Reviewerly/exHarmony huggingface dataset:
It Is Different When Items Are Older

User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect to enjoy beforehand (positivity bias). Methods exist for mitigating the effects of selection bias in user ratings on the evaluation and optimization of RSs. However, these methods treat selection bias as static, despite the fact that the popularity of an item may change drastically over time and the fact that user preferences may also change over time.
Synchronously Improving Multi-user English Translation Ability by Using AI

English has become the most widely used language in the world. Everything we do in study, life and work is closely linked with English. With the continuous development of computer technology, machine translation is becoming more and more mature. The convergence of Artificial Intelligence (AI) and language learning is getting increasingly close, which brings great impact and challenge to the language education industry, but also provides an opportunity for the synchronous promotion of the development of the language education industry. With the further development of AI, machine translation can better meet the needs of most general translation, but in the face of professional, diversified, detailed and complex communication translation tasks containing human emotion, machine translation is still difficult to replace human translation. In order to improve the English translation ability of university students, this paper uses AI to propose the innovative factor based Quantum Particle Swarm Optimization-Convolutional Neural Network (QPSO-CNN) algorithm. Through the experiment, at first, the obtained dataset can ensure the accuracy and diversity of the collected results of English translation feature samples to the maximum extent, and the trained QPSO-CNN can be used to analyze the accuracy of the English translation ability of university students. Then, by comparing the convergence curve of QPSO-CNN and back propagation-CNN (BP-CNN), it is concluded that the proposed QPSO-CNN in this paper has been greatly improved in terms of model accuracy and convergence speed.
Immunizing with information – Inoculation messages against conversational agents’ response failures

Conversational agents (CAs) are often unable to provide meaningful responses to user requests, thereby triggering user resistance and impairing the successful diffusion of CAs. Literature mostly focuses on improving CA responses but fails to address user resistance in the event of further response failures. Drawing on inoculation theory and the elaboration likelihood model, we examine how inoculation messages, as communication that seeks to prepare users for a possible response failure, can be used as an alleviation mechanism. We conducted a randomized experiment with 558 users, investigating how the performance level (high or low) and the linguistic form of the performance information (qualitative or quantitative) affected users' decision to discontinue CA usage after a response failure. We found that inoculation messages indicating a low performance level alleviate the negative effects of CA response failures on discontinuance. However, quantitative performance level information exhibits this moderating effect on users' central processing, while qualitative performance level information affected users' peripheral processing. Extending studies that primarily discuss ex-post strategies, our results provide meaningful insights for practitioners.
Dual class representation learning for few-shot image classification

Few-shot learning (FSL) models are trained on base classes that have many training examples and evaluated on novel classes that have very few training examples. Since these models cannot be properly fine-tuned on the novel classes using the few training examples, FSL methods use the features extracted by the model trained on the base classes for evaluating it on the novel classes. In this work, we propose a novel few-shot learning approach that focuses on making the network representations generic, to improve its novel class performance. Specifically, our proposed approach introduces dual class information for the base class training images using a base class label and a self-supervised class label. The self-supervised classes are synthesized by applying transformations that alter the image structure and are not dependent on the base classes. Our approach trains the network to recognize both types of classes simultaneously for each training image. This process incorporates the class-invariant information of the self-supervised classes into the model representations and helps the model learn generic features which in turn improves its novel class performance. We also propose a novel approach for creating self-supervised classes that helps the network incorporate a deeper understanding of the structure of the objects and their parts. This approach significantly outperforms existing self-supervision methods in this setting. We empirically demonstrate on multiple datasets that our approach is very effective in addressing the few-shot learning problem and significantly outperforms the single class representation learning approach that uses a single image class label per training image.
Automatic classification of literature in systematic reviews on food safety using machine learning

Systematic reviews are used to collect relevant literature to answer a research question in a way that is clear, thorough, unbiased and reproducible. They are implemented as a standard method in the domain of food safety to obtain a literature overview on the state-of-the-art research related to food safety topics of interest. A disadvantage to systematic reviews, however, is that this process is time-consuming and requires expert domain knowledge. The work reported here aims to reduce the time needed by an expert to screen all possible relevant articles by applying machine learning techniques to classify the articles automatically as either relevant or not relevant. Eight different machine learning algorithms and ensembles of all combinations of these algorithms were tested on two different systematic reviews on food safety (i.e. chemical hazards in cereals and leafy greens). The results showed that the best performance was obtained by an ensemble of naive Bayes and a support vector machine, resulting in an average decrease of 32.8% in the amount of articles the expert has to read and an average decrease in irrelevant articles of 57.8% while keeping 95% of the relevant articles. It was concluded that automatic classification of the literature in a systematic literature review can support experts in their task and save valuable time without compromising the quality of the review.
Topic-Guided Abstractive Multi-Document Summarization

A critical point of multi-document summarization (MDS) is to learn the relations among various documents.In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries.Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation.Since topic extraction can be viewed as a special type of summarization that "summarizes" texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other.Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge metrics and human evaluation, meanwhile learns high-quality topics.
Quantum remote sensing under the effect of dephasing

The quantum remote sensing (QRS) is a scheme to add security about the measurement results of a qubit-based sensor. A client delegates a measurement task to a remote server that has a quantum sensor, and eavesdropper (Eve) steals every classical information stored in the server side. By using quantum properties, the QRS provides an asymmetricity about the information gain where the client gets more information about the sensing results than Eve. However, quantum states are fragile against decoherence, and so it is not clear whether such a QRS is practically useful under the effect of realistic noise. Here, we investigate the performance of the QRS with dephasing during the interaction with the target fields. In the QRS, the client and server need to share a Bell pair, and an imperfection of the Bell pair leads to a state preparation error in a systematic way on the server side for the sensing. We consider the effect of both dephasing and state preparation error. The uncertainty of the client side decreases with the square root of the repetition number $M$ for small $M$, which is the same scaling as the standard quantum metrology. On the other hand, for large $M$, the state preparation error becomes as relevant as the dephasing, and the uncertainty decreases logarithmically with $M$. We compare the information gain between the client and Eve. This leads us to obtain the conditions for the asymmetric gain to be maintained even under the effect of dephasing. Then, we can quantify how much information the client can gain while preserving the information asymmetricity.
Gottesman-Kitaev-Preskill codes: A lattice perspective

We examine general Gottesman-Kitaev-Preskill (GKP) codes for continuous-variable quantum error correction, including concatenated GKP codes, through the lens of lattice theory, in order to better understand the structure of this class of stabilizer codes. We derive formal bounds on code parameters, show how different decoding strategies are precisely related, propose new ways to obtain GKP codes by means of glued lattices and the tensor product of lattices and point to natural resource savings that have remained hidden in recent approaches. We present general results that we illustrate through examples taken from different classes of codes, including scaled self-dual GKP codes and the concatenated surface-GKP code.
Blended Learning Method in The View of Learning and Teaching Strategy in Geography Study Programs in Higher Education

In this era of digital technology, learning is very foreign if it still uses single method methods, namely face-to-face. We firmly believe that combining traditional methods with technology must be applied to achieve outcomes, especially in postgraduate studies. For that reason, we have carried out many publications in journals, books and procedures, and other information. Then the effort has involved several steps such as understanding the data through coding evaluation and drawing conclusions that essentially want to get the answer data on this royal question as valid and accurate as possible. Based on the available evidence and the depth of the discussion of the findings, we can finally say that the learning strategy in universities, especially in the undergraduate market, is a strategy that combines the old and new ways, namely face-to-face in the classroom and is guided by collaborative learning or learning-based learning. Thus, it is hoped that the findings of this study will make the development and progress of learning and learning studies related to blended learning increase in the future.
An Overview of the Action Space for Deep Reinforcement Learning

In recent years, deep reinforcement learning has been applied to tasks in the real world gradually. Especially in the field of control, reinforcement learning has shown unprecedented popularity, such as robot control, autonomous driving, and so on. Different algorithms may be suitable for different problems, so we investigate and analyze the existing advanced deep reinforcement learning algorithms from the perspective of action space. At the same time, we analyze the differences and connections between discrete action space, continuous action space and discrete-continuous hybrid action space, and elaborate various reinforcement learning algorithms suitable for different action spaces. Applying reinforcement learning to the control problem in the real world still presents huge challenges. Finally, we summarize these challenges and discuss how reinforcement learning can be appropriately applied to satellite attitude control tasks.
Estimating objective (EEG) and subjective (SSQ) cybersickness in people with susceptibility to motion sickness

Cybersickness refers to the uncomfortable side effects, such as headaches, dizziness, and nausea, felt while experiencing virtual reality (VR). This study investigated cybersickness in people with sensitivity to motion sickness using electroencephalography (EEG), the Simulator Sickness Questionnaire (SSQ), and simple VR content. Based on the scores from the Motion Sickness Susceptibility Questionnaire (MSSQ), 40 males in their twenties were selected as the sensitive group (n = 20) and non-sensitive group (n = 20). The experiment contained two conditions: a baseline condition representing a resting state and a cybersickness condition in which watching VR content induced cybersickness. The SSQ score increased significantly after watching the VR content in both groups. The sensitive group showed significantly lower absolute power in the beta and gamma bands than the non-sensitive group. The cybersickness condition showed significantly increased delta and decreased alpha compared to the baseline condition. We evaluated EEG and SSQ to identify subjective symptoms and objective physiological changes associated with cybersickness.
Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists

Breast carcinoma grade, as determined by the Nottingham Grading System (NGS), is an important criterion for determining prognosis. The NGS is based on 3 parameters: tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). The advent of digital pathology and artificial intelligence (AI) have increased interest in virtual microscopy using digital whole slide imaging (WSI) more broadly.To compare concordance in breast carcinoma grading between AI and a multi-institutional group of breast pathologists using digital WSI.We have developed an automated NGS framework using deep learning. Six pathologists and AI independently reviewed a digitally scanned slide from 137 invasive carcinomas and assigned a grade based on scoring of the TF, NP, and MC.Interobserver agreement for the pathologists and AI for overall grade was moderate (κ = 0.471). Agreement was good (κ = 0.681), moderate (κ = 0.442), and fair (κ = 0.368) for grades 1, 3, and 2, respectively. Observer pair concordance for AI and individual pathologists ranged from fair to good (κ = 0.313-0.606). Perfect agreement was observed in 25 cases (27.4%). Interobserver agreement for the individual components was best for TF (κ = 0.471 each) followed by NP (κ = 0.342) and was worst for MC (κ = 0.233). There were no observed differences in concordance amongst pathologists alone versus pathologists + AI.Ours is the first study comparing concordance in breast carcinoma grading between a multi-institutional group of pathologists using virtual microscopy to a newly developed WSI AI methodology. Using explainable methods, AI demonstrated similar concordance to pathologists alone.
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?

Being able to explain the prediction to clinical end-users is a necessity to leverage the power of artificial intelligence (AI) models for clinical decision support. For medical images, a feature attribution map, or heatmap, is the most common form of explanation that highlights important features for AI models' prediction. However, it is unknown how well heatmaps perform on explaining decisions on multi-modal medical images, where each image modality or channel visualizes distinct clinical information of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for clinical users' interpretation of AI decisions. To tackle this clinically important but technically ignored problem, we propose the modality-specific feature importance (MSFI) metric. It encodes clinical image and explanation interpretation patterns of modality prioritization and modality-specific feature localization. We conduct a clinical requirement-grounded, systematic evaluation using computational methods and a clinician user study. Results show that the examined 16 heatmap algorithms failed to fulfill clinical requirements to correctly indicate AI model decision process or decision quality. The evaluation and MSFI metric can guide the design and selection of XAI algorithms to meet clinical requirements on multi-modal explanation.
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https://github.com/giacoballoccu/explanation-quality-recsys.
Are GAN-based morphs threatening face recognition?

Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.
A Divide-and-Conquer Approach to Dicke State Preparation

We present a divide-and-conquer approach to deterministically prepare Dicke states $\lvert D_k^n\rangle$ (i.e., equal-weight superpositions of all $n$-qubit states with Hamming Weight $k$) on quantum computers. In an experimental evaluation for up to $n=6$ qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results [Mukherjee and others, TQE'2020], [Cruz and others, QuTe'2019]. The fidelity gains are achieved through several techniques: Our circuits first "divide" the Hamming weight between blocks of $n/2$ qubits, and then "conquer" those blocks with improved versions of Dicke state unitaries [B\"artschi and others, FCT'2019]. Due to the sparse connectivity on IBM's heavy-hex-architectures, these circuits are implemented for linear nearest neighbor topologies. Further gains in (estimating) the state fidelity are due to our use of measurement error mitigation and hardware progress.
Metaheuristics for data mining: survey and opportunities for big data

In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan in Metaheuristics for big data, Wiley, Hoboken, 2016), and an article published in 4OR (Dhaenens and Jourdan in 4OR 17 (2):115–139, 2019); additionally, updated references and an analysis of the current trends are presented.
On the role of stablecoins in cryptoasset pricing dynamics

Abstract We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investors’ demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
Unveiling the Structure of Wide Flat Minima in Neural Networks

The success of deep learning has revealed the application potential of neural networks across the sciences and opened up fundamental theoretical problems. In particular, the fact that learning algorithms based on simple variants of gradient methods are able to find near-optimal minima of highly nonconvex loss functions is an unexpected feature of neural networks. Moreover, such algorithms are able to fit the data even in the presence of noise, and yet they have excellent predictive capabilities. Several empirical results have shown a reproducible correlation between the so-called flatness of the minima achieved by the algorithms and the generalization performance. At the same time, statistical physics results have shown that in nonconvex networks a multitude of narrow minima may coexist with a much smaller number of wide flat minima, which generalize well. Here we show that wide flat minima arise as complex extensive structures, from the coalescence of minima around "high-margin" (i.e., locally robust) configurations. Despite being exponentially rare compared to zero-margin ones, high-margin minima tend to concentrate in particular regions. These minima are in turn surrounded by other solutions of smaller and smaller margin, leading to dense regions of solutions over long distances. Our analysis also provides an alternative analytical method for estimating when flat minima appear and when algorithms begin to find solutions, as the number of model parameters varies.