Reviewerly/exHarmony huggingface dataset:
Eye-tracking the effect of semantic decoding on orthographic learning in Chinese

Most Chinese compound characters represent meaning explicitly and systematically via the semantic radical. Previous research has shown that readers can use the semantic radical to infer the meaning of unknown compound characters (i.e., semantic decoding). Yet it is unclear whether it influences reading in natural texts or facilitates orthographic learning. Across two eye-tracking experiments, we exposed adult native speakers (n= 25 and n=16) to sixteen pseudocharacters embedded in sentences. Half of the pseudocharacters contained semantic radicals related to the meaning conferred by the sentential context (i.e., transparent); the other half were unrelated (i.e., opaque). After reading, participants completed written cloze, orthographic decision and definition production tasks. In both experiments, eye-tracking results showed a reduction in fixation times and regressions over the five exposures for the pseudocharacters. The post-test outcomes provided evidence for orthographic and semantic learning. There was no difference in eye-movement measures or in written cloze and orthographic decision between transparent and opaque items, but definition was better for the transparent pseudocharacters. We conclude that semantic decoding does not affect the reading of novel compound characters in natural texts or orthographic learning, but that it does seem to assist in learning semantics.
Stereoscopic image quality assessment combining statistical features and binocular theory

Stereoscopic image quality evaluation is extremely significance as a performance evaluator of modern 3D display technology. Due to the complexity of human visual system (HVS) and the incomprehensive study of stereoscopic perception of human eyes, stereoscopic image quality assessment (SIQA) is still a challenging task. In this paper, combining binocular characteristics, we propose an efficient no-reference stereoscopic image quality assessment according to binocular adding and subtracting channels. Distinguished from other SIQA methods, which pay attention to complex binocular visual properties, the visual information which is closely bound up with image distortion from adding and subtracting channels to describe ocular dominance (alternate name binocularity) is proposed. To estimate the contribution of each channel in SIQA, a dynamic weighting system is proposed for binocular fusion according to local energy. Furthermore, quality awareness features based on multi-scale and multi-orientation are extracted to describe visual degradation. Comparing with existing methods, experimental results on public 3D image databases demonstrate the proposed framework achieves high consistent with the subjective quality scores.
Fault Tolerance in Iterative-Convergent Machine Learning

Machine learning (ML) training algorithms often possess an inherent self-correcting behavior due to their iterative-convergent nature. Recent systems exploit this property to achieve adaptability and efficiency in unreliable computing environments by relaxing the consistency of execution and allowing calculation errors to be self-corrected during training. However, the behavior of such systems are only well understood for specific types of calculation errors, such as those caused by staleness, reduced precision, or asynchronicity, and for specific types of training algorithms, such as stochastic gradient descent. In this paper, we develop a general framework to quantify the effects of calculation errors on iterative-convergent algorithms and use this framework to design new strategies for checkpoint-based fault tolerance. Our framework yields a worst-case upper bound on the iteration cost of arbitrary perturbations to model parameters during training. Our system, SCAR, employs strategies which reduce the iteration cost upper bound due to perturbations incurred when recovering from checkpoints. We show that SCAR can reduce the iteration cost of partial failures by 78% - 95% when compared with traditional checkpoint-based fault tolerance across a variety of ML models and training algorithms.
Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.
Combined Microspectroscopic Characterization of a Red-Colored Granite Rock Sample

Combined microspectroscopic mapping have been conducted on a red-colored Tenzan granite sample by using an original visible–fluorescence–Raman microspectrometer together with a low vacuum scanning electron microscopy–energy dispersive spectrometry (SEM-EDS) without coating. Visible darkfield reflectance spectra were converted to L*a*b* color values and Kubelka–Munk (KM) spectra. Large a* value (red) positions correspond to large band areas at 500–560 nm, possibly due to hematite-like iron oxide, while large b* value (yellow) positions to large band areas at 450–500 nm, due to epidote-like mineral. Scanning electron microscopy–energy dispersive spectrometry analyses indicated that the reddish parts are Na and K-feldspars with low Fe contents (&lt;0.5 wt%). Raman microspectroscopy could not detect hematite-like minerals. Since some hematite-like minerals were only identified by transmission electron microscope, they are considered to be submicron microcrystals disseminated in feldspar matrices. The KM spectra for prehnite-like minerals show a weak broad band around 430 nm due possibly to a ligand field band of Fe 3+ without clear Fe 2+ –Fe 3+ inter-valence charge transfer (IVCT) bands around 720 nm. Therefore, Fe in prehnite is not considered to be present as hematite-like iron oxide, but can be mainly present as Fe 3+ replacing Al 3+ in the crystal structure. Since determination of physicochemical states of Fe such as valence and coordination states (Fe 2+ or Fe 3+ , oxide or in crystal lattice, etc.) and their distributions are extremely difficult, especially in complex colored materials such as rocks, the combined microspectroscopic methods are useful for their nondestructive characterization.
Co-producing, curating, and defining design knowledge in an online practitioner community

As co-design and other participatory design practices increasingly make design outcomes more accessible to everyday citizens, it is also important to understand how designers negotiate the value of design knowledge that undergirds design action and share this knowledge within their own community to facilitate and evolve their practices. In this study, we analyze UX practitioners’ interactions on Reddit, including patterns of resource sharing and curation that point towards a collective construction of UX as a design discipline. We identified how knowledge from diverse sources was selected and shared with the subreddit community (co-production); the resources that community members engaged with and to what extent (curation); and the collective body of knowledge that characterised the design community (definition of design knowledge). We found that boundary work that sought to define the value of UX knowledge often took place at the periphery of shared resources, either expanding or rearticulating the boundary of UX knowledge in relation to trends in employment and nascent professionalisation. Implications of this work for the co-creation of knowledge to support design practices are considered, focusing on how design knowledge concomitantly shapes and is shaped by client-directed design action.
Ambiguous discrimination among linearly dependent quantum states and its application to two-way deterministic quantum key distribution

It is impossible to distinguish a set of linearly dependent quantum states, which ensures the security of quantum key distribution (QKD). When considering some features such as symmetry, one may distinguish a part of the quantum states from another part, called ambiguous discrimination. In this paper, we demonstrate that two-way deterministic QKD (TDQKD) is immune to photon-number-splitting (PNS) attacks for two-photon signals. When there are three or more photons in signals, the eavesdropper can gain the key bits probabilistically, without introducing any error rate. When the average number of photons per pulse takes the value of 0.1, the successful probability of PNS attacks on nonempty pulses is about 0.001, and the corresponding channel loss is about 30 dB. Our results show that TDQKD is robust against PNS attacks.
A New Perspective on Machine Learning: How to do Perfect Supervised Learning.

In this work, we introduce the concept of bandlimiting into the theory of machine learning because all physical processes are bandlimited by nature, including real-world machine learning tasks. After the bandlimiting constraint is taken into account, our theoretical analysis has shown that all practical machine learning tasks are asymptotically solvable in a perfect sense. Furthermore, the key towards this solvability almost solely relies on two factors: i) a sufficiently large amount of training samples beyond a threshold determined by a difficulty measurement of the underlying task; ii) a sufficiently complex and bandlimited model. Moreover, for some special cases, we have derived new error bounds for perfect learning, which can quantify the difficulty of learning. These generalization bounds are not only asymptotically convergent but also irrelevant to model complexity. Our new results on generalization have provided a new perspective to explain the recent successes of large-scale supervised learning using complex models like neural networks.
ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud

Outlier detection has numerous applications in different domains. A family of techniques, called contextual outlier detectors, are based on a single, user-specified demarcation of data attributes into indicators and contexts. In this work, we propose ConOut, a new contextual outlier detection technique that leverages multiple contexts that are automatically identified. Importantly, ConOut is a one-click algorithm—it does not require any user-specified (hyper)parameters. Through experiments on various real-world data sets, we show that ConOut outperforms existing baselines in detection accuracy. Further, we motivate and apply ConOut to the advertisement domain to identify fraudulent publishers, where ConOut not only improves detection but also provides statistically significant revenue gains to advertisers: a minimum of 57% compared to a naïve fraud detector; and $$\sim $$ 20% in revenue gains as well as $$\sim $$ 34% in mean average precision compared to its nearest competitor. Code related to this paper is available at: https://github.com/meghanathmacha/ConOut , https://cmuconout.github.io/ .
Improved security in the genetic algorithm-based image steganography scheme using Hilbert space-filling curve

This paper presents the security analysis of a recently proposed data hiding scheme by Kanan and Nazeri [A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl. 2014;41(14):6123–6130]. Security of the scheme depends upon a secret key\chromosome constructed using a genetic algorithm. The key specifies offsets and patterns for data embedding. By performing the key-sensitivity analysis and exploiting image pixel correlation, it is shown in the present paper that the secret data can be easily revealed. Simulation results demonstrate that just 11 bits out of the 27-bit secret key are sufficient to reveal the data. To overcome the problem, an improvement is suggested in the data embedding pattern that demonstrates high data security. Bitwise analysis of the secret key used in the modified approach exhibits high sensitivity to change in the key.
One- versus multi-component regular variation and extremes of Markov trees

A Markov tree is a random vector indexed by the nodes of a tree whose distribution is determined by the distributions of pairs of neighbouring variables and a list of conditional independence relations. Upon an assumption on the tails of the Markov kernels associated to these pairs, the conditional distribution of the self-normalized random vector when the variable at the root of the tree tends to infinity converges weakly to a random vector of coupled random walks called tail tree. If, in addition, the conditioning variable has a regularly varying tail, the Markov tree satisfies a form of one-component regular variation. Changing the location of the root, that is, changing the conditioning variable, yields a different tail tree. When the tails of the marginal distributions of the conditioning variables are balanced, these tail trees are connected by a formula that generalizes the time change formula for regularly varying stationary time series. The formula is most easily understood when the various one-component regular variation statements are tied up to a single multi-component statement. The theory of multi-component regular variation is worked out for general random vectors, not necessarily Markov trees, with an eye towards other models, graphical or otherwise.
TiFi: Taxonomy Induction for Fictional Domains

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.
A Comparison of Machine Learning Methods Applicable to Healthcare Claims Fraud Detection

The healthcare industry has become a very important pillar in the modern society but has witnessed an increase in fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but for certain cases they have been insufficient and time consuming. Data mining which has emerged as a very important process in knowledge discovery has been successfully applied in the health insurance claims fraud detection. We implemented a prototype that comprised different methods and a comparison of each of the methods was carried out to determine which method is most suited for the Medicare dataset. We found that while ensemble methods and neural net performed, the logistic regression and the naive bayes model did not perform well as depicted in the result.
Linking universal network quality perception and change readiness

Purpose This paper aims to examine empirically, within the context of the public sector, in what way the overall quality perception of an individual’s network relationships at work affect the psychological condition known as change readiness. Moreover, the research examines whether tacit knowledge and organizational climate can have indirect effects on the relationship between universal network quality perception and change readiness. Design/methodology/approach The data were collected through a self-reported survey from 105 individuals employed on a full-time basis by the government sector in Abu Dhabi, UAE. Using regression and mediation analysis, three hypotheses are tested relating to the direct effect of universal network quality perception and change readiness, accompanied by the indirect effects of tacit knowledge and organizational climate on the preceding relationship. Findings The results emanated from statistical analysis show that universal network quality perception has a positive association with change readiness, and tacit knowledge and organizational climate mediate significantly the relationship between the universal network quality perception and change readiness. Research limitations/implications Considering the nature of the study, conclusions with regard to causality might not be that strong as the findings relevant to the dependent variable represented by change readiness are not based on longitudinal research and the sample comprises only local people with similar cultural background. Therefore, study conclusions may not be suitable for generalization. Practical implications The mediating effects highlight the consideration of tacit knowledge and organizational climate as influential organizational mechanisms within the framework of a proactive change management practice due to their positive impact on the psychological uncertainty and lack of job content competencies that a change initiative might cause to employees and their behavioral reactions. Originality/value The paper reveals the role of universal network quality perception as a conduit of positive social influence on change readiness. This role becomes significantly elastic when tacit knowledge and organizational climate become notable links of relationship quality because of their ability to trigger favorable psychosocial behavioral patterns toward organizational change. Furthermore, this study expands the literature on organizational change readiness factors in the UAE, for which few studies currently exist.
Role of Group Level Affect to Find the Most Influential Person in Images

Group affect analysis is an important cue for predicting various group traits. Generally, the estimation of the group affect, emotional responses, eye gaze and position of people in images are the important cues to identify an important person from a group of people. The main focus of this paper is to explore the importance of group affect in finding the representative of a group. We call that person the “Most Influential Person” (for the first impression) or “leader” of a group. In order to identify the main visual cues for “Most Influential Person”, we conducted a user survey. Based on the survey statistics, we annotate the “influential persons” in 1000 images of Group AFfect database (GAF 2.0) via LabelMe toolbox and propose the “GAF-personage database”. In order to identify “Most Influential Person”, we proposed a DNN based Multiple Instance Learning (Deep MIL) method which takes deep facial features as input. To leverage the deep facial features, we first predict the individual emotion probabilities via CapsNet and rank the detected faces on the basis of it. Then, we extract deep facial features of the top-3 faces via VGG-16 network. Our method performs better than maximum facial area and saliency-based importance methods and achieves the human-level perception of “Most Influential Person” at group-level.
A new sparse representation learning of complex data: Application to dynamic clustering of web navigation

Among the variety of algorithms that have been developed for clustering, prototype-based approaches are very popular due to their low computational complexity, allowing real-life applications. In such algorithms, the data set is summarized by a small set of prototypes. Each prototype usually represents a cluster of objects. However, the definition of prototypes for complex objects defined by their relations (relational data) is not an easy task. Few works have been done yet in relational prototype-based clustering. Because relational data are described by a full matrix of dissimilarities, the most important challenge is the computation and memory costs, especially when the number of objects to analyze is very large and for the analysis of data streams (data sets with a dynamic structure varying over time). The combination of these three characteristics (size, complexity and evolution) presents a major challenge and few satisfactory solutions exist at the moment, despite increasingly evident needs. This paper focus on the development of new clustering approaches adapted to big and dynamic relational data. The main idea is to use a set of fixed support points chosen among the objects of the data set, independently from the clusters, and use these support points as a basis for the definition of a representation space, using the Barycentric Coordinates formalism. We demonstrate the qualities of the proposed approaches theoretically and experimentally on a set of artificial and real relational data. We also propose an extension adapted to relational data stream analysis, allowing a dynamic creation and suppression of prototypes to follow the dynamic of the data structure. This dynamic approach is applied on a real data set to detect and follow the dynamic of areas of interest over time in user’s web navigation. We tested different measures of similarity between URLs and different methods of automatic labeling to characterize the clusters. The results are convincing and encouraging, the clusters are homogeneous with clear associated topics. The dynamics of user’s interest can be recorded and visualized for each cluster. Remarkable patterns can be associated to precise events or usual timing and cycles in user’s interest.
Seeing Through Fog Without Seeing Fog: Deep Sensor Fusion in the Absence of Labeled Training Data.

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare edge-case scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here this https URL.