Reviewerly/exHarmony huggingface dataset:
Educational practices to transform and connect schools and communities

The current social and educational challenges force us to rethink the role of educational institutions and digital technologies in the 21st century, which requires a deeper understanding of learning activities in schools. In this article we analyze bottom-up initiatives for educational transformation implemented in public lower secondary schools from Norway, Chile and Spain that involved 230 students and 14 teachers. Three ethnographic case studies were carried out, using individual interviews, focus groups, participant observations and document analysis in six schools. The main goal was to deeply understand how bottom-up school initiatives, with a comprehensive use of digital technologies, are contributing to generate connected practices and to involve teachers, learners and communities in the discussion about what kind of society they want in their future. The results of the analysis indicate that fostering a transformative agency in secondary schools has the potential of engaging students in the exploration of contemporary social issues and that digital connectedness can contribute to connect schools with youth life trajectories and communities. This study on transformative agency and digital connectedness reveals a new path for educational transformation that may interest everybody who, in one way or another, are involved in education systems all around the world. Los desafíos sociales y educativos que enfrentamos en la actualidad nos obligan a repensar cuál es el rol de las instituciones educativas y de las tecnologías digitales en el siglo XXI, lo cual requiere una comprensión más profunda de las actividades de aprendizaje de las escuelas. En este artículo analizamos iniciativas para la transformación educativa implementadas en centros de secundaria públicos de Noruega, Chile y España que involucraron a 230 alumnos y 14 profesores. Se llevaron a cabo tres estudios de caso etnográficos a partir de entrevistas, grupos de discusión, observaciones participantes y análisis documental en seis centros. El objetivo principal fue comprender en profundidad cómo estas iniciativas contribuyen a generar prácticas conectadas y a involucrar al profesorado, el estudiantado y las comunidades en la discusión sobre el tipo de sociedad en la que quieren vivir en un futuro. Los resultados del análisis indican que el fomento de una agencia transformadora en los centros de secundaria tiene el potencial de involucrar al alumnado en el estudio de problemáticas sociales contemporáneas y que la conectividad digital puede contribuir a conectar las escuelas con las trayectorias de vida y las comunidades de los jóvenes. Este primer estudio sobre agencia transformadora y conectividad digital desvela una línea de transformación educativa que puede interesar a todos aquellos individuos que, de una forma u otra, están involucrados en los sistemas educativos de todo el mundo.
Multiphase estimation without a reference mode

Multiphase estimation is a paradigmatic example of a multiparameter problem. When measuring multiple phases embedded in interferometric networks, specially tailored input quantum states achieve enhanced sensitivities compared with both single-parameter and classical estimation schemes. Significant attention has been devoted to defining the optimal strategies for the scenario in which all of the phases are evaluated with respect to a common reference mode, in terms of optimal probe states and optimal measurement operators. Also, the strategies assume unlimited external resources, which is experimentally unrealistic. Here, we optimize a generalized scenario that treats all of the phases on an equal footing and takes into account the resources provided by external references. We show that the absence of an external reference mode reduces the number of simultaneously estimable parameters, owing to the immeasurability of global phases, and that the symmetries of the parameters being estimated dictate the symmetries of the optimal probe states. Finally, we provide insight for constructing optimal measurements in this generalized scenario. The experimental viability of this paper underlies its immediate practical importance beyond fundamental physics.
Accurate and Efficient Image Super-Resolution via Global-Local Adjusting Dense Network

Convolutional neural network-based (CNN-based) method has shown its superior performance on the image super-resolution (SR) task. However, several researches have shown that obtaining a better reconstruction result often leads to the significant increase in parameters and computation. To alleviate the burden in computational needs, we propose a novel global-local adjusting dense super-resolution network (GLADSR) to build a powerful yet lightweight CNN-based SR model. To enhance the network capacity, we present a global-local adjusting module (GLAM) which can adaptively reallocate the processing resources with local selective block (LSB) and global guided block (GGB). The GLAMs are linked with nested dense connections to make better use of the global-local adjusted features. In addition, we also introduce a separable pyramid upsampling (SPU) module to replace the regular upsampling operation, which thus brings a substantial reduction of its parameters and obtains better results. Furthermore, we show that the proposed refinement structure is capable of reducing image artifacts in SR processing. Extensive experiments on benchmark datasets show that the proposed GLADSR outperforms the state-of-the-art methods with much fewer parameters and much less computational cost.
Fault Detection Filtering Design for Discrete-Time Interval Type-2 T–S Fuzzy Systems in Finite Frequency Domain

This article focuses on the problem of fault detection filtering design for discrete-time interval type-2 Takagi-Sugeno (T-S) fuzzy systems in finite frequency domain. Considering the fact that external disturbances and faults are usually reside in finite frequency ranges, the finite frequency H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> performances are introduced to reflect the disturbance robustness and fault sensitiveness in finite frequency domain, respectively. Based on discrete-time Fourier transform and its properties, finite frequency performance analysis results are first obtained. Then, by exploiting the information on upper and lower membership functions, the membership-function-dependent filtering design conditions in the form of linear matrix inequalities are established for discrete-time interval type-2 T-S fuzzy systems in finite frequency domain. With the obtained filter, a fault detection scheme is then proposed and it is shown that the resulting fault detection system is asymptotically stable with prescribed finite frequency H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> performances. Finally, the effectiveness of the proposed method is validated by simulation studies.
Experimental three-state measurement-device-independent quantum key distribution with uncharacterized sources

The measurement-device-independent quantum key distribution (MDI-QKD) protocol plays an important role in quantum communications due to its high level of security and practicability. It can be immune to all side-channel attacks directed on the detecting devices. However, the protocol still contains strict requirements during state preparation in most existing MDI-QKD schemes, e.g., perfect state preparation or perfectly characterized sources, which are very hard to realize in practice. In this letter, we investigate uncharacterized MDI-QKD by utilizing a three-state method, greatly reducing the finite-size effect. The only requirement for state preparation is that the state are prepared in a bidimensional Hilbert space. Furthermore, a proof-of-principle demonstration over a 170 km transmission distance is achieved, representing the longest transmission distance under the same security level on record.
Robust extreme learning machine for modeling with unknown noise

Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches.
Evaluating the effectiveness of a preservice teacher technology training module incorporating SQD strategies

Abstract Preparation to use information and communication technology (ICT) is an important component of preservice language teachers’ training, and various existing teacher training models propose a range of strategies for increasing their technology knowledge and technology adoption rates. However, the relative effectiveness of these strategies remains unclear. Based on Tondeur et al.’s (2012) Synthesis of Qualitative Data model, which delineates the six main teacher preparation strategies (i.e., role modeling, reflection, instructional design, collaboration, authentic experience, and continuous feedback), the present study designed a 4 week training module for preservice language teachers and examined how these training strategies affected 63 participants’ perceived technology knowledge and attitudes toward technology adoption. Among the six training strategies, reflection and instructional design had the highest positive impacts on these preservice teachers’ self-reported knowledge about and attitudes towards using ICT. As well as revealing the relative impacts of each training strategy, the results indicate that our designed training module has considerable potential for application to teacher training in other subjects.
An empirical study on ARM disassembly tools

With the increasing popularity of embedded devices, ARM is becoming the dominant architecture for them. In the meanwhile, there is a pressing need to perform security assessments for these devices. Due to different types of peripherals, it is challenging to dynamically run the firmware of these devices in an emulated environment. Therefore, the static analysis is still commonly used. Existing work usually leverages off-the-shelf tools to disassemble stripped ARM binaries and (implicitly) assume that reliable disassembling binaries and function recognition are solved problems. However, whether this assumption really holds is unknown.
The Butterfly “Affect”: impact of development practices on cryptocurrency prices

Abstract The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how “healthy” and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it. In this paper, we investigate whether developers’ emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers’ comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers’ comments over time, and test the corresponding time series (i.e. the affect time series ) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers’ emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)—associated with the prediction of the prices of cryptocurrencies—significantly decreases when including the affect time series.
Context-Aware Sarcasm Detection Using BERT

In this paper, we present the results obtained by BERT, BiLSTM and SVM classifiers on the shared task on Sarcasm Detection held as part of The Second Workshop on Figurative Language Processing.The shared task required the use of conversational context to detect sarcasm.We experimented by varying the amount of context used along with the response (response is the text to be classified).The amount of context used includes (i) zero context, (ii) last one, two or three utterances, and (iii) all utterances.It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set.On the other hand, the best performance for the Reddit data set was obtained when using only the response without any contextual information.The BERT classifier obtained F-score of 0.743 and 0.658 for the Twitter and Reddit data set respectively.
SCARF: Detecting Side-Channel Attacks at Real-time using Low-level Hardware Features

Side-Channel Attacks (SCAs) are powerful attacks compromising the security of modern computer systems by exploiting hardware vulnerabilities. Prior studies on detection of SCAs based on low-level microarchitectural features captured from processors' hardware performance counter (HPC) registers have considered collecting hardware events of both victim applications (cryptographic application, e.g. RSA, AES and etc.) and attack applications. However, in such techniques the attack HPCs data can be easily manipulated and/or corrupted resulting in misleading the SCA detection mechanism. Furthermore, the prior works have explored the suitability of a limited number of Machine Learning (ML) algorithms in detecting SCAs without examining the instance level false alarm rate that as we show in this work is a more important evaluation metric for SCA detection techniques. In response, in this paper, we propose SCARF, a machine learning-based real-time side-channel attack detection methodology using low-level hardware features. To this aim, we first only monitor the victim applications' behavior using the HPC features and analyze the captured low-level traces of the victim applications under no attack and attack conditions to avoid manipulation of attackers' HPCs. Next, a wide range of ML classifiers with customized HPC features are implemented to determine the most effective ML technique for detecting SCAs at real-time, while improving accuracy and reducing instance-level false alarm rate of ML-based SCA detectors. Lastly, the False Alarm Minimization (FAM) technique is proposed to further reduce the instance level false positive rate of the ML-based SCA detectors. The experimental results indicate that the SCARF methodology can obtain up to 100% attack detection accuracy with 0% instance level false alarm rate for detecting SCAs.
Does mathematics training lead to better logical thinking and reasoning? A cross-sectional assessment from students to professors

Mathematics is often promoted as endowing those who study it with transferable skills such as an ability to think logically and critically or to have improved investigative skills, resourcefulness and creativity in problem solving. However, there is scant evidence to back up such claims. This project tested participants with increasing levels of mathematics training on 11 well-studied rational and logical reasoning tasks aggregated from various psychological studies. These tasks, that included the Cognitive Reflection Test and the Wason Selection Task, are of particular interest as they have typically and reliably eluded participants in all studies, and results have been uncorrelated with general intelligence, education levels and other demographic information. The results in this study revealed that in general the greater the mathematics training of the participant, the more tasks were completed correctly, and that performance on some tasks was also associated with performance on others not traditionally associated. A ceiling effect also emerged. The work is deconstructed from the viewpoint of adding to the platform from which to approach the greater, and more scientifically elusive, question: are any skills associated with mathematics training innate or do they arise from skills transfer?
A preference semantics for imperatives

Imperative sentences like Dance! do not seem to represent the world. Recent modal analyses challenge this idea, but its intuitive and historical appeal remain strong. This paper presents three new challenges for a non-representational analysis, showing that the obstacles facing it are even steeper than previously appreciated. I will argue that the only way for the non-representationalist to meet these three challenges is to adopt a dynamic semantics. Such a dynamic semantics is proposed here: imperatives introduce preferences between alternatives. This characterization of meaning focuses on what function a sentence serves in discourse, rather than what that sentence refers to (e.g., a state of the world). By representing the meaning of imperatives, connectives and declaratives in a common dynamic format, the challenges posed for non-representationalism are met. BibTeX info
Optical Phase Transitions in Photonic Networks: a Spin-System Formulation

Nonlinear optical networks can exhibit phase transitions, settling into optical states similar to ferromagnets or spin glasses, thus establishing a new class of spin models beyond those found in statistical mechanics.
There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks

Artificial Intelligence (AI) plays a fundamental role in the modern world, especially when used as an autonomous decision maker. One common concern nowadays is ``how trustworthy the AIs are''. Human operators follow a strict educational curriculum and performance assessment that could be exploited to quantify how much we entrust them. To quantify the trust of AI decision makers, we must go beyond task accuracy especially when facing limited, incomplete, misleading, controversial or noisy datasets. Towards addressing these challenges, we describe DeepTrust, a Subjective Logic (SL) inspired framework that constructs a probabilistic logic description of an AI algorithm and takes into account the trustworthiness of both dataset and inner algorithmic workings. DeepTrust identifies proper multi-layered neural network (NN) topologies that have high projected trust probabilities, even when trained with untrusted data. We show that uncertain opinion of data is not always malicious while evaluating NN's opinion and trustworthiness, whereas the disbelief opinion hurts trust the most. Also trust probability does not necessarily correlate with accuracy. DeepTrust also provides a projected trust probability of NN's prediction, which is useful when the NN generates an over-confident output under problematic datasets. These findings open new analytical avenues for designing and improving the NN topology by optimizing opinion and trustworthiness, along with accuracy, in a multi-objective optimization formulation, subject to space and time constraints.
Automating Configuration of Convolutional Neural Network Hyperparameters Using Genetic Algorithm

In recent years, Convolutional Neural Networks (CNN) have been widely used for real-world applications in the field of computer vision. Their class-leading performance, however, depends heavily on the architecture used for a given problem. In most cases, the architectures are manually optimized by the researchers, a time-consuming process hard to achieve without prior knowledge of CNN. In this paper, we propose a new genetic algorithm for the optimization of the CNN architecture for a given image classification problem. This algorithm extends and refines existing research in the field, by allowing depth exploration, introducing a novel sequential crossover operator, using an incremental selective pressure schedule over evolution (favoring higher diversity in early generations) and by evaluating individual performances over the validation set with early stopping. The technique is validated in three image classification dataset, namely, CIFAR10, MNIST and Caltech256 datasets, which are widely used benchmarks for image classification algorithms. We evaluate the performance and total execution time over these datasets, and compare our results with those achieved by the best genetic methods published so far. In all cases, we achieve better results in terms of test accuracy, consistently over different datasets, while remaining in the same orders of magnitude of execution time of the fastest approaches.
Rethinking Graph Regularization for Graph Neural Networks

The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model f(X). However, with the recent popularity of graph neural networks (GNNs), directly encoding graph structure A into a model, i.e., f(A, X), has become the more common approach. While we show that graph Laplacian regularization brings little-to-no benefit to existing GNNs, and propose a simple but non-trivial variant of graph Laplacian regularization, called Propagation-regularization (P-reg), to boost the performance of existing GNN models. We provide formal analyses to show that P-reg not only infuses extra information (that is not captured by the traditional graph Laplacian regularization) into GNNs, but also has the capacity equivalent to an infinite-depth graph convolutional network. We demonstrate that P-reg can effectively boost the performance of existing GNN models on both node-level and graph-level tasks across many different datasets.