Reviewerly/exHarmony huggingface dataset:
Indoor Scene Semantic Segmentation Based on RGB-D Image and Convolution Neural Network

Abstract In recent years, convolutional neural network has been widely used in image semantic segmentation and achieved great success. In this paper, a semantic segmentation network of indoor scene based on rgb-d image is proposed: SRNET (Strong supervision Residual Net). In this network model, the original data is processed by separate training and gradual fusion, and the mandatory supervision module is added in the decoding stage, which effectively improves the accuracy of semantic segmentation. At the same time, anti residual decoding method and jump structure are introduced to reduce information loss. Experimental results show that the segmentation accuracy of this model is better than most of the current segmentation algorithms.
Path planning control using high abstraction level environment model and industrial task-oriented knowledge

In order to face an increasing economic competition, industrial manufacturers wish to reduce the time and cost of product development. Furthermore, up-to-date products are more and more integrated, and must be assembled, disassembled or maintained under potentially very strong geometric constraints. In the context of Industry 4.0, manufacturers are therefore expressing the desire to validate all the tasks related to their products lifecycles, from design stage on, by simulation using a digital mock-up, and before building the physical prototypes. A key issue is then to find a trajectory, a movement, to show the feasibility of the simulated scenarios. Automatic path planning algorithms, developed by the robotics community from the 1980s on, have been widely used for this purpose. In this paper, we intend to improve the relevance of the trajectories proposed by such algorithms and the associated computation times. To do so, we consider: a) the use of path planning algorithms or of combinations of these; b) the involvement for the environment modelling of data with a higher abstraction level than the purely geometric data traditionally used; and c) the representation of the knowledge related to the task to be performed by using ontologies. The approaches developed and associated improvements of the state of the art are validated experimentally through the simulation of highly geometrically constrained manipulation tasks.
Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Approach

Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point cloud is difficult, so the no-reference (NR) methods have become a research hotspot. Few researches about NR objective quality metrics are conducted due to the lack of a large-scale subjective point cloud dataset. Besides, the distinctive property of the point cloud format makes it infeasible to apply blind image quality assessment (IQA) methods directly to predict the quality scores of point clouds. In this paper, we establish a large-scale PCQA dataset, which includes 104 reference point clouds and more than 24,000 distorted point clouds. In the established dataset, each reference point cloud is augmented with 33 types of impairments (e.g., Gaussian noise, contrast distortion, geometry noise, local loss, and compression loss) at 7 different distortion levels. Besides, inspired by the hierarchical perception system and considering the intrinsic attributes of point clouds, an end-to-end sparse convolutional neural network (CNN) is designed to accurately estimate the subjective quality. We conduct several experiments to evaluate the performance of the proposed network. The results demonstrate that the proposed network has reliable performance. The dataset presented in this work will be publicly accessible at this http URL.
Leakage of Dataset Properties in {Multi-Party} Machine Learning

Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that such multi-party computation can cause leakage of global dataset properties between the parties even when parties obtain only black-box access to the final model. In particular, a ``curious'' party can infer the distribution of sensitive attributes in other parties' data with high accuracy. This raises concerns regarding the confidentiality of properties pertaining to the whole dataset as opposed to individual data records. We show that our attack can leak population-level properties in datasets of different types, including tabular, text, and graph data. To understand and measure the source of leakage, we consider several models of correlation between a sensitive attribute and the rest of the data. Using multiple machine learning models, we show that leakage occurs even if the sensitive attribute is not included in the training data and has a low correlation with other attributes or the target variable.
Structure by Architecture: Disentangled Representations without Regularization

We study the problem of self-supervised structured representation learning using autoencoders for generative modeling. Unlike most methods which rely on matching an arbitrary, relatively unstructured, prior distribution for sampling, we propose a sampling technique that relies solely on the independence of latent variables, thereby avoiding the trade-off between reconstruction quality and generative performance inherent to VAEs. We design a novel autoencoder architecture capable of learning a structured representation without the need for aggressive regularization. Our structural decoders learn a hierarchy of latent variables, akin to structural causal models, thereby ordering the information without any additional regularization. We demonstrate how these models learn a representation that improves results in a variety of downstream tasks including generation, disentanglement, and extrapolation using several challenging and natural image datasets.
Differences between the Intention to Implement Hand Hygiene and Hand Hygiene Behavior in Accordance with Education Experience and Fulfillment of Infection Control among New Nurses during Undergraduate Studies

Purpose: This study aims to identify the degree of intention to hand hygiene and hand hygiene behavior in new nurses based on education experience on infection control. Method: The participants comprised 108 new nurses working in a general hospital in S city, Korea. Data were collected from September to December, 2020. Data were analyzed using descriptive statistics, independent t-test, analysis of variance (ANOVA), and Pearson correlation with SPSS 26.0 program. Results: There were statistically significant differences in intention to hand hygiene (F=3.39, p=.021) and hand hygiene behavior (F=3.33, p=.022) depending on education experience related to infection control during undergraduate courses. Hand hygiene intention (F=4.41, p=.008) and hand hygiene behavior (F=4.13, p=.008) showed statistically significant differences depending on educational satisfaction with infection control during undergraduate courses. There were significant positive correlations between intention to hand hygiene and hand hygiene behavior (r=.21, p=.026). Conclusion: This suggests that education in infection control can reinforce intention to hand hygiene and hand hygiene behavior in new nurses.
Hacking VMAF and VMAF NEG: metrics vulnerability to different preprocessing.

Video quality measurement plays a critical role in the development of videoprocessing applications. In this paper, we show how popular quality metricsVMAF and its tuning-resistant version VMAF NEG can be artificially increased byvideo preprocessing. We propose a pipeline for tuning parameters of processingalgorithms that allows increasing VMAF by up to 218.8%. A subjective comparisonof preprocessed videos showed that with the majority of methods visual qualitydrops down or stays unchanged. We show that VMAF NEG scores can also beincreased by some preprocessing methods by up to 23.6%.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification.

Many areas of science make extensive use of computer simulators that implicitly encode likelihood functions of complex systems. Classical statistical methods are poorly suited for these so-called likelihood-free inference (LFI) settings, outside the asymptotic and low-dimensional regimes. Although new machine learning methods, such as normalizing flows, have revolutionized the sample efficiency and capacity of LFI methods, it remains an open question whether they produce reliable measures of uncertainty. This paper presents a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) efficiently construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power; (2) provide practical diagnostics for assessing empirical coverage over the entire parameter space. We refer to our framework as likelihood-free frequentist inference (LF2I). Any method that estimates a test statistic, like the likelihood ratio, can be plugged into our framework to create valid confidence sets and compute diagnostics, without costly Monte Carlo samples at fixed parameter settings. In this work, we specifically study the power of two test statistics (ACORE and BFF), which, respectively, maximize versus integrate an odds function over the parameter space. Our study offers multifaceted perspectives on the challenges in LF2I.
Research on secure storage of network information resources based on data sensitivity

Aiming at the problems of low accuracy and long time consuming in traditional block based secure storage method, a block based secure storage method of network information resources based on data sensitivity analysis is proposed. Relying on the big data network environment, the required storage network information resources are classified according to their storage capacity and computing capacity, and the block processing is realized according to the classification results combined with resource sensitivity; the block security storage model of network information resources is constructed through deviation compensation. The results show that the block storage method of network information resources based on data sensitivity analysis meets the design requirements, and it is safe, efficient and implementable.
ÎNVĂŢAREA COLABORATIVĂ ÎN CADRUL UNEI CERCETĂRI ACŢIUNE DEZVOLTATĂ PENTRU A RĂSPUNDE PROVOCĂRILOR IMPUSE DE ŞCOALA ONLINE

The measures put in place to stop the spread of the Covid-19 have had a major impact on the organization of educational processes. School teachers have been faced with overnight digitalization of their activities without always receiving adequate support with this transition. The present paper reports on a participatory action research project in the form of a tutoring program that sought to understand and respond to these challenges. The project took the form of an open learning initiative addressed to teachers in April – June 2020, followed by a data collection and analysis phase. 37 teachers in four Romanian counties benefited from personalized forms of support offered by 20 student- tutors enrolled in the Educational Sciences Department at the West University in Timişoara. All project activities were carried out at a distance, in the vast majority of cases, online. In analyzing the data produced by the project (tutor reflection log entries, qualitative interviews and focus groups with teachers and tutor students) we seek to answer the following research question: How did a collaborative learning process emerge as part of a participatory action research project carried out during the onset of online teaching and learning practices?. The collaborative learning responded to teachers’ immediate and individual needs regarding the development of digital competences, as well as related to pedagogical and emotional support. Through the tutoring program, the expected roles of the educational actors were reversed: since the undergraduate students were not primarily beneficiaries of the educational processes, but took on an active part as facilitators of the teachers’ learning processes. Thus, a competence transfer from the university to the pre- university environment took place, at a faster rate than it would usually happen.
Quantum deep learning in remote sensing: achievements and challenges

In recent years, deep learning algorithms have shown promising results for different image analyzing tasks, particularly in remote sensing image processing. Inspired by the success of remote sensing sensors in geo-located imagery, many studies have been carried out on remote sensing sensors for image processing, which brings a new approach into intelligent remote sensing and photogrammetric computer vision. At the same time, algorithms for quantum processors have been shown to efficiently solve some issues that are intractable on conventional, classical processors. This study summarizes the novel techniques of deep learning and quantum deep learning and its research progress and real-world applications in remote sensing image processing, introduces the current main challenges in processing and its development of geo-located datasets, focuses on the analysis and elaboration of the research status of quantum deep learning in sensing and imaging, and on this basis, summarizes the intelligent remote sensing applications and their application effects in scene understanding.
Discussion on the Performance Evaluation Index System of Basic Course Teaching Team in Colleges and Universities

Teaching team plays an important role in integrating teaching resources, improving the quality of teachers and the quality of education and teaching. This paper studies the characteristics of the basic course teaching team, and gives the principles of building the basic course teaching team performance evaluation index system and the specific performance evaluation index system, in order to provide a direction for the high-level construction of the teaching team.
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the \textsc{MuST-C} English-German, English-French and English-Spanish language pairs.
Cryptanalysis of a Code-Based Signature Scheme Based on the Schnorr-Lyubashevsky Framework

We propose an attack on the recent attempt by Li, Xing and Yeo to produce a code-based signature scheme using the Schnorr-Lyubashevsky approach in the Hamming metric, and verify its effectiveness through numerical simulations. Differently from other (unsuccessful) proposals, this new scheme exploits rejection sampling along with dense noise vectors to hide the secret key structure in produced signatures. We show that these measures, besides yielding very slow signing times and rather long signatures, do not succeed in protecting the secret key. We are indeed able to prove the existence of a strong correlation between produced signatures, which ultimately leaks information about the secret key. To support this claim, we use both theoretical arguments and numerical evidences. Finally, we employ such a weakness to mount a full key recovery attack, which is able to recover the secret key after the observation of a bunch of signatures. Our results show that the considered scheme may be secure only for one-time usage.
Dissecting cloud game streaming platforms regarding the impacts of video encoding and networking constraints on QoE

Cloud gaming -- sometimes also referred to as game streaming -- is the concept of executing games on remote servers -- typically within a cloud -- capturing the video output normally displayed onto a screen and sending the result as a video stream to end-user devices. While many game streaming solutions have existed for many years now with first proof of concept shown in 2000, many of these solutions still seem to suffer from technical or quality limitations. Recently, several new actors entered the cloud gaming market with the promise to solve these limitations.
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings

We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs). Our algorithm for the $\ell_2$ setting achieves optimal excess population risk in near-linear time, while the best known differentially private algorithms for general convex losses run in super-linear time. Our algorithm for the $\ell_1$ setting has nearly-optimal excess population risk $\tilde{O}\big(\sqrt{\frac{\log{d}}{n}}\big)$, and circumvents the dimension dependent lower bound of [AFKT21] for general non-smooth convex losses. In the differentially private non-convex setting, we provide several new algorithms for approximating stationary points of the population risk. For the $\ell_1$-case with smooth losses and polyhedral constraint, we provide the first nearly dimension independent rate, $\tilde O\big(\frac{\log^{2/3}{d}}{n^{1/3}}\big)$ in linear time. For the constrained $\ell_2$-case, with smooth losses, we obtain a linear-time algorithm with rate $\tilde O\big(\frac{1}{n^{3/10}d^{1/10}}+\big(\frac{d}{n^2}\big)^{1/5}\big)$. Finally, for the $\ell_2$-case we provide the first method for {\em non-smooth weakly convex} stochastic optimization with rate $\tilde O\big(\frac{1}{n^{1/4}}+\big(\frac{d}{n^2}\big)^{1/6}\big)$ which matches the best existing non-private algorithm when $d= O(\sqrt{n})$. We also extend all our results above for the non-convex $\ell_2$ setting to the $\ell_p$ setting, where $1 < p \leq 2$, with only polylogarithmic (in the dimension) overhead in the rates.
Expanded Gabidulin Codes and Their Application to Cryptography

This paper presents a new family of linear codes, namely the expanded Gabidulin codes. Exploiting the existing fast decoder of Gabidulin codes, we propose an efficient algorithm to decode these new codes when the noise vector satisfies a certain condition. Furthermore, these new codes enjoy an excellent error-correcting capability because of the optimality of their parent Gabidulin codes. Based on different masking techniques, we give two encryption schemes by using expanded Gabidulin codes in the McEliece setting. According to our analysis, both of these two cryptosystems can resist the existing structural attacks. Our proposals have an obvious advantage in public-key representation without using the cyclic or quasi-cyclic structure compared to some other code-based cryptosystems.
A new construction of odd-variable rotation symmetric Boolean functions with optimal algebraic immunity and higher nonlinearity

Rotation symmetric Boolean functions are potentially rich in functions of cryptographic significance. In this paper, a new construction of odd-variable rotation symmetric Boolean functions with optimal algebraic immunity is presented. By a direct calculation, the nonlinearity of the newly constructed functions is higher than the nonlinearities of all the known odd-variable rotation symmetric Boolean functions with optimal algebraic immunity. The algebraic degree and the fast algebraic immunity of our functions are also considered.
Dynamic Performance Evaluation of Blockchain Technologies

In recent years, the rapid development of blockchain technology has focused on the world. Its application has been extended to many fields, such as digital finance, supply chain management, and digital asset transactions. For some enterprises and users, how to choose the most effective platform from many blockchains to control costs and share data is an important issue. To comprehensively evaluate the blockchain technology, we analyze from three levels: fundamentals, market, and sentiment indicators. At the same time, the efficiency of dynamic blockchain technology over time can reflect the overall change of this technology. On the one hand, based on the fact that there is no clear inputs in the blockchain performance, we use the index model. On the other hand, we combined the global productivity index with the directional distance function, and finally measured the dynamic performance of the blockchain technology.
The system of monitoring and evaluation of school nutrition

The article summarizes international approaches to creating effective management tools that enable operational management of school nutrition programs of various scales — monitoring and evaluation systems. M&amp;E system, as a project management tool, is the most relevant and appropriate way to reduce the level of uncertainty and ensure the effectiveness of management decisions, using the possibilities of digitalization. The paper suggests ways to create basic tools of the system for monitoring and evaluating national school nutrition programs and the national plan for the M&amp;E system of the school nutrition program in Russia.
Thompson Sampling for Markov Games with Piecewise Stationary Opponent Policies

Reinforcement learning problems with multiple agents pose the challenge of efficiently adapting to nonstationary dynamics arising from other agentsâ€™ strategic behavior. Although several algorithms exist for these problems with promising empirical results, regret analysis and efficient use of other-agent models in general-sum games is very limited. We propose an algorithm (TSMG) for general-sum Markov games against agents that switch between several stationary policies, combining change detection with Thompson sampling to learn parametric models of these policies. Under standard assumptions for parametric Markov decision process (MDP) learning, the expected regret of TSMG in the worst case over policy parameters and switch schedules is near-optimal in time and number of switches, up to logarithmic factors. Our experiments on simulated games show that TSMG can outperform standard Thompson sampling and a version of Thompson sampling with a static reset schedule, despite the violation of an assumption that the MDPs induced by the other player are ergodic.