersickness in virtual environments by reducing the sickness scores by approximately 66%.
A Chan-Vese model based on the Markov chain for unsupervised medical image segmentation

The accurate segmentation of medical images is crucial to medical care and research; however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan-Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan-Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel level. Our method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is rigid. The performance of our method is assessed by using four Computerized Tomography (CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.
Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility

This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion

One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we found that in the current mainstream video datasets, some action categories are highly related with the scene where the action happens, making the model tend to degrade to a solution where only the scene information is encoded. For example, a trained model may predict a video as playing football simply because it sees the field, neglecting that the subject is dancing as a cheerleader on the field. This is against our original intention towards the video representation learning and may bring scene bias on a different dataset that can not be ignored. In order to tackle this problem, we propose to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid. Specifically, we construct a positive clip and a negative clip for each video. Compared to the original video, the positive/negative is motion-untouched/broken but scene-broken/untouched by Spatial Local Disturbance and Temporal Local Disturbance. Our objective is to pull the positive closer while pushing the negative farther to the original clip in the latent space. In this way, the impact of the scene is weakened while the temporal sensitivity of the network is further enhanced. We conduct experiments on two tasks with various backbones and different pre-training datasets, and find that our method surpass the SOTA methods with a remarkable 8.1% and 8.8% improvement towards action recognition task on the UCF101 and HMDB51 datasets respectively using the same backbone.
A Sequence-to-Sequence Approach to Dialogue State Tracking

This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently.This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-tosequence problem.Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue.Seq2Seq-DU has the following advantages.It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas.In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system.Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2,MultiWOZ2.1,WOZ2.0,DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.
5* Knowledge Graph Embeddings with Projective Transformations

Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type -- often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple subgraph structures in a neighborhood (e.g.~combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model 5*E in projective geometry, which supports multiple simultaneous transformations -- specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on most widely used link prediction benchmarks