Reviewerly/exHarmony huggingface dataset:
A Tutorial on Quantum Convolutional Neural Networks (QCNN)

Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision. This model can also be used in small quantum computers, and a hybrid learning model can be designed by adding a quantum convolution layer to the CNN model or replacing it with a convolution layer. This paper also verifies whether the QCNN model is capable of efficient learning compared to CNN through training using the MNIST dataset through the TensorFlow Quantum platform.
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity. The main challenges of using Radar are the noise and measurement ambiguities which have been a struggle for existing simple input or output fusion methods. To better address this, we propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion, which learn from data to exploit both geometric and dynamic information of Radar data. RadarNet achieves state-of-the-art results on two large-scale real-world datasets in the tasks of object detection and velocity estimation. We further show that exploiting Radar improves the perception capabilities of detecting faraway objects and understanding the motion of dynamic objects.
Comparison of zero replacement strategies for compositional data with large numbers of zeros

Modern applications in chemometrics and bioinformatics result in compositional data sets with a high proportion of zeros. An example are microbiome data, where zeros refer to measurements below the detection limit of one count. When building statistical models, it is important that zeros are replaced by sensible values. Different replacement techniques from compositional data analysis are considered and compared by a simulation study and examples. The comparison also includes a recently proposed method (Templ, 2020) [1] based on deep learning. Detailed insights into the appropriateness of the methods for a problem at hand are provided, and differences in the outcomes of statistical results are discussed.
Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps. The proposed method achieves diagnosis performance of 0.898 and 0.862 in terms AUC when using ground-truth segmentation maps and a maximum of 0.880 and 0.860 when a U-Net-based automatic segmentation stage is employed, for DDSM-400 and CBIS-DDSM, respectively. ConclusionsThe experimental results demonstrate that integrating segmentation information into a CNN leads to improved performance in breast cancer diagnosis of mammographic masses.
Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning

Currently, in the face of the health crisis caused by the Coronavirus COVID-19 which has spread throughout the worldwide.The fight against this pandemic has become an unavoidable reality for many countries.It is now a matter involving many areas of research in the use of new information technologies, particularly those related to artificial intelligence.In this paper, we present a novel contribution to help in the fight against this pandemic.It concerns the detection of people wearing masks because they cannot work or move around as usual without protection against COVID-19.However, there are only a few research studies about face mask detection.In this work, we investigated using different deep Convolutional Neural Networks (CNN) to extract deep features from images of faces.The extracted features are further processed using various machine learning classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN).Were used and examined all different metrics such as accuracy and precision, to compare all model performances.The best classification rate was getting is 97.1%, which was achieved by combining SVM and the MobileNetV2 model.Despite the small dataset used (1376 images), we have obtained very satisfactory results for the detection of masks on the faces.
Formulating and Solving Routing Problems on Quantum Computers

The determination of vehicle routes fulfilling connectivity, time, and operational constraints is a well-studied combinatorial optimization problem.The NP-hard complexity of vehicle routing problems has fostered the adoption of tailored exact approaches, matheuristics, and metaheuristics on classical computing devices.The ongoing evolution of quantum computing hardware and the recent advances of quantum algorithms (i.e., VQE, QAOA, and ADMM) for mathematical programming make decision-making for routing problems an avenue of research worthwhile to be explored on quantum devices.In this article, we propose several mathematical formulations for inventory routing cast as vehicle routing with time windows and comment on their strengths and weaknesses.The optimization models are compared from a quantum computing perspective, specifically with metrics to evaluate the difficulty in solving the underlying quadratic unconstrained binary optimization problems.Finally, the solutions obtained on simulated quantum devices demonstrate the relative benefits of different algorithms and their robustness when put into practice.
A multi-resolution model for histopathology image classification and localization with multiple instance learning

Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra-observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group ≥ 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.
Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning

A rapid-growing machine learning technique called federated edge learning has emerged to allow a massive number of edge devices (e.g. smart phones) to collaboratively train globally shared models without revealing their private raw data. This technique not only ensures good machine learning performance but also maintains data privacy of the edge devices. However, the federated edge learning still faces the following critical challenges: (i) difficulty in avoiding unreliable edge devices acting as workers for federated edge learning, and (ii) lack of efficient learning task assignment schemes among task publishers and workers. To tackle these challenges, reputation is utilized as a metric to evaluate the trustworthiness and reliability of the edge devices. A many-to-one matching model is proposed to address the task assignment problem between task publishers and reliable workers with high reputation. For stimulating reliable edge devices to join model training and enable secure reputation management, blockchain is employed to store the training records and manage reputation data in a decentralized and secure manner without the risk of a single point of failure. Numerical results show that the proposed schemes can achieve significant performance improvement in terms of reliability of federated edge learning.
Comparative Analyses of Bert, Roberta, Distilbert, and Xlnet for Text-Based Emotion Recognition

Transformers' feat is attributed to its better language understanding abilities to achieve state-of-the-art results in medicine, education, and other major NLP tasks. This paper analyzes the efficacy of BERT, RoBERTa, DistilBERT, and XLNet pre-trained transformer models in recognizing emotions from texts. The paper undertakes this by analyzing each candidate model's output compared with the remaining candidate models. The implemented models are fine-tuned on the ISEAR data to distinguish emotions into anger, disgust, sadness, fear, joy, shame, and guilt. Using the same hyperparameters, the recorded model accuracies in decreasing order are 0.7431, 0.7299, 0.7009, 0.6693 for RoBERTa, XLNet, BERT, and DistilBERT, respectively.
A gravitational search algorithm with hierarchy and distributed framework

Gravitational search algorithm is an effective population-based algorithm. It simulates the law of gravity to implement the interaction among particles. Although it can effectively optimize many problems, it generally suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed. A distributed framework randomly groups several subpopulations and a three-layered hierarchy manages them. Communication among subpopulations finally enhances the search performance. Experiments discuss parameters and strategies of the proposed algorithm. Comparison between it and sixteen state-of-the-art algorithms demonstrates its superior performance. It also shows the practicality for two real-world optimization problems.
Adoption of COVID-19 Contact Tracing Apps: A Balance Between Privacy and Effectiveness

With the relative ubiquity of smartphones, contact tracing and exposure notification apps have been looked to as novel methods to help reduce the transmission of COVID-19. Many countries have created apps that lie across a spectrum from privacy-first approaches to those that have very few privacy measures. The level of privacy incorporated into an app is largely based on the societal norms and values of a particular country. Digital health technologies can be highly effective and preserve privacy at the same time, but in the case of contact tracing and exposure notification apps, there is a trade-off between increased privacy measures and the effectiveness of the app. In this article, examples from various countries are used to highlight how characteristics of contract tracing and exposure notification apps contribute to the perceived levels of privacy awarded to citizens and how this impacts an app’s effectiveness. We conclude that finding the right balance between privacy and effectiveness, while critical, is challenging because it is highly context-specific.
Internet-of-Forensic (IoF): A blockchain based digital forensics framework for IoT applications

Digital forensic in Internet-of-Thing (IoT) paradigm is critical due to its heterogeneity and lack of transparency of evidence processing. Moreover, cross-border legalization makes a hindrance in such process pertaining to the cloud forensic issues. This urges a forensic framework for IoT which provides distributed computing, decentralization, and transparency of forensic investigation of digital evidences in cross-border perspectives. To this end, we propose a framework for IoT forensics that addresses the above mentioned issues. The proposed solution called Internet-of-Forensics (IoF) considers a blockchain tailored IoT framework for digital forensics. It provides a transparent view of the investigation process that involves all the stakeholders (e.g., heterogeneous devices, and cloud service providers) in a single framework. It uses blockchain-based case chain to deal with the investigation process including chain-of-custody and evidence chain. Consensus is used for consortium to solve the problems of cross-border legalization. This is also beneficial for a transparent and ease of forensic reference. The programmable lattice-based cryptographic primitives produce reduced complexities. It shows benefits for power-aware devices and puts an add-on to the novelty of the presented idea. IoF is generic; hence, it can be used by autonomous security operation centers, cyber-forensic investigators and manually initiated evidences under chain-of-custody for man-made crimes. Security services are assured as required by the framework. IoF is experimented and compared with the other state-of-the-art frameworks. The outcomes and analysis prove the efficiency of IoF concerning complexity, time consumption, memory and CPU utilization, gas consumption, and energy analysis.
An Overview of Recommendation Techniques and Their Applications in Healthcare

With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, collaborative filtering (CF)-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.
ASR N-Best Fusion Nets

Current spoken language understanding systems heavily rely on the best hypothesis (ASR 1-best) generated by automatic speech recognition, which is used as the input for downstream models such as natural language understanding (NLU) modules. However, the potential errors and misrecognition in ASR 1-best raise challenges to NLU. It is usually difficult for NLU models to recover from ASR errors without additional signals, which leads to suboptimal SLU performance. This paper proposes a fusion network to jointly consider ASR n-best hypotheses for enhanced robustness to ASR errors. Our experiments on Alexa data show that our model achieved 21.71% error reduction compared to baseline trained on transcription for domain classification.
The perception of humanness in conversational journalism: An algorithmic information-processing perspective

How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ.
A Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation Algorithm Based on Domain-Dependent and Domain-Independent Feature Fusion

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user's characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user's characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user's characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user's domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user's features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.
Virtual products as an extension of my body: Exploring hedonic and utilitarian shopping value in a virtual reality retail environment

Consumer’s ability to touch products is an essential component of one’s retail shopping experience. When the ability to touch is not available, Virtual Reality (VR) platforms may provide a simulated medium for consumers to haptically explore products. In multiple studies, we discover that a VR retail environment (vs. an online retail website) positively impacts hedonic shopping value. Further, we demonstrate that a VR retail environment leads to consumers perceiving certain products as an extension of the body as opposed to perceiving it as a presentation of the body. Our findings reveal that a VR retail environment is more suitable for products that are perceived as an extension of the body (e.g., tools) rather than as a presentation of the body (e.g., clothes). Finally, we unveil mediating pathways to these relationships involving telepresence and need for touch.
Lightweight blockchain consensus mechanism and storage optimization for resource-constrained IoT devices

Blockchain is a distributed digital ledger with features such as tamper-proof and privacy protection, which can provide reliable security solutions for the Internet of Things, smart home, and other scenarios. However, most of the devices in these scenarios only have limited computing, storage, bandwidth, and other resources, which makes it difficult to bear the burden of the blockchain consensus process and subsequent storage of the blockchain ledger. Therefore, a lightweight blockchain needs to be designed to suit for resource-constrained device scenarios. In order to achieve lightweight blockchain, an improved PBFT blockchain consensus mechanism based on reward and punishment strategy is proposed in this paper. Moreover, to reduce the storage overhead on the premise of ensuring the recoverability of blockchain, a blockchain storage optimization scheme based on RS erasure code is proposed in this paper. Experimental results show that the strategies proposed in this paper can reduce the delay of the consensus, the communication resources required by the consensus, and the cost of blockchain storage.
Learning Passage Impacts for Inverted Indexes

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT. In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index. Compared to existing methods, DeepImpact improves impact-score modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document. Our experiments show that DeepImpact significantly outperforms prior first-stage retrieval approaches by up to 17% on effectiveness metrics w.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the same effectiveness of state-of-the-art approaches with up to 5.1x speedup in efficiency.