Reviewerly/exHarmony huggingface dataset:
A causal Bayesian network approach for consumer product safety and risk assessment

Product risk assessment is the overall process of determining whether a product is judged safe for consumers to use. Among several methods for product risk assessment, RAPEX is the primary one used by regulators in the UK and EU. Despite its widespread use we identify several limitations of RAPEX, including a limited approach to handling uncertainty, especially in the absence of data, and the inability to incorporate causal explanations for using and interpreting the data.We develop a Bayesian Network (BN) model to provide an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. BNs are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally.We use the BN approach to demonstrate risk assessments for products where relevant test and product instance data are and are not available. Whereas RAPEX can only produce results given relevant data, the BN approach produce results for products with and with no relevant data - replicating RAPEX in the former and providing deeper insights in both cases.The BN approach is powerful and flexible for systematic product risk assessment. While it can complement more traditional methods like RAPEX, it is able to provide quantified, auditable assessments in situations where such methods cannot because of lack of data. Practical Applications: Safety regulators, manufacturers, and risk professionals can use the BN approach for all types of consumer product risk assessment, including for novel products or products with little or no historical data. They can also use it to validate the results of existing methods when data becomes available. It informs risk management decisions and helps understand the effect of those decisions on the consumer risk perception.
A Nano-Joule Burst-Mode Eye-Gaze Angle and Object Distance Sensor for Smart Contact Lenses

In this article, we demonstrate an ultra-low energy eye gaze tracking system and distance ranger based on scleral coils developed specifically for smart contact lenses. The system comprises of a set of four (two transmitter and two receiver) semi-circular, copper coils which are patterned on flexible Kapton substrates conformally placed on the eye scleras. The thickness of the entire assembly is ~100 µm. A high- frequency amplitude modulated AC signal is transmitted from a transmitter coil in short bursts, which induces an EMF in a receiver coil that is a function of the eye-gaze angle. The induced voltage is demodulated using an AM receiver IC. The observed object distance from the eyeballs can be determined via triangulation of the measured eye-gaze angles. Preliminary results demonstrate that a very short burst of transmitted signal 31 µs long is sufficient for peak envelope detection of the received signal, requiring only 340 nJ of total energy per distance range measurement. This is about 1/500,000 of the energy per measurement required by miniature solid-state time- of-flight distance sensors.
An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.
Integration of Fog Computing for Health Record Management Using Blockchain Technology

Internet of Medical Things (IoMT) is a breakthrough technology in the transfer of medical data via a communication system. Wearable sensor devices collect patient data and transfer them through mobile internet, that is, the IoMT. Recently, the shift in paradigm from manual data storage to electronic health recording on fog, edge, and cloud computing has been noted. These advanced computing technologies have facilitated medical services with minimum cost and available conditions. However, the IoMT raises a high concern on network security and patient data privacy in the health care system. The main issue is the transmission of health data with high security in the fog computing model. In today's market, the best solution is blockchain technology. This technology provides high-end security and authentication in storing and transferring data. In this research, a blockchain-based fog computing model is proposed for the IoMT. The proposed technique embeds a block chain with the yet another consensus (YAC) protocol building security infrastructure into fog computing for storing and transferring IoMT data in the network. YAC is a consensus protocol that authenticates the input data in the block chain. In this scenario, the patients and their family members are allowed to access the data. The empirical outcome of the proposed technique indicates high reliability and security against dangerous threats. The major advantages of using the blockchain model are high transparency, good traceability, and high processing speed. The technique also exhibits high reliability and efficiency in accessing data with secure transmission. The proposed technique achieves 95% reliability in transferring a large number of files up to 10,000.
Discrete neural representations for explainable anomaly detection

The aim of this work is to detect and automatically generate high-level explanations of anomalous events in video. Understanding the cause of an anomalous event is crucial as the required response is dependant on its nature and severity. Recent works typically use object or action classifier to detect and provide labels for anomalous events. However, this constrains detection systems to a finite set of known classes and prevents generalisation to unknown objects or behaviours. Here we show how to robustly detect anomalies without the use of object or action classifiers yet still recover the high level reason behind the event. We make the following contributions: (1) a method using saliency maps to decouple the explanation of anomalous events from object and action classifiers, (2) show how to improve the quality of saliency maps using a novel neural architecture for learning discrete representations of video by predicting future frames and (3) beat the state-of-the-art anomaly explanation methods by 60% on a subset of the public benchmark X-MAN dataset [25].
Image-to-Image Translation-Based Data Augmentation for Robust EV Charging Inlet Detection

This work addresses the task of electric vehicle (EV) charging inlet detection for autonomous EV charging robots. Recently, automated EV charging systems have received huge attention to improve users’ experience and to efficiently utilize charging infrastructures and parking lots. However, most related works have focused on system design, robot control, planning, and manipulation. Towards robust EV charging inlet detection, we propose a new dataset (EVCI dataset) and a novel data augmentation method that is based on image-to-image translation where typical image-to-image translation methods synthesize a new image in a different domain given an image. To the best of our knowledge, the EVCI dataset is the first EV charging inlet dataset. For the data augmentation method, we focus on being able to control synthesized images’ captured environments (e.g., time, lighting) in an intuitive way. To achieve this, we first propose the environment guide vector that humans can intuitively interpret. We then propose a novel image-to-image translation network that translates a given image towards the environment described by the vector. Accordingly, it aims to synthesize a new image that has the same content as the given image while looking like captured in the provided environment by the environment guide vector. Lastly, we train a detection method using the augmented dataset. Through experiments on the EVCI dataset, we demonstrate that the proposed method outperforms the state-of-the-art methods. We also show that the proposed method is able to control synthesized images using an image and environment guide vectors.
Locally Differentially Private Sparse Vector Aggregation

Vector mean estimation is a central primitive in federated analytics. In vector mean estimation, each user $i \in[n]$ holds a real-valued vector $v_{i} \in[-1,1]^{d}$, and a server wants to estimate the mean of all n vectors; we would additionally like to protect each user’s privacy. In this paper, we consider the k-sparse version of the vector mean estimation problem. That is, suppose each user’s vector has at most k non-zero coordinates in its d-dimensional vector, and moreover, $k \ll d$. In practice, since the universe size d can be very large (e.g., the space of all possible URLs), we would like the per-user communication to be succinct, i.e., independent of or (poly-)logarithmic in the universe size.In this paper, we show matching upper- and lower-bounds for the k-sparse vector mean estimation problem under local differential privacy (LDP). Specifically, we construct new mechanisms that achieve asymptotically optimal error as well as succinct communication, either under user-level-LDP or event-level-LDP. We implement our algorithms and evaluate them on synthetic and real-world datasets. Our experiments show that we can often achieve one or two orders of magnitude reduction in error compared with prior work under typical choices of parameters, while incurring insignificant communication cost.
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach

Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite limited. Addressing such problem, we present a novel domain-adaptive approach called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design an efficient parameter-free cost normalization layer for internal feature-level alignment. Lastly, a highly related auxiliary task, self-supervised occlusion-aware reconstruction is presented to narrow the gaps in output space. We perform intensive ablation studies and break-down comparisons to validate the effectiveness of each proposed module. With no extra inference overhead and only a slight increase in training complexity, our AdaStereo models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo, even outperforming some state-of-the-art disparity networks finetuned with target-domain ground-truths. Moreover, based on two additional evaluation metrics, the superiority of our domain-adaptive stereo matching pipeline is further uncovered from more perspectives. Finally, we demonstrate that our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
TDM-CFC: Towards Document-Level Multi-label Citation Function Classification

Citation function classification is an indispensable constituent of the citation content analysis, which has numerous applications, ranging from improving informative citation indexers to facilitating resource search. Existing research works primarily simply treat citation function classification as a sentence-level single-label task, ignoring some essential realistic phenomena thereby creating problems like data bias and noise information. For instance, one scientific paper contains many citations, and each citation context may contain rich discussions of the cited paper, which may reflect multiple citation functions. In this paper, we propose a novel task of Document-level Multi-label Citation Function Classification in a bid to considerably extend the previous research works from a sentence-level single-label task to a document-level multi-label task. Given the complicated nature of the document-level citation function analysis, we propose a novel two-stage fine-tuning approach of large scale pre-trained language model. Specifically, we represent a citation as an independent token and propose a novel two-stage fine-tuning approach to better represent it in the document context. To enable this task, we accordingly introduce a new benchmark, i.e., TDMCite, encompassing 9594 citations (annotated for their function) from online scientific papers by leveraging a three-aspect citation function annotation scheme. Experimental results suggest that our approach results in a considerable improvement in contrast to the state-of-the-art BERT classification fine-tuning approaches.
Interval universal approximation for neural networks

To verify safety and robustness of neural networks, researchers have successfully applied abstract interpretation , primarily using the interval abstract domain. In this paper, we study the theoretical power and limits of the interval domain for neural-network verification. First, we introduce the interval universal approximation (IUA) theorem. IUA shows that neural networks not only can approximate any continuous function f (universal approximation) as we have known for decades, but we can find a neural network, using any well-behaved activation function, whose interval bounds are an arbitrarily close approximation of the set semantics of f (the result of applying f to a set of inputs). We call this notion of approximation interval approximation . Our theorem generalizes the recent result of Baader et al. from ReLUs to a rich class of activation functions that we call squashable functions . Additionally, the IUA theorem implies that we can always construct provably robust neural networks under ℓ ∞ -norm using almost any practical activation function. Second, we study the computational complexity of constructing neural networks that are amenable to precise interval analysis. This is a crucial question, as our constructive proof of IUA is exponential in the size of the approximation domain. We boil this question down to the problem of approximating the range of a neural network with squashable activation functions. We show that the range approximation problem (RA) is a Δ 2 -intermediate problem, which is strictly harder than NP -complete problems, assuming coNP ⊄ NP . As a result, IUA is an inherently hard problem : No matter what abstract domain or computational tools we consider to achieve interval approximation, there is no efficient construction of such a universal approximator. This implies that it is hard to construct a provably robust network, even if we have a robust network to start with.
Implementing an Open &amp; <scp>FAIR data sharing</scp> policy—A case study in the earth and environmental sciences

Abstract This paper outlines the impact of the introduction of an Open &amp; FAIR (findable, accessible, interoperable, and reusable) data sharing policy on six earth and environmental science journals published by Taylor &amp; Francis, beginning in November 2019. Notably, 18 months after implementing this new policy, we observed minimal impacts on submission, acceptance rates, or peer‐review times for the participating journals. This paper describes the changes that were required to internal systems and processes in order to implement the new policy, and compares our findings with recent literature reports on the impact of journals introducing data‐sharing policies.
International expert agenda in education: Key characteristics and problem areas

Introduction . This article presents an analysis of the global expert discourse in education prior to the Covid-2019 pandemic.Aim. The current research aims to analyse expert reports, regarding the theoretical foundations, articulated key trends, stakeholders and skills seen as educational outcomes. Methodology and research methods . The sample consists of 25 reports published in 2012–2020 by leading expert organisations (OECD, World Bank, etc.) devoted to the problems of education (school or higher education) and the relationship between education and the labour market. The main research method is critical discourse analysis. Results . Despite the dominance of the ideas of human capital in these reports, the relationship between “demand”, seen through the prism of global trends, and “supply” in the form of skills that education should provide, turns out to be problematic. Among the main stakeholders in education, managers and administration of educational institutions are emphasised in expert reports. Employers and learners are seen as rather passive players in the educational field. Physical health skills and agency skills are underestimated in global expert discourse despite their increasing relevance demonstrated by COVID-19 pandemic. These findings call for new approaches in global discussion about educational content and results in the context of global pandemic COVID-19. The scientific novelty of the research lies in the empirically grounded analysis of leading international expert discourse, about which there are many stereotypes, often without an evidence base. The practical significance of the study is in the identification of concrete problem areas of international expert discussion in education (at the time before the outbreak of the pandemic) that require strengthening, including on the basis of new research.
Effect of Input Noise Dimension in GANs

Generative Adversarial Networks (GANs) are by far the most successful generative models. Learning the transformation which maps a low dimensional input noise to the data distribution forms the foundation for GANs. Despite their application in various domains, they are prone to certain challenges like mode collapse and unstable training. To overcome the challenges, researchers have proposed novel loss functions, architectures, and optimization methods. Unlike the previous approaches, we focus on the input noise and its role in the generation in our work here.
Multi-local Collaborative AutoEncoder

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists of novel multi-local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models.
Complexity of finite-variable fragments of products with non-transitive modal logics

Abstract We show that products of propositional modal logics where at least one factor is one of the monomodal logics $\textbf {K}$, $\textbf {KT}$, $\textbf {KB}$ and $\textbf {KTB}$ are polynomial-time embeddable into their single-variable fragments. Consequently, we obtain results about the computational complexity of single-variable fragments of logics belonging to intervals bounded by such products. We generalize our embeddability results to expanding relativized products and to products with polymodal logics.
Lightweight encryption for short-range wireless biometric authentication systems in Industry 4.0

Most recent solutions for users’ authentication in Industry 4.0 scenarios are based on unique biological characteristics that are captured from users and recognized using artificial intelligence and machine learning technologies. These biometric applications tend to be computationally heavy, so to monitor users in an unobtrusive manner, sensing and processing modules are physically separated and connected through point-to-point wireless communication technologies. However, in this approach, sensors are very resource constrained, and common cryptographic techniques to protect private users’ information while traveling in the radio channel cannot be implemented because their computational cost. Thus, new security solutions for those biometric authentication systems in their short-range wireless communications are needed. Therefore, in this paper, we propose a new cryptographic approach addressing this scenario. The proposed solution employs lightweight operations to create a secure symmetric encryption solution. This cipher includes a pseudo-random number generator based, also, on simple computationally low-cost operations in order to create the secret key. In order to preserve and provide good security properties, the key generation and the encryption processes are fed with a chaotic number sequence obtained through the numerical integration of a new four-order hyperchaotic dynamic. An experimental analysis and a performance evaluation are provided in the experimental section, showing the good behavior of the described solution.