Reviewerly/exHarmony huggingface dataset:
Evaluation Model of College English Teaching Effect Based on Big Data Platform

At present, there are many methods for teaching assessment in different universities. As we know, now each university has different assess system for English teaching. Firstly, as a compulsory course, teachers and students pay great attention to English learning, but there are also many problems. At present, there are still relatively few high-quality English teachers, especially in rural universities. There are serious deficiencies in teacher resources and teaching equipment, which can not meet the urgent needs of College English teaching. The teaching method of teachers is relatively simple, and the teaching method is mostly used. In teaching, face-to-face is used to instill knowledge into students. In the teaching process, the Chinese teaching method takes a long time, and the translation teaching method is used too much. There are many grammatical errors in the teaching process, which is difficult for students to understand: in the teaching process, the passive teaching is mainly used, and the teachers teach the students to listen to the notes. There is a kind of “dumb English”; Teachers do not have enough time to interact with students. At present, most of the teaching tasks of College English substitute teachers are heavy. Under the existing education system, the goal of English education is mainly exam oriented, and the practical application is lack. As a result, many students’ interest in learning English decreases. Students with good academic performance and some interest in English often neglect their listening ability. For students with poor grades, their interest in learning is very low, and their opportunities to actually use English are also very limited. Therefore, how to improve the efficiency of College English teaching and improve the comprehensive quality of students on the basis of meeting the existing teaching and examination requirements is the key to teaching research. In view of this, this paper proposes a wide area database based College English teaching performance evaluation model.
Image Restoration Quality Assessment Based on Regional Differential Information Entropy

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study's image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people's average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.
An Approach Based on Feature Selection for Missing Value Imputation

Today, with the spread of technologies such as the internet of things and data acquisition from sensors, the data obtained has increased. The size of the data produced by other sources, especially digital platforms, is increasing day by day. This increase in data production enables the development of effective artificial intelligence applications and in-depth analysis. However, in many data collection processes, missing values are included in the data set due to operational problems or different reasons. This situation is expressed as a data quality problem in the literature. It is possible that the analysis to be made on this data will be negatively affected by this situation. Various statistical techniques and machine learning-based techniques exist in the literature for filling missing values. In this study, an approach is put forward that suggests missing values imputation based on the consistency of the sample with missing values with other samples in the data set.
Building a Semantic Layer for Early Design Trade Studies in the Development of Commercial Aircraft

To improve the adoption of Model-based Systems Engineering (MBSE), data that is distributed across engineering disciplines needs to be made available in an open and descriptive way. This paper describes a new approach to implementing a semantic layer that allows integrating and publishing MBSE data stored in heterogeneous models in a uniform way by means of Semantic Web technologies. The tool-independent views on engineering data provided by the semantic layer enable the implementation of services for accessing, classifying, checking and reuse of federated information. We report on the creation of a common vocabulary in the Ontology Modeling Language (OML) that can be automatically instantiated from distributed models into a knowledge graph. We demonstrate the benefits of our approach using a Systems Modeling Language (SysML) based early design trade study in the aeronautics domain.
Scalable Management Architecture for Electronic Health Records Based on Blockchain

The rise of communication and information technology in conjunction with the health emergency due to COVID-19 has accelerated the implementation of electronic health records (EHR). These registries are constantly growing and put people’s privacy, information security and health data at risk. The use of the blockchain is shown as an alternative for the distribution and management of health data. However, the blockchain has limitations of scalability and interoperability. This work proposes a scalable management architecture based on blockchain that will be analyzed by comparing it with other works reviewed in the document. The model proposed in three modules aims to deliver an alternative to EHR’s scalable management solution based on the private blockchain such as Hyperledger Fabric.
Fuzzy Rating System for Cohesive Development Teams

Cohesive software development teams are the most valuable engineering units in IT. However, the search of these teams is complicated by the need to analyze development artifacts (e.g. code base, project repository), assess the found teams, and order them via the rating. The article describes the rating system which allows estimating teams’ quality. The rating is built upon the system of fuzzy inference due to the fact that usually the teams’ efficiency may be estimated only by experts. The article contains the reference to the previous research when the search algorithm was designed and implemented, the joint section between the previous and current study, the description of the rating system, and why the fuzzy logic is applicable, the tests of a rating system based on the search results from the previous article, the conclusion with plans for further research. The implemented rating system is a key module in the implemented software that helps HR managers to find potential teams via the search algorithm and pick up the best for the job via the rating system.
Traceability System for an Agricultural Supply Network based on Blockchain

Blockchain is a type of database that stores data in a decentralized way using cryptography. This technology is being used in different fields, such as those involving supply chains. One such field is agriculture, where different actors interact to keep track of products and register their state as they are transported. Considering the importance of this information, it is desired to guarantee its reliability, and decrease the human error involved when capturing data. This project considers a prototype system in which information of agricultural products is registered by farmers and IoT devices, to be stored securely in a Blockchain network. It is implemented in on Amazon Web Services using Hyperledger Fabric. In order to get this information, there is a web application that can be accessed by scanning a QR code present on the products. This prototype serves to verify the traceability of the agricultural production chain, with high reliability and data security.
“Sponge Maps”: Using the Concept of Value by Area Maps for Avoiding the Area Size Bias in Choropleth Maps

Abstract A well-known problem with choropleth maps is the cognitively induced effect that larger regions are perceived as more dominant. Consequently, unsatisfactory detection rates for small areas can result, which becomes relevant when important spatial features or patterns are explored (e.g., regions with maximum values). One possible approach to avoiding the area size bias is the use of cartograms. While there is already some work on Equal Are Unit Maps, little or no research has been done on the possibility of applying the concept of Value By Area Maps to transform the reference area maps. One goal of this article is to introduce the concept of so-called “Sponge Maps”, which distort the base maps independently of thematic attributes, but depending on the need to show or emphasize certain areas of interest. The second goal of the article is to answer the overall research question whether Sponge Maps actually reduce the area size bias and improve the detectability of maximum value regions. A user study was able to verify the effectiveness of the Sponge Map approach in particular. However, it also became clear that not only the area size bias plays a role in the detection of important regions—dependencies on the absolute position (top-down bias), compactness or conspicuous shape (shape bias), the familiarity (awareness bias), the color intensity (darkness bias) and not least by the distortions in the Sponge Maps as such (distortion bias) are shown. Furthermore, special aspects of detecting minimum value regions are revealed (including the so-called inverse area size bias).
Optimization of DevOps Transformation for Cloud-Based Applications

Rapid software development is critical for meeting company objectives and competing more effectively in the competitive IoT infrastructure. DevOps is a growing technique that enables enterprises to provide high-quality software capabilities through automation, to improve team communication, and to increase efficiency across the software product lifecycle. Research problem: Due to the increased demand for new products and technologies, a huge overwork shifted on the organizations for introducing software with pace and to become stable to compete with others. Due to this, the majority of organizations prefer an automated system for product development and require cloud-based applications. The git version control system is used for version management and Docker is used to package code and provide libraries. AWS services are leveraged to deploy an application as a cloud. Jenkins is used as a CI/CD pipeline to manage various phases of development and to make the development process continuous. The ELK stack is used to monitor and visualize the execution of code. In light of the findings, DevOps is an efficient method for cloud application deployment and resource selection based on the relative importance of each optimized objective in terms of value parameters such as cost, memory, and CPU capacity, and that the method can be tailored to specific application requirements. The findings of this analysis indicate that an application can be deployed to the cloud using DevOps techniques. The proposed approach cost 60% less at full weight 1.0 and 11.3% less with no weight compared to the benchmark solution’s 15.078%
QHAN: Quantum-inspired Hierarchical Attention Mechanism Network for Question Answering

The approach to question answering is challenging because it usually requires finding useful information from within and between question and answer sentences for sentence semantic matching. The key information mined from existing question and answer sentences, as a supplement to semantic information, maybe helpful for this task. However, capturing the intra-sentence and inter-sentence semantic interactions is somewhat difficult given the implicit interrelationships between question and answer sentences. Although the learning effect of multi-layer neural network is good, it lacks explanatory and theoretical support. This paper mainly studies how to select the best answer sentence from a set of question and answer sentences, mine information more effectively, perform semantic matching, and have a certain interpretability. Therefore, we propose a hierarchical attention network (QHAN) based on the mathematical framework of quantum theory, which integrates the attention mechanism under quantum measurement and density matrix (DMATT) and the attention mechanism under quantum weak measurement and weak value (WMATT) in one in a unified model framework. QHAN can not only discover key sentences in a set of question and answer sentences and key words in a sentence, but also perform sentence semantic matching quickly and accurately. Furthermore, QHAN has the advantage of interpretability in terms of models due to the physical meaning and attentional full weight distribution implied by quantum theory. Extensive experiments on the question answering dataset show that our method is comparable to the baselines and can explain why the selected question and answer sentence is the best option in terms of intra-sentence and inter-sentence attention distributions.
Experimental demonstration of practical high-speed Gaussian coherent state continuous-variable quantum key distribution with real-time parameter monitoring and key distillation

Gaussian modulated coherent state continuous variable quantum key distribution protocol is proven to provide high security while being compatible with classical coherent communication equipment. Here, we demonstrate a high-speed Gaussian modulated coherent state continuous variable quantum key distribution with a symbol rate of 40 MHz. Our system is consisted of field deployable data acquisition and processing hardware. We have also developed a software tool chain for recording data and parameter monitoring in real time. The data is then post processed to distill secret keys. We have achieved a secret key rate of 2.3 Mbps after all the post processing stages, at a transmission distance of 25 km.
A Formalization of the Development Closedness Criterion for Left-Linear Term Rewrite Systems

Several critical pair criteria are known that guarantee confluence of left-linear term rewrite systems. The correctness of most of these have been formalized in a proof assistant. An important exception has been the development closedness criterion of van Oostrom. Its proof requires a high level of understanding about overlapping redexes and descendants as well as several intermediate results related to these concepts. We present a formalization in the proof assistant Isabelle/HOL. The result has been integrated into the certifier CeTA.
List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels

In this paper, we present list autoencoder (listAE) to mimic list decoding used in classical coding theory. With listAE, the decoder network outputs a list of decoded message word candidates. To train the listAE, a genie is assumed to be available at the output of the decoder. A specific loss function is proposed to optimize the performance of a genie-aided (GA) list decoding. The listAE is a general framework and can be used with any AE architecture. We propose a specific architecture, referred to as incremental-redundancy AE (IR-AE), which decodes the received word on a sequence of component codes with non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our simulation results show that the IR-AE under CA list decoding demonstrates meaningful coding gain over Turbo-AE and polar code at low block error rates range.
Delegated Proof of Stake Consensus with Mobile Voters and Multiple Entry PBFT Voting

In this work we combine Delegated Proof of Stake (DPoS) consensus technique with multiple entry Practical Byzantine Fault Tolerant voting in a permissioned blockchain network. Voting is organized in rounds that contain a number of PBFT consensus cycles. Nodes with low stake are forced to leave at the end of current round and may return to a new round when they repurchase the stake tokens, while nodes with sufficient stake may leave the network temporarily at the completion of current round due to mobility. We consider multiple DPoS classes based on node's initial stake and probability of truthful voting, and model their behavior using embedded Markov Chain which corresponds to a Semi Markov Process (SMP). We show that probability of reaching consensus is higher when rounds are shorter and/or there are more nodes in the network. In addition, we find that nodes from higher priority classes are mostly excluded from voting due to their mobility, while those from lower priority classes are excluded more often on account of low stake.
Zen-tor: A Zero Knowledge Known-Unknown Traffic Classification Method

Home smart devices are widely used today, but due to their inherent drawbacks, one's home might be in danger if the home network has too many unknown traffic where malicious traffic hides. We present a conceptually new method called Zero Knowledge Known-unknown Traffic Classification(Zen-tor) which utilizes Generative Adversarial Networks(GAN) and con-volutional network. Zen-tor can classify unknown network traffic from known one under the situation of knowing zero knowledge of unknown traffic, thus it can be trained to protect private home network with only known traffic. We evaluate Zen-tor on a publicly available dataset, and the results show that Zen-tor has excellent unknown classification accuracy and outperforms the state-of-the-art unknown traffic classification methods.
Divertible Searchable Symmetric Encryption for Secure Cloud Storage

Searchable Symmetric Encryption (SSE) is a promising method for users to store data in remote clouds securely and search them using keywords over an encrypted index. In this paper, we explore a new function named “keyword diverting” and propose a variant of SSE named Divertible Searchable Symmetric Encryption (DivSSE). Specifically, the index in DivSSE is encoded into an inverted, compressed, and encrypted format, by using the super-increasing sequence, symmetric homomorphic encryption (SHE), and a secure hash function. According to the homomorphic properties of SHE, users can construct a unique keyword diverting token, which can be utilized to update the encrypted index by obliviously merging data identifiers corresponding to different keywords without searching in advance and thus achieve keyword diverting. Moreover, based on function secret sharing, DivSSE can protect users' search patterns and reduce communication costs with the assistance of two independent clouds. Detailed security proof demonstrates that DivSSE can achieve parallel privacy, forward privacy, and backward privacy. Extensive performance evaluation also shows that DivSSE is efficient in terms of computational and communication overheads.
More Persuasive Explanation Method for End-to-End Driving Models

With the rapid development of autonomous driving technology, a variety of high-performance end-to-end driving models (E2EDMs) are being proposed. In order to understand the computational methods of E2EDMs, pixel-level explanations methods are used to obtain the explanations of the E2EDMs. However, little attention has been paid to the excellence of the explanations of E2EDMs. Therefore, in order to build trustworthy E2EDMs, we focus on improving the persuasibility of the explanations of E2EDMs. We propose an object-level explanation method (main approach) for E2EDMs, which masks the objects in the image and then treats the change in the prediction result as the importance of the objects, then we explain the E2EDM by the importance of each object. To further validate the effectiveness of object-level explanations, we propose another approach (validation approach), which trains E2EDMs with object information as input and generates the importance of objects using general explanation methods. Both approaches generate object-level explanations, in order to compare these object-level explanations with traditional pixel-level explanations, we propose experimental methods to measure the persuasibility of explanations of E2EDMs through a subjective and objective method. The subjective method evaluates persuasibility based on the extent to which participants think the importance of features indicated by the explanations is correct. The objective method evaluates the persuasibility based on the human annotation similarity between provided with only the important part of images and provided with the complete images. The experimental results show that the object-level explanations are more persuasive than the traditional pixel-level explanations.
Isolating Features of Object and Its State for Compositional Zero-Shot Learning

The purpose of Compositional Zero Shot Learning (CZSL) is to recognize previously unseen compositions of known objects (e.g. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">apple, banana</i> ) and their states (e.g. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ripe, unripe</i> ) as seen in an image. The CZSL is a challenging problem as it is difficult to isolate the visual features of object and its states from its compositions in images. The features of a state may have wide variation across different compositions. For example, the state <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sliced</i> has different visual features in compositions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sliced apple</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sliced tomato</i> . In this paper, we attempt to solve the problem of CZSL using a two-stage recognition approach. Each stage sequentially performs recognition task utilising two distinct modalities of compositions. The modalities are image features as well as textual features representing features of objects and states respectively. We propose a novel gradient regularized loss term for better disentanglement of object and state features from the visual features of the composition. An appropriate disentanglement of the features of visual primitives (states and objects) leads to correct identification of images of unseen state-object compositions. The proposed approach and the competing methods are evaluated on three benchmark datasets, MIT States, UT-Zappos50 k and CGQA. Our extensive experiments establish the efficacy of our proposed algorithm that outperforms other state-of-the-art approaches.