Reviewerly/exHarmony huggingface dataset:
Learning Cross-Lingual Mappings in Imperfectly Isomorphic Embedding Spaces

One mainstream method in cross-lingual word embeddings is to learn a linear mapping between two monolingual embedding spaces using a training dictionary. Successful linear mappings require isomorphic embedding spaces. However, monolingual embedding spaces are not perfectly isomorphic, and therefore, a linear mapping cannot align them accurately. In this study, we assume that two embedding spaces are composed of near-isomorphic translation pairs (NearITP) and non-isomorphic translation pairs. Owing to the nature of similar substructures, NearITP can make linear mapping work well. Motivated by this, we design a screening strategy to identify NearITP effectively. Based on this strategy, we find that the proportion of NearITP in the commonly used training dictionary is relatively low, leading to sub-optimal results. To address this problem, we propose a general framework that can be combined with any of the mapping methods, which further boosts subsequent mapping. Experimental results demonstrate that our framework is an improvement over existing mapping-based methods, and outperforms state-of-the-art models on two public data sets. Moreover, we show that our framework can be successfully generalized to contextual word embeddings such as multilingual BERT (mBERT), and further enhances the cross-lingual properties of mBERT.
Of pumpkin spice lattes and hamplanets

Abstract The present study establishes what comprises the genre of venting as opposed to ranting and how venting functions within the Reddit community Fat People Stories. In this subreddit, users compose either real or fictional narratives describing encounters with people of size who are perceived to exhibit rude behavior. Given the sensitive nature of these discussions, they are often fraught with negative emotional expression, and individuals seek a variety of outlets to release these emotions and find commiseration and diversion. An analytical corpus of 45,867 words across ten posts and corresponding comment sections was gathered and analyzed using Askehave and Nielsen’s (2005) two-dimensional genre model and supplemented with discourse analysis of the individual texts. As the study explores how venting unfolds in a digitally mediated environment, insights gained can have implications for how humans sort through negative emotions in face-to-face communication, and, more specifically, for discourse surrounding public health and body weight.
Transfer Learning Based Evolutionary Algorithm for Bi-level Optimization Problems

Evolutionary algorithms for bi-level optimization suffer from the low efficiency in dealing with the multiple lower level optimization tasks required by the population based upper level search. In this paper, we design a transfer learning based covariance matrix adaptation evolution strategy (TL-CMA-ES) for bi-level optimization problems, where the tasks of searching for multiple lower level optimal solutions are conducted by a set of CMA-ES optimizers in a parallel manner. Furthermore, a transfer learning strategy is introduced in the parallel lower level CMA-ES search such that each CMA-ES optimizer can learn and utilize useful features gained by its neighbours. Experimental comparison and analysis are carried out to verify the effectiveness and efficiency of the proposed method.
Developing 21st-Century Musicianship: Tablet-based Music Production in the General Music Classroom

Increased availability of tablets at home and in classrooms provides educators access to a powerful tool for music instruction. Music production lessons on tablets offer alternate approaches to developing music literacies while teaching valuable technology skills. These activities are ideal for general music education because they align with contemporary music practices and are adaptable to a variety of learning environments (in person, remote, and hybrid). This article will present a model for tablet-based music production instruction in the general music classroom that aligns with the National Core Arts Standards and accompanying process components grounded in five essential skills: sequencing, recording, editing, effects processing, and mixing.
An Augmented Reality Greenhouse to Demonstrate Game-Based Learning

Augmented Reality is an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by information generated by a computing device. We propose a mobile-based AR application that provides an immersive simulation of a portable plant nurturing experience. The application utilizes the Unity AR Foundation framework to augment a portal to their own virtual garden. The user is able to explore their garden, build terraria, nurture and maintain various plants, thus providing an end-to-end simulation. This is a novel approach in experimental learning. We explore and evaluate concepts of information absorption, immersion, user satisfaction and experience emotions by conducting a comparative study between traditional E-learning methodology and E-learning using AR.
Visualization as a stimulus domain for vision science

Traditionally, vision science and information/data visualization have interacted by using knowledge of human vision to help design effective displays. It is argued here, however, that this interaction can also go in the opposite direction: the investigation of successful visualizations can lead to the discovery of interesting new issues and phenomena in visual perception. Various studies are reviewed showing how this has been done for two areas of visualization, namely, graphical representations and interaction, which lend themselves to work on visual processing and the control of visual operations, respectively. The results of these studies have provided new insights into aspects of vision such as grouping, attentional selection and the sequencing of visual operations. More generally yet, such results support the view that the perception of visualizations can be a useful domain for exploring the nature of visual cognition, inspiring new kinds of questions as well as casting new light on the limits to which information can be conveyed visually.
Investigating the Factors that Influence Technology Acceptance of an Educational Game Integrating Mixed Reality and Concept Maps

With the rapid development of mobile learning technologies as well as relevant hardware and software platforms, there is a bright prospect for mixed reality (MR) applying in the field of education. However, appropriate knowledge navigation and concept arrangement methods are urgent to diminish students' cognitive load in the virtual learning environment. In this preliminary study, concept maps were selected as scaffolding tools to help students navigate through the MR learning space, and an educational game prototype named MMRCM integrating MR and concept maps was developed to investigate students' technology acceptance about it. Considering the novelty of this new type of MR application, an extended version of the Technology Acceptance Model (TAM) was constructed with the MR game design elements and the concept map usefulness as external factors. A middle school physics experiment using MMRCM was conducted to help students learn the abstract concepts of friction. The evaluation results showed that the model's external factors have significant correlations with both perceived ease of use and perceived usefulness. The findings indicated explicit intention to use MMRCM, which implied that MR gaming and concept maps could be integrated as an effective instructional tool in science education.
Lepton-Antineutrino Entanglement and Chiral Oscillations

Dirac bispinors belong to an irreducible representation of the complete Lorentz group, which includes parity as a symmetry yielding two intrinsic discrete degrees of freedom: chirality and spin. For massive particles, chirality is not dynamically conserved, which leads to chiral oscillations. In this contribution, we describe the effects of this intrinsic structure of Dirac bispinors on the quantum entanglement encoded in a lepton-antineutrino pair. We consider that the pair is generated through weak interactions, which are intrinsically chiral, such that in the initial state the lepton and the antineutrino have definite chirality but their spins are entangled. We show that chiral oscillations induce spin entanglement oscillations and redistribute the spin entanglement to chirality-spin correlations. Such a phenomenon is prominent if the momentum of the lepton is comparable with or smaller than its mass. We further show that a Bell-like spin observable exhibits the same behavior of the spin entanglement. Such correlations do not require the knowledge of the full density matrix. Our results show novel effects of the intrinsic bispinor structure and can be used as a basis for designing experiments to probe chiral oscillations via spin correlation measurements.
Generating and Measuring Similar Sentences Using Long Short-Term Memory and Generative Adversarial Networks

The two problems of measuring the semantic similarity (MSS) between two sentences and generating a similar sentence (GSS) for a given one are particularly challenging. Since these two problems naturally have logical connections, this article proposes algorithms to deal with them together. The main contributions of this article are in four aspects. 1) We propose a new algorithm called the syntactic and semantic long short-term memory (SSLSTM) for computing sentence similarity. The sentence model used by SSLSTM computes a representation vector of a given sentence by merging the results of separately running two LSTM networks, one with the given sentence and the other with a related sentence that is generated based on the semantic features of the words and syntactic features of the given sentence. The semantic similarity score of two sentences is calculated based on the distance between the two representations vectors. 2) A new GAN framework is proposed called the sentence similarity generative adversarial network (SSGAN). A GSS algorithm and an MSS algorithm are incorporated as modules in the generator and discriminator of SSGAN. A unique design of SSGAN is that, with one input triple to the GAN, the generator will produce three additional items, and the discriminator will process them. Three versions of SSGAN are proposed: the classic SSGAN (C-SSGAN), the hybrid SSGAN (H-SSGAN), and the black-box SSGAN (B-SSGAN). 3) Two new paradigms of GAN emerge from the design patterns of the black-box SSGAN and hybrid SSGAN, called the black-box GAN (B-GAN) and the hybrid GAN (H-GAN), respectively, which have the potentials to be generally applied to other NLP problems. 4) A series of experiments of different settings are designed to test the effects of B-SSGAN, and the results show that B-SSGAN has considerable boosting effects on both the chosen MSS and GSS algorithms. Several experiments are executed to compare SSLSTM with some representative and state-of-the-art MSS algorithms. The results show that SSLSTM has advantages in terms of the amount of error and the overall performance. There are new design features in these experiments. The performances of a GSS algorithm are measured by using an MSS algorithm. Multiple performance measures are considered to describe the algorithms’ performance holistically, including the efficiency of achieved performance relative to training time, which indicates that a CNN-based algorithm (SSCNN) is the most training-efficient in the comparison.
Variational Bandwidth Auto-encoder for Hybrid Recommender Systems

Hybrid recommendations have recently attracted a lot of attention where user features are utilized as auxiliary information to address the sparsity problem caused by insufficient user-item interactions. However, extracted user features generally contain rich multimodal information, and most of them are irrelevant to the recommendation purpose. In this article, we propose a variational bandwidth auto-encoder (VBAE) for recommendations, aiming to address the sparsity and noise problems simultaneously. VBAE first encodes user collaborative and feature information into Gaussian latent variables via deep neural networks to capture non-linear user similarities. Moreover, by considering the fusion of collaborative and feature variables as a virtual communication channel from an information-theoretic perspective, we introduce a user-dependent channel to dynamically control the information allowed to be accessed from the feature embeddings. A quantum-inspired uncertainty measurement of the hidden rating embeddings is proposed accordingly to infer the channel bandwidth by disentangling the uncertainty information in the ratings from the semantic information. Through this mechanism, VBAE incorporates adequate auxiliary information from user features if collaborative information is insufficient, while avoiding excessive reliance on noisy user features to improve its generalization ability to new users. Extensive experiments conducted on three datasets demonstrate the effectiveness of the proposed method.
Online Learning and Resource Allocation for User Experience Improvement in Mobile Edge Clouds

As the cloud is pushed to the edge of the network to promote the development of mobile applications, resource allocation for user experience improvement in mobile edge clouds is facing multiple challenges. In order to serve mobile users, resource allocation in mobile edge clouds needs to be adjusted continuously. Considering that the available resources in mobile edge clouds are generally limited, we propose to optimize resource allocation by minimizing the time-average complaining probability. Specifically, we advocate a feedback-based online resource allocation system, which can effectively improve the overall user experience in mobile edge clouds through online resource allocation. The system runs in a closed-loop fashion: we establish a classifier model to predict user experience, use the prediction results as the target of resource allocation model to guide the resource allocation explicitly, and then adjust the classifier model based on the updated overall dataset. The online resource allocation problem turns out to be a stochastic problem, for which we design an online queue resource allocation algorithm (OQRAA) supported by Lyapunov optimization technique. The proposed system enables us to explore the quantitative relationship between user experience and feature values and leverage this relationship to find the optimal resource allocation policy immediately upon users’ arrival. The numerical results show that the proposed system has an improvement of 200% in reducing the time-average complaining probability compared with the baseline algorithm.
Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks

Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query image against a gallery of previously-seen images. State-of-the-art techniques rely on large, computationally-intensive Deep Neural Networks (DNNs). We propose a novel hierarchical DNN architecture that uses attribute labels in the training dataset to perform efficient object reID. At each node in the hierarchy, a small DNN identifies a different attribute of the query image. The small DNN at each leaf node is specialized to re-identify a subset of the gallery-only the images with the attributes identified along the path from the root to a leaf. Thus, a query image is re-identified accurately after processing with a few small DNNs. We compare our method with state-of-the-art object reID techniques. With a $\sim 4\%$ loss in accuracy, our approach realizes significant resource savings: 74% less memory, 72% fewer operations, and 67% lower query latency, yielding 65% less energy consumption.
Analysing Cause-Specific Mortality Trends using Compositional Functional Data Analysis

Abstract We study the dynamics of cause-specific mortality rates among countries by considering them as compositions of functions. We develop a novel framework for such data structure, with particular attention to functional PCA. The application of this method to a subset of the WHO mortality database reveals the main modes of variation of cause-specific rates over years for men and women and enables us to perform clustering in the projected subspace. The results give many insights of the ongoing trends, only partially explained by past literature, that the considered countries are undergoing. We are also able to show the different evolution of cause of death undergone by men and women: for example, we can see that while lung cancer incidence is stabilizing for men, it is still increasing for women.
Embedding Sequence Model in STM32 Based Neuro-Controller

The problem of sensor data modeling, short and long term forecasting as well as the use of edge devices to operate and provide useful inferences is increasingly becoming a vital factor in modern embedded AI solutions. The paper represents the pipeline development of the sensor data processing, designing the sequence model with subsequent deployment on a STM32F407VG microcontroller. To implement the intended study, we have considered an annual smart home temperature data which allowed us to conduct numerical experiments with the developed sequence models. In order to proceed with LSTM and GRU cells, which were chosen as a basis of the main sequence models, the data preparation and transformation process was carefully described. We opted to produce multistep generalization model using the popular deep learning approaches. Since STM32 is a memory limited microcontroller and can not hold large models, it is crucial to optimize the best model configurations - hyper-parameters set and architecture. Addressing this problem, we applied and investigated the tuning routine via genetic optimization metaheuristic. The best configured and validated model has been chosen as a candidate to be deployed on STM32F407VG. The process of model conversion, serialization, deployment with X-CUBE-AI extension pack and inference step is presented and discussed. The obtained results and conclusions can be practically useful for researchers and embedded engineers who work with AI and microcontrollers of STM32 family.
Open set recognition algorithm based on Conditional Gaussian Encoder

&lt;abstract&gt; &lt;p&gt;For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the original form of the signal as much as possible and deep neural network is used to extract useful information. CG-Encoder adopts residual network structure and a new Kullback-Leibler (KL) divergence is defined. In the training phase, the known classes are approximated to different Gaussian distributions in the latent space and the discrimination between classes is increased to improve the recognition performance of the known classes. In the testing phase, a specific and effective OSR algorithm flow is designed. Simulation experiments are carried out on 9 jamming types. The results show that the CSR and OSR performance of CG-Encoder is better than that of the other three kinds of network structures. When the openness is the maximum, the open set average accuracy of CG-Encoder is more than 70%, which is about 30% higher than the worst algorithm, and about 20% higher than the better one. When the openness is the minimum, the average accuracy of OSR is more than 95%.&lt;/p&gt; &lt;/abstract&gt;
Nested Named Entity Recognition via an Independent-Layered Pretrained Model

When an entity contains one or more entities, these particular entities are referred to as nested entities. The Layered BiLSTM-CRF model can use multiple BiLSTM layers to identify nested entities. However, as the number of layers increases, the number of labels that the model can learn decreases, and it may not even predict any entities, thereby causing the model to stop stacking. Furthermore, the model will be constrained by the one-way propagation of information from the lower layer to the higher layer. The incorrect entities extracted by the outer layer will affect the performance of the inner layer. We propose a novel neural network for nested named entity recognition (NER) that dynamically stacks flat NER layers to address these issues. Each flat NER layer captures contextual information based on a pretrained model with more robust feature extraction capabilities. The model parameters of a flat NER layer and its input are entirely independent. The input of each layer is all of the word representations generated by the input sequence through the embedding layer. The independent input ensures that different flat NER layers will not be interfered with by other flat NER layers during model training and testing to reduce error propagation. Experiments show that our model obtains F1 scores of 76.9%, 78.1%, and 78.0% on the ACE2004, ACE2005, and GENIA datasets, respectively.
Introduction to Cybersecurity

In this chapter, cybersecurity principles to protect digital information against cyber threats and cyber threat actors are discussed. Three fundamental principles to protect data include confidentiality, integrity, and availability. These principles are extended by adding two more principles of accountability and authenticity. The main motivations behind launching cyber threats are data breaches, financial instability, service disruption, and politics. These motivations pave the way for securing critical organizational assets by following the CIAAA principle. Surprisingly, the world’s biggest data breaches have exposed several businesses including communication, social networking, aviation industry, financial sector, government databases, health care, media, and transportation. These breaches emphasize the need for a data-centric security management system that includes data protection, transfer, control, access, and measurement. The data-centric security management process gathers meticulous information about an organization to understand how an attacker can exploit the vulnerabilities.
Reliable AI Through SVDD and Rule Extraction

The proposed paper addresses how Support Vector Data Description (SVDD) can be used to detect safety regions with zero statistical error. It provides a detailed methodology for the applicability of SVDD in real-life applications, such as Vehicle Platooning, by addressing common machine learning problems such as parameter tuning and handling large data sets. Also, intelligible analytics for knowledge extraction with rules is presented: it is targeted to understand safety regions of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms.