Reviewerly/exHarmony huggingface dataset:
Adversarial anchor-guided feature refinement for adversarial defense

Adversarial training (AT), which is known as a robust training method for defending against adversarial examples, usually loses the performance of models for clean examples due to the feature distribution discrepancy between clean and adversarial. In this paper, we propose a novel Adversarial Anchor-guided Feature Refinement (AAFR) defense method aimed at reducing the discrepancy and delivering reliable performances for both clean and adversarial examples. We devise adversarial anchor that detects whether the feature comes from clean or adversarial example. Then, we use adversarial anchor to refine the feature to reduce the discrepancy. As a result, the proposed method substantially achieves adversarial robustness while preserving the performance for clean examples. The effectiveness of the proposed method is verified with comprehensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets.
FATMLPGS: Design of a fault-aware trust establishment model for low-power IoT deployments via generic lightweight sidechains

DoS, GH, Sybil, Masquerading, Spoofing, Man in the Middle, etc. constantly attack IoT networks. Internal or external attacks reduce end-to-end delay, throughput, energy use, and other metrics. To counter these attacks, researchers have proposed a number of security &amp; privacy mechanisms with varying computational complexity and security levels. Immutability, traceability, transparency, and distributed nature make blockchain-based models secure. QoS depends on blockchain length, so these models aren’t scalable. Researchers say sidechaining improves QoS while remaining secure. Splitting or merging complex sidechains requires machine learning. Low-power IoT networks can’t use models. This text suggests a lightweight MGWO Model that helps establish initial routes by choosing high-trust nodes, reducing sidechaining power consumption, and incorporating fault-aware trust establishment. MGWO Model determines blockchain piece count for high QoS. MGWO Model uses Q-Learning to detect network faults. Fault identification is controlled by a stochastically modelled and activated Intrinsic Genetic Algorithm (IGA). Q-Learning, MGWO, and IGA can mitigate Sybil, Masquerading, Grey Hole, DDoS, and MITM attacks. Even when attacked, the proposed model maintains high QoS, improving real-time deployment efficiency. The proposed model improves energy efficiency by 15.9%, throughput by 10.6%, communication speed by 8.3%, and packet delivery by 0.8% for different network scenarios.
More accurate simulation for insurance data based on a modified SVM polynomial method1

This study aims to present the modified SVM polynomial method in order to evaluate insurance data. The research methodology discusses classical and modified SVM polynomial methods by R programming, and uses performance profiles to create the most preferable methods. It offers a new algorithm called an accurate evaluating algorithm as the way to construct the modified SVM polynomial method. The classical SVM polynomial method is also represented as the main idea in finding the modified polynomial SVM method. Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROCs) Curve, Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds are further named an accurate evaluating algorithm, employed to build the modified SVM polynomial method. The research paper also presents the best performance profiles based on the computing time and the number of iterations of both classical and modified SVM polynomial methods. Performance profiles show numerical comparisons based on both methods involving insurance data also displayed in this paper. It can be concluded that applying an accurate evaluating algorithm on the modified SVM polynomial method will improve the data accuracy up to 86% via computing time and iterations compared to the classical SVM polynomial method, which is only 79%. This accurate evaluating algorithm can be applied to various large-sized data by utilizing R programming with changing any suitable kernels for that data. This vital discovery will offer solutions for faster and more accurate data analysis that can benefit researchers, the private sector, or governments struggling with data.
A novel 3D scanning technique for reflective metal surface based on HDR-like image from pseudo exposure image fusion method

When scanning a metal workpiece with structured light, the reflected light can cause information loss or errors, ultimately affecting the reconstruction accuracy of the metal workpiece. Most studies use high dynamic range methods to overcome this issue, and a lot of research has been done on how to choose exposure time, but this kind of 3D scanning might lack efficiency. In this paper, we propose a new method. First, we calculate the relationship of the reference images and generate pseudo-exposure images through two groups of pictures, then we use image fusion to generate high dynamic images. Through the full factorial experimental design, the standard deviation of the reconstructed surface and the number of generated point clouds are recorded. By evaluating these indicators comprehensively, the best experimental parameters can be obtained. The experimental results show that the method proposed in this paper can effectively solve the issue of scanning reflective metal surfaces. Compared with other methods, the proposed pseudo-exposure method can reach the expected accuracy, the number of photographs taken can be reduced, and the scanning efficiency can be effectively improved.
DORIS: Personalized course recommendation system based on deep learning

Course recommendation aims at finding proper and attractive courses from massive candidates for students based on their needs, and it plays a significant role in the curricula-variable system. However, nearly all students nowadays need help selecting appropriate courses from abundant ones. The emergence and application of personalized course recommendations can release students from that cognitive overload problem. However, it still needs to mature and improve its scalability, sparsity, and cold start problems resulting in poor quality recommendations. Therefore, this paper proposes a novel personalized course recommendation system based on deep factorization machine (DeepFM), namely Deep PersOnalized couRse RecommendatIon System (DORIS), which selects the most appropriate courses for students according to their basic information, interests and the details of all courses. The experimental results illustrate that our proposed method outperforms other approaches.
Research on hybrid navigation algorithm and multi-objective cooperative planning method for electric inspection robot

For mobile robots working in the power inspection environment, navigation algorithms and collaborative path planning are the keys to understanding and reproducing human power inspection’s thinking mode. Firstly, the paper proposed a hybrid navigation algorithm of reflector and laser based on the generalized inverse of the least squares solution. Secondly, to solve the problem of multi-robot inspection cooperative path planning, the traffic rule reservation table generated a dynamic weighted map representing traffic congestion in power inspection to prevent multi-robot collision. Thirdly, through the distributed control method, the robot used the dynamic weighted map to improve the RRT (Rapidly-Exploring Random Trees)algorithm to achieve the purpose of active cooperative path planning, reducing the trajectory’s critical path points and solving the cooperative congestion. Finally, the positioning and calibration compensation can be carried out through the experiment by the reflector navigation algorithm in a large-scale complex environment. Comparing the path planning methods of the A* algorithm and the dynamic weighted RRT algorithm, the dynamic weighted RRT algorithm reduces the number of extended nodes. It solves the multi-robot planning collision problem while ensuring the search path’s completeness. Meanwhile, the dynamic weighted RRT algorithm can save the running time of the multi-robot system and effectively improve the efficiency of the power inspection system.
Representation, Reference, Relevance, and Retention

The long history of keywords and their predecessors as semiotic, symbolic, and semantic pointers to key concepts over time is introduced. This chapter describes current findings on four sensory specifics that are generally not considered as being aspects of library and information science but that are keywords which ground the discipline both physically and conceptually: that is, vision for representation, voice for reference, hearing for relevance, and memory for retention.
Databases, Search Engines

This chapter describes the history of commercial databases, the rise of search engines, and the range in responses of libraries and LIS researchers to these developments during the past 50 years.
Discovery, Retrieval

This chapter describes the emergence of the differentiation between traditional librarianship with its focus on collections, which has become incorporated into today’s library discovery systems, and information science with its focus on retrieval, which has become incorporated into algorithms and ontologies for today’s global Internet, which together comprise the contemporary discipline of library and information science.
Cross-modal contrastive learning for aspect-based recommendation

Knowledge-enhanced recommender systems with aspects have improved recommendation performance by better profiling user preferences. Existing models can be divided into graph-based and text-based depending on the type of external knowledge: knowledge graph and review text. Since each knowledge provides different information from the scope and detail of aspects, it is necessary to integrate them for modeling sophisticated aspect-level preferences. However, it is difficult to directly fuse the aspects defined on two types of knowledge because they are expressed in different latent spaces. To tackle this problem, we explore self-supervised learning on multi-modal data. Specifically, we propose a novel model called COntrastive learning with croSs-MOdal aSpects (COSMOS). To take the data imbalance between knowledge graph and review texts into consideration, we devise a cross-modal contrastive learning scheme, which generates multiple views of a user or an item based on inter-modal correlation. With the correlation between aspects, COSMOS captures the inherent dependency between graph and text data. The fine-grained aspect-level preference, which contains salient features (from review text) as well as general ones (from knowledge graph), leads to providing high-quality recommendation results, even if a user only has one of the two data. Experimental results on two datasets show that COSMOS outperforms state-of-the-art recommender systems.
Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.
A BRIEF REVIEW OF THE DEVELOPMENT PATH OF ARTIFICIAL INTELLIGENCE AND ITS SUBFIELDS

The broad discipline of Artificial Intelligence (AI) aims to automate processes that currently require human intelligence. Understanding intelligence and creating intelligent systems are the two clear objectives of AI. AI is interested in decision-making tools including knowledge representation, machine learning, heuristic reasoning, and inference approaches. The roots of AI can be found in philosophy, literature, and the human imagination. Early advancements in engineering, electronics, and many other fields have inspired AI. In this paper, we begin with a general introduction to the area of AI, then move on to its inception, history, subfields, and various applications.
Optimized reform strategy for quality education of Chinese language and literature education for college students based on information fusion technology

Abstract The learning of Chinese language and literature knowledge is of great significance to cultivate students’ humanistic qualities, but the lack of change in the teaching and learning concepts of teachers and students in practical teaching has caused the problem of inefficient teaching, which affects the development of students’ humanistic qualities. In order to achieve the overall requirement and purpose of combining humanistic education with the teaching of Chinese language and literature, improving the overall quality of students, cultivating their humanistic concern, and enhancing their independent learning ability, this paper adopts the combination of humanistic quality education and Chinese language and literature teaching, the main role of university students’ ontology as a guide, and explores the current situation of humanistic quality education in Chinese language and literature teaching based on information fusion technology, and gives countermeasures to strengthen the integration of the two, involving four aspects: creating a harmonious humanistic environment, teaching contents should be related to the real situation, paying attention to students’ personality cultivation, and actively using various teaching forms. In addition, a comprehensive analysis is conducted around the quality of Chinese language and literature education for college students based on information fusion technology, which ultimately provides the right training model and method for relevant educators. This method also has positive implications for the education and development of other subjects.
Deep continual hashing for real-world multi-label image retrieval

Deep Hashing has achieved great success in large-scale image retrieval due to binary code’s storage and computation efficiency. However, its learning paradigm under real-world environments is less studied, and most existing approaches are developed in the closed scenario, e.g., simple and unchanging semantics. When images of new classes emerge, they have to retrain the model on all history training datasets, but the constant data uploading makes this impractical. This paper proposes a novel method, called continual deep semantic hashing (CDSH), for learning binary codes of multi-label images with increasing classes. The CDSH consists of two hashing networks. One learns to hash the increasing semantics of data, i.e., label, into the semantic codes and accumulates label-code pairs as long-term knowledge, incorporating empirically verified loss and designed special regularization to ensure encoding old labels unchanged. The other learns to map images to the corresponding semantic code from a probabilistic view and solid knowledge via retaining history exemplar samples and projecting model gradients. We theoretically prove this improves the probability of old data’s code unchanged after the model is updated. Extensive experiments on four widely used image datasets demonstrate that the CDSH method can continually learn hash functions and yield state-of-the-art retrieval performance.
A high-resolution record of landscape changes and land use over the last 5000 years in western Calabria (S. Eufemia Gulf, southern Tyrrhenian Sea, Italy)

Pollen analysis of a marine core collected in the Gulf of S. Eufemia (Tyrrhenian Calabria, Italy) allowed reconstructing the regional changes in vegetation and land use over the last 5000 years. Pollen diagram zonation through Constrained Cluster Analysis highlighted three compositional zones whose boundaries mark the major changes that affected the vegetation structure. A dense forest cover with a few signs of human activities characterized the wide pollen source area from 5055 to 2700 BP (Zone 1). In this period, the Pre-Protohistoric communities were mainly concentrated on the Tropea Promontory where they had a significant local impact. Minor forest rarefactions at 5000–4800 BP, ca. 4400–4000 BP, ca. 3450–3150 BP were correlated to phases of climatic shifts toward aridity. From 2700 to 2000 BP (Zone 2a), a longer and more incisive period of forest decline was connected to a time of aridity that favored the intense activities of an increasing anthropogenic pressure. Indeed, important urban centers developed in the area during the Greek and Roman colonization. Diffuse deforestation and cultivation occurred from 790 BP (Zone 3), enhancing soil erosion and fluvial discharge as testified by the sudden increase in sedimentation rates. This disruption of the slope morphodynamics was connected to the collapse of territorial management following the end of the Western Roman Empire. Compositional Data Analysis, applied to a simplified pollen dataset, highlighted both a negative correlation between Abies and Fagus and a close similarity between the AP/NAP curve and the Axis 1 scores of the Relative Variation Biplot.
Early prediction of breast cancer based on the classification of <scp>HER</scp>‐2 and <scp>ER</scp> biomarkers using deep neural network

Abstract Background Owing to the highly coarse chromatin, multi‐dimensionality of the histo image, irregularity of shape and size, texture and appearance, nuclei extraction is challenging. To address these complexities, a deep‐learning algorithm called a stacked sparse autoencoder was considered as a research factor in this study. Methods and Materials This study focuses on detecting the epithelial regions and extracting high‐level features to segment the patches based on the nuclei and classify the biomarkers concerning the nuclei patches. We used 6,53,400 microscopic image patches of 363 patients sourced from the BreakHis database, of which 4,90,050 prominent image patches containing only nuclei were utilized for biomarker classification (essentially eliminating the non‐nuclei patches from 363 whole‐slide images (WSI)). Non‐nuclei patches were eliminated owing to an imbalanced class distribution. From the nuclei patches the biomarkers oestrogen receptor (ER) and human epidermal growth factor receptor‐2 (HER‐2) that are crucial for the early diagnosis of breast cancer are predicted based on the nuclei count and Nuclei intensity. These indicators aid in predicting the risk of breast cancer as well as the severity and potential effectiveness of various treatments. About 20%–25% of breast cancer cases have an overexpression of the protein HER‐2, which makes the tumour more aggressive and on the other hand, the majority of breast cancer cases express the protein ER. Results The classifier finally classified if the nuclei detected based on the features were benign, malignant, or normal with an accuracy of 99.73%, using which early prediction is performed by extracting and classifying the biomarkers HER‐2 and ER. The overall classification rate for classifying HER‐2 and ER was 97.52%. Conclusion HER‐2 +ve was classified with an intensity above 23%, and total nuclei in the range of 150–1000 were termed ER‐positive. Based on these 40 HER‐2 +ve patients, 25 ER +ve patients were detected in 363 patients. From this observation, it is concluded that 25–40 patients are at risk of breast cancer in the next 5 years due to the cell proliferation rate of 7000.
DESIGN OF COORDINATE DEVICES FOR CYBERSPORTS BASED ON THE VISUAL LANGUAGE OF NATURAL AND INDUSTRIAL ELEMENTS OF UDMURTIA

Rethinking and styling the object of inspiration is one of the components of design to create new images and products. The visual language of an object is important for its uniqueness, recognizability and communication. A creative search for concepts can lead to original combinations, as in computer games. Currently, computer games and eSports are gaining quite active development. Imagery and conceptuality also did not leave this sphere. In the field of developing technology for convenient use of a computer, especially coordinate devices, there are many opportunities for creating conceptual object design. The object of the study is the features of the visual transformation of the natural and industrial elements of the environment of Udmurtia, which will subsequently become the basis for the design modeling of the artistic image of coordinate devices.&#x0D; The methodological basis of research and development is: semiotic modeling, graphical analysis, additive technologies, digital modeling, media technologies, photographic fixation. The formation of a visual language of creative sources for an object is one of the fundamentals in design engineering, since the adaptation of a visual image to an object forms the recognition of a product brand in the market.
Fast Non-Local Attention network for light super-resolution

Although convolutional neural network-based methods have achieved significant performance improvement for Single Image Super-Resolution (SISR), their vast computational cost hinders real-world environment application. Thus, the interest in light networks for SISR is rising. Since existing SISR light models mainly focus on extracting fine local features using convolution operation, they have a limitation in that networks hardly capture global information. To capture the long-range dependency, Non-Local (NL) attention and Transformers have been explored in the SISR task. However, they are still suffering from a balancing problem between performance and computational cost. In this paper, we propose Fast Non-Local attention NETwork (FNLNET) for a super light SISR, which can capture the global representation. To acquire global information, we propose The Fast Non-Local Attention (FNLA) module that has low computational complexity while capturing global representation that reflects long-distance relationships between patches. Then, FNLA requires only 16 times lower computational cost than conventional NL networks while improving performance. In addition, we propose a powerful module called Global Self-Intension Mining (GSIM) that fuses the multi-information resources such as local, and global representation. Our FNLNET shows outstanding performance with fewer parameters and computational costs in the experiments on the benchmark datasets against state-of-the-art light SISR models.