Reviewerly/exHarmony huggingface dataset:
Application of Image Recognition Technology in the Field of Ecological Environment Entrepreneurship Education for College Students

With the continuous development of global entrepreneurial activities, many countries have regarded entrepreneurial enthusiasm as an indicator of social progress. At the same time, our country also encourages and supports everyone to actively start businesses, and the government has also taken some measures to provide a boost to the development of innovation and entrepreneurship. As a new force in the field of innovation and entrepreneurship, college students play a very important role in the field of innovation and entrepreneurship. The work content of this paper is mainly to study the application of image recognition technology in the field of entrepreneurial education of college students' ecological environment. This article first analyzes the status quo of college student entrepreneurship and the combination of image recognition technology and college student entrepreneurship, then introduces the image recognition technology applied, and finally analyzes and contrasts the entrepreneurial ecological environment of colleges and universities, and conducts a questionnaire survey of 30 entrepreneurial students. The results show that image recognition technology can improve the entrepreneurial ecological environment in colleges and universities, and entrepreneurial students are also very satisfied with the application of image recognition technology in the process of entrepreneurship.
SeAbOM: Semi-supervised Learning for Aspect-Based Opinion Mining

Opinion-rich information generated by social media users plays a vital role in today’s market economy. The impact exists from understanding the current fashion trend to the product failure anatomy. It helps to decide the plans for the growth of an industry. To address this issue, business analysts want a detailed aspect-based analysis of user opinion. Many researchers either tried a supervised or unsupervised approach for the same. The literature review showed that the weak structuring of the corpus impacts the outcome of the state-of-the-art method. This paper illustrates the semi-supervised way to extract and summarize the aspect and sentiments associated with the user reviews. We proposed a mechanism to learn aspect-related terms (ARTs) for the seed aspect terms (ATs) from the corpus. We used the customer review dataset and SemEval Reviews-English to test the working and performance of our system. The results show that the proposed method achieves a recall upto 0.88 for the review corpus.
Automatic generation of behavior trees for the execution of robotic manipulation tasks

Robots should be able to exercise reasoning in both symbolic and geometric levels in order to plan a manipulation task. The execution of such tasks needs to be robust enough to cope with real environments. In an attempt to address this pertinent industry need, the paper proposes the use of behavior trees for effective robotic manipulation in dynamic environments. This paper presents a method to automatically generate a behavior tree and showcases its ability to enable the robot to reason at different levels and adapt to an uncertain and changing environment. This allows for a complex task to be robustly executed, pioneering the advancement towards fully functional service robots.
Integer LWE with Non-subgaussian Error and Related Attacks

This paper focuses on the security of lattice based Fiat-Shamir signatures in leakage scenarios. More specifically, how to recover the complete private key after obtaining a large number of noisy linear equations without modular about the private key. Such a set of equations can be obtained, for example, in [], by attacking the rejecting sampling step with a side-channel attack. The paper refers to the mathematical problem of recovering the secret vector from this structure as the ILWE problem and proves that it can be solved by the least squares method. A similar mathematical structure has been obtained in [] by leaking a single bit at certain specific locations of the randomness.However, the ILWE problem requires the error term to be subgaussian, which is not always the case in practice. This paper therefore extends the original ILWE problem by presenting the non-subgaussian ILWE problem, proving that it can be solved by the least squares method combined with a correction factor, and giving two attack scenarios: an attack with lower bits leakage of randomness than in [], and a careless implementation attack on the randomness. In the lower bit randomness leakage case, we are able to attack successfully with 2 or 3 bits leakage lower than those in [] experimentally, and in the careless implementation attack, we are able to recover the private key successfully when the rejection sampling partially fails.
Safety First: A Study of Users’ Perception of VR Adoption in Vehicles

The increasing ubiquity and mobility of VR devices has introduced novel use cases, one of which is using VR while in dynamic, on-the-go environments. Hence, there is a need to examine the perceptual, cognitive, and behavioral aspects of both the driving experience and VR immersion, and how they influence each other. As an initial step towards this goal, we report on the results of an online survey that investigated users’ perceived safety of using VR in an AV. The results of the survey show a mix of expected and surprising attitudes towards VR-in-the-car.
The effect of image descriptors on the performance of classifiers of footwear outsole image pairs

Shoe prints are commonly found at the scene of a crime and can sometimes help link a suspect to the scene. Because prints tend to be partially observed or smudgy, comparing crime scene prints with reference images from a putative shoe can be challenging. Footwear examiners rely on guidelines such as those published by SWGTREAD [1] to visually assess the similarity between two or more footwear impressions, one reason being that reliable, quantitative methods have yet to be validated for use in real cases. To help in the development of such methods, we created a study dataset of images of outsole impressions that shared class characteristics and degree of wear and that were subject to a specific type of degradation. We also propose a method to quantify the similarity between two outsole images that extends the capabilities of MC-COMP [2]. The proposed method is composed of three steps; (1) extracting image descriptors, (2) aligning images using the maximum clique, (3) calculating similarity values using two different classifiers; (a) degree of overlap between the two images, and (b) a score produced by a random forest. To explore the performance of the algorithm we propose, we compared degraded, crime scene-like images to high-quality reference images produced by the same or by different shoes. Even though comparisons involved matches or very close non-matches, and one of the images was blurry, the algorithm shows good source classification performance.
Polaris: Transparent Succinct Zero-Knowledge Arguments for R1CS with Efficient Verifier

Abstract We present a new zero-knowledge succinct argument of knowledge (zkSNARK) scheme for Rank-1 Constraint Satisfaction (RICS), a widely deployed NP-complete language that generalizes arithmetic circuit satisfiability. By instantiating with different commitment schemes, we obtain several zkSNARKs where the verifier’s costs and the proof size range from O (log 2 N ) to <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"><m:mrow><m:mi>O</m:mi><m:mrow><m:mo>(</m:mo><m:mrow><m:msqrt><m:mi>N</m:mi></m:msqrt></m:mrow><m:mo>)</m:mo></m:mrow></m:mrow></m:math> O\left( {\sqrt N } \right) depending on the underlying polynomial commitment schemes when applied to an N -gate arithmetic circuit. All these schemes do not require a trusted setup. It is plausibly post-quantum secure when instantiated with a secure collision-resistant hash function. We report on experiments for evaluating the performance of our proposed system. For instance, for verifying a SHA-256 preimage (less than 23k AND gates) in zero-knowledge with 128 bits security, the proof size is less than 150kB and the verification time is less than 11ms, both competitive to existing systems.
FMD-cGAN: Fast Motion Deblurring Using Conditional Generative Adversarial Networks

In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN delivers impressive structural similarity and visual appearance after deblurring an image. Like other deep neural network architectures, GANs also suffer from large model size (parameters) and computations. It is not easy to deploy the model on resource constraint devices such as mobile and robotics. With the help of MobileNet [1] based architecture that consists of depthwise separable convolution, we reduce the model size and inference time, without losing the quality of the images. More specifically, we reduce the model size by 3–60x compare to the nearest competitor. The resulting compressed Deblurring cGAN faster than its closest competitors and even qualitative and quantitative results outperform various recently proposed state-of-the-art blind motion deblurring models. We can also use our model for real-time image deblurring tasks. The current experiment on the standard datasets shows the effectiveness of the proposed method.
Environment modeling for evaluating system variants in model-based systems engineering

Model-based systems engineering is a methodology for the interdisciplinary system development using different domain models. Considering the intended system’s environment and context of usage in early design phases is a way of bridging the gap from system’s design to its real-life applicability. Simulations represent a possible approach to consider different constellations but are effortful. Therefore, we present a generic environment modeling approach to evaluate different environmental models with their respective environmental, user and system’s impact based on generated system variants derived from system requirements. In this sequential process, we consistently link and evaluate system variants to possible system contexts.
Integrating Semantic-Space Finetuning and Self-Training for Semi-Supervised Multi-label Text Classification

To meet the challenge of lack of labeled data in document classification tasks, semi-supervised learning has been studied, in which unlabeled samples are also utilized for training. Self-training is one of the iconic strategies for semi-supervised learning, in which a classifier trains itself by its own predictions. However, self-training has been mostly applied to multi-class classification, and rarely applied to the multi-label scenario. In this paper, we propose a self-training-based approach for semi-supervised multi-label document classification, in which semantic-space finetuning is introduced and integrated into the self-training process. Newly discovered credible predictions are used not only for classifier finetuning, but also for semantic-space finetuning, which further benefit label propagation for exploring more credible predictions. Experimental results confirm the effectiveness of the proposed approach and show a satisfactory improvement over the baseline methods.
A Structure Alignment Deep Graph Model for Mashup Recommendation

In this paper, we propose a novel approach to recommend services for a given mashup development task. We model service data as a heterogeneous service graph which includes multiple types of nodes and edges to capture rich information extracted from the data. We extend the design of the graph convolutional networks to learn optimal graph embeddings based on a novel structure alignment framework leveraging the latent heterogeneous graph structural features. We then design a ranking mechanism to recommend those services so that their links to the mashup can best fit the latent graph structural features. Both the embedding learning and ranking process make the use of meta-paths to incorporate prior domain knowledge into recommendation. A comprehensive experimental study is conducted on a real-world data set and the result indicates that our approach can significantly outperform the existing solutions.
Some error analysis for the quantum phase estimation algorithms

Abstract This paper is concerned with the phase estimation algorithm in quantum computing, especially the scenarios where (1) the input vector is not an eigenvector; (2) the unitary operator is approximated by Trotter or Taylor expansion methods; (3) random approximations are used for the unitary operator. We characterize the probability of computing the phase values in terms of the consistency error, including the residual error, Trotter splitting error, or statistical mean-square error. In the first two cases, we show that in order to obtain the phase value with error less or equal to 2 − n and probability at least 1 − ϵ , the required number of qubits is <?CDATA $t\geqslant n+\mathrm{log}\left(2+\frac{{\delta }^{2}}{2{\epsilon}{\Delta}{E}^{2}}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>t</mml:mi> <mml:mo>⩾</mml:mo> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mi>log</mml:mi> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mfrac> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>δ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>ϵ</mml:mi> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:msup> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> </mml:mrow> </mml:mfenced> </mml:math> . The parameter δ quantifies the error associated with the inexact eigenvector and/or the unitary operator, and Δ E characterizes the spectral gap, i.e., the separation from the rest of the phase values. This analysis generalizes the standard result (Cleve et al 1998 Phys. Rev X 11 011020; Nielsen and Chuang 2002 Quantum Computation and Quantum Information ) by including these effects. More importantly, it shows that when δ &lt; Δ E , the complexity remains the same. For the third case, we found a similar estimate, but the number of random steps has to be sufficiently large.
Research on quality control method of road engineering construction based on genetic algorithm

In order to comprehensively control the construction quality of road engineering, this paper studies the quality control method of road engineering construction. In this study area, the four objectives of construction period, cost, quality and environment are analyzed and studied quantitatively. Based on this, the multi-objective optimization model of road engineering construction quality control is constructed. Combined with genetic algorithm, the optimal solution of the comprehensive optimization model is found. Finally, a typical project is taken as an example, The validity and rationality of the model are verified. The results show that the construction period is reduced by 12% and the cost is reduced by 0.2%. The quality and environmental objectives also meet the requirements of the contract. The goal of Balanced Optimization of construction period, cost, quality, and environment is achieved, which further shows the feasibility of the optimization control model and the effectiveness of the intelligent algorithm, At the same time, it provides reference and basis for the construction quality control of the actual road engineering project.
Secure Multi-Party Computation and Statistics Sharing for ML Model Training in Multi-domain Multi-vendor Networks

We propose a secure aggregation algorithm that allows proprietary-owned domains, hosting statistically different datasets, train and operate ML models in a Horizontally Federated Learning fashion. The obtained results show a compelling test accuracy of 98.60% for a QoT estimation use-case in multi-domain multi-vendor networks
Energy-Aware Task Scheduling Approach Using DVFS and Particle Swarm Optimization for Heterogeneous Multicore Processors

Multicore processors have been the mainstream in computer architecture nowadays. Energy consumption and performance are always significant factors in the usage of multicore processors. Scheduling the multiple tasks to the cores is the key issue for high efficiency and minimal energy consumption. This paper aims at minimizing the energy consumption in DVFS-enabled heterogeneous systems with optimal energy-aware task scheduling. This is achieved with a particle swarm optimization-based scheme, which can effectively reduce the energy consumption and improve the performance with a feasible task scheduling solution. The experimental and comparative study indicates that the proposed algorithm is energy efficient.
Correlation Analysis, Optimization and Computer Simulation of Teaching Efficiency and Blended Learning of Higher Vocational English Based on POA and CoI Algorithm

Abstract Blended learning has become a popular form of teaching. In order to study a more scientific and reasonable learning plan, this paper uses CoI theory and POA theory to design the teaching plan. First of all, the use of POA theory output, input, output of the three stages of pre-class, in-class, after-class design respectively. The main task before class is to assign output tasks, preview the contents of the class and exchange doubts with students. In class, the teacher checks the students’ study before class. According to the students’ learning situation and output results, pertinently explain the knowledge points and summarize the information involved in the students’ independent output. Review the output according to what you have learned after class. At the same time, the corresponding teaching plans are made according to the three elements of CoI theory: social existence, teaching existence and cognitive existence. After that, it analyzes the correlation between teaching efficiency and teaching methods. The experimental results show that blended learning is better than traditional teaching methods. Finally, the optimization scheme is put forward, and the summary and prospect are carried out.
Research on National Education System Based on Bayesian Back Propagation

Nowadays, the expansion of higher education has become a mainstream trend in the development of higher education all over the world, and it has had a profound impact on all dimensions of society and the higher education system. This paper screened 11 indicators such as the number of papers and public education expenditures as secondary indicators, and then used factor analysis to extract 11 indicators into 3 primary indicators, which are the academic level of higher education and the research of higher education. Capital, higher education talent construction. Taking three indicators as input and higher education health evaluation as output, we use Back Propagation to construct higher education health evaluation model. In order to predict the academic level, rigorous funding and talent construction scores of higher education in the six countries after three years, this paper introduces time series analysis and found through testing that all 11 secondary indicators conform to the model. Therefore, the three-year prediction of the initial data is substituted into the higher education health evaluation model constructed in the previous article, and the evaluation obtained can be regarded as a sustainable evaluation of higher education.
Towards structured <scp>NLP</scp> interpretation via graph explainers

Abstract Natural language processing (NLP) models have been increasingly deployed in real‐world applications, and interpretation for textual data has also attracted dramatic attention recently. Most existing methods generate feature importance interpretation, which indicate the contribution of each word towards a specific model prediction. Text data typically possess highly structured characteristics and feature importance explanation cannot fully reveal the rich information contained in text. To bridge this gap, we propose to generate structured interpretations for textual data. Specifically, we pre‐process the original text using dependency parsing, which could transform the text from sequences into graphs. Then graph neural networks (GNNs) are utilized to classify the transformed graphs. In particular, we explore two kinds of structured interpretation for pre‐trained GNNs: edge‐level interpretation and subgraph‐level interpretation. Experimental results over three text datasets demonstrate that the structured interpretation can better reveal the structured knowledge encoded in the text. The experimental analysis further indicates that the proposed interpretations can faithfully reflect the decision‐making process of the GNN model.
Ambiguity and Generality in Natural Language Privacy Policies

Privacy policies are legal documents containing application data practices. These documents are well-established sources of requirements in software engineering. However, privacy policies are written in natural language, thus subject to ambiguity and abstraction. Eliciting requirements from privacy policies is a challenging task as these ambiguities can result in more than one interpretation of a given information type (e.g., ambiguous information type "device information" in the statement "we collect your device information"). To address this challenge, we propose an automated approach to infer semantic relations among information types and construct an ontology to guide requirements authors in the selection of the most appropriate information type terms. Our solution utilizes word embeddings and Convolutional Neural Networks (CNN) to classify information type pairs as either hypernymy, synonymy, or unknown. We evaluate our model on a manually-built ontology, yielding predictions that identify hypernymy relations in information type pairs with 0.904 F-1 score, suggesting a large reduction in effort required for ontology construction.
Improving the Efficiency of Autonomous Heat Supply Systems by Implementing Integrated Control Systems

Abstract In this paper we studied the actuality of the problem of optimal control of the heat supply system as a single complex object on the basis of a generalized mathematical model that takes into account the dynamics of the fuel combustion process, heat generation, its transmission through the heat network and that takes into account the dynamics of heat energy consumption and various external factors.
Quality Evaluation of the General Domain Chinese Dialogue Generation Models with a BLEURT-based Model

The development of natural language models for Chinese dialogue language generation has gradually progressed to a quite high quality over the last year. However, it still requires human to evaluate the generated dialogue, that cost time and labor to test new models. The development of automated evaluation methods has not been able to achieve a balance between human effort reduction and evaluation accuracy. Current evaluation methods are unstable and inefficient. In the past, the most popular choices of natural language evaluation methods, such as BLEU and ROUGE, performed at a certain level for translation tasks, but could not reach the same standard of stability for non-sequence-to-sequence generation types such as dialogue or questions and answers. Until recently, BLEURT model began to provide a training to evaluation approach and a guide for evaluating translation models automatically. BLEURT uses a relatively small number of manual methods to allow the model to evaluate any natural language generation task. We adopt the BLEURT pre-training model as the basic training model to learn the manual evaluation on the generated Chinese dialogue. It gives good evaluation results as human judge, and we find that some generation model can generate better dialogue than ordinary human on social media.