Reviewerly/exHarmony huggingface dataset:
Training, personal and environmental barriers of online education

The sudden change that universities experienced in the wake of the COVID-19 pandemic raised some key issues in higher education. The objectives of the present study are to discover the barriers that students experienced regarding training in the use of synchronous online teaching tools, and to analyse the barriers they faced in terms of personal and environmental aspects. A quantitative methodology was used, with an ad hoc questionnaire consisting of Likert-type questions that was completed by 670 students of Education at the University of Cordoba and the University of Lleida. The results showed that most students did not receive any training in teaching synchronous online classes. Also, the students who did not have their own devices (i.e. a computer, mobile or tablet) or had to share one with other members of their family had the most difficulties in following online courses. The efforts made by students during the crisis is notable, and teachers are encouraged to think about possible strategies for dealing with similar situations in the future.
Novel camera calibration method based on invariance of collinear points and pole–polar constraint

To address the eccentric error of circular marks in camera calibration, a circle location method based on the invariance of collinear points and pole-polar constraint is proposed in this paper.Firstly, the centers of the ellipses are extracted, and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points.Subsequently, since the infinite lines passing through the centers of the marks are parallel, the other center projection coordinates are expressed as the solution problem of linear equations.The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed, and the results are utilized as the true image points to achieve the high precision camera calibration.As demonstrated by the simulations and practical experiments, the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks.The relevant results confirm the precision and robustness of the proposed approach.
Convenient quantum controlled teleportation of two-qubit pure state with seven-qubit entangled state

Abstract Based on a seven-qubit entangled state, Chen et al (2020 Int. J. Theor. Phys. 59 1402) presented a scheme for controlled quantum teleportation. Using the same quantum channel state, we put forward a new scheme in which the quantum information sender can also transmit a Bell state to receiver under the agreement and assist of the controller. The comparison with Chen et al ’s scheme is made and reveals its distinct advantage of reducing the classical resource consumption and increasing the intrinsic efficiency as well as degrading the complexity of necessary operations.
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Geospatial object segmentation, a fundamental Earth vision task, always suffers from scale variation, the larger intra-class variance of background, and foreground-background imbalance in high spatial resolution (HSR) remote sensing imagery. Generic semantic segmentation methods mainly focus on the scale variation in natural scenarios. However, the other two problems are insufficiently considered in large area Earth observation scenarios. In this paper, we propose a foreground-aware relation network (FarSeg++) from the perspectives of relation-based, optimization-based, and objectness-based foreground modeling, alleviating the above two problems. From the perspective of the relations, the foreground-scene relation module improves the discrimination of the foreground features via the foreground-correlated contexts associated with the object-scene relation. From the perspective of optimization, foreground-aware optimization is proposed to focus on foreground examples and hard examples of the background during training to achieve a balanced optimization. Besides, from the perspective of objectness, a foreground-aware decoder is proposed to improve the objectness representation, alleviating the objectness prediction problem that is the main bottleneck revealed by an empirical upper bound analysis. We also introduce a new large-scale high-resolution urban vehicle segmentation dataset to verify the effectiveness of the proposed method and push the development of objectness prediction further forward. The experimental results suggest that FarSeg++ is superior to the state-of-the-art generic semantic segmentation methods and can achieve a better trade-off between speed and accuracy.
GraphSPD: Graph-Based Security Patch Detection with Enriched Code Semantics

With the increasing popularity of open-source software, embedded vulnerabilities have been widely propagating to downstream software. Due to different maintenance policies, software vendors may silently release security patches without providing sufficient advisories (e.g., CVE). This leaves users unaware of security patches and provides attackers good chances to exploit unpatched vulnerabilities. Thus, detecting those silent security patches becomes imperative for secure software maintenance. In this paper, we propose a graph neural network based security patch detection system named GraphSPD, which represents patches as graphs with richer semantics and utilizes a patch-tailored graph model for detection. We first develop a novel graph structure called PatchCPG to represent software patches by merging two code property graphs (CPGs) for the pre-patch and post-patch source code as well as retaining the context, deleted, and added components for the patch. By applying a slicing technique, we retain the most relevant context and reduce the size of PatchCPG. Then, we develop the first end-to-end deep learning model called PatchGNN to determine if a patch is security-related directly from its graph-structured PatchCPG. PatchGNN includes a new embedding process to convert PatchCPG into a numeric format and a new multi-attributed graph convolution mechanism to adapt diverse relationships in PatchCPG. The experimental results show GraphSPD can significantly outperform the state-of-the-art approaches on security patch detection.
Unity is strength: Improving the detection of adversarial examples with ensemble approaches

A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. In this work, we propose the adoption of ensemble approaches to leverage the effectiveness of multiple detectors in exploiting distinct properties of the input data. To this end, the ENsemble Adversarial Detector (ENAD) framework integrates scoring functions from state-of-the-art detectors based on Mahalanobis distance, Local Intrinsic Dimensionality, and One-Class Support Vector Machines, which process the hidden features of deep neural networks. ENAD is designed to ensure high standardization and reproducibility to the computational workflow. Extensive tests on benchmark datasets, models and adversarial attacks show that ENAD outperforms all competing methods in the large majority of settings. The improvement over the state-of-the-art and the intrinsic generality of the framework, which allows one to easily extend ENAD to include any set of detectors and integration strategies, set the foundations for the new area of ensemble adversarial detection.
SAD: Sensor-Based Anomaly Detection System for Smart Junctions

The low cost and advancement of sensor technology have long proven to be invaluable in their use in all areas of science. Several video-based methods have already been suggested for intelligent transportation systems (ITSs). However, the information collected through image/video processing alone is not sufficient for an error-proof traffic management system. In this article, we propose sensor-based anomaly detection (SAD), a system that integrates the capabilities of sensors with powerful image processing techniques to build an efficient, self-adaptive traffic control system. This system has two interlinked modules: accident detection and emergency stat invocation. The accident detection module uses piezoelectric sensors to measure any pressure variation that in turn invokes an image processing submodule to detect the accident. Once the accident is confirmed, the emergency stat invocation module contacts the nearest emergency service for help. The proposed model is simple and fast to detect anomalies in real-time heterogeneous traffic conditions. The combined use of sensor technology and image processing to detect anomalies significantly increases the accuracy of the system, i.e., by reducing false alarms. By integrating SAD in real-time, the emergency services are alerted instantly, thereby ensuring faster medical assistance. The proposed system, when deployed and analyzed in real-time, achieves 93%–95% accuracy in urban areas and 96%–99% accuracy on highways. SAD clearly offers more accuracy when compared with other state-of-the-art methods without compromising performance.
PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios

Perception algorithms in autonomous driving systems confront great challenges in long-tail traffic scenarios, where the problems of Safety of the Intended Functionality (SOTIF) could be triggered by the algorithm performance insufficiency and dynamic operational environment. However, such scenarios are not systematically included in current open-source datasets, and this paper fills the gap accordingly. Based on the analysis and enumeration of trigger conditions, a high-quality diverse dataset is released, including various long-tail traffic scenarios collected from multiple resources. Considering the development of probabilistic object detection (POD), this dataset marks trigger sources that may cause perception SOTIF problems in the scenarios as key objects. In addition, an evaluation protocol is suggested to verify the effectiveness of POD algorithms in identifying the key objects via uncertainty. The dataset never stops expanding, and the first batch of open-source data includes 1126 frames with an average of 2.27 key objects and 2.47 normal objects in each frame. To demonstrate how to use this dataset for SOTIF research, this paper further quantifies the perception SOTIF entropy to confirm whether a scenario is unknown and unsafe for a perception system. The experimental results show that the quantified entropy can effectively and efficiently reflect the failure of the perception algorithm.
User Interactions With a Municipality Chatbot—Lessons Learnt From Dialogue Analysis

Chatbots are increasingly taken up by the public sector, as a means to efficient provision of information and services. However, there is a lack of knowledge on how users interact with such chatbots. To address this knowledge gap, we have conducted an analysis of user interactions with a chatbot for citizens of Norwegian municipalities. We analyzed a total of 2663 user-chatbot dialogues from six municipalities, using the framework of Følstad and Taylor. The analysis showed that most user input was characterized by brief messages and a utility-oriented dialogue style whereas chatbot responses were characterized by substantial response relevance (68% of chatbot responses categorized as relevant) and helpfulness (66% of dialogues categorized as help being offered and likely used). Furthermore, message brevity and a utility-oriented dialogue style was found to be positively associated with users receiving relevant chatbot responses and helpful dialogue outcomes. Variation in chatbot design, specifically how the chatbot was presented to users, was found to impact user message brevity and dialogue style, and, by extension, response relevance and dialogue outcome. On the basis of the findings, we summarize lessons learnt and suggest directions for future research.
Beyond A ‘Noticing Stance’: Reflecting to Expand Postdigital Positionalities

This chapter offers an overview of dominant educational technology research paradigms, how they conceptualise the relationship between humans and technology and situate the researcher. As such, it aims to provide an introduction for scholars who may be new to postdigital research, as well as those who are more familiar with the postdigital paradigm. Through reflective prompts, we engage readers in a journey to think about their own postdigital positionalities. This includes exploring the positionalities of the more-than-human and what this means for postdigital research in the future. The expansion of AI has already and will continue to impact on how we think about studying such technologies and our relationships with them, and how we view ourselves as researchers as part of the research process and broader phenomena we are investigating.
TinyReptile: TinyML with Federated Meta-Learning

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly learn a global model without sharing sensitive local data. However, a common global model may not work for all devices due to the complexity of the actual deployment environment and the heterogeneity of the data available on each device. In addition, the deployment of TinyML hardware has significant computational and communication constraints, which traditional ML fails to address. Considering these challenges, we propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning, to collaboratively learn a solid initialization for a neural network (NN) across tiny devices that can be quickly adapted to a new device with respect to its data. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The evaluations on various TinyML use cases confirm a resource reduction and training time saving by at least two factors compared with baseline algorithms with comparable performance.
PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing

This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearestneighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools - image retargeting, completion and reshuffling - that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods.
HyperMixer: An MLP-based Low Cost Alternative to Transformers

Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune.In the pursuit of lower costs, we investigate simple MLP-based architectures.We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding.In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks.Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers.In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.
Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success)

Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in fewand zero-shot settings.However, it is unclear if such models are similarly capable in more specialized, high-stakes domains such as biomedicine.In this paper, we enlist domain experts (individuals with medical training) to evaluate summaries of biomedical articles generated by GPT-3, given zero supervision.We consider both single-and multi-document settings.In the former, GPT-3 is tasked with generating regular and plain-language summaries of articles describing randomized controlled trials; in the latter, we assess the degree to which GPT-3 is able to synthesize evidence reported across a collection of articles.We design an annotation scheme for evaluating model outputs, with an emphasis on assessing the factual accuracy of generated summaries.We find that while GPT-3 is able to summarize and simplify single biomedical articles faithfully, it struggles to provide accurate aggregations of findings over multiple documents.We release all data and annotations used in this work.
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages

In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation.To overcome the "curse of multilinguality", these models often opt for scaling up the number of parameters, which makes their use in resource-constrained environments challenging.We introduce SMaLL-100, a distilled version of the M2M-100 (12B) model, a massively multilingual machine translation model covering 100 languages.We train SMaLL-100 with uniform sampling across all language pairs and therefore focus on preserving the performance of low-resource languages.We evaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba, and TICO-19 and demonstrate that it outperforms previous massively multilingual models of comparable sizes (200-600M) while improving inference latency and memory usage.Additionally, our model achieves comparable results to M2M-100 (1.2B), while being 3.6× smaller and 4.3× faster at inference.
Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities

In this work, we propose a novel framework to devise features that can be used by Query Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the proposed framework as a periodic table of QPP components, practitioners can devise new predictors better suited for NIR. Through the framework, we detail what challenges and opportunities arise for QPPs at different stages of the NIR pipeline. We show the potential of the proposed framework by using it to devise two types of novel predictors. The first one, named MEMory-based QPP (MEM-QPP), exploits the similarity between test and train queries to measure how much a NIR system can memorize. The second adapts traditional QPPs into NIR-oriented ones by computing the query-corpus semantic similarity. By exploiting the inherent nature of NIR systems, the proposed predictors overcome, under various setups, the current State of the Art, highlighting -- at the same time -- the versatility of the framework in describing different types of QPPs.
Use and acceptance of crypto currencies in India: an evaluation of block chain application in financial sector using PLS SEM and ANN approach

Purpose This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose. Design/methodology/approach This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group. Findings The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design. Research limitations/implications Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity. Practical implications The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology. Social implications Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly. Originality/value Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.