Reviewerly/exHarmony huggingface dataset:
Optimization Design and Implementation of Smart Multimedia College English Classroom Integrating Internet of Things Technology

With the rise of the Internet of Things technology and the improvement of college teaching requirements, more and more schools gradually use multimedia to carry out teaching activities for students. However, in today’s teaching environment, teachers’ teaching activities still do not get rid of the traditional thinking mode, and there is no fundamental change in teaching contents. In this context, the application of smart multimedia college English classroom teaching integrating Internet of Things technology was born. This paper mainly studied the optimization effect of smart multimedia integrated with Internet of Things technology on college English classrooms. The students of Baotou Medical College were taken as the research object; first, carry out information perception and data mining analysis work related to English, and finally, carry out diversified and situational teaching through smart multimedia. The degree of effect of optimization is reflected by the changes in the scores of students with different English abilities before and after. This paper mainly studied the optimization of college English classrooms with smart multimedia integrated with IoT, and the effect of optimization was reflected by the changes in the scores of students in Baotou Medical College before and after the experiment with different English abilities. This paper selected 10 sophomore students in Baotou Medical College and used their final English test scores as the benchmark to make statistics on different ability scores. Then, these students were given smart multimedia English classroom teaching activities, and they were tested on their English scores; a comparative analysis of the performance of different students with different English language skills before and after the test had to be done. The results showed that after 30 days of smart multimedia teaching, the English ability of 10 students has been improved. Among them, the average score of listening comprehension increased by 10.2 points, with the largest increase of 53.7%, and the average score of written expression ability increased by 1.1 points, with the smallest increase of 15.3%. Through a specific analysis of its ability, it was concluded that in terms of listening comprehension ability, the score of student 5 was improved to 13 points, the largest increase was 86%, the score of student 2 was improved to 7 points, and the smallest increase was 28%. In terms of reading comprehension, student 4’s score increased by 9 points, the largest increase was 56%, and the scores of students 2 and 9 were 2 points; the smallest increase was 7%. To sum up, the smart multimedia in the framework of the Internet of Things technology has a positive feedback effect on the optimization of college English classrooms, and different students’ different English abilities have been improved to varying degrees. This provides some guiding suggestions for the subsequent changes in the learning modalities of the undergraduate English classroom.
Multi-level encryption for 3D mesh model based on 3D Lorenz chaotic map and random number generator

&lt;span lang="EN-US"&gt;The increasing 3D model applications in various areas of life and widespread use like industry leads to 3D models being stolen and attacked by hackers; therefore, 3D model protection is a fundamental matter nowadays. In this paper, the proposed scheme will provide stringent security for the 3D models by implementing multiple levels of security with preserving the original dimensionality of the 3D model using the weight factor (w). The first level of security is achieved by applying a shuffling process for the vertices based on a key from random number generator (RNG), which provides good confusion. The second level is implemented by modifying the vertices values based on 3D keys from 3D Lorenz chaotic map, which provides good diffusion. The proposed scheme was applied on different 3D models varying in the vertices and faces number. The results illustrate that the proposed scheme deforms the entire 3D model based on Hausdorff distance (HD) approximately 100 after the encryption process, making it resist statistical attack. The scheme provides high security against brute force attack because it has a large key space equal to 10,105 and high security against deferential attack through secret key sensitivity using number of pixels change rate (NPCR) near to 99:6% and unified average changing intensity (UACI) near to 33:4%.&lt;/span&gt;
DISTRIBUTION PATTERNS AND SETTLEMENT DENSITY USING NEAREST NEIGHBOR ANALYSIS AND KERNEL DENSITY ANALYSIS IN DIY COASTAL AREAS

DIY's coastal area is one of DIY's development priorities by establishing the site as an economic corridor that impacts physical development, such as settlements in the coastal area of DIY. This study aims to analyze the distribution and density of settlements. The method used in research is a quantitative descriptive and Geography Information System approach. The analysis used in this study is Nearest Neighbour Analysis (NNA) and Kernel Density Analysis with an analysis tool in ArcGIS 10.5. The results of the NNA show that the three districts located on the coast of DIY have the same distribution pattern characteristics, namely, the clustered pattern, which means the NNA index value is &lt;1 or the T value ranges from 0-0.80. The results of the kernel density analysis show that the most significant density is found in Kulon Progo Regency, precisely in Wates District, and Bantul Regency, precisely in Srandakan District. At the same time, for Gunung Kidul Regency, it is less significant, and the density is only centered on one density point. Several factors cause settlement density, including physical factors (topography, slope, soil type, and clean water sources), accessibility (proximity to transportation routes and proximity to the city center), availability of facilities and infrastructure (electricity network, educational facilities, and health), and environmental factors (natural and human resources).Keywords: Settlements, Coastal, NNA, Kernel Density
A Sponge-Based Key Expansion Scheme for Modern Block Ciphers

Many systems in use today require strong cryptographic primitives to ensure confidentiality and integrity of data. This is especially true for energy systems, such as smart grids, as their proper operation is crucial for the existence of a functioning society. Because of this, we observe new developments in the field of cryptography every year. Among the developed primitives, one of the most important and widely used are iterated block ciphers. From AES (Advanced Encryption Standard) to LEA (Lightweight Encryption Algorithm), these ciphers are omnipresent in our world. While security of the encryption process of these ciphers is often meticulously tested and verified, an important part of them is neglected—the key expansion. Many modern ciphers use key expansion algorithms which produce reversible sub-key sequences. This means that, if the attacker finds out a large-enough part of this sequence, he/she will be able to either calculate the rest of the sequence, or even the original key. This could completely compromise the cipher. This is especially concerning due to research done into side-channel attacks, which attempt to leak secret information from memory. In this paper, we propose a novel scheme which can be used to create key expansion algorithms for modern ciphers. We define two important properties that a sequence produced by such algorithm should have and ensure that our construction fulfills them, based on the research on hashing functions. In order to explain the scheme, we describe an example algorithm constructed this way, as well as a cipher called IJON which utilizes it. In addition to this, we provide results of statistical tests which show the unpredictability of the sub-key sequence produced this way. The tests were performed using a test suite standardized by NIST (National Institute for Standards and Technology). The methodology of our tests is also explained. Finally, the reference implementation of the IJON cipher is published, ready to be used in software. Based on the results of tests, we conclude that, while more research and more testing of the algorithm is advised, the proposed key expansion scheme provides a very good generation of unpredictable bits and could possibly be used in practice.
Research on Hexchess Game System based Artificial Intelligence

With the rapid development of computer technology, artificial intelligence is emerging. Hex chess became popular because of its simple rules, but it also brought complex algorithms. Although the simple Monte Carlo tree search can be applied to the Hex game system, the search process is slow due to a large number of calculations. This paper proposes an improved Monte Carlo tree search algorithm based on the Upper Confidence Bound(UCB) formula to optimize the Hex game system and reduce the randomness of the Monte Carlo algorithm. To improve the efficiency of the search algorithm in the Hex game system, an effective system is adopted. Compared with the improved algorithm, not only the searching time of the Monte Carlo algorithm tree is improved, but also the performance of the algorithm is improved. At the same time, the system uses QT Creator to realize graphic interaction and complete the design of each module.
New Technologies to Enhance Money-Making Potential

It is difficult to predict the future of social media monetization. Multiple actors, individuals, organizations, and platforms can use social media to earn profits. Their paths to monetization are diverse and follow different routes. However, we can find some clues about the attributes of future monetization strategies and models based on current social media.
A Co-training Algorithm by Predicting Confidence of Unlabeled Neighbors

As a prominent semi-supervised learning algorithm, co-training can make full use of a few labeled samples as well as a quantity of unlabeled samples to train robust classifiers, thus it is widely researched in the last decades. However, there are still two severe problems in existing co-training methods: firstly, data used in co-training needs at least two sufficient and redundant views, but few practical datasets can meet it. Secondly, because there are a few labeled samples in the initial stage, the initial classifiers trained by co-training are usually too weak to correctly label unlabeled samples, which may bring in noisy labels for the following training process. To deal with these problems, we propose a co-training algorithm by predicting confidence of unlabeled neighbors (termed LCN-CoTrain). Specifically, LCN-CoTrain first defines an entropy-based view division method to generate redundant views. Meanwhile, LCN-CoTrain introduces a novel labeling confidence prediction strategy, which takes the nearest unlabeled neighbors into consideration when calculating the labeling confidence of a certain sample. To validate the efficiency of our proposed LCN-CoTrain algorithm, we experiment on four UCI datasets and compare LCN-CoTrain with several representative co-training methods. The experimental results indicate that our proposed LCN-CoTrain can learn robust classifiers and outperform most of baseline methods.
Towards the quantification of cybersecurity footprint for SMBs using the CMMC 2.0

Organizations, small and big, are faced with major cybersecurity challenges over the past several decades, as the proliferation of information systems and mobile devices expand. While larger organizations invest significant efforts in developing approaches to deal with cybersecurity incidents, Small and Medium Businesses (SMBs) are still struggling with ways to both keep their businesses alive and secure their systems to the best of their abilities. When it comes to critical systems, such as defense industries, the interconnectivities of organizations in the supply-chain have demonstrated to be problematic given the depth required to provide a high-level cybersecurity posture. The United States (U.S.) Department of Defense (DoD) with the partnership of the Defense Industry Base (DIB) have developed the Cybersecurity Maturity Model Certification (CMMC) in 2020 with a third-party mandate for Level 1 certification. Following an outcry from many DIB organizations, a newly revised CMMC 2.0 was introduced in late 2021 where Level 1 (Fundamental) was adjusted for annual self-assessment. CMMC 2.0 provides the 17 practices that organizations should self-assess. While these 17 practices provide initial guidance for assessment, the specific level of measurement and how it impacts their overall cybersecurity posture is vague. Specifically, many of these practices use non-quantifiable terms such as “limit”, “verify”, “control”, “identify”, etc. The focus of this work is to provide SMBs with a quantifiable method to self-assess their Cybersecurity Footprint following the CMMC 2.0 Level 1 practices. This paper outlines the foundational literature work conducted in support of the proposed quantification Cybersecurity Footprint Index (CFI) using 26 elements that correspond to the relevant CMMC 2.0 Level 1 practices.
Channel Interaction Mechanism for Fine Grained Image Categorization

Fine grained image classification is a very popular research topic in the fields of computer vision and pattern recognition in recent years. At present, fine-grained image classification by deep learning is mainly based on a single image. The network cannot find the subtle differences between similar image pairs, so it is unable to find the high discriminative regions accurately and efficiently. In this paper, an image classification method based on channel interaction mechanism is proposed. By selecting image feature pairs with high similarity, features of the two images are subtracted to extract the discriminative semantic features in the image pair. Then, the vector is used for classification. This method can learn the interaction between two images in a dynamic way. The comparison with traditional CNN on different fine-grained image data sets show the effectiveness of this method.
LightPro: Lightweight Probabilistic Workload Prediction Framework for Database-as-a-Service

Nowadays, Database-as-a-Service (DBaaS) has become more and more popular among users as it can largely reduce the complexity of managing databases and applications. Considering the increasing complexity of different applications, system management and automation such as self-provisioning and performance tuning can be challenging. To better achieve autonomous optimization of the system, the ability to predict future workload patterns is of great essence. In this paper, we propose a novel lightweight probabilistic workload forecasting framework (LIGHTPRO) that is easy to train and robust, to help the system predict future workload patterns, leveraging multi-head attention mechanism and convolution operations. Experiments on real-world query traces demonstrate the superiority of LIGHTPRO in reducing training time and capturing both long-term and short-term temporal patterns of the workload compared with other baselines.
SeInspect: Defending Model Stealing via Heterogeneous Semantic Inspection

Recent works developed an emerging attack, called Model Stealing (MS), to steal the functionalities of remote models, rendering the privacy of cloud-based machine learning services under threat. In this paper, we propose a new defense against MS attacks, using Semantic Inspection (called SeInspect). SeInspect mainly achieves two breakthroughs in this line of work. First, state-of-the-art MS attacks tend to craft malicious queries within a distribution close to benign ones. Such a characteristic increases the stealthiness of these attacks and makes them able to circumvent most of the existing MS defenses. In SeInspect, we introduce a semantic feature based detection method to amplify the query distribution discrepancy between malicious and benign users. Thus, SeInspect can detect stealthy MS attacks with a higher detection rate than existing defenses. Second, in our evaluation, we notice that existing defenses cause significantly increased response latency of model service due to repetfitive user-by-user inspection (e.g., increased by 7.01 times for PRADA, EuroS &P 2019). To mitigate the problem, we propose to analyze semantic features with a two-layer defense mechanism. The first layer can achieve a “quickshot” on users in batches and pick out all potentially malicious users. Then, the second layer identifies the attacker in a user-by-user manner. In our evaluation, we experiment with SeInspect on eight typical MS attacks. The result shows that SeInspect can detect two more attacks than prior works while reducing latency by at least $$54.00\%$$ .
Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Normalisation CAM

Deep learning has been widely used to segment tumour regions in stained histopathology images. However, precise annotations are expensive and labour-consuming. To reduce the manual annotation workload, we propose a light annotation-based fine-level segmentation approach for histology images based on a VGG-based Fusion network with Global Normalisation CAM. The experts are only required to provide a rough segmentation annotation on the images, and then accurate fine-level segmentation boundaries can be produced using this method. To validate the proposed approach, three datasets with rough and fine quality segmentation annotation are built. The fine quality labels are used only as ground truth in evaluation. The VFGN-CAM method includes three main components: an annotation enhancement to boost boundary accuracy and model generalisability; a VGG Fusion module that integrates multi-scale tumour features; and a Global Normalisation CAM module that combines local and global gradient information of tumour regions. Our VGG fusion and Global Normalisation CAM outperform the existing methods with a Dice of 84.188%. The final improvement for our proposed methods against the original rough labels is around 22.8%.
Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems, it is commonly assumed that all problem instances are feasible.In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources.Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement.In this case, locating the nearest feasible solution would be valuable information for the decision-makers.In this paper, a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible.To judge the performance of the proposed algorithm, 13 well-known dynamic test problems were solved.The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40% over all the environments and it can also find a good, but infeasible solution, when an instance is infeasible.
Deep Interest Context Network for Click-Through Rate

In recent years, the proposed Deep Interest Network (DIN), Deep Interest Evolution Network (DIEN) and Deep Session Interest Network (DSIN) have further developed click-through rate prediction models. The above three models mainly focus on the evolution and development of the user’s historical behavior sequence. To a certain extent, the influence of environmental vectors on the user’s choice of the advertisement for the item to be recommended is ignored. As a result, click-through rates cannot be predicted more accurately when items have strong environmental attributes. To solve this problem, we propose a new model based on DIN, called Deep Interest Context Network (DICN). DICN combines two local activation units. It adaptively learns the user’s interest representation from the user’s historical behavior data concerning an advertisement and the context in which the advertisement is located (i.e., environmental factors). The experimental results show that DICN significantly improves the performance and model expression ability of advertisements with strong environmental attributes.
CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

Researchers have proposed many approaches to identify key classes in software from the perspective of complex networks, such as CONN-TOTAL-W, PageRankBR, and ElementRank, which can effectively help developers understand software. However, these approaches tend to rely on a single metric when measuring the importance of classes. They do not consider the aggregation of multiple metrics to select the winner classes that rank high in majority metrics. In this work, we propose a key class identification approach using Markov-Chain-based ranking aggregation, namely CRA. First, CRA constructs a weighted directed class coupling network (WDCCNet) to describe the software and further applies existing approaches on WDCCNet to calculate class importance. Second, CRA filters out some metrics according to specific rules and uses the Markov chain to aggregate the remaining metrics. When the state probability distribution reaches a fixed point and does not change anymore, the classes in the software are sorted in a descending order according to the probability distribution, and the top-15% classes are treated as key classes. To evaluate the CRA approach, we compare it with 10 baseline approaches available on 6 pieces of software. Empirical results show that our approach is superior to the baselines according to the average ranking of the Friedman Test.
Breast Sentinel Lymph Node Cancer Detection from Mammographic Images Based on Quantum Wavelet Transform and an Atrous Pyramid Convolutional Neural Network

This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.