Reviewerly/exHarmony huggingface dataset:
Restaurant recommender system based on sentiment analysis

Today, exploiting sentiment analysis has become popular in designing recommender systems in various fields, including the restaurant and food area. However, most of the sentiment analysis-based restaurant recommender systems only use static information such as food quality, price, and service quality. The analysis of users’ opinions and the extraction of their food preferences lead to the provision of personalized recommendations, which is a research gap in literature; In this paper, a context-aware recommender system is proposed that extracts the food preferences of individuals from their comments and suggests restaurants in accordance with these preferences. For this purpose, the semantic approach is used to cluster the name of foods extracted from users’ comments and analyze their sentiments about them. Finally, nearby open restaurants are recommended based on their similarity to user preferences. For evaluation, the TripAdvisor website has been used and comments from 100 different users have been collected during the first 9 months of 2018. The precision, recall and f-measure of the system are measured in three scenarios of top1, top3, and top5. The results indicate that the proposed system can provide recommendations with a precision of 92.8%, giving users a high degree of precision. Besides, the system outperforms the previous research in these criteria. • A text processing method is proposed to extract users’ preferences from their comments. • A restaurant recommender system is proposed based on semantic similarity evaluation. • The precision, recall and f-measure has been evaluated via an experiment.
Improved autoencoder for unsupervised anomaly detection

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved AutoEncoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.
Decentralized Federated Learning: Balancing Communication and Computing Costs

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. The convergence rate of DFL can be optimized to achieve the balance of communication and computing costs under constrained resources. For improving communication efficiency of DFL, compressed communication is further introduced to the proposed DFL as a new scheme, named DFL with compressed communication (C-DFL). The proposed C-DFL exhibits linear convergence for strongly convex objectives. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods and show that C-DFL further enhances communication efficiency.
Cyber Security Threats in Cloud: Literature Review

In recent years, data has been expanding every second in terms of velocity, volume, and variety. This has resulted in enormous and complex big data, raising challenges in the storage, management, analysis of these big data and security thereof. Many organizations tend to use cloud systems in order to facilitate the operation in big data without being fully aware of the security and privacy challenges that the utilization of these systems pose and consequently ignoring the important practices and techniques that should be implemented when using cloud systems. These security threats therefore require more research in order to produce solutions to the cloud system environment. The goal of this paper is collection of most common cyber security threats in the cloud system environment and most common used mitigation techniques by reviewing the published papers in the period from 2019 to 2020 followed by cloud risk assessment case study in an organization in Saudi Arabia.
Best practices for analyzing the direct energy use of blockchain technology systems: Review and policy recommendations

There are a growing number of blockchain applications in energy systems, but surprisingly little is known about their direct energy demand outside of cryptocurrency applications. Addressing this knowledge gap should be a key policy priority so that the energy use of blockchain systems can be better understood and managed as applications proliferate. To help policy makers and energy analysts achieve this goal, this article makes three contributions. First, we present a brief review of blockchain system components, energy demand drivers, and emerging applications to provide a common conceptual foundation. Second, we propose best practices for energy analysis of blockchain systems and identify best practice lapses in previous literature that presently cloud our understanding of energy use. Finally, we propose priority research areas to address identified lapses and knowledge gaps, which would ensure future research produces data and estimates that are maximally relevant to energy policy decisions.
Garbage in garbage out: The precarious link between IoT and blockchain in food supply chains

The application of blockchain in food supply chains does not resolve conventional IoT data quality issues. Data on a blockchain may simply be immutable garbage. In response, this paper reports our observations and learnings from an ongoing beef supply chain project that integrates Blockchain and IoT for supply chain event tracking and beef provenance assurance and proposes two solutions for data integrity and trust in the Blockchain and IoT-enabled food supply chain. Rather than aiming for absolute truth, we explain how applying the notion of ‘common knowledge’ fundamentally changes oracle identity and data validity practices. Based on the learnings derived from leading an IoT supply chain project with a focus on beef exports from Australia to China, our findings unshackle IoT and Blockchain from being used merely to collect lag indicators of past states and liberate their potential as lead indicators of desired future states. This contributes: (a) to limit the possibility of capricious claims on IoT data performance, and; (b) to utilise mechanism design as an approach by which supply chain behaviours that increase the probability of desired future states being realised can be encouraged.
Integrating and navigating engineering design decision-related knowledge using decision knowledge graph

Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge.
Analysis and Prediction of COVID-19 Confirmed Cases Using Deep Learning Models: A Comparative Study

COVID-19 or Novel coronavirus is an infectious disease that was first noticed in December, 2019 and it eventually emerged as a pandemic as it is highly contagious in nature. It affected the economic and social structure worldwide and caused a huge loss of human life. Due to the scarcity of medical infrastructure, it has become nearly impossible to cure every case of COVID-19 and hence the loss of lives is exceedingly increasing. So, if the cases can be forecasted beforehand, proper precautions can be taken on time and thousands of human lives can be saved. In this paper, predictions of the number of coronavirus confirmed cases for the five topmost affected countries across the world have been made. Along with it, a comparative study of ANN (Artificial Neural Network) and RNN (Recurrent Neural Network) based LSTM (Long Short Term Memory) Model has been carried out. The countries taken into consideration for this paper are USA, India, Brazil, Russia, and France. The models have been used to train the dataset and validate the prediction results against the original data based on the predefined metric of MSE or Mean Squared Error. The prediction results have been visualized graphically and it was inferred that the LSTM model outperformed the ANN model.
Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning

The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. One of the most important mechanisms is the use of facemasks. However, the wearing the facemasks incorrectly makes this prevention method useless. Artificial Intelligence (AI) and especially facial recognition techniques can be used to detect misuses and reduce virus transmission, especially indoors. In this paper, we present an intelligent method to automatically detect when facemasks are being worn incorrectly in real-time scenarios. Our proposal uses Convolutional Neural Networks (CNN) with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading. The main problem that we have detected is that there is currently no training set for this task. It is for this reason that we have requested the participation of citizens by taking different selfies through an app and placing the mask in different positions. Thus, we have been able to solve this problem. The results show that the accuracy achieved with transfer learning slightly improves the accuracy achieved with convolutional neural networks. Finally, we have also developed an Android-app demo that validates the proposal in real scenarios.
Effective Feature Fusion Network in BIFPN for Small Object Detection

In view of the difficulty and low accuracy of small object detection in remote sensing images, this paper proposes a bidirectional cross-scale connection feature fusion network with an information direct connection layer and a shallow information fusion layer. Aiming at the problem that the detection targets in remote sensing images are mainly small and medium-sized targets, we fuse the shallow feature maps with rich spatial information in the bidirectional cross-scale connection feature fusion network instead of directly using the shallow feature maps for regression and classification. While ensuring the model inference speed, the detection accuracy of small objects is improved. At the same time, we use the information direct connection layer to perform feature fusion with the initial information in each iteration of the bidirectional cross-scale connection feature fusion pyramid to prevent the loss of small object information. Experimental results show that the algorithm proposed in this paper can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset.
Global cryptocurrency trend prediction using social media

This paper aims to investigate the global crypto-currency price movement trends with respect to the social media communication data. The idea is to analyze the topical trends in the online communities and social media platforms to understand and extract insights that could be used to predict the price fluctuations in crypto-currencies. The hypothesis rests in finding the empirical evidence to exploit the relationship between price variations and social media activities. Such models and insights will help us better understand the crypto currency ecosystems in context of social media behavior which can be used for real-time trading systems.
Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks

Bootstrap Aggregative Mean-Shift SQL Query Clustering (BAMSQLQC) technique is to identify the anti-patterns in the big data logs with a minimal false-positive rate. BAMSQLQC technique collects numbers of patterns (i.e. queries) from input SQL query big data logs and creates bootstrap big data samples by using the patterns in the given dataset. The BAMSQLQC technique constructs several weak clusters for each pattern in a bootstrap sample. The clustering output of all weak mean-shift clustering is combined into a strong cluster by using the voting scheme to efficiently group similar patterns together with a lesser false-positive rate. The BAMSQLQC technique conducts the experimental results using metrics such as anti-patterns detection accuracy, false-positive rate, time and space complexity. The results show that the BAMSQLQC technique can increase the accuracy and reduce the time complexity of anti-patterns discovery for effective big data analytics in 5G networks compared to existing techniques.
<i>K</i>-Time Modifiable and Epoch-Based Redactable Blockchain

As an immutable append-only distributed ledger, blockchain allows a group of participants to reach a consensus in an untrustworthy ecosystem. Immutability is a blockchain feature that persists data forever, but it is no longer legal in reality. Blockchain has unchangeable improper contents that violate laws. Moreover, data regulation toward “the right to be forgotten” requires blockchain must be modifiable. To address this problem, redactable blockchain has been introduced to relax immutability in a controlled way. However, once a participant is authorized, she/he can rewrite any content and no penalty for the malicious behavior that hinders the wide deployment of redactable blockchain in practice. In this paper, we introduce a new notion, dubbed k-time modifiable and epoch-based redactable blockchain (KERB) with a monetary penalty to control rewriting privileges and penalize malicious behaviors. Our solution is built up from simple building blocks: digital signatures and chameleon hashes. We give a formal definition and security models of KERB, and present a generic construction along with formal proofs. The extensive comparison and experimental analysis illustrate that our solution enjoys superior functionalities and performances than the state-of-the-art solutions.
Avatars for Teleconsultation: Effects of Avatar Embodiment Techniques on User Perception in 3D Asymmetric Telepresence

A 3D Telepresence system allows users to interact with each other in a virtual, mixed, or augmented reality (VR, MR, AR) environment, creating a shared space for collaboration and communication. There are two main methods for representing users within these 3D environments. Users can be represented either as point cloud reconstruction-based avatars that resemble a physical user or as virtual character-based avatars controlled by tracking the users' body motion. This work compares both techniques to identify the differences between user representations and their fit in the reconstructed environments regarding the perceived presence, uncanny valley factors, and behavior impression. Our study uses an asymmetric VR/AR teleconsultation system that allows a remote user to join a local scene using VR. The local user observes the remote user with an AR head-mounted display, leading to facial occlusions in the 3D reconstruction. Participants perform a warm-up interaction task followed by a goal-directed collaborative puzzle task, pursuing a common goal. The local user was represented either as a point cloud reconstruction or as a virtual character-based avatar, in which case the point cloud reconstruction of the local user was masked. Our results show that the point cloud reconstruction-based avatar was superior to the virtual character avatar regarding perceived co-presence, social presence, behavioral impression, and humanness. Further, we found that the task type partly affected the perception. The point cloud reconstruction-based approach led to higher usability ratings, while objective performance measures showed no significant difference. We conclude that despite partly missing facial information, the point cloud-based reconstruction resulted in better conveyance of the user behavior and a more coherent fit into the simulation context.
M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VSNet establishes the state-of-the-arts unsupervised method and achieves better performance than previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks & Temples benchmark with effective improvement.
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible ‘in-the-wild’ properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges – predicting the level of trustworthiness – no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 percent improvement).
Quantum federated learning through blind quantum computing

Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooperation between quantum physics and machine learning may lead to unparalleled prospect for solving private distributed learning tasks. In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe. For concreteness, we first introduce a protocol for private single-party delegated training of variational quantum classifiers based on blind quantum computing and then extend this protocol to multiparty private distributed learning incorporated with differential privacy. We carry out extensive numerical simulations with different real-life datasets and encoding strategies to benchmark the effectiveness of our protocol. We find that our protocol is robust to experimental imperfections and is secure under the gradient attack after the incorporation of differential privacy. Our results show the potential for handling computationally expensive distributed learning tasks with privacy guarantees, thus providing a valuable guide for exploring quantum advantages from the security perspective in the field of machine learning with real-life applications.