Reviewerly/exHarmony huggingface dataset:
Comparison of the YOLOv3 and SSD MobileNet v2 Algorithms for Identifying Objects in Images from an Indoor Robotics Dataset

The YOLO and SSD algorithms are tools widely used for detecting objects in images or videos. This is due to the speed of detection and good performance in the identification of objects. This article presents a comparison of the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images through simulations, the dataset used is an indoor robotics dataset. In order to reach the objective, several training sessions were carried out to analyze the behavior of each model when detecting objects in images. After analyzing the results, a better performance of the YOLOv3 model was observed, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. It is worth mentioning that this work presents for the first time a comparison between the SSD MobileNet v2 and YOLOv3 algorithms.
End-to-end neural systems for automatic children speech recognition: An empirical study

A key desiderata for inclusive and accessible speech recognition technology is ensuring its robust performance to children’s speech. Notably, this includes the rapidly advancing neural network based end-to-end speech recognition systems. Children speech recognition is more challenging due to the larger intra-inter speaker variability in terms of acoustic and linguistic characteristics compared to adult speech. Furthermore, the lack of adequate and appropriate children speech resources adds to the challenge of designing robust end-to-end neural architectures. This study provides a critical assessment of automatic children speech recognition through an empirical study of contemporary state-of-the-art end-to-end speech recognition systems. Insights are provided on the aspects of training data requirements, adaptation on children data, and the effect of children age, utterance lengths, different architectures and loss functions for end-to-end systems and role of language models on the speech recognition performance.
Anonymous Traceability protocol based on Group Signature for Blockchain

In blockchain technology, data are stored on decentralized nodes and public to each node in the blockchain network. Hence, the focus of privacy protection in the blockchain guarantees the anonymity of transactions such that attackers cannot attain the users' personal information through data analysis. Among the existing privacy protection technologies, the scheme based on group signature has good anonymity, but the existing scheme exists a large number of operations that are difficult to apply to wireless terminals. In this paper, using the powerful offloading capability of edge computing, we propose a blockchain node traceable identity privacy technology scheme based on threshold group signature, and the scheme greatly reduces the computing burden of nodes while achieving node privacy protection.
Transfer Ability of Monolingual Wav2vec2.0 for Low-resource Speech Recognition

Recently, there are several domains that have their own feature extractors, such as ResNet, BERT, and GPT-x, which are widely used for various down-stream tasks. These models are pre-trained on large amounts of unlabeled data by self-supervision. In the speech domain, wav2vec2.0 starts to show its powerful representation ability and feasibility for ultra-low resource speech recognition tasks. This speech feature extractor is pre-trained on the monolingual audiobook corpus, whereas it has not been thoroughly examined in real spoken scenarios and other languages. In this work, we endeavor to transfer the knowledge from the pre-trained monolingual wav2vec2.0 to cross-lingual spoken ASR tasks with less than 20 hours of labeled data. We achieve more than 20% relative improvements in all the six languages compared with previous methods, establishing a strong benchmark on CALLHOME datasets. Compared with supervised pre-training, self-supervision training used in wav2vec2.0 has a better transfer ability. We also find that using coarse-grained modeling units, such as subword or character, usually achieves better results than fine-grained modeling units, such as phone or letter.
Age-Related Differences in Preferences for Using Voice Assistants

In the past few years, voice assistants have become broadly available in different forms of presentation and devices – not only as a personal assistant within smartphones but as smart speakers, within TV sets or as part of an in-car infotainment system. Furthermore, we live in an ageing society and considering elderly people as users of voice assistants gains more relevance driven by both trends. The goal of this study is to identify the specific age-related preferences of older people when using a conversational user interface in form of a voice assistant. We conducted a survey based on 26 elderly-related communication strategies among participants of different age. The participants had to evaluate the strategies according to their own preferences for using voice assistants. As a result, we identified 11 preferences specific to older users. Surprisingly, most of the communication strategies, when applied to voice assistants, seem to be relevant for users of all ages, and a few of the communication strategies do not apply when used in voice assistants. The preferences specific to older people help to develop new guidelines for voice user interfaces or conversational user interfaces in general. They do not automatically lead to those guidelines but provide a foundation to derive requirements, develop guidelines and evaluate those guidelines by means of user-based usability tests.
Reducing Model Cost Based on the Weights of Each Layer for Federated Learning Clustering

Federated Learning (FL) has a different learning framework from existing machine learning, which had to centralize training data. Federated learning has the advantage of protecting privacy because learning is performed on each client device rather than the central server, and only the weight parameter values, which are the learning results, are sent to the central server. However, the performance of federated learning shows relatively low performance compared to cloud computing, and in reality, it is difficult to build a federated learning environment due to the high communication cost between the server and multiple clients. In this paper, we propose Federated Learning with Clustering algorithms (FLC). The proposed FLC is a method of clustering clients with similar characteristics by analyzing the weights of each layer of a machine learning model, and performing federated learning among the clustered clients. The proposed FLC can reduce the communication cost for each model by reducing the number of clients corresponding to each model. As a result of extensive simulation, it is confirmed that the accuracy is improved by 2.4% and the loss by 47% through the proposed FLC compared to the standard federated learning.
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching

Satellite multi-view stereo (MVS) imagery is particularly suited for large-scale Earth surface reconstruction. Differing from the perspective camera model (pin-hole model) that is commonly used for close-range and aerial cameras, the cubic rational polynomial camera (RPC) model is the mainstream model for push-broom linear-array satellite cameras. However, the homography warping used in the prevailing learning based MVS methods is only applicable to pin-hole cameras. In order to apply the SOTA learning based MVS technology to the satellite MVS task for large-scale Earth surface reconstruction, RPC warping should be considered. In this work, we propose, for the first time, a rigorous RPC warping module. The rational polynomial coefficients are recorded as a tensor, and the RPC warping is formulated as a series of tensor transformations. Based on the RPC warping, we propose the deep learning based satellite MVS (SatMVS) framework for large-scale and wide depth range Earth surface reconstruction. We also introduce a large-scale satellite image dataset consisting of 519 5120×5120 images, which we call the TLC SatMVS dataset. The satellite images were acquired from a three-line camera (TLC) that catches triple-view images simultaneously, forming a valuable supplement to the existing open-source WorldView-3 datasets with single-scanline images. Experiments show that the proposed RPC warping module and the SatMVS framework can achieve a superior reconstruction accuracy compared to the pin-hole fitting method and conventional MVS methods. Code and data are available at https://github.com/WHU-GPCV/SatMVS.
Detecting Aligned Double JPEG Compressed Color Image With Same Quantization Matrix Based on the Stability of Image

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.
Multi-operator continuous ant colony optimisation for real world problems

A Multi-operator continuous Ant Colony Optimisation (MACOR) is proposed in this paper to solve the real-world problems. An adaptive multi-operator framework is proposed for selecting the suitable operator during different evolutionary stages by considering the historical performance of operators and the convergence status of the population. To improve the search accuracy, four operators are presented to construct new ant solutions in different ways. A success-based random-walk selection strategy and local search method are also combined with MACOR to better balance the algorithmic ability of exploration and exploitation. Experiments are conducted on the test suite of real-world problems to demonstrate the superiority of the proposed MACOR by comparing it to state-of-the-art algorithms. The influences of the multi-operator framework and different combinations of operators on the algorithmic performance are also investigated.
Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems

Graph embedding based methods have been used in recommendation systems recently, owing to their advances in modeling nodes as embeddings in a low-dimensional space. By effective neighborhood aggregation, graph convolutional networks can exploit high-order connections of neighbors such that the learned embeddings could be more informative thus improve the recommendation performance. However, user and item representations learned by graph aggregation inherently contain uncertainty due to sparsity of user-item interactions and noise of item features. To address these challenges, we propose a multi-modal variational graph auto-encoder (MVGAE) method. Specifically, we design modality-specific variational encoders that learn a Gaussian variable for each node whereas the mean vector represents semantic information and the variance vector denotes the noise level of the corresponding modality. Moreover, with the conditional independence assumption, the modality-specific Gaussian node embeddings are fused according to the product-of-experts principle, where the semantic information in each modality is weighted based on the estimated uncertainty level. Extensive experiments on three public datasets, Amazon Movies, Amazon Electronics and AliShop-7 C, demonstrate that our proposed method achieves competitive performance when compared with the state-of-the-art algorithms.
Towards Source-Aligned Variational Models for Cross-Domain Recommendation

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns to fit the target observations and align its hidden space with the source latent space jointly. Since we model the latent spaces by the variational posteriors, we operate at this level, and in particular, we investigate two approaches, namely rigid and soft alignments. In the former scenario, the variational model in the target domain is set equal to the source variational model. That is, we only learn a generative model in the target domain. In the soft-alignment scenario, the target VAE has its variational model, but which is encouraged to look like its source counterpart. We analyze the proposed objectives theoretically and conduct extensive experiments to illustrate the benefit of our contribution. Empirical results on six real-world datasets show that the proposed models outperform several comparable cross-domain recommendation models.
4DLFVD

We present a 4D Light Field (LF) video dataset, collected by a custom-made camera matrix, to be used for designing and testing algorithms and systems for LF video coding, processing, and streaming. Compared to existing LF datasets, ours provides LF videos, as opposed to only images, and at higher frame resolution, higher number of viewpoints, and/or higher framerate, offering the best visual quality LF video dataset. To achieve this, we built a 10 x 10 LF capture matrix composed of 100 cameras, each with a 1920 x 1056 resolution. We used this matrix to record videos in real and varying illumination and scene dynamics conditions. The dataset contains a total of nine groups of LF videos: eight groups collected with a fixed camera matrix position and orientation recording indoor potted plants, furniture, etc., and the last group collected by rotating around an outdoor environment with roadside vehicles, pedestrians, etc. Each group of LF videos consists of 100 video streams encoded with H.265/HEVC. Scene changes vary from static to slightly dynamic to highly dynamic, providing a good level of diversity. As an example, we present the results of a depth estimation method and show that our dataset can be used for applications such as objection detection, 3D modeling, and others.
Performance Analysis of PostgreSQL, MySQL, Microsoft SQL Server Systems Based on TPC-H Tests

The development of information technologies requires the processing of an increasing amount of information. A great number of tools and DBMSs for working with big data have been created. Examples of DBMS are: MS SQL Server, Oracle Database, SciDB, VoltDB, PostgreSQL XL, Clusterix, etc. Most systems of the BigData class require serious computing power to ensure acceptable performance. This article discusses the TPC-H test and 22 of its queries, the execution of which allows you to analyze the performance of database management systems (DBMS). PostgreSQL, MySQL, and Microsoft SQL Server DBMS are considered and their performance is investigated based on TPC-H test queries with database (DB) of various sizes in the DbVisualizer Free version 11.0.6.
Multi-view Multi-pose Robust Face Recognition Based on VGGNet

Face recognition has recently received momentous interest as one of the most triumphant applications of image analysis and understanding. There are many causes for increasing attentiveness in face recognition as a rising concern for public identity verification and security. There is a necessity for face analysis and modeling approaches in information management and digital entertainment. Existing face recognition systems are facing challenges like pose variation, variation in illumination, occlusion problems in the face. To deal with all these problems, this paper presents face recognition based on the VGG16 deep learning model. To present robust pose and view variant face recognition proposed system uses Multi-Task Convolutional Neural Network (MTCNN) for face detection and VGGNet for face recognition. We have used a real-time database of facial images of 50 subjects. The results of the proposed method are assessed based on cross-validation accuracy and it gives improved accuracy of 95.80%, 77.50%, and 98.20% accuracy under extreme uncontrolled conditions for ORL, FERET, and real-time face database.
Real-Time Prediction of Impending Ground Shaking: Review of Wavefield-Based (Ground-Motion-Based) Method for Earthquake Early Warning

Earthquake early warning (EEW) systems aim to provide advance warning of impending ground shaking, and the technique used for real-time prediction of shaking is a crucial element of EEW systems. Many EEW systems are designed to predict the strength of seismic ground motions (peak ground acceleration, peak ground velocity, or seismic intensity) based on rapidly estimated source parameters (the source-based method), such as hypocentral location, origin time, magnitude, and extent of fault rupture. Recently, however, the wavefield-based (or ground-motion-based) method has been developed to predict future ground motions based directly on the current wavefield, i.e., ground motions monitored in real-time at neighboring sites, skipping the process of estimation of the source parameters. The wavefield-based method works well even for large earthquakes with long duration and huge rupture extents, highly energetic earthquakes that deviate from standard empirical relations, and multiple simultaneous earthquakes, for which the conventional source-based method sometimes performs inadequately. The wavefield-based method also enables prediction of the ongoing seismic waveform itself using the physics of wave propagation, thus providing information on the duration, in addition to the strength of strong ground motion for various frequency bands. In this paper, I review recent developments of the wavefield-based method, from simple applications using relatively sparse observation networks to sophisticated data assimilation techniques exploiting dense networks.
With-in-project defect prediction using bootstrap aggregation based diverse ensemble learning technique

Predicting the defect-proneness of a module can reduce the time, effort, manpower, and consequently the cost to develop a software project. Since the causes of software defects are difficult to identify, a wide range of machine learning models are still being developed to build a high performing prediction systems. For this reason, an hybrid approach called – diverse ensemble learning technique (DELT), that adopts two diversity generation schemes such as bootstrap aggregation and multi-inducer concepts, is proposed for with-in-project defect prediction (WPDP) problem in order to mitigate the low classification rates of the prediction model. To predict the final class-label for any unlabeled test module, the proposed DELT employs the principle of majority voting. An extensive set of experiments are conducted on 43 publicly available PROMISE and NASA datasets. The experimental results are promising since it improves the generalization performance in classifying the defect proneness of the software module.
Comprehensive Importance-Based Selective Regularization for Continual Segmentation Across Multiple Sites

In clinical practice, a desirable medical image segmentation model should be able to learn from sequential training data from multiple sites, as collecting these data together could be difficult due to the storage cost and privacy restriction. However, existing methods often suffer from catastrophic forgetting problem for previous sites when learning from images from a new site. In this paper, we propose a novel comprehensive importance-based selective regularization method for continual segmentation, aiming to mitigate model forgetting by maintaining both shape and reliable semantic knowledge for previous sites. Specifically, we define a comprehensive importance weight for each model parameter, which consists of shape-aware importance and uncertainty-guided semantics-aware importance, by measuring how a segmentation's shape and reliable semantic information is sensitive to the parameter. When training model on a new site, we adopt a selective regularization scheme that penalizes changes of parameters with high comprehensive importance, avoiding the shape knowledge and reliable semantics related to previous sites being forgotten. We evaluate our method on prostate MRI data sequentially acquired from six institutes. Results show that our method outperforms many continual learning methods for relieving model forgetting issue. Code is available at https://github.com/jingyzhang/CISR.
Mobile phone use for social inclusion: the case of internally displaced people in Nigeria

Internally Displaced People (IDP) have received less attention in ICT4D research. This study examines how IDP in Africa use mobile phones to enhance their social inclusion. We employed Sen's Capability Approach as the theoretical lens and a qualitative case study as a methodology. Qualitative data obtained from 21 conflict-induced IDP in Nigeria suggests that mobile phones serve not only as a self-help commodity to overcome disconnection from their communities but also a means to enhance their individual and collective capabilities, which in turn fosters their social inclusion. However, generating these capabilities depend on the personal, social, and environmental experiences of IDP. With these findings, the study offers contributions to theory, research, and practice.