Reviewerly/exHarmony huggingface dataset:
Advances in Mobile Learning Educational Research (A.M.L.E.R.): Mobile learning as an educational reform

The journal Advances in Educational Research and Evaluation is a peer-reviewed openaccess journal aimed to be a medium for discussing a wide range of international educational experiences and assessment techniques. The journal intends to publish high-quality articles, the scope of which includes but is not limited to topics mentioned in this editorial. With the support of an international team of educational scholars who kindly volunteered to serve on the editorial board, the journal is set to adhere to the highest publishing ethics standards.
Combining EfficientNet and Vision Transformers for Video Deepfake Detection

Deepfakes are the result of digital manipulation to forge realistic yet fake imagery. With the astonishing advances in deep generative models, fake images or videos are nowadays obtained using variational autoencoders (VAEs) or Generative Adversarial Networks (GANs). These technologies are becoming more accessible and accurate, resulting in fake videos that are very difficult to be detected. Traditionally, Convolutional Neural Networks (CNNs) have been used to perform video deepfake detection, with the best results obtained using methods based on EfficientNet B7. In this study, we focus on video deep fake detection on faces, given that most methods are becoming extremely accurate in the generation of realistic human faces. Specifically, we combine various types of Vision Transformers with a convolutional EfficientNet B0 used as a feature extractor, obtaining comparable results with some very recent methods that use Vision Transformers. Differently from the state-of-the-art approaches, we use neither distillation nor ensemble methods. Furthermore, we present a straightforward inference procedure based on a simple voting scheme for handling multiple faces in the same video shot. The best model achieved an AUC of 0.951 and an F1 score of 88.0%, very close to the state-of-the-art on the DeepFake Detection Challenge (DFDC). The code for reproducing our results is publicly available here: https://tinyurl.com/cnn-vit-dfd .
Fault-Tolerant Continuous-Variable Measurement-based Quantum Computation Architecture

Continuous-variable measurement-based quantum computation on cluster states has in recent years shown great potential for scalable, universal, and fault-tolerant quantum computation when combined with the Gottesman-Kitaev-Preskill (GKP) code and quantum error correction. However, no complete fault-tolerant architecture exists that includes everything from cluster-state generation with finite squeezing to gate implementations with realistic noise and error correction. In this work, we propose a simple architecture for the preparation of a cluster state in three dimensions in which gates can be efficiently implemented by gate teleportation. To accommodate scalability, we propose architectures that allow both spatial and temporal multiplexing, with the temporally encoded version requiring as little as two squeezed light sources. Because of its three-dimensional structure, the architecture supports topological qubit error correction, while GKP error correction is efficiently realized within the architecture by teleportation. To validate fault tolerance, the architecture is simulated using surface-GKP codes, including noise from GKP states as well as gate noise caused by finite squeezing in the cluster state. We find a fault-tolerant squeezing threshold of 12.7 dB, with room for further improvement.Received 11 January 2021Revised 18 May 2021Accepted 23 July 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030325Published 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 AreasMeasurement-based quantum computingOptical quantum information processingQuantum information processing with continuous variablesQuantum InformationAtomic, Molecular & Optical
Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders

Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex images, such as those encountered in the medical domain. Barely visible abnormalities in chest X-rays or metastases in lymph nodes on the scans of the pathology slides resemble normal images and are very difficult to detect. To address this problem, we introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a redesigned training pipeline to handle high-resolution, complex images, and a robust way of computing an image abnormality score. We revisit the very problem statement of fully unsupervised anomaly detection, where no abnormal examples are provided during the model setup. We propose to relax this unrealistic assumption by using a very small number of anomalies of confined variability merely to initiate the search of hyperparameters of the model. We evaluate our solution on two medical datasets containing radiology and digital pathology images, where the state-of-the-art anomaly detection models, originally devised for natural image benchmarks, fail to perform sufficiently well. The proposed approach suggests a new baseline for anomaly detection in medical image analysis tasksa.
Modular Deep Reinforcement Learning for Continuous Motion Planning With Temporal Logic

This letter investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces. Linear temporal logic (LTL) is used to specify high-level tasks over infinite horizon, which can be converted into a limit deterministic generalized Büchi automaton (LDGBA) with several accepting sets. The novelty is to design an embedded product MDP (EP-MDP) between the LDGBA and the MDP by incorporating a synchronous tracking-frontier function to record unvisited accepting sets of the automaton, and to facilitate the satisfaction of the accepting conditions. The proposed LDGBA-based reward shaping and discounting schemes for the model-free reinforcement learning (RL) only depend on the EP-MDP states and can overcome the issues of sparse rewards. Rigorous analysis shows that any RL method that optimizes the expected discounted return is guaranteed to find an optimal policy whose traces maximize the satisfaction probability. A modular deep deterministic policy gradient (DDPG) is then developed to generate such policies over continuous state and action spaces. The performance of our framework is evaluated via an array of OpenAI gym environments.
Ethical Responsibility and Sustainability (ERS) Development in a Metaverse Business Model

Businesses are starting to use the Metaverse to expand their service network and establish new value co-creation for customers. However, businesses may need to carefully assess the ethical implications of their data collection and utilisation procedures for business sustainability. This paper examines the ethical concerns surrounding the usage of the Metaverse by organisations to obtain a competitive edge. This research was based on an exploratory assessment of business ethics and a Metaverse business model. A structured literature review was selected as the study’s design to get a better understanding of the issue. This research provides preliminary insights into the Metaverse and its business ethics, suggesting that any business must have a transparent policy regarding its Metaverse applications to foster a culture of ethics. This research aims to promote a constructive discussion on the issue of ethics in the context of the Metaverse that arises when an organisation conducts a violation or misuses user data. This paper is useful for people in the fields of technology and public policy, such as academics, businesspeople, and policymakers.
Is Twitter a Good Place for Asking Questions? A Characterization Study

People often turn to their social networks to fulfill their information needs. We conducted a study of question asking and answering (Q&amp;A) behavior on Twitter. We found that the most popular question types were rhetorical and factual. Surprisingly, along with entertainment and technology questions, people asked personal and health-related questions. The majority of questions received no response, while a handful of questions received a high number of responses. The larger the askers’ network, the more responses she received; however, posting more tweets or posting more frequently did not increase chances of receiving a response. Most often the ‘follow’ relationship between asker and answerer was one-way. We provide a rich characterization of Q&amp;A in social information streams and discuss implications for design.
Predictive performance modeling for distributed batch processing using black box monitoring and machine learning

In many domains, the previous decade was characterized by increasing data volumes and growing complexity of data analyses, creating new demands for batch processing on distributed systems. Effective operation of these systems is challenging when facing uncertainties about the performance of jobs and tasks under varying resource configurations, e. g., for scheduling and resource allocation. We survey predictive performance modeling (PPM) approaches to estimate performance metrics such as execution duration, required memory or wait times of future jobs and tasks based on past performance observations. We focus on non-intrusive methods, i. e., methods that can be applied to any workload without modification, since the workload is usually a black box from the perspective of the systems managing the computational infrastructure. We classify and compare sources of performance variation, predicted performance metrics, limitations and challenges, required training data, use cases, and the underlying prediction techniques. We conclude by identifying several open problems and pressing research needs in the field.
Automatic short answer grading and feedback using text mining methods

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required.
Accurate, Efficient and Scalable Graph Embedding

The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs.However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency.In this paper, we propose novel parallelization techniques for graph samplingbased GCNs that achieve superior scalable performance on very large graphs without compromising accuracy.Specifically, our GCN guarantees work-efficient training and produces order of magnitude savings in computation and communication.To scale GCN training on tightly-coupled shared memory systems, we develop parallelization strategies for the key steps in training: For the graph sampling step, we exploit parallelism within and across multiple sampling instances, and devise an efficient data structure for concurrent accesses that provides theoretical guarantee of near-linear speedup with number of processing units.For the feature propagation step within the sampled graph, we improve cache utilization and reduce DRAM communication by data partitioning.We prove that our partitioning strategy is a 2approximation for minimizing the communication time compared to the optimal strategy.We demonstrate that our parallel graph embedding outperforms state-of-the-art methods in scalability (with respect to number of processors, graph size and GCN model size), efficiency and accuracy on several large datasets.On a 40-core Xeon platform, our parallel training achieves 64× speedup (with AVX) in the sampling step and 25× speedup in the feature propagation step, compared to the serial implementation, resulting in a net speedup of 21×.Our scalable algorithm enables deeper GCN, as demonstrated by 1306× speedup on a 3-layer GCN compared to Tensorflow implementation of state-of-the-art.
Multi-Agent-Based Unsupervised Detection of Energy Consumption Anomalies on Smart Campus

The smart campus is becoming a reality with the advancement of information and communication technologies.For energy efficiency, it is essential to detect abnormal energy consumption in a smart campus, which is important for a ''smart'' campus.However, the obtained data are usually continuously generated by ubiquitous sensing devices, and the abnormal patterns hidden in the data are usually unknown, which makes detecting anomalies in such a context more challenging.Moreover, evaluating the quality of anomaly detection algorithms is difficult without labeled datasets.If the data are annotated well, classical criteria such as the receiver operating characteristic or precision recall curves can be used to compare the performance of different anomaly detection algorithms.In a smart campus environment, it is difficult to acquire labeled data to train a model due to the limited capabilities of the sensing devices.Therefore, distributed intelligence is preferred.In this paper, we present a multi-agent-based unsupervised anomaly detection method.We tackle these challenges in two stages with this method.First, we label the data using ensemble models.Second, we propose a method based on deep learning techniques to detect anomalies in an unsupervised fashion.The result of the first stage is used to evaluate the performance of the proposed method.We validate the proposed method with several datasets, and the experimental results demonstrate the effectiveness of our method.
Optimal verification and fidelity estimation of maximally entangled states

We study the verification of maximally entangled states by virtue of the simplest measurement settings: local projective measurements without adaption. We show that optimal protocols are in one-to-one correspondence with complex projective 2-designs constructed from orthonormal bases. Optimal protocols with minimal measurement settings are in one-to-one correspondence with complete sets of mutually unbiased bases. Based on this observation, optimal protocols are constructed explicitly for any local dimension, which can also be applied to estimating the fidelity with the target state and to detecting entanglement. In addition, we show that incomplete sets of mutually unbiased bases are optimal for verifying maximally entangled states when the number of measurement settings is restricted. Moreover, we construct optimal protocols for the adversarial scenario in which state preparation is not trusted. The number of tests has the same scaling behavior as the counterpart for the nonadversarial scenario; the overhead is no more than three times. We also show that the entanglement of the maximally entangled state can be certified with any given significance level using only one test as long as the local dimension is large enough.
How to “DODGE” Complex Software Analytics

Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring "redundant tunings"', i.e., pairs of tunings which lead to indistinguishable results. By ignoring redundant tunings, DODGE, a tuning tool, runs orders of magnitude faster, while also generating learners with more accurate predictions than seen in prior state-of-the-art approaches.
Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary.
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer

Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision.Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable.To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a lowlevel agent that alters the sentence.We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents.Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.
Partial Label Learning by Semantic Difference Maximization

Partial label learning is a weakly supervised learning framework, in which each instance is provided with multiple candidate labels while only one of them is correct. Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities. This paper gives a positive answer to this question for the first time. Specifically, if two instances do not share any common candidate labels, they cannot have the same ground-truth label. By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts.
Two-photon blockade and photon-induced tunneling generated by squeezing

Inspired by the recent experiment of Hamsen et al. [Phys. Rev. Lett. 118, 133604 (2017)], which demonstrated two-photon blockade in a driven nonlinear system (composed of a harmonic cavity with a driven atom), we show that two-photon blockade and other nonstandard types of photon blockade and photon-induced tunneling can be generated in a driven harmonic cavity without an atom or any other kind of nonlinearity, but instead coupled to a nonlinear (i.e., squeezed) reservoir. We also simulate these single- and two-photon effects with squeezed coherent states and displaced squeezed thermal states.
Language Learning Using Speech to Image Retrieval

Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoken utterances. Using a combination of a multi-layer GRU, importance sampling, cyclic learning rates, ensembling and vectorial self-attention our results show a remarkable increase in image-caption retrieval performance over previous work. Furthermore, we investigate which layers in the model learn to recognise words in the input. We find that deeper network layers are better at encoding word presence, although the final layer has slightly lower performance. This shows that our visually grounded sentence encoder learns to recognise words from the input even though it is not explicitly trained for word recognition.