Reviewerly/exHarmony huggingface dataset:
Feature fusion via Deep Random Forest for facial age estimation

In the last few years, human age estimation from face images attracted the attention of many researchers in computer vision and machine learning fields. This is due to its numerous applications. In this paper, we propose a new architecture for age estimation based on facial images. It is mainly based on a cascade of classification trees ensembles, which are known recently as a Deep Random Forest. Our architecture is composed of two types of DRF. The first type extends and enhances the feature representation of a given facial descriptor. The second type operates on the fused form of all enhanced representations in order to provide a prediction for the age while taking into account the fuzziness property of the human age. While the proposed methodology is able to work with all kinds of image features, the face descriptors adopted in this work used off-the-shelf deep features allowing to retain both the rich deep features and the powerful enhancement and decision provided by the proposed architecture. Experiments conducted on six public databases prove the superiority of the proposed architecture over other state-of-the-art methods.
AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation

This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several appearance-describing images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.
Progress Report of Cyber Society v1.0 Development as A Learning Media for Indonesian Society to Support EFA

In this paper, we present the progress in developing the Cyber Society (CS) v.1.0, which is a prototype of intelligent learning concepts that can be employed in the 21st century and become the future-proof technology. This paper is a continuation of the previous work that discussed there a high-possible to develop specific e-learning for Indonesian society to support Education for All (EFA); therefore, this work will focus on the realization of our idea. It will start from “analysis” to the “evaluation” steps. The discussion of this paper comprises of (1) analyzing the needs and minimum requirements of the CS prototype; (2) designing; (3) developing, and (4) evaluating the CS prototype. Once the CS has been developed, then, we evaluated the CS website v1.0 using the black-box method; it shows that the system but-tons can be run well as expected. Later, we tested its Usability through the SUS questionnaire that obtained from 50 participants, we got eighty (80) of the mean score. According to the SUS measurement, the feasibility of CS website v1.0 can be categorized as follows: (1) Promoter NPS; (2) Acceptable; (3) Excellent Adjective; and (4) A- Grade. This work has usefulness in developing modern teaching &amp; learning patterns. Moreover, it is expected to be a medium of online-based learning with real-time concepts without any limitations on time, age, and distance. Our system can be used as a reference model in developing future technology-based learning media.
COVID-19 prediction models should adhere to methodological and reporting standards

The coronavirus disease 2019 (COVID-19) pandemic has led to a proliferation of clinical prediction models to aid diagnosis, disease severity assessment and prognosis. A systematic review has identified 66 COVID-19 prediction models: concluding that all, with no exception, are at high risk of bias due to concerns surrounding the data quality, statistical analysis and reporting, and none are recommended for use [1]. Therefore, we read with interest the recent paper by Wu et al. [2] describing the development of a model to identify COVID-19 patients with severe disease on admission to facilitate triage. However, our enthusiasm was dampened by a number of concerns surrounding the design, analysis and reporting of the study which deserve highlighting to readers. COVID-19 prediction models should adhere to methodological and reporting standards <https://bit.ly/3ebnook>
Augmented Reality Dynamic Image Recognition Technology Based on Deep Learning Algorithm

Augmented reality is a research hotspot developed on the basis of virtual reality.Friendly human-computer interaction interface makes the application prospect of augmented reality technology very broad.Convolutional neural networks in deep learning have been widely used in the field of computer vision and become an important weapon in dynamic image recognition tasks.Combining deep learning and traditional machine learning techniques, this article uses convolutional neural networks to extract features from image data.The convolutional neural network uses the last layer of features and uses the softmax recognizer for recognition.This article combines a convolutional neural network that can learn good feature information with integrated learning that has good recognition effects.In the recognition tasks of the MNIST database and the CIFAR-10 database, comparison experiments were performed by adjusting the hierarchical structure, activation function, descent algorithm, data enhancement, pooling selection, and number of feature maps of the improved convolutional neural network.The convolutional neural network uses a pooling size of 3 * 3, and uses more cores (above 64), small receptive fields (2 * 2), and more hierarchical structures.In addition, the Relu activation function, gradient descent algorithm with momentum, and enhanced data set are also used.The research results show that under certain experimental conditions, the dynamic image recognition results have dropped to a very low error rate in the MNIST database, and the error rate in the CIFAR-10 database is also ideal.
Monolithic InP optical unitary converter based on multi-plane light conversion

Integrated reconfigurable optical unitary converters (OUCs) are crucial in realizing all-optical spatial mode demultiplexing for mode-division-multiplexed transmission systems and programmable photonic processing for optical neural networks. In this work, we present the first experimental demonstration of 4×4 OUC monolithically integrated on InP. To avoid the difficulty of integrating a large number of Mach-Zehnder interferometer couplers on the InP platform, we apply the concept of multi-plane light conversion and use cascaded stages of 4-port multimode interference couplers, which are more scalable and easier to fabricate on InP. By optimizing the phase shifters, we demonstrate reconfigurable 4-mode sorting as well as error-free switching of 40-Gbit/s signal.
Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing video analyses in the era of big data and cloud computing.
Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN

The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community.The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation.In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances.The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion.First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector.The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI.The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines.
Image Coding With Data-Driven Transforms: Methodology, Performance and Potential

Image compression has always been an important topic in the last decades due to the explosive increase of images. The popular image compression formats are based on different transforms which convert images from the spatial domain into compact frequency domain to remove the spatial correlation. In this paper, we focus on the exploration of data-driven transform, Karhunen-Loéve transform (KLT), the kernels of which are derived from specific images via Principal Component Analysis (PCA), and design a high efficient KLT based image compression algorithm with variable transform sizes. To explore the optimal compression performance, the multiple transform sizes and categories are utilized and determined adaptively according to their rate-distortion (RD) costs. Moreover, comprehensive analyses on the transform coefficients are provided and a band-adaptive quantization scheme is proposed based on the coefficient RD performance. Extensive experiments are performed on several class-specific images as well as general images, and the proposed method achieves significant coding gain over the popular image compression standards including JPEG, JPEG 2000, and the state-of-the-art dictionary learning based methods.
The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.
Digital Teaching Competence and Space Competence with TPACK in Social Sciences

Research on training in knowledge related to geolocation with technologies shows the training potentials and limitations that these resources offer for teachers in training. This study examines the perceptions of the instructional effectiveness of mobile devices and geolocation software, by analyzing the performance in the TPACK skills dimensions of future teachers (n = 421). A mixed methodology has been implemented, with a descriptive cross-sectional approach as well as inferential analysis based on a questionnaire (α ≥ .918) and the tasks carried out by the sample. The results show a poor didactic use of technologies while confirming the acquisition of knowledge about geolocation. This corroborates the benefits of applying an improvement in the digital competence of teachers in training.
Multi-label Emotion Classification using Content-Based Features in Twitter

Multi-label Emotion Classification is a supervised classification problem that aims to classify multiple emotion labels from a given text. Recently, Multi-label Emotion Classification has appealed to the research community due to possible applications inE-learning, marketing, education, and health care, etc. We applied content-based methods (words and character n-grams) on tweets to show how our purposed content-based method can be used for the development and evaluation of the Multi-label Emotion Classification task. The results achieved after our extensive experimentation demonstrate that content-based word unigram surpassed other content-based features (Multi-label Accuracy = 0.452, MicroF1 = 0.573, MacroF1 = 0.559, Exact Match = 0.141, Hamming Loss= 0.179).
Converting Anyone’s Emotion: Towards Speaker-Independent Emotional Voice Conversion

Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.
The Brevity Law as a Scaling Law, and a Possible Origin of Zipf’s Law for Word Frequencies

An important body of quantitative linguistics is constituted by a series of statistical laws about language usage. Despite the importance of these linguistic laws, some of them are poorly formulated, and, more importantly, there is no unified framework that encompasses all them. This paper presents a new perspective to establish a connection between different statistical linguistic laws. Characterizing each word type by two random variables, length (in number of characters) and absolute frequency, we show that the corresponding bivariate joint probability distribution shows a rich and precise phenomenology, with the type-length and the type-frequency distributions as its two marginals, and the conditional distribution of frequency at fixed length providing a clear formulation for the brevity-frequency phenomenon. The type-length distribution turns out to be well fitted by a gamma distribution (much better than with the previously proposed lognormal), and the conditional frequency distributions at fixed length display power-law-decay behavior with a fixed exponent $\alpha\simeq 1.4$ and a characteristic-frequency crossover that scales as an inverse power $\delta\simeq 2.8$ of length, which implies the fulfilment of a scaling law analogous to those found in the thermodynamics of critical phenomena. As a by-product, we find a possible model-free explanation for the origin of Zipf's law, which should arise as a mixture of conditional frequency distributions governed by the crossover length-dependent frequency.
Sampling of Graph Signals via Randomized Local Aggregations

Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing. While in classical signal processing sampling is a well defined operation, when we consider a graph signal many new challenges arise and defining an efficient sampling strategy is not straightforward. Recently, several works have addressed this problem. The most common techniques select a subset of nodes to reconstruct the entire signal. However, such methods often require the knowledge of the signal support and the computation of the sparsity basis before sampling. Instead, in this paper we propose a new approach to this issue. We introduce a novel technique that combines localized sampling with compressed sensing. We first choose a subset of nodes and then, for each node of the subset, we compute random linear combinations of signal coefficients localized at the node itself and its neighborhood. The proposed method provides theoretical guarantees in terms of reconstruction and stability to noise for any graph and any orthonormal basis, even when the support is not known.
Class-Incremental Domain Adaptation

We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods enable learning of new classes in absence of source training data, but fail under a domain-shift without labeled supervision. In this work, we effectively identify the limitations of these approaches in the CIDA paradigm. Motivated by theoretical and empirical observations, we propose an effective method, inspired by prototypical networks, that enables classification of target samples into both shared and novel (one-shot) target classes, even under a domain-shift. Our approach yields superior performance as compared to both DA and CI methods in the CIDA paradigm.
SIRUS: Stable and Interpretable RUle Set for classification

State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism.This lack of interpretability is a strong limitation for applications involving critical decisions, typically the analysis of production processes in the manufacturing industry.In such critical contexts, models have to be interpretable, i.e., simple, stable, and predictive.To address this issue, we design SIRUS (Stable and Interpretable RUle Set), a new classification algorithm based on random forests, which takes the form of a short list of rules.While simple models are usually unstable with respect to data perturbation, SIRUS achieves a remarkable stability improvement over cutting-edge methods.Furthermore, SIRUS inherits a predictive accuracy close to random forests, combined with the simplicity of decision trees.These properties are assessed both from a theoretical and empirical point of view, through extensive numerical experiments based on our R/C++ software implementation sirus available from CRAN.
BanglaWriting: A multi-purpose offline Bangla handwriting dataset

This article presents a Bangla handwriting dataset named BanglaWriting that contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,787 characters in total. Moreover, this dataset includes 5,470 unique words of Bangla vocabulary. Apart from the usual words, the dataset comprises 261 comprehensible overwriting and 450 handwritten strikes and mistakes. All of the bounding-boxes and word labels are manually-generated. The dataset can be used for complex optical character/word recognition, writer identification, handwritten word segmentation, and word generation. Furthermore, this dataset is suitable for extracting age-based and gender-based variation of handwriting.
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020

The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications (#SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovigilance, and epidemiology of birth defects. In addition to re-runs of three tasks, #SMM4H 2020 included new tasks for detecting adverse effects of medications in French and Russian tweets, characterizing chatter related to prescription medication abuse, and detecting self reports of birth defect pregnancy outcomes. The five tasks required methods for binary classification, multi-class classification, and named entity recognition (NER). With 29 teams and a total of 130 system submissions, participation in the #SMM4H shared tasks continues to grow.
CAP: Context-Aware Pruning for Semantic Segmentation

Network pruning for deep convolutional neural networks (CNNs) has recently achieved notable research progress on image-level classification. However, most existing pruning methods are not catered to or evaluated on semantic segmentation networks. In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework. Concretely, we formulate the embedded contextual information by leveraging the layer-wise channels interdependency via the Context-aware Guiding Module (CAGM) and introduce the Context-aware Guided Sparsification (CAGS) to adaptively identify the informative channels on the cumbersome model by inducing channel-wise sparsity on the scaling factors in batch normalization (BN) layers. The resulting pruned models require significantly lesser operations for inference while maintaining comparable performance to (at times outperforming) the original models. We evaluated our framework on widely-used benchmarks and showed its effectiveness on both large and lightweight models. On Cityscapes dataset, our framework reduces the number of parameters by 32%, 47%, 54%, and 63%, on PSPNet101, PSPNet50, ICNet, and SegNet, respectively, while preserving the performance.