Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of recommendation system research based on knowledge graph

With the development of information technology, big data technology, and mobile Internet technology, people can quickly obtain and process a large amount of information. However, a significant quantity of detailed information makes it difficult for people to get the information they need efficiently. The emergence of recommendation systems solves this problem very well. It can actively push the information that users are interested in. Knowledge graph-based recommendation technology is mainly used to solve problems, such as the interpretability and cold start encountered by recommendation systems in analysing large data volumes. This paper introduces four aspects of recommendation technology based on knowledge graphs: recommendation algorithm, evaluation metrics, datasets, and models.
Coupled flxed point theorems for weak compatible mappings in fuzzy metric spaces

In this paper, flrst we introduce the notion of common cou- pled coincidence point for pairs of mappings. Secondly, we introduce the concept of weakly commuting and variants of weakly commuting mappings (R-weakly commuting, R-weakly commuting of type (Af), type (Ag), type (P) mappings). Thirdly, we introduce the notion of weakly f-compatible maps and weakly g-compatible maps to obtain common coupled coinci- dence points in fuzzy metric spaces. At the end, we prove common flxed point theorems for pairs of weakly compatible map and their variants, which generalize the result of various authors present in flxed point theory literature. 2010 AMS Classiflcation: 47H10, 54H25
Research on AI algorithm education system of educational robot

To meet the needs of artificial intelligence education, an educational robot with teaching as its function is developed, and the education system of AI algorithm based on KNN algorithm is designed. On the basis of the design of the ESP32 development board, the combination of ESP32 and artificial intelligence algorithm is applied to the robot's artificial intelligence teaching system. On the basis of the ESP32 embedded system, the KNN algorithm of the educational robot is developed and applied. The system consists of upper computer (PC) and lower computer (ESP32). SIP is used to realize the data transmission of wireless communication between PC and ESP32. The image classification method based on KNN algorithm is studied to provide reference for better application of educational robot in artificial intelligence education.
Research and implementation of tomato disease and insect pest detection method based on improved SVM

In this paper, an improved method was proposed to solve the shortcomings of SVM in the application of tomato disease and insect pest detection algorithm: extracting features of color texture features CCL (CM, CCV, LBP) for fusion, selecting the optimal parameters c and g through 9-fold cross verification, and calculating the OPTIMAL parameters by RBFSVM to obtain the CCL-SVM model. Experimental results show that the improved SVM algorithm improves the accuracy and efficiency of detection.
Power prediction of photovoltaic power plant based on double-loop distributed gradient boosting algorithm

With the increasingly prominent problems of energy carbon emissions, environmental pollution, and climate change, China has further accelerated the construction of new energy sources to deal with a series of ecological problems. China's new energy structure mainly relies on photovoltaic and wind power generation. In addition, wind and solar power generation also has problems such as instability and strong randomness, so existing big data and artificial intelligence can be used to solve this problem and improve wind and solar forecasting. In this paper, a double-loop distributed gradient enhancement algorithm is used to improve the power prediction accuracy of photovoltaic power plants, which effectively solves the shortcomings of slow speed and poor prediction stability in photovoltaic power prediction, and ensures the power dispatching problem based on new power systems.
A transfer learning framework based on adaptive spatial spectral relational networks for hyperspectral image classification

Hyperspectral image classification is one of the key technologies in hyperspectral image application. Since the number of bands in hyperspectral images is usually much larger than that in RGB images or multispectral images, it is necessary to reduce the dimensionality of the hyperspectral data of the target by using pre-trained models from other fields. However, this situation may lead to the loss of effective spectral information. Therefore, to overcome this problem, we propose a novel transfer learning framework based on Adaptive Spatial Spectral Relational Networks (ASSRN), which can be applied to knowledge transfer between different hyperspectral datasets. Finally, we conduct extensive experiments on several databases. The inspiring experimental results demonstrate the effectiveness of the proposed framework.
A vulnerability automation exploitation method based on symbolic execution

With increasing number of software vulnerabilities, the quantity of attacks utilizing malicious samples is also on rise, leading to intensified adversarial competition. In particular, the application of automatic vulnerability mining techniques has resulted in a significant increase in the number of vulnerabilities, but security researchers often do not have enough time to deal with them. This paper proposes a symbol-execution-based automated vulnerability exploitation method, which can achieve automation of vulnerability detection, classification and exploitation. Finally, this paper designs and implements a prototype system for symbol-execution-based automated vulnerability exploitation and verifies its effectiveness through experiments. This research provides security analysts with an in-depth understanding of the types of vulnerabilities and determines methods for vulnerability exploitability, further improving the efficiency of analyzing and fixing vulnerabilities.
Image-based method to defend against Android malware adversarial examples

Classifier based on deep learning has greatly improved the ability of identifying malware. However, these detectors may be affected by adversarial input disturbances. Any vulnerability in the malware detector may pose a significant threat to the platform they protected. Therefore, in order to improve the defense against malware and adversarial examples, we conducted the study on adversarial deep learning for Android malware. On this basis, this paper proposes a new method to improve the robustness of the malware detector which is based on visualizing and deep learning. Different from the traditional method with feature extraction, this paper first converts the malware binaries of the Android application package into grayscale images and use the Generative Adversarial Network (GAN) to optimize the boundary samples. Then conduct adversarial training to generate a robust malware detector based on deep learning. A large number of experiments have proved that this method can resist different attacks without occupying too many resources. The results show that it has significant advantages compared with other current classifiers.
Improved timing synchronization algorithm for OFDM system based on ZC sequence

An improved timing synchronization algorithm based on ZC (Zadoff-Chu) sequence is proposed in order to improve timing synchronization accuracy of OFDM (Orthogonal Frequency Division Multiplexing) system. This algorithm combined the sequence inversion of Minn algorithm with the sequence conjugate of Park algorithm. The new sequences replaced PN sequences with ZC sequences that have good autocorrelation and low cross-correlation. Even in the timing position error, the system can still be precisely timing synchronized. Under the channel of AWGN (Additive Gaussian White Noise), the number of subcarriers is 25616QAM modulation and demodulation. Matlab simulation shows that the improved algorithm eliminates the influence of minor peak on timing synchronization in Minn algorithm and Park algorithm Timing deviation and MSE (Mean Square Error) are lower than Minn algorithm and Park algorithm within -15 to approximately15-dB, which improves timing accuracy. When the SNR is 15-dB, the timing MSE of the improved algorithm is 4.2×10-6, which is the optimal state for estimating performance.
“This App Just Gets Me”: Assessing the Quality, Features and User Reviews of Menopause Smartphone Apps

Abstract Mobile health (mHealth) apps are becoming a popular digital resource and in the last few years menopause smartphone apps have become more popular. While these apps often contain little healthcare professional (HCP) input or evidence-based information less is known about the specific features of the apps and whether users find the apps helpful in relation to their menopause experience. We conducted a quality, feature and written review analysis of 18 menopause apps. We assessed the overall quality of the apps using the Mobile App Rating Scale (MAR), identified features including tracking, data visualization and community elements and undertook a thematic analysis of the written reviews. The analysis showed that app quality was variable and features ranged from simple trackers to community elements, audio guides and personalized content. Reviews of apps were overwhelmingly positive in nature and the thematic analysis highlighted four themes: “empowering functions”, “I’m not alone,” “function wish list,” and “usability and privacy. ” The apps encouraged self-reflection and built confidence around menopause discussions, especially with HCPs, while the community features encouraged a sense of togetherness. Additional, tailored functionality, alongside ease of use remains important to users. Menopause apps currently available on the market vary in terms of their quality and features but reviews indicate they are generally perceived as positive. Apps may have a role to play in supporting women’s menopause experience and further consideration of their role in relation to HCP consultations is required.
Adoption of Mobile and Wearable Technology for Older Adults’ Physical Activity: A Preliminary Model

Abstract This study explores the adoption of mobile and wearable technology in the physical activity of older adults. The data arose from qualitative ethnographic observations and interviews (N = 7). Findings led to the conceptualization of a model describing interviewees’ decision-making processes for adoption. The model outlines these phases: awareness; attitudes; appraisals; initial use, which could lead to abandonment or adoption; and post-trial abandonment, which could lead to rejection or probable adoption through redeveloping awareness and attitudes, and reappraising the technology. The findings highlight the need to consider for age-specific barriers to adoption and to understand adoption as a stage-wise process with intricate layers. Certain well-formed habits and the lack of understanding of the parameters provided by these technologies can lead to older adults’ rejection of tools that are potentially useful for physical activity motivation. The paper suggests practical ways for promoting mobile and wearable technology to older adults.
Willingness of diabetes mellitus patients to use mHealth applications and its associated factors for self-care management in a low-income country: an input for digital health implementation

Background Although mHealth applications are becoming more widely available and used, there is no evidence about why people are willing to use them. Therefore, this study aimed to assess the willingness of patients with diabetes to use mHealth applications and associated factors for self-care management in Ethiopia. Methods An institutional cross-sectional study was conducted among 422 patients with diabetes. Data were collected using pretested interviewer-administered questionnaire. Epi Data V.4. 6 for entering the data and STATA V.14 for analysing the data were used. A multivariable logistic regression analysis was carried out to identify factors associated with patient’s willingness to use mobile health applications. Results A total of 398 study participants were included in the study. About 284 (71.4%) 95% CI (66.8% to 75.9%)). Of participants were willing to use mobile health applications. Patients below 30 years of age (adjusted OR, AOR 2.21; 95% CI (1.22 to 4.10)), urban residents (AOR 2.12; 95% CI (1.12 to 3.98)), internet access (AOR 3.91; 95% CI (1.31 to 11.5)), favourable attitude (AOR 5.20; 95% CI (2.60 to 10.40)), perceived ease of use (AOR 2.57; 95% CI (1.34 to 4.85)) and perceived usefulness (AOR 4.67; 95% CI (1.95 to 5.77)) were significantly associated with patients’ willingness to use mobile health applications. Conclusions Overall, diabetes patients’ willingness to use mobile health applications was high. Patients’ age, place of residence, internet access, attitude, perceived ease of use and perceived usefulness were significant factors concerning their willingness to use mobile health applications. Considering these factors could provide insight for developing and adopting diabetes management applications on mobile devices in Ethiopia.
Bayesian Nonparametrics: An invitation to Bayesian nonparametrics

This introduction explains why you are right to be curious about Bayesian nonparametrics – why you may actually need it and how you can manage to understand it and use it. We also give an overview of the aims and contents of this book and how it came into existence, delve briefly into the history of the still relatively young field of Bayesian nonparametrics, and offer some concluding remarks about challenges and likely future developments in the area.
Morphogenesis in Trypanosoma cruzi epimastigotes proceeds via a highly asymmetric cell division

Trypanosoma cruzi is a protist parasite that is the causative agent of Chagas’ disease, a neglected tropical disease endemic to the Americas. T. cruzi cells are highly polarized and undergo morphological changes as they cycle within their insect and mammalian hosts. Work on related trypanosomatids has described cell division mechanisms in several life-cycle stages and identified a set of essential morphogenic proteins that serve as markers for key events during trypanosomatid division. Here, we use Cas9-based tagging of morphogenic genes, live-cell imaging, and expansion microscopy to study the cell division mechanism of the insect-resident epimastigote form of T. cruzi, which represents an understudied trypanosomatid morphotype. We find that T. cruzi epimastigote cell division is highly asymmetric, producing one daughter cell that is significantly smaller than the other. Daughter cell division rates differ by 4.9 h, which may be a consequence of this size disparity. Many of the morphogenic proteins identified in T. brucei have altered localization patterns in T. cruzi epimastigoes, which may reflect fundamental differences in the cell division mechanism of this life cycle stage, which widens and shortens the cell body to accommodate the duplicated organelles and cleavage furrow rather than elongating the cell body along the long axis of the cell, as is the case in life-cycle stages that have been studied in T. brucei. This work provides a foundation for further investigations of T. cruzi cell division and shows that subtle differences in trypansomatid cell morphology can alter how these parasites divide. Author Summary Trypanosoma cruzi causes Chagas’ disease, which is among the most neglected of tropical diseases, affecting millions of people in South and Central America along with immigrant populations around the world. T. cruzi is related to other important pathogens such as Trypanosoma brucei and Leishmania spp, which have been the subject of molecular and cellular characterizations that have provided an understanding of how these organisms shape their cells and undergo division. Work in T. cruzi has lagged due to an absence of molecular tools for manipulating the parasite and the complexity of the original published genome; these issues have recently been resolved. Building on work in T. brucei, we have studied the localization of key cell cycle proteins and quantified changes in cell shape during division in an insect-resident form of T. cruzi. This work has uncovered unique adaptations to the cell division process in T. cruzi and provides insight into the range of mechanisms this family of important pathogens can employ to colonize their hosts.
Imbalanced classification by learning hidden data structure

ABSTRACT Approaches to solve the imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of the hidden structure within the majority class. The purpose of this article is to first highlight the effect of sub-clusters within the majority class on the detection of the minority instances and then handle the imbalanced classification problem by learning the structure in the data. We propose a decomposition-based approach to a two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm and thus transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity toward the minority class. We also provide a metric for selecting the clustering algorithm by comparing estimates of the stability of the decomposition, which appears necessary for good classifier performance. We demonstrate the performance of the proposed approach through various real data sets.
Dissecting endocytic mechanisms reveals new molecular targets to enhance sodium iodide symporter activity with clinical relevance to radioiodide therapy

The sodium/iodide symporter (NIS) frequently shows diminished plasma membrane (PM) targeting in differentiated thyroid cancer (DTC), resulting in suboptimal radioiodide (RAI) treatment and poor prognosis. The mechanisms which govern the endocytosis of NIS away from the PM are ill-defined. Here, we challenged the hypothesis that new mechanistic understanding of NIS endocytosis would facilitate prediction of patient outcomes and enable specific drug modulation of RAI uptake in vivo. Through mutagenesis, NanoBiT interaction assays, cell surface biotinylation assays, RAI uptake and NanoBRET, we identify an acidic dipeptide within the NIS C-terminus which mediates binding to the σ2 subunit of the Adaptor Protein 2 (AP2) heterotetramer. We discovered that the FDA-approved drug chloroquine modulates NIS accumulation at the PM in a functional manner that is AP2 dependent. In vivo, chloroquine treatment of BALB/c mice significantly enhanced thyroidal uptake of 99mTc pertechnetate in combination with the histone deacetylase (HDAC) inhibitor SAHA, accompanied by increased thyroidal NIS mRNA. Bioinformatic analyses validated the clinical relevance of AP2 genes with disease-free survival in RAI-treated DTC, enabling construction of an AP2 gene-related risk score classifier for predicting recurrence. We propose that NIS internalisation is orchestrated by the interaction of a C-terminal diacidic motif with AP2σ2, together with the proto-oncogene PBF acting via AP2μ2. Given that NIS internalisation was specifically druggable in vivo, our data provide new translatable potential for improving RAI therapy using FDA-approved drugs in patients with aggressive thyroid cancer. Summary We delineate the role of endocytic genes in regulating NIS activity at the plasma membrane and highlight the potential for systemic targeting of endocytosis to enhance radioiodine effectiveness in radioiodine-refractory cancer cells.
Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks

While artificial machine learning systems achieve superhuman performance in specific tasks such as language processing, image and video recognition, they do so use extremely large datasets and huge amounts of power. On the other hand, the brain remains superior in several cognitively challenging tasks while operating with the energy of a small lightbulb. We use a biologically constrained spiking neural network model to explore how the neural tissue achieves such high efficiency and assess its learning capacity on discrimination tasks. We found that synaptic turnover, a form of structural plasticity, which is the ability of the brain to form and eliminate synapses continuously, increases both the speed and the performance of our network on all tasks tested. Moreover, it allows accurate learning using a smaller number of examples. Importantly, these improvements are most significant under conditions of resource scarcity, such as when the number of trainable parameters is halved and when the task difficulty is increased. Our findings provide new insights into the mechanisms that underlie efficient learning in the brain and can inspire the development of more efficient and flexible machine learning algorithms.
Neo-functionalization and co-option of Pif genes facilitate the evolution of a novel shell microstructure in oysters

Molluscan shell, composed of a diverse range of architectures and microstructures, is a classic model system to study the relationships between molecular evolution and biomineralized structure formation. The shells of oysters differ from those of other molluscs by possessing a novel microstructure, chalky calcite, which facilitates adaptation to the sessile lifestyle. However, the genetic basis and evolutionary origin of this adaptive innovation remain largely unknown. Here, we present the first chromosome-level genome and shell proteomes of the Iwagaki oyster Crassostrea nippona. Multi-omic integrative analyses revealed that independently evolved and co-opted genes as well as lineage-specific domains are involved in the formation of chalky layer in the oysters. Rapid mineralization involving chalky calcite are essential for reconstruction of the shell. Importantly, von Willebrand factor type A and chitin-binding domains are identified as basic members of molluscan biomineralization toolkit. We show that the well-known Pif shared a common origin in the last common ancestor of Bilateria. Furthermore, Pif and LamG3 genes acquire new genetic function for shell mineralization in bivalves and the chalky layer formation in oysters through a combination of gene duplication and domain reorganization. Our findings highlight neo-functionalization as a crucial mechanism for shell diversity, which may be applied more widely for studies on the evolution of metazoan biomineralization. This study also has potential implications for material science and biomimetic research.