Reviewerly/exHarmony huggingface dataset:
Quality evaluation of practical training of innovative and entrepreneurial talents in universities based on statistical learning theory after COVID-19 epidemic

During the COVID-19 epidemic, college students could not return to the school, which had a great impact on the talent training of colleges and universities. Based on the statistical learning theory, this paper puts forward an evaluation model for the cultivation of innovative talents in universities after the epidemic. In this paper, the evaluation index system of the quality of innovative and entrepreneurial personnel training in Universities, which is composed of four first-class indexes: environment, teaching links, teachers and students, is constructed. At the same time, this paper uses the fuzzy comprehensive evaluation method for empirical research. Firstly, the factor set of the evaluation object and the grade domain of the comprehensive evaluation are determined. Then, AHP is used to determine the weight of evaluation indexes and expert scoring method is used to determine the single factor fuzzy comprehensive evaluation matrix of each level. According to the evaluation matrix, the fuzzy relation between evaluation object and evaluation set is calculated. Finally, according to the principle of maximum membership degree, the evaluation grade corresponding to the maximum value in the fuzzy relation set is calculated as the evaluation result of the final evaluation object. The empirical results show that this method can improve the accuracy of the evaluation model of innovation and entrepreneurship talent training, and has a certain reference value for the talent training in Universities.
Towards Post-Quantum Security for Signal’s X3DH Handshake

Modern key exchange protocols are usually based on the Diffie–Hellman (DH) primitive. The beauty of this primitive, among other things, is its potential reusage of key shares: DH shares can be either used a single time or in multiple runs. Since DH-based protocols are insecure against quantum adversaries, alternative solutions have to be found when moving to the post-quantum setting. However, most post-quantum candidates, including schemes based on lattices and even supersingular isogeny DH, are not known to be secure under key reuse. In particular, this means that they cannot be necessarily deployed as an immediate DH substitute in protocols. In this paper, we introduce the notion of a split key encapsulation mechanism (split KEM) to translate the desired key-reusability of a DH-based protocol to a KEM-based flow. We provide the relevant security notions of split KEMs and show how the formalism lends itself to lifting Signal's $$\mathsf {X3DH}$$ handshake to the post-quantum KEM setting without additional message flows. Although the proposed framework conceptually solves the raised issues, instantiating it securely from post-quantum assumptions proved to be non-trivial. We give passively secure instantiations from $$\mathsf {(R)LWE}$$ , yet overcoming the above-mentioned insecurities under key reuse in the presence of active adversaries remains an open problem. Approaching one-sided key reuse, we provide a split KEM instantiation that allows such reuse based on the KEM introduced by Kiltz (PKC 2007), which may serve as a post-quantum blueprint if the underlying hardness assumption (gap hashed Diffie–Hellman) holds for the commutative group action of CSIDH (Asiacrypt 2018). The intention of this paper hence is to raise awareness of the challenges arising when moving to KEM-based key exchange protocols with key-reusability, and to propose split KEMs as a specific target for instantiation in future research.
Effective schizophrenia recognition using discriminative eye movement features and model-metric based features

Eye movement abnormalities have been effective biomarkers that provide the possibility of distinguishing patients with schizophrenia from healthy controls. The existing methods for measuring eye movement abnormalities mostly focus on synchronic parameters, such as fixation duration and saccade amplitude, which can be directly obtained from eye movement data, while lack of considering more thorough features. In this paper, to better characterize eye-tracking dysfunction, we create a dataset containing 100 images with eye movement data of 40 patients and 30 healthy controls via a free-viewing task, and propose two types of features for effective schizophrenia recognition, i.e. the hand-crafted discriminative eye movement features and the model-metric based features via utilizing the computational models of fixation prediction and the metrics of evaluating their prediction performance. Using the proposed features, two commonly used classifiers including support vector machine and random forest have been trained for classification between patients and controls. Experimental results demonstrate the effectiveness of the proposed features for improving classification performance, and the potential that our method can serve as an alternative and promising approach for the computer-aided diagnosis of schizophrenia.
For real: a thorough look at numeric attributes in subgroup discovery

Abstract Subgroup discovery (SD) is an exploratory pattern mining paradigm that comes into its own when dealing with large real-world data, which typically involves many attributes, of a mixture of data types. Essential is the ability to deal with numeric attributes, whether they concern the target (a regression setting) or the description attributes (by which subgroups are identified). Various specific algorithms have been proposed in the literature for both cases, but a systematic review of the available options is missing. This paper presents a generic framework that can be instantiated in various ways in order to create different strategies for dealing with numeric data. The bulk of the work in this paper describes an experimental comparison of a considerable range of numeric strategies in SD, where these strategies are organised according to four central dimensions. These experiments are furthermore repeated for both the classification task (target is nominal) and regression task (target is numeric), and the strategies are compared based on the quality of the top subgroup, and the quality and redundancy of the top- k result set. Results of three search strategies are compared: traditional beam search, complete search, and a variant of diverse subgroup set discovery called cover-based subgroup selection. Although there are various subtleties in the outcome of the experiments, the following general conclusions can be drawn: it is often best to determine numeric thresholds dynamically (locally), in a fine-grained manner, with binary splits, while considering multiple candidate thresholds per attribute.
Philosophy of education in a new key: Snapshot 2020 from the United States and Canada

This article shares reflections from members of the community of philosophers of education in the United States and Canada who were invited to express their insights in response to the theme ‘Snaps...
Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.
Introduction to the Special Issue Challenges for mineral exploration in the 21st century: Targeting mineral deposits under cover

The global demand for mineral resources has dramatically increased in the last 50 years. The challenge to keep pace with this demand is the driving force for innovation, and also drives mineral exploration in regions where mineral deposits are under cover, and therefore not exposed or near surface, for which novel techniques are required to locate these mineral deposits hidden beneath this often hundreds of meters thick cover material. Generalizing, regions under cover can be globally divided into three end-member types: (1) regions with intensely weathered cover in tropical or dry (desert and semi-arid) climatic domains (based on Köppen-Geiger’s climate classification); (2) regions covered by sedimentary sequences (that can span from the Proterozoic to the Cenozoic) and glacial sediments; and (3) regions under water (seawater, freshwater or ice). This special issue aims to contribute to the debate in the field of mineral exploration in regions that are dominated by and present the complexity of cover by presenting different case studies and technology approaches to detect the distal footprint of mineral systems and deposits at depth.
ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles

Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing attacks. However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or auxiliary data regarding the victim DNN. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> , a novel model stealing attack without any data hurdles. By using heuristically generated synthetic data, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> iteratively trains a substitute model and eventually achieves a functionally equivalent copy of the victim DNN. The experimental results reveal the severity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> : i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> successfully steals the victim model without data hurdles, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> ; iii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES Attack</i> facilitates further attacks relying on the stolen model.
Insight as discovery

Performance on the triangle of coins problem was observed when participants could physically change the configuration of the problem as they explored potential solutions. Participants were filmed as they worked on the problem. One group of participants could touch and move the “coins” on the screen, thereby enacting changes to the physical configuration of the problem (high interactivity) while a second group could not (low interactivity). In Experiment 1, participants could record as many possible solutions as they wished (with feedback), whereas in Experiment 2, they were limited to only one announcement; if the proposed answer was incorrect, the session was terminated. In both experiments, solution rates were generally better in the high than in the low interactivity environment, although participants were slower and made more moves before announcing their solution in the high interactivity condition of Experiment 2. Detailed analysis of the video data for the participants in the high interactivity task environment revealed a gradual appreciation of the solution, punctuated by many discontinuities in move latencies.
Deconfounding User Satisfaction Estimation from Response Rate Bias

Improving user satisfaction is at the forefront of industrial recommender systems. While significant progress has been made by utilizing logged implicit data of user-item interactions (i.e., clicks, dwell/watch time, and other user engagement signals), there has been a recent surge of interest in measuring and modeling user satisfaction, as provided by orthogonal data sources. Such data sources typically originate from responses to user satisfaction surveys, which explicitly ask users to rate their experience with the system and/or specific items they have consumed in the recent past. This data can be valuable for measuring and modeling the degree to which a user has had a satisfactory experience on the recommendation platform, since what users do (engagement) does not always align with what users say they want (satisfaction as measured by surveys).
Fuzzy knowledge graph system for artificial intelligence-based smart education

Fuzzy knowledge graph system is a semantic network that reveals the relationships between entities, and a tool or methodology that can formally describe things in the real world and their relationships. Smart education is an educational concept or model that uses advanced information technology to build a smart environment, integrates theory and practice to build an educational framework for information age, and provides paths to practice it. Artificial intelligence (AI) is a comprehensive discipline developed by the interpenetration of computer science, cybernetics, information theory, linguistics, neurophysiology and other disciplines, which is a direction for the development of information technology in the future. On the basis of summarizing and analyzing of previous research works, this paper expounded the research status and significance of AI technology, elaborated the development background, current status and future challenges of the construction and application of fuzzy knowledge graph system for smart education, introduced the methods and principles of data acquisition methods and digitalized apprenticeship, realized the process design, information extraction, entity recognition and relationship mining of smart education, constructed a systematic framework for fuzzy knowledge graph, and analyzed the high-quality resources sharing and personalized service of AI-assisted smart education, discussed automatic knowledge acquisition and fusion of fuzzy knowledge graph, performed co-occurrence relationship analysis, and finally conducted application case analysis. The results show that the smart education knowledge graph for AI-assisted smart education can integrate teaching experience and domain knowledge of discipline experts, enhance explainable and robust machine intelligence for AI-assisted smart education, and provide data-driven and knowledge-driven information processing methods; it can also discover the analysis hotspots and main content of research objects through clustering of high-frequency topic words, reveal the corresponding research structure in depth, and then systematically explore its research dimensions, subject background and theoretical basis.
QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distribution

Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in context of stability and convergence.
User-Centered Evaluation of an Augmented Reality-based Assistance System for Maintenance

Mobile Augmented Reality solutions are a promising technology for supporting service technicians in maintenance. The possibilities of augmenting the technicians' field of vision with virtual information are confronted with the challenges of new forms of interaction and thus changing requirements for the design of user interfaces compared to desktop applications. The optimal interaction between humans and technology remains the decisive criterion for the development of usable interactive systems. Therefore, we have conducted a user-centered evaluation study in this paper and collected both quantitative and qualitative data. In two consecutive laboratory studies and a final field study we could show that user participation has a significant impact on the usability of the developed AR-based assistance system for the maintenance of heating, air conditioning, and cooling systems.
Differential privacy protection on weighted graph in wireless networks

With the development of 5G communication technology, the Internet of Things technology has ushered in the development opportunity. In the application of Internet of Things, spatial and social relations can be used to provide users with convenience in life and work, meanwhile there is also the risk of personal privacy disclosure. The data transmitted in the wireless network contains a large number of graph structure data, and the edge weight in weighted graph increases the risk of privacy disclosure, therefore in this paper we design a privacy protection algorithm for weighted graph, and adopts the privacy protection model to realize the privacy protection of edge weight and graph structure. Firstly, the whole graph sets are disturbed and the noises are added during the process of graph generation. Secondly, the privacy budget is allocated to protect the weight values of edges. The graph is encoded to deal with the structure of graph conveniently without separating from the information of edges, and then the disturbed edge weight is integrated into the graph. After that the privacy protection of the graph structure is realized in the process of frequent graph mining combined with differential privacy. Finally, the algorithm proposed in this paper is validated by experiments.
From statistical regularities in multisensory inputs to peripersonal space representation and body ownership: Insights from a neural network model

Peripersonal space (PPS), the interface between the self and the environment, is represented by a network of multisensory neurons with visual (or auditory) receptive fields anchored to specific body parts, and tactile receptive fields covering the same body parts. Neurophysiological and behavioural features of hand PPS representation have been previously modelled through a neural network constituted by one multisensory population integrating tactile inputs with visual/auditory external stimuli. Reference frame transformations were not explicitly modelled, as stimuli were encoded in pre-computed hand-centred coordinates. Here we present a novel model, aiming to overcome this limitation by including a proprioceptive population encoding hand position. We confirmed behaviourally the plausibility of the proposed architecture, showing that visuo-proprioceptive information is integrated to enhance tactile processing on the hand. Moreover, the network's connectivity was spontaneously tuned through a Hebbian-like mechanism, under two minimal assumptions. First, the plasticity rule was designed to learn the statistical regularities of visual, proprioceptive and tactile inputs. Second, such statistical regularities were simply those imposed by the body structure. The network learned to integrate proprioceptive and visual stimuli, and to compute their hand-centred coordinates to predict tactile stimulation. Through the same mechanism, the network reproduced behavioural correlates of manipulations implicated in subjective body ownership: the invisible and the rubber hand illusion. We thus propose that PPS representation and body ownership may emerge through a unified neurocomputational process; the integration of multisensory information consistently with a model of the body in the environment, learned from the natural statistics of sensory inputs.
Chinese Sentiment Analysis of Online Education and Internet Buzzwords Based on BERT

Abstract In recent years, sentiment analysis has made a great breakthrough in the field of natural language processing. Many researchers attempt to build a user comment analysis model of e-commerce products to infer the quality of goods. However, the application of sentiment analysis technology in online education platform and intelligent feedback to students and managers is almost irrelevant. In this paper, we collected a large number of online education platform web resource reviews to build a large corpus to pre-train the Word2vec model for comparative experiments. At the same time, the BERT+BiGRU model is designed for the Feedback System Structure (FSS), which achieves the effect of intelligent interaction between students and teachers and the improvement of the curriculum. On the self-built data set, the BERT+BiGRU model has the best prediction effect, with an accuracy of 98.82%. Compared with traditional machine learning methods such as Naive Bayes, the classification accuracy rate is improved by 21.54%. The experiment result shows that we use BERT+BiGRU on the FSS system to achieve the effect of teacher-student interaction and intelligent feedback of academic intelligence. In addition, we also carry out sentiment analysis on the text with Chinese Internet buzzwords. The experimental results show that the sentiment analysis model of our intelligent feedback system (FSS) has strong analytical capability.
Toward more data publication of long‐term ecological observations

Abstract Data papers, such as those published by Ecological Research , encourage the retrieval and archiving of valuable unpublished, undigitized ecological observational data. However, scientists remain hesitant to submit their data to such forums. In this perspective paper, we describe lessons learned from the Long‐Term Ecological Research, the Global Biodiversity Information Facility and marine biological databases and discuss how data sharing and publication are both powerful and important for ecological research. Our aim is to encourage readers to submit their unpublished, undigitized ecological observational data then the data may be archived, published and used by other researchers to advance knowledge in the field of ecology. Coupling data sharing and syntheses with the development of innovative informatics would allow ecology to enter the realm of big science and provide seeds for a new and robust agenda of future ecological studies.
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System

The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing studies do not consider the iterative behavior of the system where the closed feedback loop plays a crucial role in incorporating different biases into several parts of the recommendation steps.