Reviewerly/exHarmony huggingface dataset:
A new IoT geosensor network for cost-effective landslide early warning systems

&amp;lt;p&amp;gt;Worldwide, cities in mountainous areas struggle with increasing landslide risk as consequence of global warming and population growth, especially in low-income informal settlements. For these situations, current monitoring systems are often too expensive and too difficult to maintain. Therefore, innovative monitoring systems are needed in order to facilitate low-cost landslide early warning systems (LEWS) which can be applied easily.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Based on technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication standard, we are currently developing a cost-effective IoT (Internet of Things) geosensor network. It is specifically designed for local scale LEWS in informal settlements.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The system, which is open source and can be replicated without restrictions, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g. tilt sensor) on board and to which various additional sensors can be connected. The nodes are autonomous and can operate on standard batteries or solar panels. The sensor nodes can be installed on critical infrastructure such as house walls or foundations. Two of the possible additions are the Subsurface Sensor Node and the Low-Cost Inclinometer. Both are installed underground and offer tilt- and groundwater-measurements of the subsurface.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed monitoring system offers a great cost to benefit ratio and easy application for similar sites and LEWS, especially in urbanized areas in developing countries.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work is being developed as part of the project Inform@Risk, where the monitoring system will be installed as part of an early warning system in Medell&amp;amp;#237;n, Colombia. It is funded by the German Ministry of Education and Research (BMBF).&amp;lt;/p&amp;gt;
Ensemble Model to Predict Credit Card Fraud Detection Using Random Forest and Generative Adversarial Networks

Recently, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. The detection of credit card fraud detection will be considered as a classification problem, and it is evaluated using various state-of-the-art algorithms, i.e., random forest, naive Bayes (NB), and generative adversarial networks. This paper improves the accuracy of the credit card fraud detection rate further by designing an ensemble model by considering the random forest and generative adversarial networks. Extensive experiments show that the proposed technique outperforms the existing techniques.
Bangla Sign Language Recognition and Sentence Building Using Deep Learning

Modern age being the era of Information technology, it would not have come this far without the piled up data or information. Whereas communication is the basis of collecting or gathering data or information, almost 5% of the world's population is not blessed with the ability of verbal communication [1]. Sign language varies from the verbal language in every form and rule. This creates a gap between people conversing in verbal language and those communicating in sign language. Verbal languages are easy to interpret for having a common rule-following but sign language differs from region to region. This hampers the communication between normal people and those interacting in sign languages. Human to human interpretation is tough because of the enriched word wise signs and vocabs. To eradicate this issue, we are proposing a machine-based approach for training and detecting the Bangla Sign Language. Our aim is to create a multi modal system to for recognising Bangla signs. In addition, we hope to train the system with enough samples containing different signs used in Bangla Sign Language. In this research, we are using the Convolutional Neural Network (CNN) for training each individual sign. In addition to working as a medium of communication between the deaf and mute with the remaining society, this approach would also serve as a tool for the hearing deprived to learn and use the sign language properly.
Automatic structured variational inference

Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we introduce automatic structured variational inference (ASVI), a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These pseudo-conjugate families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Pseudo-conjugate families have the same space and time complexity of the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables. We validate our automatic variational method on a wide range of both low- and high-dimensional inference problems. We find that ASVI provides a clear improvement in performance when compared with other popular approaches such as mean field family and inverse autoregressive flows. We provide a fully automatic open source implementation of ASVI in TensorFlow Probability.
Predictive Learning on Hidden Tree-Structured Ising Models

We provide high-probability sample complexity guarantees for exact structure recovery and accurate predictive learning using noise-corrupted samples from an acyclic (tree-shaped) graphical model. The hidden variables follow a tree-structured Ising model distribution, whereas the observable variables are generated by a binary symmetric channel taking the hidden variables as its input (flipping each bit independently with some constant probability $q\in [0,1/2)$). In the absence of noise, predictive learning on Ising models was recently studied by Bresler and Karzand (2020); this paper quantifies how noise in the hidden model impacts the tasks of structure recovery and marginal distribution estimation by proving upper and lower bounds on the sample complexity. Our results generalize state-of-the-art bounds reported in prior work, and they exactly recover the noiseless case ($q=0$). In fact, for any tree with $p$ vertices and probability of incorrect recovery $\delta>0$, the sufficient number of samples remains logarithmic as in the noiseless case, i.e., $\mathcal{O}(\log(p/\delta))$, while the dependence on $q$ is $\mathcal{O}\big( 1/(1-2q)^{4} \big)$, for both aforementioned tasks. We also present a new equivalent of Isserlis' Theorem for sign-valued tree-structured distributions, yielding a new low-complexity algorithm for higher-order moment estimation.
Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching

Machine Learning techniques have been employed in virtually all domains in the past few years. New applications demand the ability to cope with dynamic environments like data streams with transient behavior. Such environments present new requirements like incrementally process incoming data instances in a single pass, under both memory and time constraints. Furthermore, prediction models often need to adapt to concept drifts observed in non-stationary data streams. Ensemble learning comprises a class of stream mining algorithms that achieved remarkable prediction performance in this scenario. Implemented as a set of (several) individual component classifiers whose predictions are combined to predict new incoming instances, ensembles are naturally amendable for task parallelism. Despite its relevance, an efficient implementation of ensemble algorithms is still challenging. For example, dynamic data structures used to model non-stationary data behavior and detect concept drifts cause inefficient memory usage patterns and poor cache memory performance in multi-core environments. In this paper, we propose a minibatching strategy which can significantly reduce cache misses and improve the performance of several ensemble algorithms for stream mining in multi-core environments. We assess our strategy on four different state-of-art ensemble algorithms applying four widely used machine learning benchmark datasets with varied characteristics. Results from two different hardware show speedups of up to 5X on 8-core processors with ensembles of 100 and 150 learners. The benefits come at the cost of changes in predictive performances.
Doctor Recommendation Model for Pre-Diagnosis Online in China: Integrating Ontology Characteristics and Disease Text Mining

Objective: The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients' information of symptoms, diagnosis and geographical location, as well as doctor's specialty and their department. Methods: Methods: Utilizing crawler technique, five hospital departments were selected from the online medical service platform. a dataset consisting of 20000 consultation questions by patients were built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients' pre-diagnosis and doctors' specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement. Results: In the online medical field, compared with traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. Conclusions: The proposed online pre-diagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice. Furthermore, the model also gives full consideration on patients” location factors. As a result, the proposed online pre-diagnosis doctor recommendation model would improve patients' online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.
The difference of emotional arousal between traditional 2D and VR movies: A Comparative Study Based on EEG Signals

This paper explores the impact of different emotional films on the audience in the three-dimensional virtual x,2107,(1'R-3D) environment and the traditional desktop 2D display environment. We collected and analyzed the EEG signals of 40 subjects when they watched positive and negative films in two environments. The results showed that there were significant differences in z and ß hand activities between VR and desktop mode, which were reflected in frontal, central, temporal and occipital regions, at the same time, the viewer's frontal and central region can distinguish emotional valence. These results show that VR movies can trigger stronger cortical activity and bring greater emotional arousal to the audience.
Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN), to perform classification tasks. There have been many proposals on how to use variational quantum circuits as quantum perceptrons or as QNNs. The aim of this work is to introduce a teacher-student scheme that could systematically compare any QNN architectures and evaluate their relative expressive power. Specifically, the teacher model generates the datasets mapping random inputs to outputs which then have to be learned by the student models. This way, we avoid training on arbitrary data sets and allow to compare the learning capacity of different models directly via the loss, the prediction map, the accuracy and the relative entropy between the prediction maps. Here, we focus particularly on a quantum perceptron model inspired by the recent work of Tacchino et al. (2019) and compare it to the data re-uploading scheme that was originally introduced by Pérez-Salinas et al. (2020). We discuss alterations of the perceptron model and the formation of deep QNN to better understand the role of hidden units and the non-linearities in these architectures.
Virtual Control Interface: Discover and Control IoT Devices Intuitively Through AR Glasses with Multi-model Interactions

The number of smart home devices will increase exponentially. The current Internet of Things (IoT) control interfaces on smartphones are spatially separated from the devices they operate, making them less intuitive and progressively more complicated. We developed VCI, a Virtual Reality (VR) simulation for HCI researchers to explore multimodal interactions with IoT in a future smart home setting using virtual control interfaces projected on emulated AR glasses.
Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end multi-scale residual pyramid attention network (MRPAN) to mitigate these problems. First, we propose a multi-scale attention context aggregation (MACA) module, which consists of spatial attention module (SAM) and global attention module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover complex structures in the scene. Then we proposed an improved residual refinement module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promising performance in object boundaries and local details compared with other state-of-the-art methods.
Forecast of photovoltaic generated power based on WOA-LSTM

With the injection of a large amount of electricity from photovoltaic (PV) panels, there is an increasing impact on power girds. In order to lessen the uncertainties of photovoltaic output power, a Long-Short Term Memory (LSTM) Neural Networks, based on Whale Optimization Algorithm (WOA), is proposed to predict the photovoltaic generated power. First of all, the data is pre-processed to analyze gray relativity, which helps reduce the dimensionality of variables. Then the similar day samples are given by selected input variables and Gray Relativity Analysis. Moreover, the global optimization is improved, uncertainties reduced, for WOA is used to optimize network levels and learning rate of LSTM Neural Networks. Finally, optimized LSTM Neural Networks are used to predict PV output power compared to selected samples on similar days. The prediction results prove the model efficient.
Symmetrical Cognition Between Physical Humans and Virtual Agents

Cognition is a crucial problem that affects human performance and subjective experiences in the given environment. However, the cognition problem will be different if we consider it in a symmetrical reality environment. Though researchers have studied the cognition problem for many years, there are still many different points within a symmetrical reality environment. In this paper, we discuss the cognition problem from some perspectives, i.e., attention, perception, pattern recognition, and communication. We show how the symmetrical reality system can change the traditional attention mechanism, the perception process, the recognition and the communication between different agents. Finally, we introduce the application of symmetrical cognition to expand the current cognition research and give some useful suggestions for studying the cognition problem.
Fostering Equitable Help-Seeking for K-3 Students in Low Income and Rural Contexts

Adaptive Collaborative Learning Support (ACLS) systems improve collaboration and learning for students over individual work or collaboration with non-adaptive support. However, many ACLS systems are ill-suited for rural contexts where students often need multiple kinds of support to complete tasks, may speak languages unsupported by the system, and require more than pre-assigned tutor-tutee student pairs for more equitable learning. We designed an intervention that fosters more equitable help-seeking by automatically detecting student struggles and prompts them to seek help from specific peers that can help. We conducted a mixed-methods experimental study with 98 K-3 students in a rural village in Tanzania over a one-month period, evaluating how the system affects student interactions, system engagement, and student learning. Our intervention increased student interactions by almost 4 times compared to the control condition, increased domain knowledge interactions, and propelled students to engage in more cognitively challenging activities.
Attack Agnostic Adversarial Defense via Visual Imperceptible Bound

The high susceptibility of deep learning algorithms against structured and unstructured perturbations has motivated the development of efficient adversarial defense algorithms. However, the lack of generalizability of existing defense algorithms and the high variability in the performance of the attack algorithms for different databases raises several questions on the effectiveness of the defense algorithms. In this research, we aim to design a defense model that is robust within a certain bound against both seen and unseen adversarial attacks. This bound is related to the visual appearance of an image, and we termed it as Visual Imperceptible Bound (VIB). To compute this bound, we propose a novel method that uses the database characteristics. The VIB is further used to measure the effectiveness of attack algorithms. The performance of the proposed defense model is evaluated on the MNIST, CIFAR-10, and Tiny ImageNet databases on multiple attacks that include C&W ( l2) and DeepFool. The proposed defense model is not only able to increase the robustness against several attacks but also retain or improve the classification accuracy on an original clean test set. The proposed algorithm is attack agnostic, i.e. it does not require any knowledge of the attack algorithm.
Pengaruh Mutu Pembelajaran Online Dan Tingkat Kepuasan Mahasiswa Terhadap Hasil Belajar Saat Pandemi COVID-19

The application of physical distancing policies to reduce the spread of the COVID19 virus changed learning activities from face-to-face models to online learning models. Online lectures, even if they are held online, must pay attention to the quality of learning so that learning outcomes are met. This research was conducted to find out (1) the effect of online learning quality on learning outcomes, (2) the effect of student satisfaction levels on learning outcomes, (3) the effect of online learning quality and student satisfaction levels on learning outcomes during pandemic COVID19. The respondents of this study were 34 Vocational Learning Media students. Data collection using questionnaires and documentation methods. The data analysis technique used is a simple regression analysis technique for the first and second hypotheses and multiple regression analysis for the third hypothesis. The results showed there was an influence of the quality of online learning on learning outcomes with t arithmetic higher than the t table (3.024> 1.65) or p (0.01 1.65) or p (0.01 <0.05). Based on the results of the analysis showed that the coefficient of determination X1 and X2 to Y amounted to 0.155. The results of the analysis show that the variables of the Online Learning Quality and Satisfaction Level have a contributing effect on Learning Outcomes.
Ideal Words

Abstract In this theoretical paper, we consider the notion of semantic competence and its relation to general language understanding—one of the most sough-after goals of Artificial Intelligence. We come back to three main accounts of competence involving (a) lexical knowledge; (b) truth-theoretic reference; and (c) causal chains in language use. We argue that all three are needed to reach a notion of meaning in artificial agents and suggest that they can be combined in a single formalisation, where competence develops from exposure to observable performance data. We introduce a theoretical framework which translates set theory into vector-space semantics by applying distributional techniques to a corpus of utterances associated with truth values. The resulting meaning space naturally satisfies the requirements of a causal theory of competence, but it can also be regarded as some ‘ideal’ model of the world, allowing for extensions and standard lexical relations to be retrieved.
A New Approach in Query Expansion Methods for Improving Information Retrieval

This research develops a new approach to query expansion by integrating Association Rules (AR) and Ontology. In the proposed approach, there are several steps to expand the query, namely (1) the document retrieval step; (2) the step of query expansion using AR; (3) the step of query expansion using Ontology. In the initial step, the system retrieved the top documents via the user's initial query. Next is the initial processing step (stopword removal, POS Tagging, TF-IDF). Then do a Frequent Itemset (FI) search from the list of terms generated from the previous step using FP-Growth. The association rules search by using the results of FI. The output from the AR step expanded using Ontology. The results of the expansion with Ontology use as new queries. The dataset used is a collection of learning documents. Ten queries used for the testing, the test results are measured by three measuring devices, namely recall, precision, and f-measure. Based on testing and analysis results, integrating AR and Ontology can increase the relevance of documents with the value of recall, precision, and f-measure by 87.28, 79.07, and 82.85.