Reviewerly/exHarmony huggingface dataset:
Design of Chinese Teaching Evaluation System for International Students under the Background of Data Mining

In the Chinese teaching for foreign students, Chinese teachers do not participate in the assessment of their institutions, which is not conducive to the sustainable development of Chinese language education. The potential value of the teaching evaluation is not floating on the surface, so the data mining is used to discover implicit correlations between the data. The application of data mining in the evaluation of Chinese teaching for foreign students helps to ensure safety of the teaching evaluation system for foreign students. In this paper, a Chinese teaching evaluation system that is based on the data mining is built, in which the DA is applied to the evaluation of Chinese teaching. Finally, the performance of the algorithm is tested, the results show that the DA greatly improves the classification accuracy of the model, the improved model can largely alleviate overfitting, and the feature dimension data extracted by DA are more representative. The system proposed in this paper has good performance, which can greatly improve the security of the Chinese teaching evaluation system.
Public Fund Care Tracking System based on Blockchain

In India, charities lack transparency and are difficult to monitor. This negatively affects people’s willingness to contribute. Blockchain technically offers new currency system solutions as the core technology of the Bitcoin system. This white paper proposes a security system based on blockchain technology and discloses design patterns, architectures, and workflows. Internal Blockchain is becoming more and more important in philanthropy. The applicant provides a blockchain-based decentralized donation tracking system. Built on Ethereum and provides full transparency, accountability, and direct access to target recipients [9].
Research on Augmented Reality College English Listening and Speaking Teaching Mode Supported by Wearable Technology

With the rapid development of Internet technology, two new terms, such as wearable devices and AR, have begun to appear in front of people. AR technology refers to things that are difficult to experience in reality. It appears in front of people’s eyes in space and time, and these things actually exist in the real world. The development process of AR technology shows that the boundary between teachers and students is gradually disappearing. If the school purchases a wearable device, they can use this device for university English listening and speaking teaching. This article will discuss the advanced equipment of AR supported by wearable technology applied in the field of university English teaching and complete the discussion and analysis of the following aspects: first, analyze the current situation of the application of AR technology in education and collect some domestic research cases outside, finding the problems. Then, this article introduces the theoretical basis and technical support for the education application of wearable AR technology, and a total of 110 students in the experimental class and control class of a university’s English department are interviewed and investigated. Finally, this article applies wearable AR technology to the university. In the English listening and speaking teaching model, we use questionnaire surveys, interviews, and quasi-experimental research methods to understand the teaching model of wearable devices. We used AR technology in English listening and speaking learning and discussed the application of mobile AR technology in education and found that wearable AR technology is of great help to college English listening and speaking teaching.
Quantum Optics Parity Effect on Generalized NOON States and Its Implications for Quantum Metrology

Abstract NOON states are entangled states of great interest in the development of quantum technologies, because they enable high‐precision phase measurements in interferometric schemes relevant to quantum metrology and sensing. This study shows that two degenerate modes of a cavity field coupled to an atom or molecule that can be modeled as a two‐level system through two‐photon interactions, initially prepared in a (generalized) NOON state, experience a dynamical evolution that distinctly depends on the parity of the photon number. When the total photon number is even, the system dynamics is characterized by a time range in which the state of the system evolves near maximally entangled states that may be fed to an optical interferometer to make phase measurements essentially at the Heisenberg limit. Such entangled states cannot be achieved for odd photon numbers instead. The parity effect thus emerges as a potential control factor in quantum metrology, sensing, and lithography.
A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis

Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks. This paper proposes a new sentiment-enhanced word embedding (S-EWE) method to improve the effectiveness of sentence-level sentiment classification. This sentiment enhancement method takes full advantage of the mapping relationship between word embeddings and their corresponding sentiment orientations. This method first converts words to word embeddings and assigns sentiment mapping vectors to all word embeddings. Then, word embeddings and their corresponding sentiment mapping vectors are fused to S-EWEs. After reducing the dimensions of S-EWEs through a fully connected layer, the predicted sentiment orientations are obtained. The S-EWE method adopts the cross-entropy function to calculate the loss between predicted and true sentiment orientations, and backpropagates the loss to train the sentiment mapping vectors. Experiments show that the accuracy and macro-F1 values of six sentiment classification models using Word2Vec and GloVe with the S-EWEs are on average 1.07% and 1.58% higher than those without the S-EWEs on the SemEval-2013 dataset, and on average 1.23% and 1.26% higher than those without the S-EWEs on the SST-2 dataset. In all baseline models with S-EWEs, the convergence time of the attention-based bidirectional CNN-RNN deep model (ABCDM) with S-EWEs was significantly decreased by 51.21% of ABCDM on the SemEval-2013 dataset. The convergence time of CNN-LSTM with S-EWEs was vastly reduced by 41.34% of CNN-LSTM on the SST-2 dataset. In addition, the S-EWE method is not valid for contextualized word embedding models. The main reasons are that the S-EWE method only enhances the embedding layer of the models and has no effect on the models themselves.
Preparing Vocational High School Students’ 21st Century Skills Needed through Pair Work Strategy and YouTube Channel Integration

There is no denying that the usage of digital media has extended throughout all parts of life as we enter the era of digital transformation. YouTube is the social media app on Android phones that people use the most, both in terms of total time and average time per user. Predicted outcomes of teaching and learning English with the use of YouTube are considered positive. This research aimed to get empirical evidence of the effect of a pair work strategy integrated with YouTube channels in increasing students’ speaking performance. This study applied a quasi-experimental design. The population of this research was the tenth-grade students at a State Vocational High School in Indonesia. There were 64 students who participated in this study. The instrument used in this research was the speaking test. A paired sample t-test was conducted to evaluate the impact of the intervention on students’ speaking performance. There was statistically significant improvement on students speaking score from Time 1 (M = 69, 41, SD = 5.988) to Time 2 (M = 86.16, SD = 4.444), t(31) = 15.516, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>P</mi><mo>&lt;</mo><mn>0.0005</mn></math> . The eta squares statistic (eta squared = 0.886) indicated a large effect size. An independent sample t-test was also conducted to compare the score between experimental and control groups. The result indicated that there was significant difference in score between experimental (M = 86.16, SD = 4.444) and Control group (M = 78.50, SD = 42.65; t(62) = 7.031, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>p</mi><mo>=</mo><mn>0.001</mn></math> ). The magnitude of the difference in the means was large (eta squared = 0.443). It indicated that 44.3% of students’ speaking score was influenced by the integration of paired work strategies and YouTube channels.
Learning Gait Emotions Using Affective and Deep Features

We present a novel data-driven algorithm to learn the perceived emotions of individuals based on their walking motion or gaits. Given an RGB video of an individual walking, we extract their walking gait as a sequence of 3D poses. Our goal is to exploit the gait features to learn and model the emotional state of the individual into one of four categorical emotions: happy, sad, angry, or neutral. Our perceived emotion identification approach uses deep features learned using long short-term memory networks (LSTMs) on datasets with labeled emotive gaits. We combine these features with gait-based affective features consisting of posture and movement measures. Our algorithm identifies both the categorical emotions from the gaits and the corresponding values for the dimensional emotion components - valence and arousal. We also introduce and benchmark a dataset called Emotion Walk (EWalk), consisting of videos of gaits of individuals annotated with emotions. We show that our algorithm mapping the combined feature space to the perceived emotional state provides an accuracy of 80.07% on the EWalk dataset, outperforming the current baselines by an absolute 13–24%.
Digging into primary financial market: The issues of primary financial market issuance and investigations from the perspective of blockchain

With the characteristics of anonymity, trust, tamper-proof, etc., blockchain technology can effectively solve some problems faced by the financial market, such as trust issues and information asymmetry issues. To deeply understand the application scenarios of blockchain in the financial market, the issue of securities issuance and trading in the primary market is studied. The authors conducted an empirical study to investigate the main difficulties faced by primary market participants in their business practices and the potential challenges of the deepening adoption of blockchain technology in the primary market. The authors adopted a hybrid method by combing interviews (qualitative methods) and surveys (quantitative methods) to conduct this research in two stages. In the first stage, authors interview 15 major primary market participants with different backgrounds and expertise. In the second phase, authors conducted a verification survey of 54 primary market practitioners to confirm various insights from the interviews, including challenges and desired improvements. The interviews and survey results reveal several significant challenges facing blockchain applications in the primary market: complex due diligence, mismatching, and difficult monitoring. The research shows that primary market participants have a positive attitude towards the application of blockchain technology to the primary market and are ready to adopt blockchain technology to solve some of the current issues.
Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers

Multi-stage tasks are a challenge for reinforcement learning methods, and require either specific task knowledge (e.g., task segmentation) or big amount of interaction times to be learned. In this paper, we propose Behavior Policy Learning (BPL) that effectively combines 1) only few solution sketches, that is demonstrations without the actions, but only the states, 2) model-based controllers, and 3) simulations to effectively solve multi-stage tasks without strong knowledge about the underlying task. Our main intuition is that solution sketches alone can provide strong data for learning a high-level trajectory by imitation, and model-based controllers can be used to follow this trajectory (we call it behavior) effectively. Finally, we utilize robotic simulations to further improve the policy and make it robust in a Sim2Real style. We evaluate our method in simulation with a robotic manipulator that has to perform two tasks with variations: 1) grasp a box and place it in a basket, and 2) re-place a book on a different level within a bookcase. We also validate the Sim2Real capabilities of our method by performing real-world experiments and realistic simulated experiments where the objects are tracked through an RGB-D camera for the first task.
Collaboration graph for feature set partitioning in data classification

The curse of dimensionality of features in data classification is still an open issue. An approach to solve this problem is to partition features into several sub-sets of features hence the data classification task for every subset is performed. Then, an ensemble of these classifications are reported as the result of the classification problem. However, the feature set partitioning into sub-sets of features is still an area of research interest. Thus, in this paper, an innovative framework is proposed in which, first, a collaboration measure between each two features is defined and measured. Then, the collaboration graph, consisted of features as nodes and measured collaborations as edges’ weights, is generated according to the collaboration measures calculated. After that, a community detection method is used to find the graph communities. The communities are considered as the feature subsets and a base classifier is trained for each subset based on the corresponding training data of the subsets. Then, the ensemble classifier is created by a combination of base classifiers according to the AdaBoost Aggreagation. The simulation results of the proposed approach over the real and synthetic datasets indicate that the proposed approach considerably increases the classification accuracy in comparison to previous methods.
Shadow IT in higher education: survey and case study for cybersecurity

We explore shadow information technology (IT) at institutions of higher education through a two-tiered approach involving a detailed case study and comprehensive survey of IT professionals. In its many forms, shadow IT is the software or hardware present in a computer system or network that lies outside the typical review process of the responsible IT unit. We carry out a case study of an internally built legacy grants management system at the University of Maryland, Baltimore County that exemplifies the vulnerabilities, including cross-site scripting and SQL injection, typical of such unauthorized and ad-hoc software. We also conduct a survey of IT professionals at universities, colleges, and community colleges that reveals new and actionable information regarding the prevalence, usage patterns, types, benefits, and risks of shadow IT at their respective institutions. Further, we propose a security-based profile of shadow IT, involving a subset of elements from existing shadow IT taxonomies, which categorizes shadow IT from a security perspective. Based on this profile, survey respondents identified the predominant form of shadow IT at their institutions, revealing close similarities to findings from our case study. Through this work, we are the first to identify possible susceptibility factors associated with the occurrence of shadow IT related security incidents within academic institutions. Correlations of significance include the presence of certain graduate schools, the level of decentralization of the IT department, the types of shadow IT present, the percentage of security violations related to shadow IT, and the institution's overall attitude toward shadow IT. The combined elements of our case study, profile, and survey provide the first multifaceted view of shadow IT security at academic institutions, highlighting tension between its risks and benefits, and suggesting strategies for managing it successfully.
TPEIP: Thumbnail preserving encryption based on sum preserving for image privacy

Image privacy technology is a growing important topic in cloud storage services. User can use the cloud services to browse or manage their own images, however, the user will face the problem of image privacy. Accordingly, the traditional image encryption scheme is a way to ensure the safety of images. However, encrypted images will lose their image usable property when users want to search and browse their own images online without downloading and decrypting them. Marohn et al. designed two thumbnail-preserving-encryption (TPE) methods to balance the image privacy and usability. In this paper, a novel TPEIP method based on sum-preserving-encryption (SPE) is proposed for enhancing the image privacy. SPE can avoid the statement of fail encryption using the approximate TPE method. We also applied formulas to present the correctness of the proposed scheme. The combination of thumbnail and encryption approach allows the user to quickly compare encrypted images using thumbnail. Experimental results show that our proposed method not only retained the same thumbnail appearance before and after encryption but also held the image encryption with sufficiently confidential.
Beliefs and Expectations of Primary Student Teachers in Informatics

With the new "Curriculum 21" in the German-speaking part of Switzerland, media education and informatics have become much more important for compulsory education. However, introducing a new subject is a challenging task. Most university students had no or only very limited opportunities to be exposed to topics of computer science (CS) topics during their school years. To learn more about students' beliefs and experiences, a preliminary survey was conducted. Open questions were asked to first-year students (n=251) and students in their last semester who had not yet taken any informatics classes during their studies (n=212). However, the students in their last semester had already completed internships in which they could participate in or conduct "Media and Informatics (MI)" classes. This difference is reflected in the responses: The students of the first semester more often mentioned application skills, and a negative attitude towards CS became apparent. In contrast, the students of the last semester frequently named teaching methods and characteristics of informatics classes.
Ultrasound Image Segmentation Algorithm of Thyroid Nodules Based on Improved U-Net Network

Thyroid nodule is a common clinical case, and there are different cancer risks according to the benign and malignant grades of the nodules. The shape, edge, and size of thyroid nodules are all important basis for judging the nature of thyroid nodules in clinical practice. High-precision ultrasound image segmentation of thyroid nodules plays a crucial role in auxiliary diagnosis and judgment of benign and malignant thyroid nodules. It has a high clinical significance for the efficiency and accuracy of diagnosis. In order to improve the accuracy of thyroid nodule ultrasound image segmentation, this paper proposes an improved U-Net model thyroid nodule ultrasound image segmentation method. First, an improved residual module combined with soft-pool pooling is used to form an encoder path, and downscale feature extraction is performed while reducing the loss of feature information. Then, the full-channel attention-assisted skip connection based on the polarized self-attention mechanism is used to help the model to better find the attention region at the multi-scale level and avoid the interference of similar regions to the nodule segmentation task. The experimental results show that the Dice similarity coefficient (DSC) of the proposed method can reach 0.8065, which is better than the contrast model used in the experiment, and can well achieve the segmentation of thyroid nodules.
Binary residual feature pyramid network: An improved feature fusion module based on double‐channel residual pyramid structure for autonomous detection algorithm

Abstract The vehicle detection algorithm based on visual perception has been applied in all types of automatic driving scenes. However, there are still flaws in the current detection algorithm model, especially for small objects. The detection effect of vehicle objects with small pixels in the image is often missed and wrongly detected. This research proposes an improved feature fusion module based on double‐channel residual pyramid (DRP) structure for autonomous detection algorithm which named binary residual feature pyramid network (BiResFPN) to solve the above problems. Firstly, a DRP structure, which can effectively supplement the shallow information of the network, is proposed. The residual structure is added to the output feature map for further supplement. Then, an average sampling method of positive and negative samples based on intersection‐over‐union (IOU) value is proposed on the basis of this structure, aimed at the unbalanced sampling of positive and negative samples in the training stage of faster regions with CNN features (RCNN). It leads to the reduction of the interference of a large number of simple negative samples, which makes the learned model better. The experimental results based on the KITTI and BDD100K dataset datasets show that the capability of the feature fusion module based on DRP structure is strong for small object detection. Compared with Faster‐RCNN (FPN), the detection algorithm of small object detection accuracy AP small was increased by 2.6%, AP medium and AP large was increased by 1.1% and 0.3%.
Spatio-temporal dual-attention network for view-invariant human action recognition

Due to the action occlusion and information loss caused by the view changes, view-invariant human action recognition is challenging in plenty of real-world applications. One possible solution to this problem is minimizing representation discrepancy in different views while learning discriminative feature representation for view-invariant action recognition. To solve the problem, we propose a Spatio-temporal Dual-Attention Network (SDA-Net) for view-invariant human action recognition. The SDA-Net is composed of a spatial/temporal self-attention and spatial/temporal cross-attention modules. The spatial/temporal self-attention module captures global long-range dependencies of action features. The cross-attention module is designed to learn view-invariant co-occurrence attention maps and generates discriminative features for a semantic representation of actions in different views. We exhaustively evaluate our approach on the NTU- 60, NTU-120, and UESTC datasets with multi-type evaluations, i.e., Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. Extensive experiment results demonstrate that our approach exceeds the state-of-the-art approaches with a significant margin in view-invariant human action recognition.