Reviewerly/exHarmony huggingface dataset:
A blockchain-based rice supply chain system

With the continuous development of blockchain technology, “blockchain+” has gradually evolved into a new form of social and economic development. Blockchain technology can be applied to the rice supply chain management field. Consider integrating the encryption algorithm, timestamp technology, consensus algorithm, and sidechain technology of blockchain with the rice supply chain, analliance chainbetween companies upstream and downstream of the supply chain, including producers, suppliers, and vendors can be built to create a safe and efficient supply chain information management. This information system uses the unique encryption technology of the blockchain and its unforgeable features of irreversibility and decentralization to help the supply chain break the current bottleneck, solve the problem of information security in the rice supply chain and realize the practical traceability of rice. At the same time, the involvement of the blockchain will also open up new opportunities for supply chain finance and bring about fundamental changes in supply chain management.
GCMCSR: A New Graph Convolution Matrix Complete Method with Side-Information Reconstruction

In this work, we propose a novel Graph Convolutional Matrix Completion with Side-information Reconstruction (GCMCSR) model for the recommender system. For most recommender systems, the side-informations of users and items are usually utilized as the input of the model. However, when new users or new projects are included, the system's performance can degrade significantly. In GCMCSR, to solve this problem, we take the side-information as labels to predict under a multi-task learning framework, which contains a graph-based matrix completion task and a side-information reconstruction task. We borrow the idea of Graph Convolutional Matrix Completion (GCMC) to acquire user/item representation by spatial information extracted from the user-item bipartite graph. The experiment results show that our model achieved state-of-the-art performance on all three public datasets.
Detecting adversarial examples by additional evidence from noise domain

Deep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two-stream pseudo-siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state-of-the-art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.
Digital Skills for Communication and Content Creation: Can B-learning Greatly Influence Them?

This article reports the results of a study that took place at a private university in Bogota, Colombia. An English language course was the scenario for applying a set of sessions that sought to determine how blended learning could influence the development of digital skills for creativity and communication via a pedagogical intervention during an academic term. A quasi-experimental design was carried out to measure the variable ‘communication and content creation skills’ by conducting a pre- and post-test survey. From the statistical test, the general hypothesis was accepted for the scope of this study, stating that the b-learning model did not develop the skills chosen in this implementation meaningfully. These findings imply that creativity and communication skills can be influenced by this model; also, thought-content-creation skills received a major influence according to the internal statistical tests. However, the statistical tests that comprised this method also suggested that the impact was not high enough to prove the main hypothesis. Furthermore, findings provide an opportunity to explore research designs that are not common in this field of education.
Metatheory.jl: Fast and Elegant Algebraic Computation in Julia with Extensible Equality Saturation

We introduce Metatheory.jl: a lightweight and performant general purpose symbolics and metaprogramming framework meant to simplify the act of writing complex Julia metaprograms and to significantly enhance Julia with a native term rewriting system, based on state-of-the-art equality saturation techniques, and a dynamic first class Abstract Syntax Tree (AST) pattern matching system that is dynamically composable in an algebraic fashion, taking full advantage of the language's powerful reflection capabilities. Our contribution allows to perform general purpose symbolic mathematics, manipulation, optimization, synthesis or analysis of syntactically valid Julia expressions with a clean and concise programming interface, both during compilation or execution of programs.
Two-Component Mixture Model in the Presence of Covariates

In this article, we study a generalization of the two-groups model in the presence of covariates—a problem that has recently received much attention in the statistical literature due to its applicability in multiple hypotheses testing problems. The model we consider allows for infinite dimensional parameters and offers flexibility in modeling the dependence of the response on the covariates. We discuss the identifiability issues arising in this model and systematically study several estimation strategies. We propose a tuning parameter-free nonparametric maximum likelihood method, implementable via the expectation-maximization algorithm, to estimate the unknown parameters. Further, we derive the rate of convergence of the proposed estimators—in particular we show that the finite sample Hellinger risk for every ‘approximate’ nonparametric maximum likelihood estimator achieves a near-parametric rate (up to logarithmic multiplicative factors). In addition, we propose and theoretically study two ‘marginal’ methods that are more scalable and easily implementable. We demonstrate the efficacy of our procedures through extensive simulation studies and relevant data analyses—one arising from neuroscience and the other from astronomy. We also outline the application of our methods to multiple testing. The companion R package NPMLEmix implements all the procedures proposed in this article.
Neural Network Effectiveness Evaluation While the Intersection of Images in the Receptor Field

The study and application of neural networks is one of the main areas in the field of artificial intelligence. The effectiveness of the neural network depends significantly on both its architecture and the structure of the training set. This paper proposes a probabilistic approach to evaluate the effectiveness of the neural network if the images intersect in the receptor field. A theorem and its corollaries are proved, which are consistent with the results obtained by a different path for a perceptron-type neural network.
Explainable natural language processing with matrix product states

Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations. We systematically analyze RNNs' behaviors in a ubiquitous NLP task, the sentiment analysis of movie reviews, via the mapping between a class of RNNs called recurrent arithmetic circuits (RACs) and a matrix product state (MPS). Using the von-Neumann entanglement entropy (EE) as a proxy for information propagation, we show that single-layer RACs possess a maximum information propagation capacity, reflected by the saturation of the EE. Enlarging the bond dimension beyond the EE saturation threshold does not increase model prediction accuracies, so a minimal model that best estimates the data statistics can be inferred. Although the saturated EE is smaller than the maximum EE allowed by the area law, our minimal model still achieves ~99% training accuracies in realistic sentiment analysis data sets. Thus, low EE is not a warrant against the adoption of single-layer RACs for NLP. Contrary to a common belief that long-range information propagation is the main source of RNNs' successes, we show that single-layer RACs harness high expressiveness from the subtle interplay between the information propagation and the word vector embeddings. Our work sheds light on the phenomenology of learning in RACs, and more generally on the explainability of RNNs for NLP, using tools from many-body quantum physics.
A comparative study of calibration methods for imbalanced class incremental learning

Deep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the capacity to handle streams of visual information and the management of class imbalance in datasets. Existing research approaches these two problems separately while they co-occur in real world applications. Here, we study the problem of learning incrementally from imbalanced datasets. We focus on algorithms which have a constant deep model complexity and use a bounded memory to store exemplars of old classes across incremental states. Since memory is bounded, old classes are learned with fewer images than new classes and an imbalance due to incremental learning is added to the initial dataset imbalance. A score prediction bias in favor of new classes appears and we evaluate a comprehensive set of score calibration methods to reduce it. Evaluation is carried with three datasets, using two dataset imbalance configurations and three bounded memory sizes. Results show that most calibration methods have beneficial effect and that they are most useful for lower bounded memory sizes, which are most interesting in practice. As a secondary contribution, we remove the usual distillation component from the loss function of incremental learning algorithms. We show that simpler vanilla fine tuning is a stronger backbone for imbalanced incremental learning algorithms.
GoFast: Graph-based optimization for efficient and scalable query evaluation

The popularity of the Resource Description Framework (RDF) and SPARQL has thrust the development of high-performance systems to manage data represented with this model. Former approaches adapted the well-established relational model applying its storage, query processing, and optimization strategies. However, the borrowed techniques from the relational model are not universally applicable in the RDF context. First, the schema-free nature of RDF induces intensive joins overheads. Also, optimization strategies trying to find the optimal join order rely on error-prone statistics unable to capture all the correlations among triples. Graph-based approaches keep the graph structure of RDF representing the data directly as a graph. Their execution model leans on graph exploration operators to find subgraph matches to a query. Even if they have shown to outperform relational-based systems in complex queries, they are barely scalable and optimization techniques are completely system dependent. Recently, some systems such as RDF_QDAG have shown that by combining graph exploration and triples clustering one can achieve a good compromise between performance and scalability. In this paper, we propose optimization strategies for this kind of RDF management systems. First, we define novel statistics collected for clusters of triples to better capture the dependencies found in the original graph. Second, we redefine an execution plan based on these logical structures which allows to represent the RDF graph exploration process. Third, we introduce an algorithm for selecting the optimal execution plan based on a customized cost model. Finally, we propose a new approach to refine the chosen plan by pruning invalid clusters that do not participate in the construction of the final query results. All our proposals are validated experimentally using well-known RDF benchmarks.
Design and implementation of a distributed data acquisition function architecture based on DOA/Handle technology

With the development of new generation Internet technology, Digital Object Architecture (DOA)/Handle system plays an important role in industrial system. The development and technical characteristics of DOA/Handle technology is studied in this paper. Then function architecture of distributed data acquisition system based on DOA/Handle technology is proposed. Through the detailed design of the functions and characteristics of the architecture, the core functions such as distributed data acquisition, distributed data management, data security sharing, and data security governance are realized in the industrial Internet environment. At the end of the paper, the typical scenarios of this architecture are summarized, and a prospect of the application prospect of DOA/Handle technology is introduced.
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning

Designing agents that acquire knowledge autonomously and use it to solve new tasks efficiently is an important challenge in reinforcement learning. Unsupervised learning provides a useful paradigm for autonomous acquisition of task-agnostic knowledge. In supervised settings, representations discovered through unsupervised pre-training offer important benefits when transferred to downstream tasks. Given the nature of the reinforcement learning problem, we explore how to transfer knowledge through behavior instead of representations. The behavior of pre-trained policies may be used for solving the task at hand (exploitation), as well as for collecting useful data to solve the problem (exploration). We argue that pre-training policies to maximize coverage will result in behavior that is useful for both strategies. When using these policies for both exploitation and exploration, our agents discover solutions that lead to larger returns. The largest gains are generally observed in domains requiring structured exploration, including settings where the behavior of the pre-trained policies is misaligned with the downstream task.
The Design of Ideological and Political Classroom Teaching Resource Sharing System Based on Streaming Media Technology

The traditional ideological and political classroom teaching resource sharing system has a small number of concurrent users and long information retrieval time. Aiming at the above problems, a ideological and political classroom teaching resource sharing system based on streaming media technology is designed. In the hardware design, the type of ARM development board is designed, the functions of each interface are allocated, and the digital mobile TV receiver stick and the built-in WIFI module are designed. In the software design, the streaming media technology is introduced, and the data packet format in the transmission protocol is standardized to ensure that the playback will not be paused due to temporary network congestion. Next, software modeling of the system is performed to create a user architecture use case diagram, Set up the database, and complete the design of the ideological and political classroom teaching resource sharing system based on streaming media technology through the hardware design and software design of the system. The simulation experiment results show that in the simulation experiment of the designed teaching system, compared with the conventional teaching system, the number of concurrent users is higher and the information retrieval time is faster.
Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network

The simulation of realistic ultrasound (US) images has many applications in image-guided surgery such as image registration, data augmentation, or education. We simulated intraoperative US images of the brain after tumor resection surgery. A Generative Adversarial Networks first generated an US image with resection from a resection cavity map. This generated cavity texture was then blended into a real pre-resection patient-specific US image. A validation study showed that two neurosurgeons correctly labelled only 56% and 53% of the simulated images, which indicate that these synthesized images are hardly distinguishable from real post-resection US images.
Experiments on whole-body control of a dual-arm mobile robot with the Set-Based Task-Priority Inverse Kinematics algorithm

In this paper an experimental study of set-based task-priority kinematic control for a dual-arm mobile robot is developed. The control strategy for the coordination of the two manipulators and the mobile base relies on the definition of a set of elementary tasks to be properly handled depending on their functional role. In particular, the tasks have been grouped into three categories: safety, operational and optimization tasks. The effectiveness of the resulting task hierarchy has been validated through experiments on a Kinova Movo robot, in a domestic use case scenario.
Research on Introduction to Famous Works and Cultivation of Humanistic Quality in the Teaching of British and American Literature Based on Network Information

Abstract Classics reading can significantly improve our cultural literacy, It is the main content of British and American literature teaching in the classroom. But due to some teaching ideas, many universities in the process of curriculum and teaching practice rejection of British and American literature teaching content. Now few people can calm down and deep reading, the reading atmosphere also gradually lost humanities, this article expounds the defects of traditional British and American literature teaching mode, and how to overcome these disadvantages, looking for solutions, innovative ideas and make full use of big data under the environment of network resources for classics reading, to cultivate people’s humanistic quality.
VGGNet and AlexNet Networks

After a certain point, even an extremely complex solution ceases to improve. And then it indicates, we have to improve the design of the solution. We go back to the drawing board and put our heads together to improve the capabilities. With more options available, we can iterate and test multiple solutions. And then based on the business problem at hand, the best solution will be chosen and implemented. We follow the same principle in deep learning architectures. We work on a network architecture, improve it, and make it more robust, accurate, and efficient. The selection of the neural network architectures is based on the testing done of various architectures.
The M-DUCG Methodology to Calculate the Joint Probability Distribution of Directed Cycle Graph With Local Data and Domain Causal Knowledge

Modeling a complex system with a large number of variables at one time and collecting global data on a large scale is usually not practical. It is ideal to model the global system according to only local data and structures, and then synthesize them together. Multiple local structures can be easily synthesized as a global structure through fusing same nodes, while multiple sets of local data cannot be combined as the global data. Therefore, we need a method to obtain the joint probability distribution (JPD) of the global system by utilizing local data only. When the global structure synthesized does not include directed cyclic graph (DCG), it is easy to calculate the JPD with Bayesian network (BN). When DCGs are included, BN does not work. This paper presents the multi-valued Dynamic Uncertain Causality Graph (M-DUCG) methodology to calculate the global JPD in the case of DCGs with only local data. The idea is to use multiple sets of local data to learn the parameters of the synthesized M-DUCG structure with DCGs, and then use these parameters to calculate the global JPD.
A Brief Talk on How to Use Multimedia Technology to Construct A Diversified Evaluation System in English Teaching

Abstract With the continuous deepening of college English teaching reform, the traditional summative evaluation can no longer meet the actual needs of college English teaching. Establishing a diversified student learning evaluation system in English teaching is an inevitable requirement of quality education, and also a need to develop students in accordance with their aptitude and individuality. This article briefly analyzes the current situation of using multimedia technology to construct a diversified evaluation system in English teaching, and discusses the construction of a diversified evaluation system for English teaching to mobilize students’ enthusiasm, initiative and creativity, and improve the quality of college English teaching.
Research on the Establishment of Oral Japanese Teaching System with Japanese Language Sense as the Main Content based on Computer Software

Abstract With the rapid development of computer technology, information technology has become an indispensable auxiliary equipment in the education industry, which can help students learn better, especially language education, including oral Japanese. Japanese is a difficult language, which requires students to constantly exercise and learn oral English. Under the computer technology, learners can break the limitation of time and space, which will better study and improve the level of oral Japanese. At the same time, through computer technology, we can better establish oral Japanese teaching system, which can effectively stimulate learners’ interest in learning oral Japanese. Through the construction of a new oral Japanese teaching mode, we can meet the different needs of different learners, which will effectively improve the oral and communicative competence of Japanese. First of all, this paper analyzes the necessity of computer application in oral Japanese teaching. Then, this paper puts forward some problems. Finally, some suggestions are put forward.
Broadband fiber-based entangled photon-pair source at telecom O-band

In this Letter, we report a polarization-entangled photon-pair source based on type-II spontaneous parametric downconversion at telecom O-band in periodically poled silica fiber (PPSF). The photon-pair source exhibits more than 130 nm ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼<!-- ∼ --></mml:mo> </mml:mrow> <mml:mn>24</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">T</mml:mi> <mml:mi mathvariant="normal">H</mml:mi> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:math> ) emission bandwidth centered at 1306.6 nm. The broad emission spectrum results in a short biphoton correlation time, and we experimentally demonstrate a Hong–Ou–Mandel interference dip with a full width of 26.6 fs at half-maximum. Owing to the low birefringence of the PPSF, the biphotons generated from type-II SPDC are polarization-entangled over the entire emission bandwidth, with a measured fidelity to a maximally entangled state greater than 95.4%. The biphoton source provides the broadest bandwidth entangled biphotons at O-band to our knowledge.