Reviewerly/exHarmony huggingface dataset:
Categorifying Non-Idempotent Intersection Types

Non-idempotent intersection types can be seen as a syntactic presentation of a well-known denotational semantics for the lambda-calculus, the category of sets and relations. Building on previous work, we present a categorification of this line of thought in the framework of the bang calculus, an untyped version of Levy’s call-by-push-value. We define a bicategorical model for the bang calculus, whose syntactic counterpart is a suitable category of types. In the framework of distributors, we introduce intersection type distributors, a bicategorical proof relevant refinement of relational semantics. Finally, we prove that intersection type distributors characterize normalization at depth 0.
Dayak Onion (Eleutherine palmifolia (L) Merr) as An Alternative Treatment in Early Detection of Dental Caries using Certainty Factor

Dayak onion plants are traditionally used by the Dayak tribe as a medicinal plant to treat dental caries. This plant contains compounds that can inhibit the growth of bacteria that cause dental caries. The public has not yet known about alternative dental caries treatment derived from Dayak onions. This is due to the lack of public knowledge about how to early diagnose dental caries and how to treat using Dayak onions. Expert systems with the Certainty Factor method can be used as a solution in diagnosing early dental caries. The data used in this study consisted of 20 symptoms of dental caries and 6 types of dental caries. This study shows the percentage level of confidence in the results of the initial diagnosis of the type of dental caries suffered by using the certainty factor method and the handling of the diagnosis using the Dayak plant as an initial treatment solution. The results of the accuracy-test showed that the early dental caries diagnosis system was working well.
Analysis of Mobile Based Software Development Model: Systematic Review

Software development has various methods, so the choice of software software method for a developer is very important in order to produce quality software according to standards. Based on this description, the importance of knowledge for a developer in choosing a development method, the authors will conduct a literature review in mobile software development methods. The choice of mobile software development method itself is due to the current mass shift among software providers from desktop and web to mobile. This systematic review aims to analyze a mobile-based software development model. In the compilation of this systematic review we use the new PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis). This review shows the predominant method used in the development of mobile-based male devices. The conclusion of this systematic review is that the most commonly used software development models in mobile software.
Privacy Preserved Cyber-Physical Searching for Information-Centric Intelligent Agriculture

Technological evolution has brought great changes to traditional industries. Nowadays farmers obtain great benefits such as intelligent production and convenient management from intelligent agriculture. Nevertheless, there still exists many flaws in current solutions. Deficiencies in energy management, data transmission and privacy preservation are supposed to be solved. In this paper, the privacy preserved cyber-physical searching framework is proposed to tackle these problems. This four-layer framework can provide a convenient way for farmers to manage production by simply searching for information they interested in. The data source layer contains many sensors for data collection. The data aggregation layer consists of data aggregation nodes deployed in the farms. The data transfer layer employs drones for retrieving data from data aggregation nodes. The cloud control layer is responsible for searching requests processing and privacy preservation. To realize better data collection and searching. The information centric network is introduced into this framework. Moreover, some methods are employed for privacy preservation. The simulation results show the feasibility of this framework.
A Survey of Image Dehazing Algorithm Based on Retinex Theory

With the development of computer vision systems, image enhancement has become an important research direction in computer vision. Image defogging technology is widely used in the systems of satellite remote sensing, aerial photography, target recognition and outdoor monitoring. People use image defogging technology to enhance or repair those low-quality pictures affected by fog and haze to improve visual effects and facilitate later image processing. This paper introduces the application field of image enhancement technology, and introduces the classic defogging algorithm in image defogging technology: the Retinex algorithm. In this paper, the algorithm is used to defog the pictures affected by smog in different scenes, and the advantages and disadvantages of the Retinex defogging algorithm are discussed according to the enhanced effect. Finally, this paper analyzes the effectiveness and practicality of using the Retinex algorithm for image enhancement in different scenes.
Fast and Universal Inter-Slice Handover Authentication with Privacy Protection in 5G Network

In a 5G network-sliced environment, mobility management introduces a new form of handover called inter-slice handover among network slices. Users can change their slices as their preferences or requirements vary over time. However, existing handover-authentication mechanisms cannot support inter-slice handover because of the fine-grained demand among network slice services, which could cause challenging issues, such as the compromise of service quality, anonymity, and universality. In this paper, we address these issues by introducing a fast and universal inter-slice (FUIS) handover authentication framework based on blockchain, chameleon hash, and ring signature. To address these issues, we introduce an anonymous service-oriented authentication protocol with a key agreement for inter-slice handover by constructing an anonymous ticket with the trapdoor collision property of chameleon hash functions. In order to reduce the computation overhead of the user side in the process of authentication, a privacy-preserving ticket validation with a ring signature is designed to finish in the consensus phase of the blockchain in advance. Thanks to the edge computing capabilities in 5G, distributed edge nodes help to store the anonymous ticket information, which guarantees that the legal users can finish authentication swiftly during handover. Our scheme's performance is evaluated through simulation experiments to testify the efficiency and feasibility in a 5G network-sliced environment. The results show that compared to other authentication schemes of the same type, the overall inter-slice handover delay has been reduced by 97.94%.
Reduction of speckling noise of SAR Images using Dual Tree Complex Wavelet (DTCW) and Shearlet Transforms

The Synthetic Aperture Radar (SAR) images are difficult to interpret owing to the multiplicative speckle noise from coherent acquisition systems.Therefore, despeckling of SAR images always plays a primary pre-processing task in SAR image processing.There are many methods using various spatial domain filters and transform domain algorithms which focus on speckle reduction but not all methods can preserve the image edge features.The article suggests a de-speckling algorithm via sparse representation using combined Shearlet Transform and DTCW transform which possess direction selectivity and shift invariant property.The experimental results, the suggested method has better PSNR, ENL, and EPI values than the existing state of art methods.The proposed methodology not only preserves the edges, also improves visual effect by enhancing the texture of SAR images.
Next point-of-interest recommendation by sequential feature mining and public preference awareness

With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI recommendation, since human movement exhibits sequential patterns in LBSNs. However, due to the check-ins’ sparsity problem, exploiting sequential patterns in next POI recommendation is a challenging issue, which makes the learned sequential patterns unreliable. Inspired by the fact that auxiliary information can be incorporated to alleviate this situation, in this paper, we model sequential transition based on both item-wise check-in sequences and region-wise spatial information. Besides, we propose an attention-aware recurrent neural network (ATTRNN) to learn the contribution of different time steps. Furthermore, considering users’ decision-making is influenced by public’s common preference to some extent, we design a novel framework, namely HSP (short for “Hybrid model based on Sequential feature mining and Public preference awareness”), to recommend POIs for a given user. We conduct a comprehensive performance evaluation for HSP on two real-world datasets. Experimental results demonstrate that compared to other state-of-the-art techniques, the proposed HSP achieves significantly improvements.
MSDAN: Multi-Scale Self-Attention Unsupervised Domain Adaptation Network for Thyroid Ultrasound Images

With the maturity of artificial intelligence, AI-aided diagnosis technology is gradually widely applied in clinical medicine. However, for the same pathological tissue, medical images produced by different types of instruments usually possess different data distributions. Because of the domain shift phenomenon, AI-aided diagnosis cannot accurately diagnose medical images in other domains, which is a waste of precious medical images. This paper proposes a Multi-Scale Self-Attention Unsupervised Domain Adaptive framework (MSDAN), which consists of three modules. First, the multi-scale framework constrains the source domain features and target domain features by optimizing adversarial losses with different level features. Second, the mix-up discriminator extracts latent spatial features by mixing up source domain and target domain features. Finally, MSDAN learns the geometric information of the pathological tissues in medical images through the self-attention module, thereby improving the transfer effect of the semantic information in medical images. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity, especially for thyroid ultrasound images.
Organization Evolution of Fuzzy System Based on Financial Risk Degree of Commercial Banks

After the market-oriented reform of China’s financial industry, there have been some problems in financial risk assessment. In recent years, commercial bank finance has made rapid development, but on the whole, the financial risk assessment of commercial banks is still the weakest link in the Chinese financial system. This experiment selects data from state-owned commercial banks and foreign-funded commercial banks. Through the analysis and deconstruction of the macroenvironment, participants, and business models, this paper systematically combines the factors influencing the financial risk of commercial banks, which can identify the main sources of financial risk in this complex way of financing and clarify the effects of the transfer of financial risk between different participants. Based on this, the paper studies the differences between the assets and liabilities between banks on the risk-taking of banks and the reform of the organizational evolution of fuzzy system. According to the application scenarios and actual needs of commercial banks’ financial risks, the entropy weight analysis method is used to reflect the weight of indicators by the difference degree of observed index values. The information quantity of indicators is measured to ensure that the established indicators can reflect most of the initial information. The experimental results show that, compared with state-owned banks, the proportion of foreign banks’ assets in 2018 is very small. The highest value of public debt assets is 9.2 billion yuan, followed by financial institutions with 2.58 billion yuan, and deposit institutions with 280 million yuan. The central bank has no debt amount.
Using 360-Video Virtual Reality to Influence Caregiver Emotions and Behaviors for Childhood Literacy

Through the design and exploratory evaluation of a narrative-based 360-video virtual reality experience, the authors aimed at building empathy in adults towards children who experience challenges in early literacy. This contributes to a limited literature on VR empathetic design by specifically studying caregivers in relation to reading difficulties and utilizing a low-cost immersive medium. This research performed a quasi-experimental pilot study following a pretest-posttest design with 27 participants, collecting measures such as participant empathy, anxiety, immersion, and emotional reactions. This paper explored changes in pre-post measures, correlations between variables, and possible explanations for the observed results. The VR experience increased positive caregiver attitudes towards struggling readers. Participants who reported a high degree of emotional reactions showed increased willingness to donate to help reading difficulties. Participants with teaching experience or with lower starting empathy scores were less likely to be affected.
Evaluation of Information Value for Solar Power Plants in Market Environment

Uncertainty is not negligible in power systems, it affects the generation, transmission and utilization of electricity. Especially in electricity markets, the randomness and variability of generation or load will cause notable forecast errors and lead to economic losses of market participants. Due to the data explosion in recent few years, data have gradually become valuable information that can be mined, analyzed, and applied. Meanwhile, the utility that more abundant and accurate information could eliminate uncertainty is emerging. In this paper, solar power plants (SPPs) are selected as the object, the general mechanism in electricity markets is briefly introduced and the corresponding market optimization model is constructed. With the optimization model analytically solved, the impact of uncertainty is analyzed and the expressions of information value are derived. Besides, uniform, normal, logistic, and Laplace distribution are used in the evaluation of information value and compared. Finally, the case study is conducted to verify the theoretical results of information value and illustrate its significance.
Clustering Based Approach to Enhance Association Rule Mining

Association rule mining algorithms such as Apriori and FPGrowth are extensively being used in the retail industry to uncover consumer buying patterns. However, the scalability of these algorithms to deal with the voraciously increasing data is the major challenge. This research presents a novel Clustering based approach by reducing the dataset size as a solution. The products are clustered based on their frequency and price. Another important aspect of this study is to find interesting rules by performing differential market basket analysis to identify association rules which are likely ignored in the trivial approach. When using a cluster-based approach, it is observed that the same set of rules can be generated by using only 7% of the total 16210 items, which in turn directly contributes to reducing the processing overheads and thus reducing the computation time. Furthermore, results obtained from differential market basket analysis have highlighted a few interesting rules which were missing from the original set of rules. A clustering-based approach used in this study not only consists of frequent items but also considers their contribution to the overall revenue generation by considering its price. In addition to this, the least contributing product exclusion rate is also improved from 45% to 93 %. These results evidently suggest that the computation cost can be significantly reduced, and more accurate rules can be generated by applying differential market basket analysis.
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity

Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function approximation and Markovian sampling, and the algorithm is shown to achieve an ϵ-stationary point with a sample complexity in the order of O(ϵ−3). Such a high sample complexity is due to the large variance induced by the Markovian samples. In this paper, we propose a variance-reduced Greedy-GQ (VR-Greedy-GQ) algorithm for off-policy optimal control. In particular, the algorithm applies the SVRG-based variance reduction scheme to reduce the stochastic variance of the two time-scale updates. We study the finite-time convergence of VR-Greedy-GQ under linear function approximation and Markovian sampling and show that the algorithm achieves a much smaller bias and variance error than the original Greedy-GQ. In particular, we prove that VR-Greedy-GQ achieves an improved sample complexity that is in the order of O(ϵ−2). We further compare the performance of VR-Greedy-GQ with that of Greedy-GQ in various RL experiments to corroborate our theoretical findings.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation

Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillation has been used to successfully regularize the student by transferring knowledge from the teacher. However, the teacher and the student are trained on the same dataset and tend to learn similar feature representations, whereas the most general knowledge should be found through differences. The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher. To this end, we propose a novel training framework, where the learning of general knowledge is more in line with the idea of reaching consensus, i.e., finding common knowledge that is beneficial to different yet all datasets through diversified learning partners. Concretely, the training task is divided into a group of subtasks with the same number of students. Each student assigned to one subtask not only is optimized on the allocated subtask but also imitates multi-view feature representation aggregated from other students (i.e., student peers), which induces students to capture common knowledge among different subtasks and alleviates the over-fitting of students on the allocated subtasks. To further enhance generalization, we extend the unidirectional distillation to the bidirectional distillation that encourages the student and its student peers to co-evolve by exchanging complementary knowledge with each other. Empirical results and analysis demonstrate that our training framework effectively improves the model generalization without sacrificing training efficiency.
Accurate estimation of total intracranial volume in MRI using a multi-tasked image-to-image translation network

Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegenerative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.
Construction of Personalized Education Model for College Students Driven by Big Data and Artificial Intelligence

Abstract The combination of big data, artificial intelligence and education has become a new mode of university education reform in China. This paper combs the help of big data and artificial intelligence for the individualized teaching of university teachers, the individualized management of college educators and the individualized learning of college students through the mining of common and individual data, the equal emphasis of process and result, and the combination of task learning and autonomous learning. The model of individualized education for college students is constructed, which is mainly composed of two sub-models: the individualized learning model of college students and the individualized education model of college educators. Thees two models promote each other and act together on the process of college students’ education.
Self-supervised monocular depth estimation in gastroendoscopy using GAN-augmented images

Gastroendoscopy is the golden standard procedure that enables medical doctors to investigate the inside of a patient's stomach. Monocular depth estimation from an endoscopic image enables the simultaneous acquisition of RGB and depth data, which can boost the capability of the endoscopy for various potential diagnostic applications, such as the RGB-D data acquisition toward whole stomach 3D reconstruction for lesion localization and local view expansion for lesion inspection. Therefore, deep-learning-based approaches are gaining traction to provide depth information in monocular endoscopy. Since it is very difficult to obtain ground-truth RGB and depth image pairs in clinical settings, computer-generated (CG) data is usually used for training the depth estimation network. However, CG data has a limitation to generate realistic RGB and depth data. In this paper, we propose a novel data generation strategy for self-supervised training to predict the depth in gastroendoscopy. To obtain dense reference depth data for training, we first reconstruct a whole stomach 3D model by exploiting chromoendoscopic images sprayed with indigo carmine (IC) blue dye. We then generate virtual no-IC images from chromoendoscopic images using CycleGAN to make our depth estimation network applicable to general endoscopic images without IC dye. We experimentally demonstrate that our proposed approach achieves plausible depth prediction on both chromoendoscopic and general white-light endoscopic images.