Reviewerly/exHarmony huggingface dataset:
Research on Efficient Pyramid Attention Back-Projection Network for Medical Image Super-Resolution

Clear medical images are crucial for doctors to diagnose and treat patients quickly and accurately. However, due to limitations such as equipment, imaging principles, and patient safety considerations, obtaining ideal high-resolution medical images can be challenging. To address the issues of low utilization of intermediate layer feature correlation and insufficient use of the original LR image in current deep learning-based medical image super-resolution reconstruction techniques, an efficient pyramid attentional back-projection network is proposed. The back-projection concept to the deep feature extraction and reconstruction stages are applied and the non-linear interrelationships between low- and high-resolution images is learned through enhanced back-projection blocks and efficient pyramid channel attention. The original low-resolution image is connected to the final super-resolution image through the back-projection reconstruction module to achieve superior reconstruction performance. Experimental comparisons on several publicly available medical image datasets demonstrate that the proposed network outperforms other state-of-the-art methods in both objective quantitative metrics and subjective visual evaluation.
Accurate Classroom Person Detection Based on Multi-Frame Feature Fusion with Attention

In recent years, object detection technology has developed rapidly and is widely used in the field of intelligent video monitoring, especially in the classroom scenes, where incorporating the intelligent object detection technique into the video monitoring systems for intelligent monitoring purpose has important practical significance. However, surveillance videos may encounter issues such as object occlusion, motion blur, posture changes, and video defocus, which poses great challenges to object detection in the surveillance video scenarios. Therefore, in this paper, we conduct in-depth research on human detection in classroom multi camera surveillance video scenes and propose a multi frame feature fusion network based on the attention mechanism. The algorithm first adds the dilated convolution layers to the ResNet50 backbone network to expand the receptive field and retain more feature information; Secondly, a multi frame feature fusion strategy based on the attention mechanism was proposed to enhance the feature representation ability of the deep detector by fusing the feature information of adjacent frames, thereby improving the accuracy of human detection in the classroom monitoring video. To verify the effectiveness of the proposed method, we have compiled and produced a large-scale classroom monitoring video dataset, named OUC-ClassVideo. The proposed object detection network, which is developed based on the attention mechanism and multi frame feature fusion strategy, has achieved satisfactory performance on both the public dataset ImageNet VID and the OUC-ClassVideo dataset collected by ourselves.
CVulDetector: Cross-domain Vulnerability Detection Based on Transfer Learning

Software vulnerabilities pose a significant threat to computer security, and accurate identification of vulnerabilities has become a challenging task. Although previous work on deep learning-based vulnerability detection has achieved some success, it still faces issues such as 1) insufficient feature extraction capability of the models, and 2) unrealistic assumption of identical distribution between the source and target domains. To address these problems, we propose CVulDetector, a cross-domain vulnerability detection scheme based on transfer learning. Our approach incorporates both natural language characteristics and semantic syntax information of programs, enabling a more comprehensive description of program features. By employing two neural network models, we extract vulnerability-rich features and fine-tune the model using limited labeled data from the target domain after pre-training on a wide range of features from the source domain. Experimental evaluation on three CWE datasets shows an F1-score of 90.6%.
A Two-stage Method for Ship Target Detection and Identification in Complex Marine Environment

Ships are the main means of transportation at sea,and the hull number is the “identity card” of the ship. Therefore, it is necessary to effectively supervise ships through the identification of hull numbers. Due to the complex environment of the ocean and shooting angle limitations, hull number images are usually blurred and distorted, which has a great impact on the recognition results. Therefore, we propose a two-stage method for the detection and recognition of hull numbers, and a Super Resolution-Correction Recognition network is proposed for ambiguity and distortion of hull numbers. The accuracy of hull number detection reaches 92.1%, and the accuracy of hull number recognition reaches 91.3%. Experiments show that the method in this paper can effectively identify hull numbers.
A Method for Detecting Road Spills Under the Constraint of Short Term-Long Term Dual Background Model

With the development of urban traffic and the occurrence of a large number of traffic accidents, the use of video-based traffic event detection instead of human real-time monitoring of traffic scenes, automatic detection and identification of road obstacles such as spills that may cause traffic accidents has become a hot spot in the research of intelligent transportation systems. This paper addresses the problem of low accuracy of existing video surveillance spills detection, proposes a short term-long term dual background model constrained road spills detection method and applies it to the detection of road spills traffic events, combines the classification recognition results of classifier to eliminate misjudgment and realize the event detection of road spills, and also realizes the 3D information recovery of spills and extracts 3D height information to complete the spills target recognition. It has good application value for constructing a perfect highway event detection, management and diversion system. At the same time, the research of this paper also lays a good foundation for the subsequent work of traffic event detection, computer vision reconstruction, etc. It has important guiding significance and reference value.
Cigarette Authentication by Using Deep Learning-Based Anomaly Detection of Adhesive Mark Images

Machine vision has demonstrated effective discrimination between genuine and counterfeit cigarettes. However, the constructed identification model is limited to only a few specifications of cigarette products, and its versatility is constrained. Additionally, due to challenges in obtaining fake cigarettes and the instability of their features, it is difficult to extract representative fake sample features. This can severely compromise the reliability of the identification method. To address these limitations, we propose a novel cigarette authentication method that employs deep learning-based anomaly detection of adhesive mark images. Our approach involves a self-designed image acquisition device to capture images of cigarette packet adhesive marks. We then remove the partial background of images to obtain adhesive mark image samples. Finally, we train the convolutional neural network classification model using only real samples, then generate the maximum predicted probability value based on the trained model for the sample to be detected and compare this value with a threshold to achieve authentication. Genuine cigarette packets produced by 10 types of packaging machines and counterfeit cigarette packets were used as objects, covering 37 cigarette specifications. Three classic neural network models were selected for experiments and 787 samples were identified. The results demonstrate that all models have authentication capabilities when using the mean value as the threshold, with InceptionV1 having a discrimination accuracy, sensitivity, and specificity of 94.41%, 94.22%, and 94.69%, respectively. And the time for all models to identify a single sample is around 10 ms. The proposed method is both simple and efficient, as it can construct a discrimination model without using any fake samples. Furthermore, the ability to a variety of cigarette specifications indicates a high practical application value.
Pedestrian Detection Model in Underground Coal Mine Based on Active and Semi-supervised Learning

In the field of unmanned driving and safety monitoring of underground coal mine auxiliary transport vehicles, pedestrian detection technology is very important. At present, many works have carried out corresponding research on the influence of special working conditions such as dim light, uneven illumination, complex backgrounds, small and dense pedestrian targets in coal mines. However, these studies require the use of a large number of accurately labeled images, and it is difficult to obtain underground coal mine images, and the labeling is also very difficult. These factors have greatly affected the application and promotion of related models. To solve this problem, this paper proposes a pedestrian detection model in coal mines based on active learning and semi-supervised learning. Active learning can select indistinguishable data from existing unlabeled data for a small amount of labeling, while semi-supervised learning can use a small number of labeled samples and a large number of unlabeled samples to train the detection model and can achieve the same level as training with a large amount of labeled data. The detection accuracy of the model is similar. Therefore, the use of active learning and semi-supervised learning frameworks can effectively reduce the large demand for high-quality labeled data for the pedestrian detection model in coal mines. Experiments show that the coal mine underground pedestrian detection model based on braking learning and semi-supervised learning can use only 5% of the original data set for training to obtain a more effective detection model, which significantly reduces the dependence on labeled data and helps the rapid application and promotion of the pedestrian detection model in underground coal mines.
Raster map path planning based on intelligent optimization algorithms

To solve the problem of obstacle avoidance cars find the shortest path without collision between two locations. Firstly, the method of building a raster map is used in MATLAB to simulate the working environment of the trolley. Secondly, a hybrid algorithm combining particle swarm algorithm and genetic algorithm is proposed, and it is applied to the path optimization of the trolley. The hybrid algorithm combines the traditional particle swarm algorithm and the genetic algorithm, retains the speed and position iteration mode of the particle swarm algorithm, and integrates the cross and mutation operations in the genetic algorithm. The results show that compared with the traditional particle swarm algorithm and genetic algorithm, the hybrid algorithm can enhance the ability to jump out of local extremes in the process of population search, improve the convergence progress of the algorithm, and ensure that the obstacle avoidance trolley can find a more suitable moving path between the starting point and the target point.
Mask-guided two-stream attention network with modality alignment for infrared-visible person re-identification

In the existing person re-identification research, most of the studies are based on the person re-identification of visible light images, and the illumination conditions can affect the experimental results. Therefore, cross-modal person re-identification research has emerged, and the most commonly used method is visible-infrared person re-identification. Due to different cameras, different camera angles, different scenes, and other factors, cross-modal person re-identification faces great challenges of cross-modal differences and intra-class variations. Therefore, Mask-guided Two-stream Attention Network (MTAN) with modality alignment for infrared-visible person re-identification is proposed in this paper. First, the mask-guided two-stream feature extraction network is proposed to extract shape-aware modality features. Then, the modality alignment module is designed to eliminate cross-modal feature distribution bias. Overall, the proposed method combines the mask-guided two-stream feature extraction network, modality alignment module and the loss functions, to effectively extract modality-aware distinguishable features and reduce cross-modal distribution bias.
Tapping into Permutation Symmetry for Improved Detection of k-Symmetric Extensions

Symmetric extensions are essential in quantum mechanics, providing a lens through which to investigate the correlations of entangled quantum systems and to address challenges like the quantum marginal problem. Though semi-definite programming (SDP) is a recognized method for handling symmetric extensions, it struggles with computational constraints, especially due to the large real parameters in generalized qudit systems. In this study, we introduce an approach that adeptly leverages permutation symmetry. By fine-tuning the SDP problem for detecting k-symmetric extensions, our method markedly diminishes the searching space dimensionality and trims the number of parameters essential for positive-definiteness tests. This leads to an algorithmic enhancement, reducing the complexity from O(d2k) to O(kd2) in the qudit k-symmetric extension scenario. Additionally, our approach streamlines the process of verifying the positive definiteness of the results. These advancements pave the way for deeper insights into quantum correlations, highlighting potential avenues for refined research and innovations in quantum information theory.
IMPLEMENTASI DATA MINING DALAM PREDIKSI KEPUASAN BELAJAR SAAT PANDEMIC COVID MENGGUNAKAN ALGORITMA C 4.5

In 2020, the Covid-virus pandemic has spread throughout the world, including in Indonesia, of course affecting all aspects, especially the lives of Indonesian people, including education during the online learning process and the transition from face-to-face processes, of course, various obstacles arise including the lack of students understanding the teaching and learning process, and teachers who are unable to convey material optimally, that's why this online learning system requires the implementation of Data Mining to maximize learning and help related parties. The method used in this research is analysis and data mining. Analysis is an attempt to observe something in detail. things or things by outlining its constituent components or constituents for further study. Data mining, also known as knowledge, is one of the fields whose rapid growth is due to the high demand for added value from large-scale database batteries. From the research that has been done, it indicates that the decision tree can be a reference for schools in order to increase student and female learning satisfaction in the learning process, and based on the results of system testing it can be concluded that this application was successful in increasing student satisfaction in online learning during the covid pandemic. Data mining is used by the C4.5 algorithm to interest students in online learning during the corona virus.
ANALISIS KEPUASAN KONSUMEN TERHADAP KUALITAS PRODUK MENGGUNAKAN ALGORITMA NAIVE BAYES

Current technological developments have brought changes to human lifestyles, such as lifestyle, selection of antic goods and communication equipment such as smartphones and other devices. Nowadays it is known that the collection of branded items is also a lifestyle trend such as bags, rings, clothes and other fashions. Batam as an industrial city encourages investors to invest in Batam, because Batam is the most strategic city and is an international trade route directly adjacent to Singapore. The problem that often occurs at PT Tas Import Sukses in serving consumers is that the goods sent are not on demand, so consumers suspect that the products traded do not have good quality. And if there is a problem, the company responds with a very slow time. The purpose of this study is to analyze consumer satisfaction with the quality of products at PT Tas Import Sukses using the Naïve Bayes method, to determine the level of consumer satisfaction with the quality of bag products. And the last is to find out the level of accuracy in determining product satisfaction and quality with the Naïve Bayes method. While the results of this study are that satisfaction is strongly influenced by the level of service, while the second order is product quality, where the average level of satisfaction accuracy using the naïve bayes algorithm is 91%, pression 89% and recall 88.33%.
Speech data collection system for KUI, a Low resourced tribal language

&lt;p&gt;A new generation of speech translation technology is being developed to enable natural cross-language communication. Research efforts must focus on large vocabulary, spontaneous speech, and speaker variances to accommodate the varying demands of speech recognition technologies. These are important issues that need to be resolved for the general application of voice recognition in realistic settings. Most languages with limited resources don’t even have any speech data. Creating speech corpora is extremely difficult and time-consuming. Among all, KUI is regarded as one of the low-resource languages. In this paper, we developed the speech dataset for the KUI language to document and preserve their culture, tradition, and history for future generations. We also discuss the design, data collection procedures, and implementations and outline the different research possibilities using our KUI dataset. This paper mainly describes the GUI and method for the collection of KUI speech more quickly. In this section, the statistics of the people who helped and contributed to the collection of this KUI dataset have been provided. This study details a novel method of gathering data for any speech dataset. Using this process, we collected 60 hours of speech data sampled at 16 kHz by three different devices such as a Zoom recorder, Mobile, and Laptop from 80 different speakers. Each speaker contributed 500 sentences in the KUI language. A GUI application is designed to capture the speeches of numerous speakers in the KUI language. Several guidelines are proposed and used for the collection of the KUI speech dataset. All the guidelines are based on real-time experience gained during the data collection process by our team members.&lt;/p&gt;
Kesiapsiagaan Masyarakat dalam Menghadapi Ancaman Kebakaran

Fire disasters still occur frequently so that people are encouraged to have knowledge of preparedness in anticipating fire disasters. However, information about the community's ability to anticipate fire disasters is still very limited. The research objective was to determine community preparedness in facing the threat of fire in Penggilingan Village, Cakung District, East Jakarta. The method used is descriptive research using quantitative data. The study population consisted of all heads of households living in Penggilingan sub-district, totaling 40,641 families, while the sample was part of the heads of families, totaling 125 families. Sampling used proportional random sampling technique. The research variable was community preparedness in facing the threat of fire as measured by 4 parameters from LIPI-UNESCO/ISDR, 2006 namely knowledge and attitude, disaster warning system, emergency response plan, and ability to mobilize resources. Data were analyzed using frequency distribution tables and preparedness indexes. The results showed that the community is prepared to face the threat of fire in Penggilingan Village, Kec. Cakung is categorized as ready with an index value of 79.03. Even though it is in the ready category, it would be better if this preparedness continues to be improved to become very ready. Especially preparedness regarding the disaster warning system and the ability to mobilize resources that still need to be evaluated by the community and stakeholders. As well as fire socialization or training programs should be further intensified so that information about fire disasters can be received by all levels of society.
Impact of Mobile Phone Usage on Behavioral Change of Rural Youth in India - A Cross-Sectional Analysis

The study was taken up among the rural youths in the age group of 15-35 to find out the impact of mobile phone use on their behavioral changes in Salem District of Tamil Nadu. The study was based on primary data. Five demographic variables such as age, gender, marital status, family size and education were used as background variables to understand their inter-relationship between them. One common finding emerged from the background variables that the majority of mobile phone users were unmarried and school and college going students. To determine the impacting factors on behavioral changes due to more use of mobile phones, factor analysis was used. The factor analysis was clearly indicated psychological factors (stress, anxiety and depression) attributed to more use of mobile phone followed by increasing possibility of health hazards like non-communicable diseases and sedentary behavior on behavioral changes of respondents. Therefore, it is understood that the mobile phone usage can cause behavioral changes of the age group between (15-35), as this group prone to accept anything without understanding the long-term effects. Although mobile phone has become an integral part of daily life, it cannot be avoided but its judicial use could overcome some of its negative effects.
IMPLEMENTASI KEAMANAN JARINGAN MENGGUNAKAN PORT KNOCKING

Technological advancements continue to progress rapidly, allowing experts to enhance their knowledge across various industries, including health, network security, and others. PT Air Batam Hulu is a water company situated at Building A-WTP Moya Muka Kuning Jl. Letjend Supranto in Batam, responsible for managing the security of drinking water. However, the company's existing system is susceptible to data security breaches due to unauthorized hacking attempts. In addition, computer network systems face both physical and logical threats, such as Sniffer, Spoofing, Preaking, Remote Attack, Hole, Hacker, Cracker, and more. Among the current successful hacking attempts, UML sites, Malware, Trojans, Viruses, and port scanning are commonly encountered. To address security concerns, the company implements a security system called "port knocking," which involves opening or closing access to specific ports through the use of a firewall on network devices. This system works by sending certain packets or connections, including TCP, UDP, and ICMP protocols. To gain access to restricted ports, users are required to perform a "knocking" action by inputting the necessary rule beforehand. This ensures that only authorized users can access and use specific ports, enhancing data security and reducing the risk of unauthorized infiltration.