Reviewerly/exHarmony huggingface dataset:
MRIN: Fine-grained Feature Interaction based on Multiple Feature Relationships for CTR Prediction

CTR prediction is an important task in personalized recommendation and online advertising. In recent years, many models have been applied in CTR prediction systems to imporve the predcition accuracy. Although the current work has achieved results, the diversity of relationships between the different features was ignored: 1) most existing models usually use the vector-product to realize the interaction of each feature pairs, which ignore the potential contexts between the different feature interactions and lack the different interactive behaviors between the same pair of features; 2) most existing models usually only consider the importance of features of the same order, which ignore the weight relationships between the different feature representations.In this paper, we propose a novel deep learning model called Multi-Relational Interaction Network (MRIN), which learns fine-grained feature interactions and captures latent relationships between features. On the one hand, we design two interactive modules that take different interactive methods to learn the feature interactions in the potential contexts. On the other hand, we flexibly apply attention network to further automatically learn the weight relationships between different feature representations. Extensive experiments conducted on two public datasets prove the effectiveness of our proposed model, which has better performance than the state-of-the-art models.
Zero-Shot Offline Handwritten Chinese Character Recognition with Graph Embedding

Handwritten Chinese character recognition (HCCR) is a challenging topic in the field of computer vision due to its numerous categories, complex structure, shape similarity, casual writing style, and lack of training data. Some studies have attempted to develop radicals-based approaches with zero-shot recognition capacity to alleviate the data dependency problem of deep learning. However, previous studies tend to treat Chinese characters as isolated individuals and focus solely on the structural characteristics, ignoring the correlation information among Chinese characters. In this paper, we construct a graph to represent the correlation information and propose a novelty zero-shot HCCR method based on graph embedding. In addition, we present a pre-training approach for the encoder based on contrastive learning. The experimental results show that our method outperforms the other radical-based zero-shot recognition methods and also achieves a competitive performance on traditional experiment setting.
Collaborative Elaboration of Design Fiction Narratives with Participatory Design Fiction Extend

The elaboration of future narratives in Design Fiction has always been an individual activity, showing the unique vision of those who elaborated them and proposed the diegetic artifacts. Creating narratives collectively became possible with the Participatory Design Fiction methodology, which recommends, however, that participants are in the same physical location. We present a methodology to enable the creation of future narratives and their smart devices by a group of geographically dispersed volunteers, the Participatory Design Fiction Extend. With it, it was possible to train people, without knowledge about Design Fiction, in the elaboration of narratives, as well as about smart devices in the future and diegetic artifacts. Volunteers produced narratives and proposed intelligent devices that were later analyzed looking for similarities, differences and complementary ideas, in the light of the concepts of the collective unconscious, enabling the construction of a unique narrative and an intelligent device that contemplated most of the foreseen functionalities.
DarkFT: Automatic Scanning Behavior Analysis with FastText in Darknet Traffic

Network telescopes (Darknets) collect and record unsolicited Internet-wide traffic destined for a routed but unused address space, which provides a global perspective on Inter-net scanning behavior. However, it’s very difficult to extract meaningful information from Darknet traffic including a large number of unlabeled packets. In recent years, some work has used NLP techniques for self-supervised learning to generate embeddings as a rich representation of the Darknet. However, we found that previous resulting embeddings are not general enough. This paper proposed a new traffic representation model called Darknet Traffic Representations using FastText (DarkFT) which trains contextualized representation from large-scale unlabeled data. The embedding features can be applied to semi-supervised and unsupervised tasks. We conduct experiments on a dataset collected by network telescopes located in Japan and get better accuracy compared with state-of-the-art NLP-based approaches, especially for unknown senders.
Time Variant Resource Allocation With Secured Dynamic Priority Weighted Scheduling Process Based On Virtualized Subnet Transmission Support Factor In Cloud Computing

Resource allocation is required and evolution embodiments in many cloud computing data center management problems. For instance, a cloud service offers frequently allocated servers to border Virtual Machines (VM) based on CPU, memory space and disk availability and according to the needs of the VMs. Later, the service provider has raised the resources of the standard and the allocated network bandwidth to virtual machines that are assigned a boundary. The problem arise in number of virtual machines takes will not be able to start to allocate to change during the processing of the prediction task but later, the service establishes a new fault-tolerant regeneration strategy and provides a unique copy of the virtual machine and data between the fault domains.to resolve this problem we propose a, Secured Dynamic priority weighted scheduling process based on Virtualized Subnet transmission support factor in cloud computing. In addition, Secure Time variant Traffic analysis, the packet transmission on network on virtualized security to enhance the cloud scheduling process. This proposed system improves the scheduling performance as well in the security and trust factor compared to the other system.
Big Data Analytics-Based Recommendation System Using Ensemble Model

In the realm of computer science, RSS is a set of tools and methods for making useful product recommendations to end users. To maintain footholds in competitive industry, telecoms provide a wide range of offerings. It is challenging for a client to choose the best-fit product from the huge bouquet of products available. It is possible to increase suggestion quality by using the large amounts of textual contextual data detailing item qualities which are accessible with rating data in various recommender’s domains. Users have a hard time making purchases in the telecom industry. Here, fresh strategy for improving recommendation systems in the telecommunications industry is proposed. Users may choose the recommended services which is loaded onto their devices. Using a recommendation engine is a simple way for telecoms to increase trust and customer satisfaction index. The suggested recommendation engine allows users to pick and choose services they need. The present study compared two distinct recommendation frameworks: a single algorithm and an ensemble algorithm model. Experiments were conducted to compare the efficacy of separate algorithms and ensemble algorithm. Interestingly, the ensemble algorithm-based recommendation engine has proven to provide better recommendations in comparison to individual algorithms.
The Modified Binary Sparrow Search Algorithm (mbSSA) and Its Implementation

In our large world, there are various optimization problems that cannot be solved exactly through heuristic and meta-heuristic algorithms due to their large randomness in their initial population. In this paper, we opted the SSA which is known for its best optimal capability as well as speedy convergence including many more advantages. But still, SSA is facing some disadvantages such as massive randomness in the initial population and fall into the local optima. To overcome this deprivation, we have modified SSA by converting the random initial population into the binary initial population. And this modified SSA is named modified binary SSA (mbSSA). In this paper, the newly invented modified binary SSA (mbSSA) is implemented on 10 benchmark test functions as well as the outcomes of mbSSA are contrasted with the original SSA, PSO, and GWO. In this work, we analyzed mbSSA from various aspects like optimal value, mean for convergence accuracy, the standard deviation for stability, and convergence curves to check the convergence rate, and also we applied the Wilcoxon signed-rank test on mbSSA. In all aspects, the output of the experiment demonstrates that the modified binary SSA (mbSSA) is best in relation to SSA, GWO, and PSO.
Preventing Frauds in Industrial Drying Systems

Financial frauds are common in all social systems and place heavy economic and personal burdens on every individual living in any social system. Although social poverty does impact fraud occurrence, the primary driving force is greed.
Shufflenetv2: An Effective Technique for Recommendation System in E-Learning by User Preferences

Recommendation systems for e-learning can help learners to discover new courses, improve their learning outcomes, and enhance their overall experience. Learning improved by technology provides a variety of communication and data tools for learning and teaching. The lack of data, scalability, cold start, time requirements, and accuracy continues to be challenging. To overcome the challenges, we propose deep learning-based techniques for recommendation systems in e-learning. In this paper, we utilize a deep learning method for a recommendation system. Initially, we collect the information of the student or user from the student or user profile. Secondly, extracting the features such as comments, likes, dislikes, favorites, and ratings using the ResNet-152 approach. Using the ShuffleNet V2 technique, the number of users who have recommended and have not recommended the associated learning site will be predicted at the finish. To improve the accuracy of the proposed technique, we utilize the Modified Butterfly Optimization Algorithm (MBOA). The performance analysis in terms of several performance indicators serves as evidence for the proposed method's superiority.
A Decentralized Secured Data sharing Framework for IoT Networks

As data collected from devices keeps increasing at an exponential rate, IoT keeps blooming in many different kinds of industries and brings a consistent change in our everyday lives. However, the price of this growth is security and privacy, as data flows are increasingly more dense, which can also contain sensitive and private information. Therefore, it is evident that in order to further advance in the field of IoT and enable its adoption, it is essential to safeguard data flows in such networks, an aspect that can be supported by blockchain technology. Blockchain has some crucial advantages, such as immutability, non-repudiation and availability, building upon a decentralized ledger that is shared among participants, thus increasing the security and trust of the system. Given that fact, this paper proposes a conceptual framework that enables secure and private data sharing on a decentralized and tamper-proof consortium blockchain, along with two use cases that illustrate its conceptual soundness and potential.
Single image defogging and evaluation with deep neural networks without the need of ground truth

Fog dramatically compromises the overall visibility of any scene, critically affecting features such as objects' illumination, contrast, and contours. The decrease in visibility compromises the performance of Computer Vision algorithms such as pattern recognition and segmentation, some of them very relevant to decision-making in the rise of autonomous-driven vehicles. Many dehazing methods have been proposed. However, to the best of our knowledge, all currently used metrics do compare the defogged image to its ground truth, usually the same scene on a non-foggy day, or estimate physical parameters from the scene. This hinders progress in the field, as obtaining proper ground truth images is not always possible and becomes costly and time-consuming because physical parameters greatly depend on the scene conditions. This work aims to tackle this issue by proposing a real-time operating defogging network that only takes an RGB image of the fogged scene as input, performs the defogging, and uses a contour-based metric for Single Image Defogging evaluation even when the ground truth is not available, which is the most common situation. The proposed metric only requires the original hazy image and the image after the defogging procedure. We trained our network using a novel two-stage pipeline with the DENSE dataset and compared our method and metric with currently used metrics and other defogging techniques with the NTIRE 2018 defogging challenge to prove their effectiveness.
Analysis on the Implementation Path of College Student Education Management under the Background of "Micro Era"

With the continuous development of the Internet, more and more "WeChat" software has appeared, such as "WeChat" and "Tiktok".The emergence of these platforms has changed people's habits in daily life, and has also had a significant impact on college students.The emergence of the "micro era" gives colleges and universities an opportunity to carry out educational reform, so that colleges and universities can fully combine the characteristics of the "micro era" to carry out more effective student education management.This article will take the college student education management as the main research direction to explore the effective measures of student education management under the "micro era".
Extraction of use case diagram elements using natural language processing and network science

Software engineering artifact extraction from natural language requirements without human intervention is a challenging task. Out of these artifacts, the use case plays a prominent role in software design and development. In the literature, most of the approaches are either semi-automated or necessitate formalism or make use of restricted natural language for the extraction of use cases from textual requirements. In this paper, we resolve the challenge of automated artifact extraction from natural language requirements. We propose an automated approach to generate use cases, actors, and their relationships from natural language requirements. Our proposed approach involves no human intervention or formalism. To automate the proposed approach, we have used Natural Language Processing and Network Science. Our proposed approach provides promising results for the extraction of use case elements from natural language requirements. We validate the proposed approach using several literature-based case studies. The proposed approach significantly improves the results in comparison to an existing approach. On average, the proposed approach achieves around 71.5% accuracy (F-Measure), whereas the baseline method achieves around 16% accuracy (F-Measure) on average. The evaluation of the proposed approach on the literature-based case studies shows its significance for the extraction of use case elements from natural language requirements. The approach reduces human effort in software design and development.
Design of an Ethical Framework for Artificial Intelligence in Cultural Heritage

In recent years Artificial Intelligence (AI) has found its way into the creative and cultural industries, opening up new challenges and opportunities, poorly sufficiently explored today. However, when it comes to culture and creativity, many social and political cost factors should be taken into account. It is crucial to identify the opportunities that AI can offer in terms of the preservation, use, promotion, and accessibility of Cultural Heritage (CH). However, ethical concerns should be outlined when applying AI in cultural settings, such as for the digital replica of official UNESCO heritage sites or an unbiased explanation and interpretation of a work of art. This paper provides a first attempt to define the main ethical findings on this topic and propose it as an ethical framework to assess different risks arising from the use of AI in the CH domain. The application of this disruptive technology in the arts is evaluated through the lens of ethical principles for trustworthy AI and it explores whether the wider accessibility and improved preservation techniques enabled by AI come at some cost in terms of interpretation, social and cultural inclusion, subjectivism, or other forms of bias. The main ethical principles that emerge from the literature for the application of AI in the cultural domain are the following: Shared Responsibility, Meaningful Participation, Explainability, Accessibility, Sustainability, Reliability and Dignity. The findings underline the importance of establishing specific sectoral ethical guidelines for AI in the field of tangible and intangible CH to support and enhance its sustainable development without compromising its values, significance, sense of belonging and strong social impact.
Learning pixel-adaptive weights for portrait photo retouching

The lookup table-based methods achieve promising retouching performance by learning image-adaptive weights to combine 3-dimensional lookup tables (3D LUTs) and conducting pixel-to-pixel color transformation. However, this paradigm ignores the local context cues and applies the same transformation to portrait pixels and background pixels that exhibit the same raw RGB values. In contrast, an expert usually conducts different operations to adjust the color temperatures, tones of portrait regions, and background regions. This inspires us to model local context cues to improve the retouching quality explicitly.Thus, the center pixel of an image patch is first retouched by predicting pixel-adaptive lookup table weights. To modulate the influence of neighboring pixels, as neighboring pixels exhibit different affinities to the center pixel, a local attention mask is estimated. Then, the quality of the local attention mask is further improved by applying supervision, which is based on the affinity map calculated by the ground-truth portrait mask. For group-level consistency, we propose to directly constrain the variance of mean color components in the Lab space. Extensive experiments on the PPR10K dataset demonstrate the effectiveness of the proposed method, the retouching performance on high-resolution photos is improved by over 0.5dB in terms of PSNR, and the group-level inconsistency is reduced by 2.1.
Effective Use of Remote Learning in Sustaining Teaching and Learning of Fine and Applied Arts in Covid-19 Era In Colleges of Education

The study determined how remote learning can be used effectively in sustaining teaching and learning of fine and applied arts in colleges of education in Benue State, Kogi States, and Enugu State, Nigeria. We adopted a cross-sectional survey for the study. We used a sample size of 218 respondents for the study. We used a structured questionnaire for the study. We used frequency percentage and Chi-square analysis. Moreover, four hypotheses were tested. The finding of the study showed that the ICT devices are not available, respondents do not use the effective technique for teaching and learning fine and applied arts in remote learning, that respondents ascertained that there are challenges concerning the use of technology in the teaching and learning of fine and applied arts, and respondents agree on the solutions in mitigating the adverse effect of COVID-19 lockdown on teaching and learning of fine and applied arts. We highlighted the educational implications of the findings and the recommendations. We also made some limitations of the study and suggestions for further studies.
ACADEMIC PERFORMANCE OF ADOLESCENT STUDENTS HAVING M-LEARNING HABITS IN RELATION TO THEIR INTELLIGENCE

Mobile devices are widely used in the digital age. The educational use of mobile devices in and outside classrooms helps to develop positive attitude among students towards courses. The aim of this research is to find out the influence of M-learning habits on academic performance of adolescent students in relation to their intelligence. A sample of about 350 students of +1 and +2 grade both boys and girls from the private and government schools of Amritsar district was randomly selected for collecting data. Academic scores of final examinations of adolescent students were used to find out the academic performance of the students. M-learning Habits Scale constructed by the investigator was used to find out mobile learning habits of the students. General Mental Ability Test by Jalota was used to measure the intelligence of the students. The findings suggest that more than sixty percent of adolescent students are having M-learning habits. There is no significant relationship between academic performance of adolescent students having M-learning habits and their intelligence. There is no significant difference in the academic performance of adolescent students having M-learning habits in relation to their level of intelligence.
Running Record as Method to Improve The students' Reading Ability

The purpose of this study was to determine whether the used of running records as a learning method could improved students' reading skills at Mts Muhammadiyah Likuboddong. The research method used was the pre-experiment method with one group pretest and posttest design. The researcher collected data by gave pretest and posttest by gave essay test. The subjects of this study were 2nd grade students of Mts Muhammadiyah Likuboddong. Sampling used total sampling. The total of samples was 18 students.The results showed that the used of running records as a learning method could improved students' reading skills. This was evidenced by the average value of the main idea in the pre test was 44.9 which increased in the posttest to 65.2 with an increased of 45%. While the average value of supporting ideas in the pretest was 40.6, increased in the posttest to 58.8 with an increased of 44%. This means that there was an increased in students' reading ability, especially in literal understanding related to main ideas and supporting ideas and it was concluded that running records as a learning method can improved students' reading skills at Mts Muhammadiyah Likuboddong. Thus it was stated that the Null Hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted.
Novel Automatic Food Trading System Using Blockchain

Abstract: The blockchain functions as a decentralized and collectively maintained ledger that allows anyone to contribute. It provides a secure and readily accessible source of information concerning farms, inventories, and contracts in the agricultural sector, where acquiring such data can sometimes be costly. By tracing the origin of food, blockchain technology supports the establishment of trustworthy food supply chains and fosters trust between producers and consumers. Its reliable data storage capabilities enable the implementation of data-driven solutions to enhance farming practices. Furthermore, when combined with smart contracts, blockchain technology facilitates prompt payments by linking them to recorded data changes within the blockchain.