Reviewerly/exHarmony huggingface dataset:
One-shot active learning for image segmentation via contrastive learning and diversity-based sampling

Image segmentation tasks based on deep learning usually require a large number of labeled samples to obtain great performance of Convolutional Neural Networks (CNNs). However, even if samples are abundant, a major issue remains that labeling samples is usually time-consuming and costly. Active learning can select valuable samples for annotation, so as to reduce the annotation cost as much as possible while maintaining the performance of CNNs. Most existing active learning approaches work in an iterative way. However, this iterative scheme needs more interaction with experts, more labor and more computing resources. In this paper, we propose a one-shot active learning framework, i.e. Contrastive Annotation (CA) based on contrastive self-supervised learning and diversity-based query strategy, aiming to select valuable samples in one-shot. Extensive experiments on three segmentation datasets, i.e., skin lesion segmentation, remote sensing image segmentation and chest X-ray segmentation, show that our proposed CA framework outperforms state-of-the-art methods by large margins.
Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system

In light of the widespread use of algorithmic (intelligent) systems across numerous domains, there is an increasing awareness about the need to explain their underlying decision-making process and resulting outcomes. Since oftentimes these systems are being considered as black boxes, adding explanations to their outcomes may contribute to the perception of their transparency and, as a result, increase users’ trust and fairness perception towards the system, regardless of its actual fairness, which can be measured using various fairness tests and measurements. Different explanation styles may have a different impact on users’ perception of fairness towards the system and on their understanding of the outcome of the system. Hence, there is a need to understand how various explanation styles may impact non-expert users’ perceptions of fairness and understanding of the system’s outcome. In this study we aimed at fulfilling this need. We performed a between-subject user study in order to examine the effect of various explanation styles on users’ fairness perception and understanding of the outcome. In the experiment we examined four known styles of textual explanations (case-based, demographic-based, input influence-based and sensitivity-based) along with a new style (certification-based) that reflect the results of an auditing process of the system. The results suggest that providing some kind of explanation contributes to users’ understanding of the outcome and that some explanation styles are more beneficial than others. Moreover, while explanations provided by the system are important and can indeed enhance users’ perception of fairness, their perception mainly depends on the outcome of the system. The results may shed light on one of the main problems in explainability of algorithmic systems, which is choosing the best explanation to promote users’ fairness perception towards a particular system, with respect to the outcome of the system. The contribution of this study is reflected in the new and realistic case study that was examined, in the creation and evaluation of a new explanation style that can be used as the link between the actual (computational) fairness of the system and users’ fairness perception and in the need of analyzing and evaluating explanations while taking into account the outcome of the system.
Provable data deletion from efficient data integrity auditing and insertion in cloud storage

With the widespread popularity of cloud storage, a growing number of data owners are willing to outsource their massive data to remote cloud servers, thus effectively avoiding the heavy burden of storing and managing the massive data by themselves in local physical storage mediums. Despite the tremendous advantages, cloud storage inescapably suffers from some severe security challenges resulting from the separation of outsourced data management and its ownership, such as data integrity, data deletion, and data insertion. In this paper, we aim to deal with the problems of integrity auditing, dynamic insertion and provable deletion over outsourced data. Specifically, we design a novel authentication data structure, namely, number-rank-based Merkle hash tree (NR-MHT), which can achieve data dynamic operations. Meanwhile, NR-MHT can maintain many data blocks in each leaf node, thus effectively solving the problem of the height indefinitely increases with the total number of data blocks. Subsequently, we adopt NR-MHT to propose a new provable data deletion scheme based on efficient data integrity auditing and dynamic data insertion. In our presented scheme, the data owner can not only permanently delete the unnecessary outsourced data blocks to save storage expenditures but also efficiently insert new data blocks to dynamically update the outsourced data set. Meanwhile, we analyze the security to formally demonstrate that our presented scheme can satisfy all of the expected security requirements without interacting with a third party auditor. Finally, we also develop a prototype implementation of our presented scheme and provide an accurate efficiency evaluation. The numerical analysis and experimental results show that compared with the existing schemes, our presented scheme is more attractive in efficiency and practicability.
Uncertainty relations with the variance and the quantum Fisher information based on convex decompositions of density matrices

We present several inequalities related to the Robertson-Schr\"odinger uncertainty relation. In all these inequalities, we consider a decomposition of the density matrix into a mixture of states, and use the fact that the Robertson-Schr\"odinger uncertainty relation is valid for all these components. By considering a convex roof of the bound, we obtain an alternative derivation of the relation in Fr\"owis et al. [Phys. Rev. A 92, 012102 (2015)], and we can also list a number of conditions that are needed to saturate the relation. We present a formulation of the Cram\'er-Rao bound involving the convex roof of the variance. By considering a concave roof of the bound in the Robertson-Schr\"odinger uncertainty relation over decompositions to mixed states, we obtain an improvement of the Robertson-Schr\"odinger uncertainty relation. We consider similar techniques for uncertainty relations with three variances. Finally, we present further uncertainty relations that provide lower bounds on the metrological usefulness of bipartite quantum states based on the variances of the canonical position and momentum operators for two-mode continuous variable systems. We show that the violation of well-known entanglement conditions in these systems discussed in Duan et al., [Phys. Rev. Lett. 84, 2722 (2000)] and Simon [Phys. Rev. Lett. 84, 2726 (2000)] implies that the state is more useful metrologically than certain relevant subsets of separable states. We present similar results concerning entanglement conditions with angular momentum operators for spin systems.
Classification of 74 facial emoji’s emotional states on the valence-arousal axes

Emojis are frequently used by people worldwide as a tool to express one's emotional states and have recently been considered for assessment in research. However, details regarding the ways in which they correspond to human emotional states remain unidentified. Thus, this study aimed to understand how emojis are classified on the valence and arousal axes and to examine the relationship between the former and human emotional states. In an online survey involving 1082 participants, a nine-point scale was employed to evaluate the valence and arousal levels of 74 facial emojis. Results from the cluster analysis revealed these emojis to be categorized into six different clusters on the two axes of valence and arousal. Further, the one-way analysis of variance indicated that these clusters have six valence and four arousal levels. From the results, each cluster was interpreted as (1) a strong negative sentiment, (2) a moderately negative sentiment, (3) a neutral sentiment with a negative bias, (4) a neutral sentiment with a positive bias, (5) a moderately positive sentiment, and (6) a strong positive sentiment. Therefore, facial emojis were found to comprehensively express the human emotional states.
Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data

Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities’ latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering (NF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> D) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed NF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> D method has a significant superiority over state-of-the-art fuzzy clustering methods.
Augmented Reality (AR) Supporting Citizen Engagement in Circular Economy

Improving the public's awareness of and engagement with sustainable practices is essential for transitioning from a linear to a circular economy (CE). This task is not trivial; however, research into new ways of effectively supporting awareness and engagement, especially in view of digitalization trends, is still missing. In this study, we investigate the ability of augmented reality (AR) technology to improve awareness of CE practices and to engage citizens, inclusively, in the CE approach. Specifically, we report on the development of a novel AR engagement tool and present the results of two studies undertaken using this tool to assess the reliability and validity of this engagement practice. The first study focuses on the citizens of a municipality in Greece, and the second focuses on the general public outside the municipality boundaries. Attributes related to user engagement, such as novelty, aesthetics, perceived usability, endurability, focused attention, self-efficacy, perceived learning, and interest in the topic, were assessed, as were factors affecting these attributes related to the participants' knowledge of CE principles and exposure to similar technologies. The two studies indicate that the AR engagement tool increased social inclusion and community cohesion and was able to successfully bring CE principles and benefits to the public's attention. Higher engagement levels were observed in participants who had limited exposure to the concept of CE prior to using the AR tool, and its use was found to improve their confidence and interest in CE. This study aspires to contribute to the ongoing discourse on the use of mobile AR and to support further evidence-based development of digital tools for public awareness and engagement.The online version contains supplementary material available at 10.1007/s43615-021-00137-7.
EGNN: Constructing explainable graph neural networks via knowledge distillation

Graph neural networks are widely utilized for processing data represented by graphs, which renders them ubiquitous in daily life. Due to their excellent performance in extracting features from structural data, graph neural networks have attracted an increasing amount of attention from both academia and industry. Essentially, most GNN models learn representations of nodes by fully/randomly aggregating their neighbor features. However, these unsophisticatedly-designed aggregation schemes always lead to a lack of interpretability, compromising the scope of adoption of GNN models. This study attempts to construct a transparent and explainable GNN model by distilling knowledge from pretrained "black-box" models. Specifically, by simultaneously preserving fidelity to the behaviors of the original model and optimizing the loss of prediction, a shallow graph neural network with explicit "contribution" weights between two nodes is trained. Next, a neighbor selection strategy is built upon these explicit weights to ensure high levels of performance and interpretability. To evaluate the proposed framework, our method is incorporated into four state-of-the-art models: GCN, GAT, GraphSAGE, and AM-GCN. Experimental results on three real-world datasets show the effectiveness of the proposed framework.
A Stable Deep Reinforcement Learning Framework for Recommendation

Recommender system (RS) solves the problem of information overload, which is crucial in industrial fields. Recently, reinforcement learning (RL) combined with RS has attracted researchers’ attention. These new methods model the interaction between RS and users as a process of serialization decision-making. However, these studies suffer from several disadvantages: 1) they fail to model the accumulated long-term interest tied to high reward, and 2) these algorithms need a lot of interactive data to learn a good strategy and are unstable in the scenario of recommendation. In this article, we propose a stable reinforcement learning framework for recommendation. We redefine the Markov decision process of RL-based recommendation, and add a stable module to model high feedback behavior of users. Second, an advanced RL algorithm is introduced to ensure stability and exploratory. The experiments verify the effectiveness of the proposed algorithm.
Strong inconsistency

Minimal inconsistent subsets of knowledge bases play an important role in propositional logic, most notably for diagnosis, axiom pinpointing, and inconsistency measurement. It turns out that for nonmonotonic reasoning a stronger notion is needed. In this paper we develop such a notion, called strong inconsistency. We show that—in an arbitrary logic, monotonic or not—minimal strongly inconsistent subsets play a similar role as minimal inconsistent subsets in propositional logic. In particular, we show that the well-known duality between hitting sets of minimal inconsistent subsets and maximal consistent subsets generalizes to arbitrary logics if the strong notion of inconsistency is used. We investigate the complexity of various related reasoning problems and present a generic algorithm for computing minimal strongly inconsistent subsets of a knowledge base. We also demonstrate the potential of our new notion for applications, focusing on axiom pinpointing and inconsistency measurement.
Entanglement and Entropic Uncertainty of Two Two‐Level Atoms

Abstract In a system of two two‐level atoms with a cavity field, the dynamics of entropic uncertainty relations, nonlocality, entanglement, and the degree of mixedness are investigated. In addition to the dynamical map, the interaction between two two‐level systems and the Fock state cavity field, the effects of various parameters, such as temperature dependence, Ising interaction, and dipole–dipole interaction are discussed. It is shown that Ising and dipole–dipole interactions promote entanglement and nonlocal correlations while lowering mixedness and entropy. In order to maintain optimal entanglement and nonlocal correlations, numerical values for improving the current cavity fields are also proposed. The cavity field exhibits both Markovian and non‐Markovian behavior, depending on the type of interaction.
Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues

Connected and automated vehicles (CAVs) are becoming a reality. Prototyping and testing of self-driving vehicle technology are becoming more popular around the world. The secure deployment of self-driving vehicles necessitates a wide range of technology, competencies, and procedures, all of which must be thoroughly checked and assessed, as road safety may be a risk. As a result, it's critical to recognize and develop a thorough understanding of the cyber security and privacy concerns with CAVs and of the way these can be prioritized as well as addressed. This chapter investigates falsified information attacks against the RSU's ongoing FL operation. We discovered a variety of attack tactics used by malicious CAVs to disrupt global system training in vehicular ad hoc networks (VANETs). In which, demonstrate the attacks effectively increased the convergence time and reduced the model's accuracy.
Injecting Text in Self-Supervised Speech Pretraining

Self-supervised pretraining for Automated Speech Recognition (ASR) has shown varied degrees of success. In this paper, we propose to jointly learn representations during pretraining from two different modalities: speech and text. The proposed method, tts4pretrain complements the power of contrastive learning in self-supervision with linguistic/lexical representations derived from synthesized speech, effectively learning from untranscribed speech and unspoken text. Lexical learning in the speech encoder is enforced through an additional sequence loss term that is coupled with contrastive loss during pretraining. We demonstrate that this novel pretraining method yields Word Error Rate (WER) reductions of 10% relative on the well-benchmarked, Librispeech task over a state-of-the-art baseline pretrained with wav2vec2.0 only. The proposed method also serves as an effective strategy to compensate for the lack of transcribed speech, effectively matching the performance of 5000 hours of transcribed speech with just 100 hours of transcribed speech on the AMI meeting transcription task. Finally, we demonstrate WER reductions of up to 15% on an inhouse Voice Search task over traditional pretraining. Incorporating text into encoder pretraining is complimentary to rescoring with a larger or in-domain language model, resulting in additional 6% relative reduction in WER.
Research data management systems and the organization of universities and research institutes: A systematic literature review

New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.
A Graph Adaptive Density Peaks Clustering algorithm for automatic centroid selection and effective aggregation

As a clustering approach based on density, Density Peaks Clustering algorithm (DPC) has conspicuous superiorities in searching and finding density peaks. Nevertheless, DPC has obvious deficiencies in centroid selection and aggregation process affected by differences in data shape and density distribution, which can easily cause problems in centroid selection and trigger domino effect. Therefore, a Graph Adaptive Density Peaks Clustering algorithm based on Graph Theory (called GADPC) is proposed to automatically select centroid and aggregate more effectively. The improvement of GADPC can be subdivided into the two steps. First, the clustering centroids are automatically selected based on the turning angle θ and the graph connectivity of centroids. Second, the remaining points are aggregated towards the corresponding clustering centroid. According to the improved principle, they belong to the closer point which has stronger graph connectivity and higher density. Theoretical analyses and experimental data indicate that GADPC, compared with DBSCAN, K-means and DPC, is more feasible and effective in processing some data sets with varying density and non-spherical distribution such as Jain and Spiral.
Performance Optimization for Semantic Communications: An Attention-based Learning Approach

In this paper, a semantic communication framework is proposed for wireless networks. In the proposed framework, a base station (BS) extracts the semantic information from textual data, and, transmits it to each user. This semantic information is modeled by a knowledge graph (KG) and hence, the semantic information consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the studied semantic communication system, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS can only transmit partial semantic information to each user so as to satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user and determine partial semantic information to be transmitted. This problem is formulated as an optimization problem whose goal is to maximize the total MSS by optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm integrated with the attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Simulation results demonstrate that the proposed semantic communication framework can reduce the size of data that the BS needs to transmit by up to 46&#x0025; and yield a two-fold improvement in the total MSS compared to a standard communication network that does not consider semantic communications.