Reviewerly/exHarmony huggingface dataset:
Linked open data-based explanations for transparent recommender systems

In this article we propose a framework that generates natural language explanations supporting the suggestions generated by a recommendation algorithm. The cornerstone of our approach is the usage of Linked Open Data (LOD) for explanation aims. Indeed, the descriptive properties freely available in the LOD cloud (e.g., the author of a book or the director of a movie) can be used to build a graph that connects the recommendations the user received to the items she previously liked via the properties extracted from the LOD cloud. In a nutshell, our approach is based on the insight that properties describing the items the user previously liked as well as the suggestions she received can be effectively used to explain the recommendations. Such a framework is both algorithm-independent and domain-independent, thus it can generate a natural language explanation for every kind of recommendation algorithm, and it can be used to explain a single recommendation (Top-1 scenario) as well as a group of recommendations (Top-N scenario). It is worth noting that the algorithm-independent characteristic does not mean that the framework is able to explain to the user how the recommendations have been generated and how the recommendation algorithm works. The framework explains to users why they might like the recommended items, independently from the recommendation algorithm that generated the recommendations. In the experimental evaluation, we carried out a user study (N = 680) aiming to investigate the effectiveness of our framework in three different domains, as movies, books and music. Results showed that our technique leads to transparent explanations for all the domains, and such explanations resulted independent of the specific recommendation algorithm in most of the experimental settings. Moreover, we also showed the goodness of our strategy when an entire group of recommendations has to be explained. As a case study, we integrated the framework in a real-world application, a conversational recommender system implemented as a Telegram Bot. The idea is to use the explanation for supporting both the training phase (when the user expresses her preferences) and the recommendation step (when the user receives the recommendations). Interesting outcomes emerge from these preliminary experiments.
Trapped Ion Quantum Information Processing with Squeezed Phonons

Trapped ions offer a pristine platform for quantum computation and simulation, but improving their coherence remains a crucial challenge. Here, we propose and analyze a new strategy to enhance the coherent interactions in trapped ion systems via parametric amplification of the ions' motion-by squeezing the collective motional modes (phonons), the spin-spin interactions they mediate can be significantly enhanced. We illustrate the power of this approach by showing how it can enhance collective spin states useful for quantum metrology, and how it can improve the speed and fidelity of two-qubit gates in multi-ion systems, important ingredients for scalable trapped ion quantum computation. Our results are also directly relevant to numerous other physical platforms in which spin interactions are mediated by bosons.
Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field, NW Desert, Egypt

The machine learning methods and well log mathematical models have been used for predicting total organic carbon (TOC) in Jurassic source rock formations in Northwestern Desert, Egypt. Conventional well log data from two wells have been utilized for source rock study of Jurassic source rocks from Khatatba and Ras Qattara formations in the study area. The source rock is first studied based on geochemical parameters, which include assessments on the type and amount of kerogen present within the source rock samples. The Jurassic source rock samples have great generative potential and consist of mixed kerogen type III and kerogen type II-III. TOC content reaches up to 46.90% for Khatatba and 16.80% Ras Qattara. In the second part of this research, we attempt to characterize the Jurassic source rocks by using mathematical well log models and machine learning methods. GR, RHOB and NPHI well log data were used for TOC prediction using both methods. The quantified TOC results show that the R2 values of well log models are above 0.9 for both formations, whereas the machine learning method using Artificial Neural Network showed R2 value of 0.4. The results from the well log models suggest that they are applicable in the study area. This study has proven that well log data can be used with confidence to evaluate organic source quantity of Jurassic rocks in Northwestern Desert in the absence of geochemical data.
Towards Achieving Robust Universal Neural Vocoding

This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality. When the recordings show significant changes in quality, or when moving towards non-speech vocalizations or singing, the vocoder still significantly outperforms speaker-dependent vocoders, but operates at a lower average relative MUSHRA of 75%. These results are shown to be consistent across languages, regardless of them being seen during training (e.g. English or Japanese) or unseen (e.g. Wolof, Swahili, Ahmaric).
Viral modernity? Epidemics, infodemics, and the ‘bioinformational’ paradigm

Viral modernity is a concept based upon the nature of viruses, the ancient and critical role they play in evolution and culture, and the basic application to understanding the role of information and forms of bioinformation in the social world. The concept draws a close association between viral biology on the one hand, and information science on the other – it is an illustration and prime example of bioinformationalism that brings together two of the most powerful forces that now drive cultural evolution. The concept of viral modernity applies to viral technologies, codes and ecosystems in information, publishing, education and emerging knowledge (journal) systems. This paper traces the relationship between epidemics, quarantine, and public health management and outlines elements of viral-digital philosophy (VDP) based on the fusion of living and technological systems. We discuss Covid-19 as a ‘bioinformationalist’ response that represents historically unprecedented level of sharing information from the sequencing of the genome to testing for a vaccination. Finally, we look at the US response to Covid-19 through the lens of infodemics and post-truth. The paper is followed by three open reviews, which further refine its conclusions as they relate to (educational) philosophy and the notion of the virus as Pharmakon.
Real-Time Face Mask Identification Using Facemasknet Deep Learning Network

The COVID - 19 pandemic is devastating mankind irrespective of caste, creed, gender, and religion. Until a vaccine is discovered, we should do our bit to constrain the expanse of the corona-virus. Using a face mask can undoubtedly help in managing the spread of the virus. COVID - 19 face mask detector uses or owns Facemasknet, deep learning techniques to successfully test whether a person is with wearing a face mask or not. The manuscript presents three-class classification namely person is wearing a mask, or improperly worn masks or no mask detected. Using our deep learning method called Facemasknet, we got an accuracy of 98.6 %. The Facemasknet can work with still images and also works with a live video stream. Cases in which the mask is improperly worn are when the nose and mouth are partially covered. Our face mask identifier is least complex in structure and gives quick results and hence can be used in CCTV footage to detect whether a person is wearing a mask perfectly so that he does not pose any danger to others. Mass screening is possible and hence can be used in crowded places like railway stations, bus stops, markets, streets, mall entrances, schools, colleges, etc. By monitoring the placement of the face mask on the face, we can make sure that an individual wears it the right way and helps to curb the scope of the virus.
BAFL: A Blockchain-Based Asynchronous Federated Learning Framework

As an emerging distributed machine learning (ML) method, federated learning (FL) can protect data privacy through collaborative learning of artificial intelligence (AI) models across a large number of devices. However, inefficiency and vulnerability to poisoning attacks have slowed FL performance. Therefore, a blockchain-based asynchronous federated learning (BAFL) framework is proposed to ensure the security and efficiency required by FL. The blockchain ensures that the model data cannot be tampered with while asynchronous learning speeds up global aggregation. A novel entropy weight method is used to evaluate the participating rank and proportion of the local model trained in BAFL of the devices. The energy consumption and local model update efficiency are balanced by adjusting the local training and communication delay and optimizing the block generation rate. The extensive evaluation results show that the proposed BAFL framework has higher efficiency and higher performance for preventing poisoning attacks than other distributed ML methods.
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

Prithviraj Ammanabrolu, Mark Riedl. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Topic-Oriented Image Captioning Based on Order-Embedding

We present an image captioning framework that generates captions under a given topic. The topic candidates are extracted from the caption corpus. A given image's topics are then selected from these candidates by a CNN-based multi-label classifier. The input to the caption generation model is an image-topic pair, and the output is a caption of the image. For this purpose, a cross-modal embedding method is learned for the images, topics, and captions. In the proposed framework, the topic, caption, and image are organized in a hierarchical structure, which is preserved in the embedding space by using the order-embedding method. The caption embedding is upper bounded by the corresponding image embedding and lower bounded by the topic embedding. The lower bound pushes the images and captions about the same topic closer together in the embedding space. A bidirectional caption-image retrieval task is conducted on the learned embedding space and achieves the state-of-the-art performance on the MS-COCO and Flickr30K datasets, demonstrating the effectiveness of the embedding method. To generate a caption for an image, an embedding vector is sampled from the region bounded by the embeddings of the image and the topic, then a language model decodes it to a sentence as the output. The lower bound set by the topic shrinks the output space of the language model, which may help the model to learn to match images and captions better. Experiments on the image captioning task on the MS-COCO and Flickr30K datasets validate the usefulness of this framework by showing that the different given topics can lead to different captions describing specific aspects of the given image and that the quality of generated captions is higher than the control model without a topic as input. In addition, the proposed method is competitive with many state-of-the-art methods in terms of standard evaluation metrics.
Blockchain: a paradigm shift in business practices

Purpose The purpose of this paper is to introduce and explore blockchain technology and its potential implementation to hospitality and tourism firms’ wide range of business operations and transactions from a technological and functional point of view. This study’s central interest is to produce novel and rigorous in-depth-review analysis and foundations for a broad discussion and outlook on the potential applications of blockchain technology benefiting hospitality and tourism research, as well as the industry as a whole. Design/methodology/approach This study identifies and proposes several potential areas of the adoption and implementation of blockchain technology to the hospitality and tourism industry, including payment and cryptocurrencies, tracking and service customization, the disintermediation of hospitality and tourism, innovative loyalty programs, smart contracts, integrated property management systems, verified rating and review systems, collaborative initiatives and due diligence and smart tourism, each of which represents fertile avenues for future research. Findings This paper provides extensive critical discussions, reviews and answers to a fundamental question: “What critical functions of Blockchain mechanisms can be implemented to the existing core operational (i.e. booking and reservation systems, guest management, etc.) and business functions (i.e. loyalty/reward programs, agent transactions, etc.) of hospitality and tourism companies?”. Research limitations/implications Future studies should specifically delve further into various angles of this “BizTech” environment based not only on business operations and competition but also on vendor and customer collaboration. Practical implications This study intends to serve as a guidance for future research, facilitate knowledge accumulation and create a new understanding and awareness in both practice and academia. One of the most important applications of blockchain in this industry would be that pertaining to direct booking, online reservation systems (i.e. airlines and online travel agencies) and check-in/out with digital identities. With industry-wide blockchain adoption, guests’ personal information can be digitally validated, saved and secured as previously established cryptographically secured codes verify one’s identity without disclosing essential personal information. Originality/value It is obvious that the hospitality and tourism industry needs urgent technological transformation, industrial innovations and new growth avenues such as the adoption of blockchain technology and systems to maintain its global market share in the future. Therefore, the implementation of blockchain systems can promote the formation of multi-center (i.e. guest operations and customer service), weakly intermediated (i.e. loyalty programs and/or review and rating systems) areas in this industry.
Quantifying the Carbon Emissions of Machine Learning

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.
Content-based image retrieval: A review of recent trends

With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.
ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-identification in Multispectral Dataset

We propose a ThermalGAN framework for cross-modality color-thermal person re-identification (ReID). We use a stack of generative adversarial networks (GAN) to translate a single color probe image to a multimodal thermal probe set. We use thermal histograms and feature descriptors as a thermal signature. We collected a large-scale multispectral ThermalWorld dataset for extensive training of our GAN model. In total the dataset includes 20216 color-thermal image pairs, 516 person ID, and ground truth pixel-level object annotations. We made the dataset freely available ( http://www.zefirus.org/ThermalGAN/ ). We evaluate our framework on the ThermalWorld dataset to show that it delivers robust matching that competes and surpasses the state-of-the-art in cross-modality color-thermal ReID.
TrustChain: A Privacy Preserving Blockchain with Edge Computing

Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.
Mixed Effects Neural Networks (MeNets) With Applications to Gaze Estimation

There is much interest in computer vision to utilize commodity hardware for gaze estimation. A number of papers have shown that algorithms based on deep convolutional architectures are approaching accuracies where streaming data from mass-market devices can offer good gaze tracking performance, although a gap still remains between what is possible and the performance users will expect in real deployments. We observe that one obvious avenue for improvement relates to a gap between some basic technical assumptions behind most existing approaches and the statistical properties of the data used for training. Specifically, most training datasets involve tens of users with a few hundreds (or more) repeated acquisitions per user. The non i.i.d. nature of this data suggests better estimation may be possible if the model explicitly made use of such “repeated measurements” from each user as is commonly done in classical statistical analysis using so-called mixed effects models. The goal of this paper is to adapt these “mixed effects” ideas from statistics within a deep neural network architecture for gaze estimation, based on eye images. Such a formulation seeks to specifically utilize information regarding the hierarchical structure of the training data - each node in the hierarchy is a user who provides tens or hundreds of repeated samples. This modification yields an architecture that offers state of the art performance on various publicly available datasets improving results by 10-20%.
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data. In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution. We observe that BIVA, in contrast to recent results, can be used for anomaly detection. We attribute this to the hierarchy of latent variables which is able to extract high-level semantic features. Finally, we extend BIVA to semi-supervised classification tasks and show that it performs comparably to state-of-the-art results by generative adversarial networks.
Watch, Listen and Tell: Multi-Modal Weakly Supervised Dense Event Captioning

Multi-modal learning, particularly among imaging and linguistic modalities, has made amazing strides in many high-level fundamental visual understanding problems, ranging from language grounding to dense event captioning. However, much of the research has been limited to approaches that either do not take audio corresponding to video into account at all, or those that model the audio-visual correlations in service of sound or sound source localization. In this paper, we present the evidence, that audio signals can carry surprising amount of information when it comes to high-level visual-lingual tasks. Specifically, we focus on the problem of weakly-supervised dense event captioning in videos and show that audio on its own can nearly rival performance of a state-of-the-art visual model and, combined with video, can improve on the state-of-the-art performance. Extensive experiments on the ActivityNet Captions dataset show that our proposed multi-modal approach outperforms state-of-the-art unimodal methods, as well as validate specific feature representation and architecture design choices.