Reviewerly/exHarmony huggingface dataset:
Research on Multimedia Technology-Assisted College English Grammar Teaching

The English Curriculum Standard for Compulsory Education lists the specific requirements for the integration of English discipline and information technology to optimize the presentation of education. For college students’ English teaching, the most appropriate and effective way is to widely apply multimedia courses to English teaching. Using the rich multimedia knowledge of multimedia courses to expand students’ oral English and teaching skills, widen the reading path of reading and writing, multimedia tutoring teaching can concretize and visualize abstract knowledge well, and rich multimedia knowledge can facilitate students’ learning and play an important role in understanding knowledge. This paper focuses on multimedia technology-assisted college English grammar teaching. The multimedia technology is analyzed by using the multimedia data hashing algorithm with semantic fusion. Then, the application of multimedia teaching materials in teaching and traditional teaching methods is experimented. By comparing their learning effects, the author draws a conclusion that multimedia technology has been applied in English grammar teaching to a certain extent, and the present situation of its application is relatively good. Most of them have an encouraging attitude, especially in improving students’ learning attitude and increasing their interest in learning. Due to the limitations of awareness and conditions, the extent and progress of the integration of information technology and disciplines have lagged behind relatively, and there are many difficulties in the application of multimedia course materials; there are certain difficulties in integrating multimedia course materials and English subjects.
Online Labor Education Optimization Method Based on Computer Intelligent Algorithm

People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education.
Context sight

Model interpretation is increasingly important for successful model development and deployment. In recent years, many explanation methods are introduced to help humans understand how a machine learning model makes a decision on a specific instance. Recent studies show that contextualizing an individual model decision within a set of relevant examples can improve the model understanding. However, there is a lack of systematic study on what factors are considered when generating and using the context examples to explain model predictions, and how context examples help with model understanding and debugging in practice. In this work, we first identify a taxonomy of context generation and summarization through literature review. We then present Context Sight, a visual analytics system that integrates customized context generation and multiple-level context summarization to assist context exploration and interpretation. We evaluate the usefulness of the system through a detailed use case. This work is an initial step for a set of systematic research on how contextualization can help data scientists and practitioners understand and diagnose model behaviors, based on which we will gain a better understanding of the usage of context.
Compressed Sensing-Based Privacy Preserving in Labeled Dynamic Social Networks

With the rapid development of social networks, privacy- preserving is an important issue. Nowadays, privacy-preserving mainly involves static social networks. In fact, social networks are dynamic. The sequential release of social networks will lead to privacy leakage. Many studies have employed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -anonymity to protect users' privacy, but because <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> affects data utility in social networks, a smaller <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> cannot provide enough privacy preserving, whereas a larger <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> will lead to a significant loss of data utility. This article proposes a new privacy-preserving approach based on compressed sensing (CS) to protect privacy in the labeled dynamic social networks. The scheme first uses the CS to compress the updated node information at each time point. Then, in the form of label grouping, the node link relationship is randomly deleted/changed to blur the node degrees to protect privacy. Finally, the reconstruction algorithm and Gaussian measurement matrix are combined to ensure operating efficiency while balancing data privacy and data utility. The simulation results show that our scheme retains data utility better than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -diversity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -anonymity. Furthermore, according to privacy analysis, the scheme can also protect link relationship privacy and label privacy at the same time, and prevent background knowledge attacks.
A dimensional learning squirrel search algorithm based on roulette strategy

Squirrel search algorithm (SSA) is an effective intelligent optimization algorithm, but it also has the problems of slow convergence speed and easy to fall into local optima. To solve these problems, a dimensional learning squirrel search algorithm (DLSSA) based on roulette strategy is proposed. Roulette strategy was introduced to update the position of squirrels on normal trees to increase the diversity of the algorithm and avoid falling into local optimal solution. Using the method of dimensional learning to guide the location update of squirrels on oak trees, the excellent information of hickory tree can be accurately obtained to increase the efficiency of information flow transmission. Cauchy mutation was performed on squirrel on hickory tree to accelerate the convergence of the algorithm. Optimization experiments are carried out on 10 benchmark functions. Compared with other 7 algorithms, DLSSA has obvious advantages in the success rate of optimization and convergence speed.
An Electronic Contract Management System Based on Blockchain A case study of technology framework with improved algorithms

Contract management is the core of management business that implements enterprise risk management and internal control. The quality of contract management directly affects the success or failure of the company&#x0027;s operations. However, the contract approval process of manual management is not clear: the level of information utilization is not high, resulting in low business processing efficiency with wasting huge man-power and material resources. Hence, many companies have been working on finding a simple and practical software to manage contracts. The article advances an electronic contract management software. The existing electronic contract system is not secure enough in encryption because of the shortcoming of easily forgetting and pretending. The new system adds a private key login through a coin-free open chain technology which is used in hospital, government, military, university and other special scenes, achieved perfect security guarantee. This paper discusses several crucial mechanisms implemented for the goals of designing the system, and gives out relevant methods and process of the implementation. Guided by system engineering theory, this thesis also discusses the modeling and optimization problems of consensus mechanism. The article finally makes analysis on the research process and results, discusses the advantages and prospects of the technology, and points out problems remaining to be solved.
A Quantum Computing Approach to Human Behavior Prediction

As quantum computing technologies become more mature, their applicability increases. One of the main challenges in intelligent environments is to correctly model and ascertain the users' behavior in order to react to it and cater to their needs. One of the main challenges in human behavior modeling is predicting the users' next actions. In this paper we propose using two different quantum computing algorithms in order to predict human behavior: Quantum Kernel Alignment and Quantum Support Vector Machines. Our experiments show that those algorithms outperform other traditional machine learning algorithms in this task. We also present a study that analyzes the influence of qubit noise in the performance of the quantum approach. This helps to understand how the accuracy of the quantum computing algorithms will increase as the underlying hardware matures and qubit noise is reduced.
Semantic Image Segmentation Methods in the Duckietown Project

The article focuses on evaluation of the applicability of existing semantic segmentation algorithms for the Duckietown simulator. Duckietown is an open research project in the field of autonomously controlled robots. The article explores classical semantic image segmentation algorithms. Their analysis for applicability in Duckietown is carried out. With the help of them, we want to make a dataset for training neural networks. The following was investigated: edge-detection techniques, threshold algorithms, region growing, segmentation algorithms based on clustering, neural networks. The article also reviewed networks designed for semantic image segmentation and machine learning frameworks, taking into account all the limitations of the Duckietown simulator. Experiments were conducted to evaluate the accuracy of semantic segmentation algorithms on such classes of Duckietown objects as road and background. Based on the results of the analysis, region growing algorithms and clustering algorithms were selected and implemented. Experiments were conducted to evaluate the accuracy on such classes of Duckietown objects as road, background and traffic signs. After evaluating the accuracy of the algorithms considered, it was decided to use Color segmentation, Mean Shift, Thresholding algorithms and Segmentation of signs by April-tag for image preprocessing. For neural networks, experiments were conducted to evaluate the accuracy of semantic segmentation algorithms on such classes of Duckietown objects as road and background. After evaluating the accuracy of the algorithms considered, it was decided to select the DeepLab-v3 neural network. Separate module was created for semantic image segmentation in Duckietown.
Low Light Image Enhancement With Small Training Dataset Using EnlightenGAN

Low light images have a lot of issues with visibility, low contrast, and a lot of noise. These cause difficulties in both manual human vision and systems that rely on computer vision in their decision-making. So far, various methods have been proposed to enhance low light images, ranging from traditional approaches, deep learning approaches, to adversarial learning approaches. All of these approaches must be trained on large unpaired datasets. These large datasets take a long time to collect. In addition, the training time also becomes longer due to a large number of datasets. Based on these conditions, in this study, we propose EnlightenGAN in a smaller dataset environment to reduce training and processing time but with optimal results. We tried to experiment with training using a smaller dataset, namely 100 low/normal light images, 50 low/normal light images, and 25 low/normal light images respectively. The results of the test scores obtained show that the EnlightenGAN 100 method can compete with the full EnlightenGAN dataset by each getting the highest score 5 times. Meanwhile, EnlightenGAN 50 was able to get the highest score twice and EnlightenGAN 25 got the poorest result with no highest score achieved at all. From the histogram results, it was found that the most optimal methods for equalizing histograms were EnlightenGAN 100 and EnlightenGAN 50. And by reducing this dataset, the training time is significantly reduced. From these experiments, we can conclude that the optimal minimum dataset number is above 50 training images.
Types of Infrareds Focusing on Face Recognition: Promises, Advances and Challenges

This review paper is considered most of the previous research regarding Thermal IR (Infrared) camera. Showing all types of IR with a brief information for each one. Then introducing the historical background of thermal technology and state of art for various thermal face analysis. This study aims to highlight the spot on all types of IR imaging on face recognition. Also, this review presents the most popular methods of IR camera as well as the benefits and drawbacks for each type of them in order to build a new system using real-time.
Towards a Tutoring System to Support Robotics Activities in Classrooms – Two Wizard-of-Oz Studies

Abstract This paper develops a method for the construction and evaluation of cognitive models to support students in their problem-solving skills during robotics in school, aiming to build a basis for an implementation of a tutoring system in the future. Two Wizard-of-Oz studies were conducted, one in the classroom and one in the lab. Based on the cognitive model, the human wizards gave support to 20 students working in pairs. The studies were video recorded and a qualitative analysis was conducted. This qualitative research approach is described in detail. The evaluation of the studies showed that students reacted mostly positively to the wizards. We also uncovered ways in which students’ problem-solving skills could be improved. Based on the evaluation and observations of the Wizard-of-Oz studies, the paper proposes a design for a future robotics skills tutoring system.
Trends in Remote User Authentication Based on Smart Card and External Memory

This research performs a literature survey of remote user authentication researches based on smart card and external memory. The main security confidence of smart card based schemes is temper-resistance property. Other reasons are small physical size, portability, convenience of non-volatile memory, and security provided by a single chip computer embedded in a plastic card. The most efficient schemes are those that used hash function or ECC. The high cost of the cards and readers and their deployment remains a burden to issuers or users. This is what motivates the use of external memory instead of smart card. But the problem of non-temper resistance property associated with external memory limited researches in that direction. There are also, absence of other activities that are essential in user authentication such as forgot/reset password and re-registration in case the external memory or smart card is stolen or lost in all the reviewed researches.
Control of population and entanglement of two qubits under the action of different types of dissipative noise

We investigate the quantum control of two qubits interacting with a Markovian environment and evolving as an $X$ state. The control is implemented via a simple applied field, whose profile is determined by means of the piecewise time-independent quantum control method. The goal is to make either the population or the system concurrence to follow a specified tracking control trajectory. By considering the system under the influence of three kinds of noise---phase damping, amplitude damping, or a combination of both---we unveil the conditions (like energy balance) under which the quantum control is properly achieved and fidelity (monitored through the trace distance) is kept satisfactorily high, even if the qubits interact differently with the dissipative medium. We further find that the effects of phase damping, a type of noise which notoriously destroys coherence and entanglement, can be mitigated if during the control the system is also exposed to amplitude damping. Of potential interest for the usage of entanglement as resource for quantum information, our results indicate that distinct entanglement measures and coherence can be maintained relatively high (even at long times) as a consequence of the tracking quantum control process.
A new end-to-end framework based on non-local network structure and spatial attention mechanism for image rain removal

Image rain removal is an important topic in the field of computer vision. In the rainy environment, the rain will seriously affect the quality of imaging, resulting in image deformation, blur, poor visibility, and other problems. So the outdoor vision system cannot accurately detect object, monitor, and other works. Therefore, how to effectively eliminate the rain-weather interference to imaging has a very important practical value. In the absence of the time series information between frame and frame, the bottleneck problem of image rain removal technology is how to effectively remove multi-density rain-fringes while preserving the detailed structure information of the image background. To solve the above problems, a new image rain removal algorithm based on non-local network structure and spatial attention mechanism is proposed in this paper. Firstly, the position relation information between different pixels is obtained by the non-local operation to obtain the global image representation. Secondly, the spatial attention mechanism is used to recalibrate the global information in the spatial dimension. In other words, the nonlinear modeling is carried out on the channel dimension to gather similar features and useful information. Finally, deconvolution and long-distance residual connection are used to restore the size of the rain-removing image layer by layer. The analysis and experimental results show that the proposed algorithm in this paper is effective in removing rain marks, effectively solves the practical difficulties of removing rain stripes with different rain densities, and preserves the details and edge information of the image well.
Feature difference for single‐shot object detection

The one-stage detectors achieve a good trade-off between performance and latency, owing to the plain architecture and divergent learning mechanism for classification and localization. However, the two sub-tasks require features with various inherency with which to generate inconsistent detections, fettering detectors. In this study, the misalignment is deeply analyzed via kernel density estimation (KDE) for the first time. Moreover, to address the misalignment, a plug-and-play detection head, named Diff-Head, is devised and embedded in one-stage detectors. Concretely, the authors merge parallel branches into a semi-parallel structure, establishing the correlation between classification and regression. In the regression branch, a feature difference module (FDM) gets rid of the features that favour classification by subtracting salient object features from the original feature map, and position encoding (PE) modules enhance the absolute position information. The flexibility and efficiency of the detection head are retained. Experiments on Pascal visual object classes (VOC) and MS COCO demonstrate that Diff-Head is effective and achieves competitive performance with state-of-the-art detectors. Meanwhile, the amount of parameters is reduced at least 30% and 83.0% average precision (AP) is achieved on Pascal VOC. The analyses of consistency and error show that Diff-Head has better localization and the capability of mitigating the misalignment.
The impact of training data selection on the software defect prediction performance and data complexity

Directly learning a defect prediction model from cross-project datasets results in a model with poor performance. Hence, training data selection becomes a feasible solution to this problem. Limited comparative studies investigating the effect of training data selection on the prediction performance have presented contradictory results. Those studies also did not analyze why a training data selection method underperforms. This study aims to investigate the impact of training data selection on the defect prediction model and data complexity measures. The method is based on an empirical comparison between prediction performance and data complexity measure before and after selection. This study compared 13 training data selection methods on 61 projects using six classification algorithms and measured the data complexity using six complexity measures focusing on overlap class, noise level, and class imbalanced ratio. Experimental results indicate that the best method for each dataset varies depending on the dataset and classifiers. The training data selection most affects noise rate and class imbalance. We concluded that critically selecting the training data method could improve the performance of the prediction model. We recommend dealing with noise and unbalanced classes when designing training data methods.
Quantum firefly algorithm with stochastic search strategies

The firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. The FA is used to solve various optimization problems, but it still has some deficiencies, such as high complexity, slow convergence rate, and low accuracy of the solution. This paper proposes a highly efficient quantum firefly algorithm with stochastic search strategies (QSSFA). In QSSFA, individuals are generated in the way of quantum angle coding by introducing the laws of quantum physics and quantum gates, and combined with the random neighborhood attraction model, an adaptive step size strategy is also introduced in the optimization. The complexity of the algorithm is greatly reduced, and the global search ability of the algorithm is optimized. The convergence speed of the algorithm, the ability to jump out of the local optimum, and the algorithm accuracy are improved. The proposed QSSFA’s performance is tested on ten mathematical test functions. The obtained results show that the QSSFA algorithm is very competitive compared to the firefly algorithm and three other FA variants.