Reviewerly/exHarmony huggingface dataset:
New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests

The aim of recommender systems is to provide users with items that could be of their interest. However one of the biggest drawbacks from recommender systems is the so called cold start problem, which occurs when new users or products are added to the system and therefore there is no previous information about them. There are many proposals in the literature that aim to deal with this issue. In some cases the user is required to provide some explicit information about them, which demands some effort on their part. Because of that and due to the great boom of social networks, we will focus on extracting implicit information from user’s social stream. In this paper we will present an approach on which social media data will be used to create a behavioural profile to classify the users and based on this classification will create predictions making use of machine learning techniques such as classification trees and random forests. Thus the user will not have to provide actively any kind of data explicitly but their social media source, alleviating in this way the cold start problem since the system would use this data in order to create user profiles, which will be the input for the engine of the recommender systems. We have carried out numerous experiments, as well as a comparison with some other state-of-the-art new user cold-start algorithms, obtaining very satisfactory results.
Explaining and Interpreting LSTMs

While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved. In this chapter, we explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.
Real-time Arm Gesture Recognition in Smart Home Scenarios via Millimeter Wave Sensing

"In air" gesture recognition using millimeter wave (mmWave) radar and its applications in natural human-computer-interaction for smart home has shown its potential. However, the state-of-the-art works still fall short in terms of limited gesture space, vulnerable to surrounding interference, and off-line recognition. In this paper, we propose mHomeGes, a real-time mmWave arm gesture recognition system for practical smart home-usage. To this end, we first distill arm gesture's position and dynamic variation, and then custom-design a lightweight convolution neural network to recognize fine-grained gestures. Next, we propose a user discovery method to focus on the target human gesture, thus eliminating the adverse impact of surrounding interference. Finally, we design a hidden Markov model-based voting mechanism to handle continuous gesture signals at run-time, which leads to continuous gesture recognition in real-time. We implement mHomeGes on a commodity mmWave radar and also perform a user study, which demonstrates that mHomeGes achieves high recognition accuracy above 95.30% in real-time across various smart home scenarios, regardless of the impact of surrounding movements, concurrent gestures, human physiological conditions, and outer packing materials. Moreover, we have also publicly archived a mmWave gesture data-set collected during developing mHomeGes, which consists of about 22,000 instances from 25 persons and may have an independent value of facilitating future research.
Neural Light Transport for Relighting and View Synthesis

The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel views under arbitrary lighting. In this paper, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach to learn a neural representation of LT that is embedded in the space of a texture atlas of known geometric properties, and model all non-diffuse and global LT as residuals added to a physically-accurate diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination, while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse, previously seen observations. Qualitative and quantitative experiments demonstrate that our neural LT (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without separate treatment for both problems that prior work requires.
Using Mobile ICT for Online Learning During COVID-19 Lockdown

The research is aimed at theoretical substantiation, development and experimental verification of methods of applying mobile technologies to university students training during the COVID-19 lockdown. The research objective implies adapting mobile ICT to online learning during the COVID-19 lockdown. There are analyzed Ukrainian and foreign researches into the issues of mobile ICT application to the University students training. The authors have developed methods of applying Audience Response Systems by taking Plickers as an example; Mobile Multimedia Authoring Tools development through using augmented reality; Mobile Learning Management Systems by taking Moodle; Mobile Modeling and Programming Environments through using Pydroid; Mobile Database Management Systems by taking Google Cloud SQL. There is comparative assessment of functionality of five systems of mobile ICT for online learning during the COVID-19 lockdown. Efficiency of the developed technology is experimentally verified and confirmed.
Medical imaging deep learning with differential privacy

Abstract The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm. We here present deepee , a free-and-open-source framework for differentially private deep learning for use with the PyTorch deep learning framework. Our framework is based on parallelised execution of neural network operations to obtain and modify the per-sample gradients. The process is efficiently abstracted via a data structure maintaining shared memory references to neural network weights to maintain memory efficiency. We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. We benchmark our framework’s computational performance against other open-source DP frameworks and evaluate its application on the paediatric pneumonia dataset , an image classification task and on the Medical Segmentation Decathlon Liver dataset in the task of medical image segmentation. We find that neural network training with rigorous privacy guarantees is possible while maintaining acceptable classification performance and excellent segmentation performance. Our framework compares favourably to related work with respect to memory consumption and computational performance. Our work presents an open-source software framework for differentially private deep learning, which we demonstrate in medical imaging analysis tasks. It serves to further the utilisation of privacy-enhancing techniques in medicine and beyond in order to assist researchers and practitioners in addressing the numerous outstanding challenges towards their widespread implementation.
Kafnets: Kernel-based non-parametric activation functions for neural networks

Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions. To increase their flexibility, several authors have proposed methods for adapting the activation functions themselves, endowing them with varying degrees of flexibility. None of these approaches, however, have gained wide acceptance in practice, and research in this topic remains open. In this paper, we introduce a novel family of flexible activation functions that are based on an inexpensive kernel expansion at every neuron. Leveraging several properties of kernel-based models, we propose multiple variations for designing and initializing these kernel activation functions (KAFs), including a multidimensional scheme allowing to nonlinearly combine information from different paths in the network. The resulting KAFs can approximate any mapping defined over a subset of the real line, either convex or non-convex. Furthermore, they are smooth over their entire domain, linear in their parameters, and they can be regularized using any known scheme, including the use of ℓ1 penalties to enforce sparseness. To the best of our knowledge, no other known model satisfies all these properties simultaneously. In addition, we provide an overview on alternative techniques for adapting the activation functions, which is currently lacking in the literature. A large set of experiments validates our proposal.
First-arrival picking with a U-net convolutional network

In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-to-noise ratio, and dramatic phase change, and they are difficult to pick accurately with traditional automatic procedures. We have approached this problem by using a U-shaped fully convolutional network (U-net) to first-arrival picking, which is formulated as a binary segmentation problem. U-net has the ability to recognize inherent patterns of the first arrivals by combining attributes of arrivals in space and time on data of varying quality. An effective workflow based on U-net is presented for fast and accurate picking. A set of seismic waveform data and their corresponding first-arrival times are used to train the network in a supervised learning approach, then the trained model is used to detect the first arrivals for other seismic data. Our method is applied on one synthetic data set and three field data sets of low quality to identify the first arrivals. Results indicate that U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey. With the increasing training data of various first arrivals, a trained U-net has the potential to directly identify the first arrivals on new seismic data.
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses.However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs.This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task.Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation.Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs.To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models.The proposed corpus and model can motivate the future research directions 1 .
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG).However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs.In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space.Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively.We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time.Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction.In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.
A Bibliometric Analysis of Blockchain Technology Research Using VOSviewer

Blockchain technology remains popular for several reasons. The main one is that it has facilitated the rise of digital currencies over the past several years and many other uses of non-crypto currency. There is a belief that the technology itself could far exceed cryptocurrencies by its impact. Thus, researchers are still discovering the real potential of blockchain. This study aims to conduct a comprehensive blockchain analysis with a bibliometric study. The data was retrieved from the Scopus database and was analyzed using the VOSviewer software, developed at Leiden University’s Centre for Science and Technology Studies (CWTS), Leiden University, the Netherlands. The study is based on the analysis of 1842 documents published in the 2007–2021 period using Scopus. From the visualization, three main groups of six clusters are generated. The red area includes topics related to blockchain technology, supply chain management, and sustainable development. The green cluster stands for such keywords as blockchains, smart contracts, electronic money, and Bitcoin and Ethereum. The blue cluster area focuses on issues related to artificial intelligence, big data, health care, and COVID-19. The analysis helps to improve the quality of the review by directing researchers to the most significant documents and mapping areas of publications.
A new semantic-based feature selection method for spam filtering

The Internet emerged as a powerful infrastructure for the worldwide communication and interaction of people. Some unethical uses of this technology (for instance spam or viruses) generated challenges in the development of mechanisms to guarantee an affordable and secure experience concerning its usage. This study deals with the massive delivery of unwanted content or advertising campaigns without the accordance of target users (also known as spam). Currently, words (tokens) are selected by using feature selection schemes; they are then used to create feature vectors for training different Machine Learning (ML) approaches. This study introduces a new feature selection method able to take advantage of a semantic ontology to group words into topics and use them to build feature vectors. To this end, we have compared the performance of nine well-known ML approaches in conjunction with (i) Information Gain, the most popular feature selection method in the spam-filtering domain and (ii) Latent Dirichlet Allocation, a generative statistical model that allows sets of observations to be explained by unobserved groups that describe why some parts of the data are similar, and (iii) our semantic-based feature selection proposal. Results have shown the suitability and additional benefits of topic-driven methods to develop and deploy high-performance spam filters.
OVERCOMING CATASTROPHIC FORGETTING FOR CONTINUAL LEARNING VIA MODEL ADAPTATION

Learning multiple tasks sequentially is important for the development of AI and lifelong learning systems. However, standard neural network architectures suffer from catastrophic forgetting which makes it difficult for them to learn a sequence of tasks. Several continual learning methods have been proposed to address the problem. In this paper, we propose a very different approach, called Parameter Generation and Model Adaptation (PGMA), to dealing with the problem. The proposed approach learns to build a model, called the solver, with two sets of parameters. The first set is shared by all tasks learned so far and the second set is dynamically generated to adapt the solver to suit each test example in order to classify it. Extensive experiments have been carried out to demonstrate the effectiveness of the proposed approach.
ArASL: Arabic Alphabets Sign Language Dataset

A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing people using machine learning, computer vision and deep learning algorithms. The contribution is a large fully-labelled dataset for Arabic Sign Language (ArSL) which is made publically available and free for all researchers. The dataset which is named ArSL2018 consists of 54,049 images for the 32 Arabic sign language sign and alphabets collected from 40 participants in different age groups. Different dimensions and different variations were present in images which can be cleared using pre-processing techniques to remove noise, center the image, etc. The dataset is made available publicly at https://data.mendeley.com/datasets/y7pckrw6z2/1.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data.However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points.In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications.This paper presents an incremental DR solution.We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results.Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization.We demonstrate the effectiveness of our design with two case studies using real-world datasets.
Logistic Regression on Homomorphic Encrypted Data at Scale

Machine learning on (homomorphic) encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and outputs an encrypted model without ever decrypting. In the prediction phase, it uses the encrypted model to predict results on new encrypted data. In each phase, no decryption key is needed, and thus the data privacy is ultimately guaranteed. It has many applications in various areas such as finance, education, genomics, and medical field that have sensitive private data. While several studies have been reported on the prediction phase, few studies have been conducted on the training phase.In this paper, we present an efficient algorithm for logistic regression on homomorphic encrypted data, and evaluate our algorithm on real financial data consisting of 422,108 samples over 200 features. Our experiment shows that an encrypted model with a sufficient Kolmogorov Smirnow statistic value can be obtained in ∼17 hours in a single machine. We also evaluate our algorithm on the public MNIST dataset, and it takes ∼2 hours to learn an encrypted model with 96.4% accuracy. Considering the inefficiency of homomorphic encryption, our result is encouraging and demonstrates the practical feasibility of the logistic regression training on large encrypted data, for the first time to the best of our knowledge.
Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current models, leading to image distortion and temporal inconsistency. We point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to uniformly deal with spatial and temporal information. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multilevel temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multifrequency motions under a fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Source code and videos are available at https://github.com/Bei-Jin/STMFANet.
A survey on knowledge graph-based recommender systems

Recommender system (RS) targets at providing accurate item recommendations to users with respect to their preferences; it has been widely employed in various online applications for addressing the problem of information explosion and improving user experience. In the past decades, while tremendous efforts have been made in enhancing the performance of RSs, some long-standing challenges, such as data sparsity, cold start, and result diversity, are unaddressed. Along this line, an emerging research trend is to exploit the rich semantic information contained in the knowledge graph (KG); it has been proven to be an effective way to enhance the capability of RSs. To this end, we provide a focused survey on KG-based RS via a holistic perspective of both technologies and applications. Specifically, firstly, we briefly review the core concepts and classical algorithms of the RSs and KGs. Secondly, we comprehensively introduce the representative and state-of-the-art works in this field based on different strategies of exploiting KGs for RSs. Meanwhile, we also summarize some typical application scenarios of KG-based RSs, for facilitating the hands-on practices of corresponding algorithms. Finally, we present our opinions on the prospects of KG-based RS and suggest some future research directions in this area.