Reviewerly/exHarmony huggingface dataset:
Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

The rapid development of technologies such as tablet PCs and 4G/5G networks has further enhanced the benefits of mobile learning. Although mobile devices are convenient and provide a variety of learning benefits, they are unable to improve students’ learning outcomes without an appropriate learning strategy. Furthermore, little research has been conducted to examine the effects of using collaborative learning on mobile devices. This study proposed a cooperative learning framework using Google Docs to explore the learning outcomes of students of natural science in an elementary curriculum. The study was of a quasi-experimental design with an experimental group (cooperative learning) and a control group (personal learning). The results show that a cooperative learning approach using Google Docs improved learning outcomes, teaching interest, and understanding of campus plants, and reduced cognitive load. One conclusion of the study is that the collaborative learning approach associated with mobile learning is more effective than personal learning. In addition, this paper also provides brief recommendations to expand on the study’s limitations. Future work should investigate the impact of collaborative learning on different environments for mobile learning.
Representation Learning from Limited Educational Data with Crowdsourced Labels

Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation. The majority of existing representation learning approaches often require a large number of consistent and noise-free labels. However, due to various reasons such as budget constraints and privacy concerns, labels are very limited in many real-world scenarios. Directly applying standard representation learning approaches on small labeled data sets will easily run into over-fitting problems and lead to sub-optimal solutions. Even worse, in some domains such as education, the limited labels are usually annotated by multiple workers with diverse expertise, which yields noises and inconsistency in such crowdsourcing settings. In this paper, we propose a novel framework which aims to learn effective representations from limited data with crowdsourced labels. Specifically, we design a grouping based deep neural network to learn embeddings from a limited number of training samples and present a Bayesian confidence estimator to capture the inconsistency among crowdsourced labels. Furthermore, to expedite the training process, we develop a hard example selection procedure to adaptively pick up training examples that are misclassified by the model. Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines. In addition, we provide a comprehensive analysis on each of the main components of our proposed framework and also introduce the promising results it achieved in our real production to fully understand the proposed framework. To encourage reproducible results, we make our code available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tal-ai/RECLE</uri> .
On the Information Dimension of Stochastic Processes

The authors have recently defined the R\'enyi information dimension rate $d(\{X_t\})$ of a stationary stochastic process $\{X_t,\,t\in\mathbb{Z}\}$ as the entropy rate of the uniformly-quantized process divided by minus the logarithm of the quantizer step size $1/m$ in the limit as $m\to\infty$ (B. Geiger and T. Koch, "On the information dimension rate of stochastic processes," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Aachen, Germany, June 2017). For Gaussian processes with a given spectral distribution function $F_X$, they showed that the information dimension rate equals the Lebesgue measure of the set of harmonics where the derivative of $F_X$ is positive. This paper extends this result to multivariate Gaussian processes with a given matrix-valued spectral distribution function $F_{\mathbf{X}}$. It is demonstrated that the information dimension rate equals the average rank of the derivative of $F_{\mathbf{X}}$. As a side result, it is shown that the scale and translation invariance of information dimension carries over from random variables to stochastic processes.
Reconfigurable multiphoton entangled states based on quantum photonic chips

Multipartite entanglement is one of the most prominent features of quantum mechanics and is the key ingredient in quantum information processing. Seeking for an advantageous way to generate it is of great value. Here we propose two different schemes to prepare multiphoton entangled states on a quantum photonic chip that are both based on the theory of entanglement on the graph. The first scheme is to construct graphs for multiphoton states by the network of spatially anti-bunching two-photon sources. The second one is to construct graphs by the linear beam-splitter network, which can generate W and Dicke states efficiently with simple structure. Both schemes can be scaled up in the photon number and can be reconfigured for different types of multiphoton states. This study supplies a systematic solution for the on-chip generation of multiphoton entangled states and will promote the practical development of multiphoton quantum technologies.
Understanding the task demands for powered wheelchair driving: a think-aloud task analysis

Background Powered wheelchair use promotes participation in individuals with limited mobility, however training is required for safe and effective use. There is limited evidence on the task demands of powered wheelchair use to inform an evidence-based skills training programme.Objective To conduct a systematic exploration of the task demands of indoor powered wheelchair use to identify frequently used skills, abilities, and knowledge.Methods We used a two-phased think aloud process to conduct a task analysis of powered wheelchair use with experienced powered wheelchair users (n = 5) and expert clinicians (n = 5). Participants completed seven indoor driving tasks while speaking aloud (concurrent think aloud) and subsequently engaged in a structured qualitative interview to discuss skills, abilities, and knowledge used across each of the seven tasks (retrospective think aloud). We used directed content analysis to map the skills and abilities to the ICF framework and conventional content analysis to develop thematic areas of knowledge used while operating a powered wheelchair.Results One-hundred and ten (110) distinct skills and abilities were identified and mapped to the ICF; 80 in the Body Structures and Functions domain, and 30 in the Activities and Participation domain. Approximately 50% of skills and abilities were mental functions. Four thematic knowledge domains were identified: knowledge of self, environment, wheelchair, and task.Conclusion Powered wheelchair use is complex and requires a variety of skills and abilities from all areas of human functioning, in addition to a wide range of knowledge. Training programmes should address a range of areas of skill development.IMPLICATIONS FOR REHABILITATIONPowered wheelchair use is a complex skill; training should develop skills from all.Domains of the ICF, including mental and physical functions.A range of knowledge is used while operating a powered wheelchair; training programs.Should include the development and application of necessary knowledge.Clinicians may consider a range of factors when assessing suitability for powered.Wheelchair user, however should acknowledge that while the range of skills idenotified.May be useful, they may not be critical for success in powered wheelchair use.
An Exploratory Analysis of the Characteristics of Ideologically Motivated Cyberattacks

Web defacement is a form of hacking that involves altering the content of a website, resulting in repairs to the website code, loss of revenue, internal loss of productivity, and reputational damage. Limited research has examined the frequency of web defacements, the factors that distinguish them from other hacking motives, and the extent to which the correlates mirror research on physical acts of ideologically-motivated crime. The current study examined over 2.4 million web defacements hosted in the U.S. from 2012 to 2016 to assess aspects of routine activities theory associated with target selection and attack methods among ideologically-motivated defacements. A binary logistic regression analysis revealed that ideologically-motivated defacers were more likely to use unknown vulnerabilities; engage in repeated attacks; target top-level domains linked to foreign nations; domains ending in.edu; and homepages within websites. The findings of this study suggest that the target selection process of ideologically-motivated defacers are more purposive and designed to draw attention to their cause, resembling target preferences of individuals who engage in physical violence in support of an ideological agenda.
Kalman filtering with censored measurements

This paper concerns Kalman filtering when the measurements of the process are censored. The censored measurements are addressed by the Tobit model of Type I and are one-dimensional with two censoring limits, while the (hidden) state vectors are multidimensional. For this model, Bayesian estimates for the state vectors are provided through a recursive algorithm of Kalman filtering type. Experiments are presented to illustrate the effectiveness and applicability of the algorithm. The experiments show that the proposed method outperforms other filtering methodologies in minimizing the computational cost as well as the overall Root Mean Square Error (RMSE) for synthetic and real data sets.
Research on electronic jamming identification based on CNN

In this paper, we apply the idea of deep learning to radar interference recognition. Firstly we introduced history and concept of Convolutional Neural Network, and summarized the method of interference recognition. Secondly, the structure of improved LeNet CNN is described, considering the character of radar echo wave. Thirdly, 7 kinds of jamming are introduced. Fourthly, several important parameters of net structure such as kernel size and batch size are adjusted to achieve best performance, through measured interference radar echo. The accuracy rate reaches up to 99.7%. Finally, we summarize advantages of the method proposed in this paper.
Quantifying Susceptibility to Spear Phishing in a High School Environment Using Signal Detection Theory

Spear phishing is a deceptive attack that uses social engineering to obtain confidential information through targeted victimization. It is distinguished by its use of social cues and personalized information to target specific victims. Previous work on resilience to spear phishing has focused on convenience samples, with a disproportionate focus on students. In contrast, here, we report on an evaluation of a high school community. We engaged 57 high school students and faculty members (12 high school students, 45 staff members) as participants in research utilizing signal detection theory (SDT). Through scenario-based analysis, participants tasked with distinguishing phishing emails from authentic emails. The results revealed an overconfidence bias in self-detection from the participants, regardless of their technical background. These findings are critical for evaluating the decision-making of underrepresented populations and protecting people from potential spear phishing attacks by examining human susceptibility.
Using lexical language models to detect borrowings in monolingual wordlists

Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational approaches in comparative linguistics, automated approaches to lexical borrowing detection are still in their infancy, disregarding many aspects of the evidence that is routinely considered by human experts. One example for this kind of evidence are phonological and phonotactic clues that are especially useful for the detection of recent borrowings that have not yet been adapted to the structure of their recipient languages. In this study, we test how these clues can be exploited in automated frameworks for borrowing detection. By modeling phonology and phonotactics with the support of Support Vector Machines, Markov models, and recurrent neural networks, we propose a framework for the supervised detection of borrowings in mono-lingual wordlists. Based on a substantially revised dataset in which lexical borrowings have been thoroughly annotated for 41 different languages from different families, featuring a large typological diversity, we use these models to conduct a series of experiments to investigate their performance in mono-lingual borrowing detection. While the general results appear largely unsatisfying at a first glance, further tests show that the performance of our models improves with increasing amounts of attested borrowings and in those cases where most borrowings were introduced by one donor language alone. Our results show that phonological and phonotactic clues derived from monolingual language data alone are often not sufficient to detect borrowings when using them in isolation. Based on our detailed findings, however, we express hope that they could prove to be useful in integrated approaches that take multi-lingual information into account.
Manufacturing rage: The Russian Internet Research Agency’s political astroturfing on social media

This paper examines social media ads purchased by the Russian Internet Research Agency (IRA). Using a public dataset, we employ a mixed method to analyze the thematic and strategic patterns of these ads. Due to Facebook and Instagram’s promotional features, IRA managed to microtarget audiences mostly located in the United States fitting its messages to suit the audiences’ political, racial, gendered, and in some cases religious backgrounds. The findings reveal the divisive nature and topics that are dominant in the dataset including: race, immigration, and police brutality. By expanding on the theoretical conceptualization of astroturfing strategy that focuses on carefully concealing the identity and intention of actors behind social media activities, we argue that IRA has added political astroturfing as a powerful tool at a low cost contributing to the broader Russian geopolitical disinformation campaign strategies. The IRA made use of the business model of Facebook and Instagram in an attempt to further divide its targeted audiences and by highlighting mostly negative issues with a potential goal of fuelling political rage.
Research on the construction of the semantic model for Chinese ancient architectures based on architectural narratives

Purpose This paper aims to reorganize the relevant information of Chinese ancient architectures with the use of Semantic Web technologies and thus facilitate its deep discovery and usage. Design/methodology/approach This paper proposes an ontology model for Chinese ancient architectures based on architectural narratives theory. To verify the availability of the ancient architecture ontology, we designed and implemented three experiments, including semantic retrieval based on SPARQL query, semantic reasoning with the use of Jena reasoner and visual analysis based on the Chinese Online Digital Humanities Resources Platform. Findings The proposed ontology provided a solution for the semantic annotation of the unstructured information of Chinese ancient architectures. On this basis, deep knowledge services such as semantic retrieval, semantic reasoning and visual analysis can be provided. Practical implications The proposed semantic model of ancient architectures can effectively improve the organization and access quality of the semantic content of Chinese ancient architectures. Originality/value This paper focuses on the semantic modelling for the unstructured information of Chinese ancient architectures to semantically describe the related entities (e.g. persons, events, places and times) and uncover their relationships, and thus it made contribution to the deep semantic annotations on ancient architectures.
New Correlations for Determination of Optimum Slope Angle of Solar Collectors

The energy coming from solar radiation could be harvested and transformed into electricity through the use of solar-thermal power generation and photovoltaic (PV) power generation.Placement of solar collectors (thermal and photovoltaic) affects the amount of incoming radiation and the absorption rate.In this research, new correlations for finding the monthly optimum slope angle (OSA) on flat-plate collectors are proposed.Twelve equations are developed to calculate the monthly OSA by the linear regression model, for the northern and the southern hemisphere stations from 15°to 55°and -20°to -45°, respectively.Also, a new equation for calculating the yearly tilt angle is developed and compared with several other calculation methods from the literature.Results confirm a 20% increase in solar energy absorption by adjusting the collectors' tilt angle in monthly time periods.This is while the adjusted collectors with the yearly optimum slope angle receive approximately 7% higher solar radiation compared to the horizontal collectors.Furthermore, the proposed equations outperformed the other calculation methods in the literature.
Fault Detection and Isolation Methods in Subsea Observation Networks

Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and the voltage and current values of the operating nodes under different fault types are collected. Numerous calibrated data samples are supervised by a deep learning algorithm, and a fault location system model is built in the laboratory to verify the feasibility and superiority of the scheme. This paper also studies the fault isolation of the observation network, focusing on the communication protocol and the design of the fault isolation system. Experimental results verify the effectiveness of the proposed algorithm for the location and prediction of high-impedance and open-circuit faults, and the feasibility of the fault isolation system has also been verified. Moreover, the proposed methods greatly improve the reliability of undersea observation network systems.
Million-channel parallelism Fourier-optic convolutional filter and neural network processor

Here we report on a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel. Testing the system on MNIST and CIFAR-10 datasets shows 96% and 54% accuracy, respectively.
Overcoming Barriers in Education: Research and Social Practice

The paper discusses the socio-cultural, economic, territorial, and other barriers in the educational sphere and the possibilities of overcoming them. The first part presents the results of research carried out on national empirical data from the 1960s to the present. The significant impact of barriers on the formation of educational and professional careers of young people is shown. It is noted that overcoming barriers becomes possible if the family has the resources to get over them; otherwise, children are forced to adjust their intentions by lowering their demands for education. The second part of the article analyzes the experience of several low-resource schools with students from low-status families. That schools undertake targeted efforts to help students overcome barriers. The teachers and management of these schools use special strategies for their work. Pedagogical measures are a significant part of these strategies; however, complex social conditions require a response by means of social measures. The most important is the formation of the motivation of all participants in the educational process. As a result of the school’s efforts, academic performance is improved, students’ intentions for further education are growing, their understanding of educational and professional careers, on the possibilities of social mobility are expanding. Changing orientations associated with building a life path is taking place. Examination of the experience of the social practice of these schools makes it possible to conclude: their activity manifests itself as a powerful resource that schools provides to families when they do not have the opportunity to overcome barriers. This resource can increase not only the potential of the educational organization but also the life chances of students, helping them no less effective than other types of resources are able to do it.
Human Activity Recognition Using Gaussian Naïve Bayes Algorithm in Smart Home

Abstract Human activity recognition (HAR), as an important research issue, aims to identify human activities in smart homes. In this paper, we apply Gaussian Naive Bayes (GNB) algorithm to HAR and evaluate the model based on smart environment sensor data. Experimental results show that the effective selection and processing of features are helpful to improve the accuracy of activity recognition of the model. Compared with NB whose accuracy rate is 82.7%, GNB has a better accuracy rate of 89.5% and even has a higher recognition accuracy in almost every category of activities. Selecting the feature variables as good and useful as possible to get a better model in the process of activity recognition is conducive to the correct classification of samples by machine learning algorithm and improves the classification performance of the model.
Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach

The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8+ T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and automatic fashion. Based on immunofluorescence (IF) images of CD8+ T lymphocytes and cancer cells, we develop a machine-learning approach which can predict the risk of relapse for patients with Triple Negative Breast Cancer (TNBC). Tumor-section images from 9 patients with poor outcome and 15 patients with good outcome were used as a training set. Tumor-section images of 29 patients in an independent cohort were used to test the predictive power of our algorithm. In the test cohort, 6 (out of 29) patients who belong to the poor-outcome group were all correctly identified by our algorithm; for the 23 (out of 29) patients who belong to the good-outcome group, 17 were correctly predicted with some evidence that improvement is possible if other measures, such as the grade of tumors, are factored in. Our approach does not involve arbitrarily defined metrics and can be applied to other types of cancer in which the abundance/location of CD8+ T lymphocytes/other types of cells is an indicator of prognosis.