Reviewerly/exHarmony huggingface dataset:
Causal Relationships Between Emotions and Dialog Acts

Emotions and Dialog Acts (DAs) are phenomena in interpersonal communication that are informative of a person’s underlying thoughts and feelings. Thus, affective computing and dialog system researchers are interested in understanding and detecting emotions and DAs in dialogs. Previous studies on emotion recognition and dialog act classification observed that utilizing one feature for classification of the other can improve model performance, which benefits the advancement of affective dialog systems. However, theoretical explanation of such gain remains unclear. In linguistic research, the relationship between an emotion and a DA is often investigated qualitatively. In this work, we conducted both qualitative and quantitative analyses examining the relationships between emotions and DAs. Through statistical analyses, causal discovery methods, and a crowd-sourcing study, using two datasets of emotional dialog we identified emotion-DA causal pairs that provide empirical evidences to existing linguistic theories, while supporting addressing these two features together in affective dialog systems. Moreover, our work offers an effective methodology for revealing linguistic discoveries with a data-driven approach.
Actual Image Data Generation and Application via Generative Adversarial Network

Using convolutional neural networks to identify aluminum mold numbers is an effective method. A data set with good generalization performance has a key role in improving the recognition rate of the network. The confrontation generation network can be generated and the real scene is almost the same False pictures. This paper collects aluminum mold number pictures and generates contour data pictures in general scenarios, and inputs these two types of pictures into CycleGAN for training, thereby generating fake pictures, and using pix2pix and consinGAN to generate fake pictures in IS indicators, Frechet is compared on the initial distance. Finally, the masktextspotter text recognition network is used to identify the mold number data set. The experimental results show that cycleGAN is efficient in generating aluminum mold number pictures.
Finite-key analysis based on neural network of practical wavelength division multiplexed decoy-state quantum key distribution

The integration of quantum key distribution (QKD) devices with the existing optical fiber networks is of great significance in reducing the deployment costs and saving fiber resources. Wavelength division multiplexing (WDM) is expected to be a desirable approach to fulfill this ultimate task. In this paper, we analyze the dominant noises in WDM-based QKD system and optimize the key parameters based on a modified model with 200 GHz channel spacing. Then, an appropriate decoy-state method is adopted to estimate the system performance considering statistical fluctuations. Finally, a three-layer artificial neural network is used to train and predict the optimal mean photon numbers within different situations. Our work provides a useful method for the parameters optimization of WDM-QKD system and accelerates the practical development of QKD that coexists with the current backbone fiber infrastructure.
Construction of College English Mixed Teaching Mode Based on Data Mining Technology

Big data is fundamentally different from traditional data. Instant, microscopic, interactive, optional and personalized will bring innovation to higher education. Under the background of highly shared information, various teaching modes relying on the network emerge one after another, and the mixed teaching method combining traditional classroom learning with online learning has become a trend. In order to promote information education, most colleges and universities have built a series of digital teaching information in view of cloud platform space, such as teaching, micro-courses and massive open online course, so as to promote students' autonomous learning. In view of this, this paper briefly introduces the challenges faced by college English in the times of big data, and discusses the multi-dimensional teaching method of college English in view of data mining, so that with effect promote the improvement of English teaching quality.
Analysis of Web Scraping Techniques to Get Keywords Suggestion and Allintitle Automatically from Google Search Engines

Keyword research is one of the essential activities in Search Engine Optimization (SEO). One of the techniques in doing keyword research is to find out how many articles titles on a website indexed by the Google search engine contain a particular keyword or so-called "allintitle". Moreover, search engines are also able to provide keywords suggestion. Getting keywords suggestions and allintitle will not be effective, efficient, and economical if done manually for relatively extensive keyword research. It will take a long time to decide whether a keyword is needed to be optimized. Based on these problems, this study aimed to analyze the implementation of the web scraping technique to get relevant keyword suggestions from the Google search engine and the number of "allintitle" that are owned automatically. The data used as an experiment in this test consists of ten keywords, which each keyword would generate a maximum of ten keywords suggestion. Therefore, from ten keywords, it will produce at most 100 keywords suggestions and the number of allintitles. Based on the evaluation result, we got an accuracy of 100%. It indicated that the technique could be applied to get keywords suggestions and allintitle from Google search engines with outstanding accuracy values.
Effect of Taking the Liberal Arts Course ‘Creative Thinking and Problem Solving’ on the Improvement of Creative Competencies of College Students in Science and Engineering －Focusing on Comparison Before and After COVID-19

This study was conducted to derive implications on how educational activities should be organized and operated for improving creative competency in online class situations by analyzing how it has changed in classes before and after COVID-19. These classes were taken by science and engineering students who took the ‘Creative Thinking and Problem Solving’ subject operated by N University. In order to achieve the goals of this study, a pre-post test related to creative competency was administered at the beginning and end of the semester to 92 students who took the face-to-face class in the second semester of 2019, and to 120 students who took the on-line class in the second semester of 2020. The research results are as follows: Creative competency was significantly improved in the face-to-face class, but not in the online class. Looking at each sub-competency of creative competency, both the challenge spirit and problem solving ability were significantly improved in the face-to-face class. Moreover, the student’s challenge spirit ability was significantly improved in the online class, but their problem solving ability was not significantly improved. These results were similar regardless of whether the instructor changed, or if the students’ grades changed. Also, the students’ gender had no bearing on these results either. Based on these research results, as well as on our educational goals and the content of the classes, the project activities involved in the ‘Creative Thinking and Problem Solving’ subject can be used in online classes in their current form. But this study suggested that strategies for improving students' problem-solving abilities need to be supplemented. Specifically, instructors should increase communication opportunities through the parallel use of the real-time remote video system, and should guide their students to become faithful to the team-based project activities through the sub-session function of the real-time remote video system. To this end, the university may consider changing two credits of two hours into two credits of three hours, or three credits of three hours. Universities also need to operate this subject in connection with extra-activities. It is also necessary for instructors to provide a support environment where students can freely meet using the real-time remote video system, and can engage in solving problems through their own initiative.
Rebalancing Bike Sharing Systems under Uncertainty using Quantum Bayesian Networks

Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferral of bikes across various bike stations to ensure the supply meets expected demand. A critical part for a bike sharing system operations is the effective management of rebalancing vehicle carrier operations that ensures bikes are restored in each station to its target value during every pick-up and drop-off operations. In this work, we present potential applications of Quantum Bayesian networks, which are quantum-equivalent to classical Bayesian networks for probabilistic rebalancing cost prediction under uncertainty. In this preliminary work, we demonstrate the proposed approach using IBM-Qiskit and compared the results classically using Netica for a case study involving rebalancing across three bike stations.
Research on Key Technologies Of UAV Navigation and Positioning System

UAV (Unmanned Aerial Vehicle) visual navigation technology is one of the current research hotpots in the field of computer vision. Its purpose is to use the visual information of aerial images to achieve continuous, stable and accurate positioning of UAV. With the continuous upgrading of hardware platforms, UAV are widely used in many fields such as national defense, military, disaster detection, and geological exploration. Most UAVs rely on the equipped Global Navigation Satellite System (GNSS) for navigation and positioning. However, radios that acquire the system are prone to signal loss or deliberate attacks. UAV are expensive, and positioning systems play a vital role in their stability and safety during flight. Vision-based navigation and positioning technology does not rely on satellite positioning systems, and is of great significance for unmanned aerial vehicles to achieve autonomous flight tasks when CNSS signals are lost. This article analyzes and designs the UAV visual positioning system in outdoor environment, conducts in-depth research on visual information extraction and positioning estimation, and proposes a series of improvement measures.
MRDRL-ROS: a Multi Robot Deep Reinforcement Learning Platform based on Robot Operating System

Abstract Deep reinforcement learning (DRL) has greatly improved the intelligence of AI in recent years and the community has proposed several common software to facilitate the development of DRL. However, in robotics the utility of common DRL software is limited and the development is time-consuming due to the complexity of various robot software. In this paper, we propose a software engineering approach leveraging modularity to facilitate robot DRL development. The platform decouples learning environment into task, simulator and hierarchical robot modules, which in turn enables diverse environment generation using existing modules as building blocks, regardless of the underlying robot software details. Experimental results show that our platform provides composable environment building, introduces high module reuse and efficiently facilitates robot DRL.
Status of Cloud Computing in Data Intensive Data Center

Abstract With the advent of big data era, data intensive computing has been widely concerned by the industry, but the research on data intensive center is still in its infancy. With the development of science and technology, the emergence of cloud computing has brought great convenience to people, and has become a research hotspot for a time. This paper analyzes the application status of cloud computing in data intensive data center. In the research, we analyzed the application status of cloud computing in data intensive data center. Taking data decision-making as an example, we studied the application effect of cloud computing in data intensive data center by analyzing the influence of different number of data sets and data centers on the time cost of data transmission. The results of this paper show that by 2019, the corresponding rate of cloud computing in data decision-making, data security and data management has reached 88%, 92% and 95% respectively. Moreover, the research results show that the application of cloud computing in data intensive data center can effectively reduce the time cost of data transmission and achieve good application effect.
Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph

The OS kernel, which has full system privileges, is an attractive attack surface. A kernel fuzzer that targets system calls in fuzzing is a popular tool for discovering kernel bugs that can induce kernel privilege escalation attacks. To the best of our knowledge, the relevance of code coverage, which is obtained by fuzzing, to the system call has not been studied yet. For instance, modern coverage-guided kernel fuzzers, such as Syzkaller, estimate code coverage by comparing the entire set of executed basic blocks (or edges) regardless of the system call relevancy. Our insight is that the system call relevancy could be an essential performance indicator for realizing kernel fuzzing. In this regard, this study aims to assess the system call-related code coverage of kernel fuzzers. For this purpose, we have developed a practical assessment system that leverages the Intel PT and KCOV and assessed the Linux kernel fuzzers, such as Syzkaller, Trinity, and ext4 fuzzer. The experiments on different kernel versions demonstrated that approximately 32,000–47,000 functions are implemented in the Linux kernel, and approximately 9.7–15.2% are related to the system call. Our finding is that fuzzers that achieve higher code coverage in conventional metrics do not execute more basic blocks related to system calls. Thus, we recommend that kernel fuzzers use both system call-related functions and regular basic blocks in coverage metrics to assess fuzzing performance or to improve coverage feedback.
Experimental Certification of Quantum Entanglement Based on the Classical Complementary Correlations of Two-Qubit States

Quantum entanglement is one of the essential resources in quantum information processing. It is of importance to verify whether a quantum state is entangled. At present, a typical quantum certification focused on the classical correlations has attracted widespread attention. Here, we experimentally investigate the relation between quantum entanglement and the classical complementary correlations based on the mutual information, Pearson correlation coefficient and mutual predictability of two-qubit states. Our experimental results show the classical correlations for complementary properties have strong resolution capability to verify entanglement for two qubit pure states and Werner states. We find that the resolution capability has great performance improvement when the eigenstates of the measurement observables constitute a complete set of mutually unbiased bases. For Werner states in particular, the classical complementary correlations based on the Pearson correlation coefficient and mutual predictability can provide the ultimate bounds to certify entanglement.
Sorrel: an IDE Plugin for Managing Licenses and Detecting License Incompatibilities

Software development is a complex process that includes many different tasks besides just writing code. One of the aspects of software engineering is selecting and managing licenses for the given project. In this paper, we present SORREL-a plugin for managing licenses and detecting potential incompatibilities for IntelliJ IDEA, a popular Java IDE. The plugin scans the project in search of information about the project license and the licenses of its libraries. If the project does not yet have a license, the plugin provides the developer with recommendations for choosing the most suitable open license, and if there is a license, it informs the programmer about potential licensing violations. The tool makes it easier for developers to choose a proper license for a project and avoid most of the licensing errors-all inside the familiar IDE editor. The plugin and its source code are available online on GitHub: https://github.com/JetBrains-Research/sorrel. A demonstration video can be found at https://youtu.be/doUeAwPjcPE.
The lrd package: An R package and Shiny application for processing lexical data

Recall testing is a common assessment to gauge memory retrieval. Responses from these tests can be analyzed in several ways; however, the output generated from a recall study typically requires manual coding that can be time intensive and error-prone before analyses can be conducted. To address this issue, this article introduces lrd (Lexical Response Data), a set of open-source tools for quickly and accurately processing lexical response data that can be used either from the R command line or through an R Shiny graphical user interface. First, we provide an overview of this package and include a step-by-step user guide for processing both cued- and free-recall responses. For validation of lrd, we used lrd to recode output from cued, free, and sentence-recall studies with large samples and examined whether the results replicated using lrd-scored data. We then assessed the inter-rater reliability and sensitivity and specificity of the scoring algorithm relative to human-coded data. Overall, lrd is highly reliable and shows excellent sensitivity and specificity, indicating that recall data processed using this package are remarkably consistent with data processed by a human coder.
Coalescing Image Steganography and Cryptography for Information Security

Steganography is the process by which we hide a secret message within a larger text or image in such a way that the presence or contents of the hidden message are not noticeable. Cryptography is a technique of securing information by making it unreadable. The information is secured using keys so that only the intended people can understand the data, thus preventing access to any unauthorized people. Using steganography alone could expose the secret message in case of a security breach and using cryptography alone would garner unnecessary suspicion. Hence, we propose a new technique by combining the two state of art techniques to ensure an improved version of information security that will care for the requirements of security and robustness in transmitting important information over open channels.
Predicting Document Coverage for Relation Extraction

Abstract This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): Does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity, and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation.
Enhancing NL Requirements Formalisation Using a Quality Checking Model

The formalisation of natural language (NL) requirements is a challenging problem because NL is inherently vague and imprecise. Existing formalisation approaches only support requirements adhering to specific boilerplates or templates, and are affected by the requirements quality issues. Several quality models are developed to assess the quality of NL requirements. However, they do not focus on the quality issues affecting the formalisability of requirements. Such issues can greatly compromise the operation of complex systems and even lead to catastrophic consequences or loss of life (in case of critical systems). In this paper, we propose a requirements quality checking approach utilising natural language processing (NLP) analysis. The approach assesses the quality of the requirements against a quality model that we developed to enhance the formalisability of NL requirements. We evaluate the effectiveness of our approach by comparing the formalisation efficiency of a recent automatic formalisation technique before and after utilising our approach. The results show an increase of approximately 15% in the F-measure (from 83.8% to 98%).
A Similarity Calculation Method for Originality Evaluation

In this paper, we propose a similarity calculation method, which can get the similarity score between a document and a document library from the aspect of originality evaluation. Compared with the traditional similarity evaluation method, this method can find the similarity in multi-source combination and rewriting behavior, which can improve the recognition in originality evaluation.
Detecting Malicious Gradients from Asynchronous SGD on Variational Autoencoder

In asynchronous distributed learning, the parameter server updates the global model as soon as a new gradient is received from any device. The asynchronous systems are designed to address the existence of lagging devices which is inevitable due to device heterogeneity and network unreliability. However, the lack of synchrony incurs additional noise and makes detecting and defending against malicious model gradients a challenging task. Unlike existing works that struggle to design robust methods to tolerate untargeted model poisoning gradients, the paper considers detecting and removing targeted model poisoning gradients from the normal asynchronous training process. This paper proposes Asynvae, a robust distributed asynchronous learning framework where the parameter server uses variational autoencoder to detect and exclude malicious gradients. Since the reconstruction error of malicious updates is much larger than that of benign ones, it can be used as an anomaly score. We formulate a threshold of reconstruction error to differentiate malicious updates from normal ones based on this idea. Asynvae is tested with extensive experiments on distributed learning benchmarks, showing a competitive performance over existing distributed learning methods under untargeted model poisoning attack, targeted model poisoning attack and lagging attack.