Reviewerly/exHarmony huggingface dataset:
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis

Classic and deep learning-based generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple “views” (e.g., audio and image) using linear transformations and neural networks, respectively. When the views are acquired and stored at different computing agents (e.g., organizations and edge devices) and data sharing is undesired due to privacy or communication cost considerations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> -based GCCA is well-motivated. In federated learning, the views are kept locally at the agents and only derived, limited information exchange with a central server is allowed. However, applying existing GCCA algorithms onto such federated learning setting may incur prohibitively high communication overhead. This work puts forth a communication-efficient federated learning framework for both linear and deep GCCA under the maximum variance (MAX-VAR) formulation. The overhead issue is addressed by aggressively compressing (via quantization) the exchanging information between the computing agents and a central controller. Compared to the unquantized version, our empirical study shows that the proposed algorithm enjoys a substantial reduction of communication overheads with virtually no loss in accuracy and convergence speed. Rigorous convergence analyses are also presented, which is a nontrivial effort. Generic federated optimization results do not cover the special problem structure of GCCA, which is a manifold constrained multi-block nonconvex eigen problem. Our result shows that the proposed algorithms for both linear and deep GCCA converge to critical points in a sublinear rate, even under heavy quantization and stochastic approximations. In addition, in the linear MAX-VAR case, the quantized algorithm approaches a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global optimum</i> in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geometric</i> rate under reasonable conditions. Synthetic and real-data experiments are used to showcase the effectiveness of the proposed approach.
Better Security Estimates for Approximate, IoT-Friendly R-LWE Cryptosystems

This work explores the effect of approximation on the Ring-learning with errors (R-LWE) based public-key cryptography (PKE) systems. Lattice-based problems such as LWE have proven to be a viable alternative to the currently used PKE systems in a post-quantum world, with one such scheme taken up for standardisation in NIST's post-quantum cryptography standardisation process (CRYSTALS-Kyber). In the modern world, the Internet of Things (IoT) applications and devices have become ubiquitous. Often, they are not secure, and with the threat of quantum computing looming on the horizon, R-LWE could provide a solution. R-LWE-based schemes come at a higher computational cost, making them challenging to be implemented on IoT devices that require low energy, low area, and efficient cryptography. To enable this, approximation could aid in lowering the necessary power and memory by removing some of the complexity in R-LWE scheme. This work provides improved security estimates for an earlier presented approximate R-LWE scheme (AxRLWE), that explores the approximation via truncation of the Gaussian distribution samples values and using an approximate modular multiplier. In addition, this work calculates an effective failure rate for different truncation levels of 2 typical medium level R-LWE parameter sets and concludes a truncation of 2-bits to be the best balance between security and approximation.
Peculiarities of Identification and Determination of Copyright Objects in the Russian Federation

Statutory criteria for the protection of copyright objects (the objective form of expression and the presence of the creative work of the author-citizen) are often insufficient when determining the objects of copyright and their differentiation with other objects of civil rights. The courts and law enforcement officers as a whole, are entrusted with a rather difficult creative task here. In this context, an important theoretical and, at the same time, practical tool, namely, linguistic (grammatical) interpretation, is of particular importance. The use of grammatical interpretation in relation to the collective terms «works of science, literature and art» used in the Civil Code of the Russian Federation, in particular in Articles 1225, 1259, seems appropriate due to the lack of sufficient and clear criteria for the protection of copyright objects and the spatial nature of the existing ones.
Fast and Accurate Color Image Classification Based on Quaternion Tchebichef Moments and Quaternion Convolutional Neural Network

This paper introduces a new architecture named QTMCNN for color image classification based on quaternion discrete Tchebichef moments (QTM) and quaternion convolutional neural network (QCNN) to improve the classification accuracy and to reduce the time of learning process. Color image is represented as a single quaternion matrix where each color pixel is represented as a pure quaternion. From this representation, quaternion Tchebichef moments are used to generate a matrix of low-dimensional significant features and fed to QCNN as input layer instead of color image. The proposed architecture reduces tremendously the number of parameters and consequently decreases the computational complexity while improving the classification rates. Experiments are conducted on Coil-100 and ETH-80 datasets to demonstrate the performance of the proposed architecture. The obtained results outperform other approaches in terms of classification accuracy and GPU elapsed time.
Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means

In this research, we use unsupervised machine learning clustering techniques, notably K-means (Jain in Pattern Recogn Lett 31:651–666, 2010 [1]), to explore human navigation using the VR Magic Carpet (Berthoz and Zaoui in Dev Med Child Neurol 57:15–20, 2015 [2]). This is a variant of the Corsi Block Tapping task (CBT) (Corsi in Human memory and the medial temporal region of the brain. McGill University, 1972 [3]) that was carried out within the experimental framework of virtual reality. The participant’s trajectory was captured as raw spatial data and afterward kinematically evaluated. Our previous research (Annaki et al. in Digital technologies and applications. ICDTA 2021. Lecture notes in networks and systems, vol 211. Springer, Cham, 2021 [4]) found three distinct groups. However, the classification remained unclear, suggesting that they include both types of people (ordinary and patients with cognitive spatial impairments). Based on this premise, we used K-means to distinguish patients’ navigation behavior from that of healthy people, highlighting the most significant differences and validating the feature on which our previous analysis was based.
The three-dot sign in language contact

Abstract In this study, we investigate the three-dot sign as a discourse marker (DM) with textual, subjective and intersubjective discourse functions. As a graphical marker that is used across languages, the three-dot sign is especially suitable for comparative studies and dynamics in language contact. Our corpus study targeting instant messages of different languages (English, German, Greek, Russian, Turkish) and speaker groups (monolinguals and bilingual heritage speakers) suggests that graphical DMs are prone to cross-linguistic influence. This depends on the specific contact situation and does not seem to be a general effect of bilingualism. The societal status of a language might further influence the use of such markers in digital informal writing. Language-specific developments that relate to emerging functions indicate that functional versatility promotes frequent use of (graphical) DMs.
Multimodality-aided Multicarrier Waveform Recognition in low SNR Regimes based on Denoised Cyclic Autocorrelation Transformation

Wireless signal recognition driven by artificial intelligence (AI) plays a pivotal role in 6G ultra-reliable wireless communications, facilitating spectrum surveillance to impede illegal radio interference. As a promising wireless technique, multicarrier waveform recognition (MWR) has been explored to enhance the reliability of wireless data transmission. However, existing works fail to achieve reliable recognition accuracy under strong noise. It remains a daunting task for MWR in signal-to-noise ratio (SNR) regimes. To deal with this issue, we propose a denoised cyclic autocorrelation-based multimodality fusion network (DCA-MFNet). Specifically, we first leverage the cyclic autocorrelation transformation to convert intercepted signals into cyclic autocorrelation (CA) features in cyclic frequency domains, which have the robust property of being insensitive to low SNR. Next, the singular value decomposition (SVD) method is employed to weaken the strong noise effects on useful CA peak values. Based on the denoised CA matrix (DCAM), the projection accumulation strategy is proposed to generate the time delay accumulation vector (TDV) and cyclic frequency accumulation vector (CFV), which can enlarge the discrimination among multicarrier signals. Finally, we fed the multimodality features of DCAM, TDV, and CFV into the developed DCA-MFNet to perform hierarchical learning, feature aggregation training, and multicarrier type prediction. Experimental results demonstrate that the proposed DCA-MFNet obtains better recognition performance than existing algorithms. Moreover, DCA-MFNet can effectively identify six multicarrier signals with a recognition accuracy of 100% at a low SNR of even -2 dB.
Sweet Streams Are Made of This: The System Engineer’s View on Energy Efficiency in Video Communications [Feature]

In recent years, the global use of online video services has increased rapidly.Today, a manifold of applications, such as video streaming, video conferencing, live broadcasting, and social networks, make use of this technology.A recent study found that the development and the success of these services had as a consequence that, nowadays, more than 1% of the global greenhouse-gas emissions are related to online video, with growth rates close to 10% per year.This article reviews the latest findings concerning energy consumption of online video from the system engineer's perspective, where the system engineer is the designer and operator of a typical online video service.We discuss all relevant energy sinks, highlight dependencies with qualityof-service variables as well as video properties, review energy consumption models for different devices from the literature, and aggregate these existing models into a global model for the overall energy consumption of a generic online video service.Analyzing this model and its implications, we find that end-user devices and video encoding have the largest potential for energy savings.Finally, we provide an overview of recent advances in energy efficiency improvement for video streaming and propose future research directions for energy-efficient video streaming services.
Privacy-Preserving Offloading in Edge Intelligence Systems With Inductive Learning and Local Differential Privacy

We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxiliary datasets with no privacy risks or pre-trained models to build the client side feature extractor. However, finding auxiliary datasets or pre-trained models is not always guaranteed and may be challenging. To bridge this gap and eliminate the reliance on auxiliary datasets or pre-trained models of existing solutions, this paper presents DeepGuess, a privacy-preserving and latency-aware deep-learning framework. DeepGuess introduces a new learning mechanism enabled by the AutoEncoder architecture: inductive learning. With inductive learning, sensitive data stays on devices and is not explicitly sent to the central server to engage in back-propagations. To further enhance privacy, we propose a new local differential privacy algorithm that allows edge devices to apply random noise to features extracted from their sensitive data before being transferred to the non-trusted central server. The experimental evaluation of DeepGuess with various datasets and in a real-world scenario shows that our solution achieves comparable or even higher accuracy than existing solutions while reducing data transfer over the network by more than 50%.
An Empirical Investigation of the Antecedents and Consequences of Virtual Reality Tourism Experiences in Tunisia

Virtual tourism is related to the use of extended reality technologies, such as augmented reality and virtual reality, in order to generate a travel experience without the need to move from one place. Virtual reality represents one of the most interesting and promising digital technologies that offers significant benefits in numerous areas such as medicine, entertainment, training, and tourism. This research paper aims at examining the relationships between enjoyment, emotional involvement, flow, positive emotions, and VR experience on one hand. On the other hand, it investigates the linkage between VR experience and attitudes towards tourism destination. A quantitative approach is used to test the hypotheses. Simple linear regression shows that all hypotheses are accepted except for the flow state VR tourist experience that is rejected. Managerial implications will be drawn.
Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching

Mixed reality technologies, including virtual and augmented reality, and also referred to as extended reality, have been in continuous development for nearly 60 years. The current generation of technologies is widely applied in diverse contexts including education. Although most educational applications are currently limited in their pedagogical framing, being dominated by content experiences, mixed realities have a number of emerging, emergent, and emerged benefits able to support a virtual university which are reviewed in this chapter. Three themes are identified: the value that these technologies play in bringing information into the environment of the learner; the ability to change the learner’s perceptions; and the implications for the virtual university as an evolving organization able to take new forms through the application of mixed reality technologies. A conceptual framework arising from the review is presented to provide a classification system for the diverse forms of new and emerging mixed reality.
SME: A Systolic Multiply-accumulate Engine for MLP-based Neural Network

In this paper, we propose an output stationary systolic multiply-accumulate engine (SME) with an optimized dataflow for multilayer perceptron (MLP) computation in the state-of-the-art Neural Radiance Field (NeRF) algorithms. We also analyze activation patterns of the NeRF algorithm which uses ReLU as the activation function, and find that the activation can be sparse, especially in the last several layers. We therefore further take advantage of activation sparsity by gating corresponding multiplications in the SME for power saving. The proposed SME is implemented using SpinalHDL, which is translated to VerilogHDL for VLSI implementation based on 40nm CMOS technology. Evaluation results show that, working at 400MHz, the proposed SME occupies 31.371mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> circuit area, and consumes 873.7mW power, translating 12,708.10 ksamples/J and 360.06 ksamples/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Time‐resolved strain and deformation measurement on the vibrating saxophone reed

Abstract Single‐reed musical instruments, such as the saxophone, generate sound through a complex interplay between the mechanics of the reed and the hydrodynamic and acoustic pressure in the instrument mouthpiece. To understand this complex mechanism, experimental data are lacking. This paper presents full‐field, time‐resolved measurements of strain and displacement of a vibrating saxophone reed, measured under mimicked realistic playing conditions. It is found that strain along the length axis of the reed is mainly expansive, except in a small zone near the tip where it becomes compressive when the reed touches the front edge of the mouthpiece. At the instant in the vibration phase where the reed touches the mouthpiece, significant bending and compressive strain appear along the direction perpendicular to the reed axis. Strain magnitudes in both directions are similar, with absolute values of 0.1%. Full‐field strain maps reveal subtle characteristics which are not revealed by displacement measurements. Bi‐axial bending and strain may be an essential component in reed mechanics, which up till now has been fully neglected in modelling.
Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes.
Computing Finite Mixture Estimators in the Tails

Abstract The finite mixtures approach identifies homogeneous groups within the sample. The data are aggregated into classes sharing similar patterns without any prior knowledge or assumption on the clustering. These clusters are characterized by group-specific regression coefficients to account for between groups heterogeneity. Two different approaches have been independently defined in the literature to compute this estimator not only at the conditional mean but also in the tails. One approach allows the grouping to change according to the selected location. The other defines the clusters once and for all at the conditional mean, and then moves the estimation to the tails, focusing on cluster specific estimates and allowing between groups comparison. Here we compare the behavior of both approaches, and in addition we consider a closely related estimator based on expectiles, together with few others more robust, quantile-based estimators. A case study on students’ performance concludes the analysis.
Visually clear diffusive light panel with embedded semi-reflective rough-surface planar outcouplers

Abstract We present a visually transparent light-diffusing panel in the form of a light guide, which has semi-reflective, thin-film blades buried within it acting as diffusive outcouplers. The semi-reflective blades can be tilted relative to the length of the light guide and are textured to partially scatter the light incident upon them in the reflection domain, while transmitting the rest. We show that the texturization of the outcouplers can be achieved following a simple molding process using a mold with well-defined roughness regions to subsequently create light-scattering surfaces. The mold is either based on frosted glass or on a photolithographic process using a polymer photomask. We demonstrate with a prototype that the view through the textured outcouplers is visually clear. Also, we demonstrate that by varying the angular difference between the incidence of the propagating light rays and the outcoupler angle, the ratio of the amount of diffused light emanating from the two surfaces of the light guide can be biased from ∼1:2 to ∼1:20. The proposed device can be used as a transparent light source that provides an occlusion-free and undistorted see-through view.
Enhanced Data Security Unlocks the Power of Real-World Data

Inside Precision MedicineVol. 10, No. 2 Sponsored ContentEnhanced Data Security Unlocks the Power of Real-World DataPublished Online:12 Apr 2023https://doi.org/10.1089/ipm.10.02.13AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 10Issue 2Apr 2023 InformationCopyright © GEN PublishingTo cite this article:Enhanced Data Security Unlocks the Power of Real-World Data.Inside Precision Medicine.Apr 2023.50-51.http://doi.org/10.1089/ipm.10.02.13Published in Volume: 10 Issue 2: April 12, 2023PDF download
Automatic sentence segmentation for classical Chinese: <i>The Spring and Autumn Annals</i> as an example

Abstract There exists no sentence boundary in most classical Chinese literature texts. Since it is difficult to read literature of this kind, experts in literature or linguistics would segment the sentence manually. This article explores the effectiveness of classical Chinese sentence segmentation method so as to provide a reference for classical Chinese punctuation. On the basis of the machine learning methods, we chose three components of machine learning, namely models, tagging schemes, and features, to compare the learning results. The models include conditional random field (CRF) models, long short term memory (LSTM) models, BiLSTM–CRF models, and three Bidirectional Encoder Representation from Transformers (BERT) models. There are five tagging schemes in this article and three features including the statistical feature, Guangyun, and Fanqie. Finally, the performance of the combined feature template is evaluated by ten-fold cross-validation on four classical Chinese texts in different genres. The SikuBERT model is proved to be the most effective model for sentence segmentation at present. Different tagging schemes and various features are introduced. The results show that 5-tag-J tagging schemes can improve performance. Statistical feature, as an important clue for classical Chinese sentence segmentation, is useful in related tasks, but Guangyun and Fanqie have little impact. Other important factors of sentence segmentation are genres and writing styles.