Reviewerly/exHarmony huggingface dataset:
Remarks on fixed point assertions in digital topology, 3

We continue the work of [5] and [3], in which are considered papers in the literature that discuss fixed point assertions in digital topology. We discuss published assertions that are incorrect or incorrectly proven; that are severely limited or reduce to triviality under "usual" conditions; or that we improve upon.
Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.
Privacy‐preserving quality control of neuroimaging datasets in federated environments

Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP-dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP-dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.
Universal product learning with errors: A new variant of LWE for lattice-based cryptography

The middle product learning with errors problem (MP-LWE) is a variant of the learning with errors problem (LWE). The public-key cryptography under MP-LWE assumption gets an equilibrium between “efficiency” and “security”. However, the hardness of MP-LWE has not been sufficiently explored since its introduction. So in this paper, we focus on the hardness of MP-LWE. By the axiomatic method, we first investigate the essential attributes about the middle product in MP-LWE. Then we present the formal definition of universal product and the corresponding universal product learning with errors problem (UP-LWE). In this sense, MP-LWE can be viewed as an instance of UP-LWE. Then we proved that for more polynomials than those have been proved in the literature, PLWE(f) can be reduced to MP-LWE. In other words, we improve the result about the hardness of MP-LWE. Furthermore, we define the asymmetric middle product which can be viewed as a construction of universal product. The corresponding UP-LWE is discussed. It provides much more flexible parameter setting than MP-LWE.
Tell, Imagine, and Search: End-to-end Learning for Composing Text and Image to Image Retrieval

Composing Text and Image to Image Retrieval ( CTI-IR ) is an emerging task in computer vision, which allows retrieving images relevant to a query image with text describing desired modifications to the query image. Most conventional cross-modal retrieval approaches usually take one modality data as the query to retrieve relevant data of another modality. Different from the existing methods, in this article, we propose an end-to-end trainable network for simultaneous image generation and CTI-IR . The proposed model is based on Generative Adversarial Network (GAN) and enjoys several merits. First, it can learn a generative and discriminative feature for the query (a query image with text description) by jointly training a generative model and a retrieval model. Second, our model can automatically manipulate the visual features of the reference image in terms of the text description by the adversarial learning between the synthesized image and target image. Third, global-local collaborative discriminators and attention-based generators are exploited, allowing our approach to focus on both the global and local differences between the query image and the target image. As a result, the semantic consistency and fine-grained details of the generated images can be better enhanced in our model. The generated image can also be used to interpret and empower our retrieval model. Quantitative and qualitative evaluations on three benchmark datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.
Multi-Modality Reconstruction Attention and Difference Enhancement Network for Brain MRI Image Segmentation

An important prerequisite for brain disease diagnosis is to segment brain tissues of Magnetic Resonance Imaging (MRI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). To improve performance, we propose a Multi-modality Reconstruction Attention and Difference Enhancement Network (MRADE-Net). We first stack three inputs of multiple MRI modalities along axial, sagittal, and coronal axes to form three enlarged volumes. Then we adopt global average pooling along each axis, fully connected layers, and activation functions to produce three orthogonal 2D coefficient maps, which are used to reconstruct a 3D attention map for weighting the three inputs. These weighted inputs are added together to generate a feature map of Multi-modality Reconstruction Attention (MRA). Similarly, we present Single-modality Reconstruction Attention (SRA) for improving feature representation abilities in middle stages. In addition, the difference of encoding features between adjacent layers is used to compensate the loss of spatial information caused by down-sampling. Experimental results show that the proposed approach is more effective than the existing state-of-the-art segmentation methods and its performance is verified on several datasets.
Learning From Imbalanced Data With Deep Density Hybrid Sampling

Learning from imbalanced data is an important and challenging topic in machine learning. Many works have devised methods to cope with imbalanced data, but most methods only consider minority or majority classes without considering the relationship between the two classes. In addition, many synthetic minority oversampling technique-based methods generate synthetic samples from the original feature space and use the Euclidean distance to search for the nearest neighbors. However, the Euclidean distance is not a precise distance metric in a high-dimensional space. This article proposes a novel method, called deep density hybrid sampling (DDHS), to address imbalanced data problems. The proposed method learns an embedding network to project the data samples into a low-dimensional separable latent space. The goal is to preserve class proximity during data projection, and we use within-class and between-class concepts to devise loss functions. We propose to use density as a criterion to select minority and majority samples. Subsequently, we apply a feature-level approach to the selected minority samples and generate diverse and valid synthetic samples for the minority class. This work conducts extensive experiments to assess our proposed method and compare it with several methods. The experimental results show that the proposed method can yield promising and stable results. The proposed method is a data-level algorithm, and we combine the proposed method with the boosting technique to develop a method called DDHS-boosting. We compare DDHS-boosting with several ensemble methods, and DDHS-boosting shows promising results.
BCNPYLIB CHAT BOT: The artificial intelligence Chatbot for library services in college of nursing

Although in the present, there is email notification, a posting, and a phone notification that connect directly with the user, they are insufficient to keep the user interested, which becomes a problem impacting the service users' library satisfaction. The user eventually forgets what they have been told since they likely tend to just skim through a notification [1]. The researcher experimented with using AI technology to answer questions from library users via the LINE application in order to meet their requirements and improve the quality of library services. Regarding AI technology, when users text a message that matches the response requirements, it can answer the user's questions about the location of the book, book categorization, opening hours, and other relevant questions, which frees up the librarian's time to focus on other tasks. AI answering services can gather users' requests and utilize them to enhance the development of answers. Users can also interact with the system without time limitations since AI technology is always acting as a librarian [2] to meet user satisfaction. In regard to the BCNPYLIB CHAT BOT experiment, it was discovered that the satisfaction assessment results encouraged learning (libraries), which led to an improvement in the quality of library service management. A library subtopic and an assessment list of “consistent notification of information related to the service, new activities, and information resources of the library” demonstrated a significant increase of 0.45 percent among nursing students in years 1-4 and nursing teachers, leading to the development of the quality of library service management. The developed system will contribute to user satisfaction with the library's services in the future
Leveraging structural properties of source code graphs for just-in-time bug prediction

The most common use of data visualization is to minimize the complexity for proper understanding. A graph is one of the most commonly used representations for understanding relational data. It produces a simplified representation of data that is challenging to comprehend if kept in a textual format. In this study, we propose a methodology to utilize the relational properties of source code in the form of a graph to identify Just-in-Time (JIT) bug prediction in software systems during different revisions of software evolution and maintenance. We presented a method to convert the source codes of commit patches to equivalent graph representations and named it Source Code Graph (SCG). To understand and compare multiple source code graphs, we extracted several structural properties of these .graphs, such as the density, number of cycles, nodes, edges, etc. We then utilized the attribute values of those SCGs to visualize and detect buggy software commits. We process more than 246 K software commits from 12 subject systems in this investigation. Our investigation on these 12 open-source software projects written in C++ and Java programming languages shows that if we combine the features from SCG with conventional features used in similar studies, we will get the increased performance of Machine Learning (ML) based buggy commit detection models. We also find the increase of F1 Scores in predicting buggy and non-buggy commits statistically significant using the Wilcoxon Signed Rank Test. Since SCG-based feature values represent the style or structural properties of source code updates or changes in the software system, it suggests the importance of careful maintenance of source code style or structure for keeping a software system bug-free.
Interactive Re-ranking via Object Entropy-Guided Question Answering for Cross-Modal Image Retrieval

Cross-modal image-retrieval methods retrieve desired images from a query text by learning relationships between texts and images. Such a retrieval approach is one of the most effective ways of achieving the easiness of query preparation. Recent cross-modal image-retrieval methods are convenient and accurate when users input a query text that can be used to uniquely identify the desired image. However, in reality, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain desired images. To overcome these difficulties, in this study, we propose a novel interactive cross-modal image-retrieval method based on question answering. The proposed method analyzes candidate images and asks users questions to obtain information that can narrow down retrieval candidates. By only answering questions generated by the proposed method, users can reach their desired images, even when using an ambiguous query text. Experimental results show the proposed method’s effectiveness.
Evaluation of Contextual and Game-Based Training for Phishing Detection

Cybersecurity is a pressing matter, and a lot of the responsibility for cybersecurity is put on the individual user. The individual user is expected to engage in secure behavior by selecting good passwords, identifying malicious emails, and more. Typical support for users comes from Information Security Awareness Training (ISAT), which makes the effectiveness of ISAT a key cybersecurity issue. This paper presents an evaluation of how two promising methods for ISAT support users in acheiving secure behavior using a simulated experiment with 41 participants. The methods were game-based training, where users learn by playing a game, and Context-Based Micro-Training (CBMT), where users are presented with short information in a situation where the information is of direct relevance. Participants were asked to identify phishing emails while their behavior was monitored using eye-tracking technique. The research shows that both training methods can support users towards secure behavior and that CBMT does so to a higher degree than game-based training. The research further shows that most participants were susceptible to phishing, even after training, which suggests that training alone is insufficient to make users behave securely. Consequently, future research ideas, where training is combined with other support systems, are proposed.
Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.
Knowledge Mining: A Cross-disciplinary Survey

Abstract Knowledge mining is a widely active research area across disciplines such as natural language processing (NLP), data mining (DM), and machine learning (ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better understand such data and operate upon it to build applications. Each mentioned discipline has come up with an ample body of research, proposing different methods that can be applied to different data types. A significant number of surveys have been carried out to summarize research works in each discipline. However, no survey has presented a cross-disciplinary review where traits from different fields were exposed to further stimulate research ideas and to try to build bridges among these fields. In this work, we present such a survey.
Robust multi-view discriminant analysis with view-consistency

Multi-view discriminant analysis (MvDA) is a successful method to learn a single discriminant common space of multiple views. However, MvDA may encounter the robustness issue theoretically because of using F-norm as the metric. In this paper, a robust multi-view discriminant analysis with view-consistency is proposed by employing L1-norm as the metric, called as L1-MvDA-VC. where both inter-view and intra-view distances are characterized by L1-norm. The proposed L1-MvDA-VC not only can obtain discriminant common space, but also is robust to outliers. In addition, a simple and effective learning algorithm is designed for L1-MvDA-VC, and its convergence is proved theoretically. Experimental results on real data sets demonstrate that L1-MvDA-VC has better performance on contaminated data than that of MvDA, MvDA-VC, MvCCDA, and MULDA.
Structure Guided Deep Neural Network for Unsupervised Active Learning

Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches rely on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of the selected samples, and then take these selected samples as the representative ones for manual labeling. However, the data do not necessarily conform to the linear models in many real-world scenarios, and how to model nonlinearity of data often becomes the key point of unsupervised active learning. Moreover, the existing works often aim to well reconstruct the whole dataset, while ignore the important cluster structure, especially for imbalanced data. In this paper, we present a novel deep unsupervised active learning framework. The proposed method can explicitly learn a nonlinear embedding to map each input into a latent space via a deep neural network, and introduce a selection block to select the representative samples in the learnt latent space through a self-supervised learning strategy. In the selection block, we aim to not only preserve the global structure of the data, but also capture the cluster structure of the data in order to well handle the data imbalance issue during sample selection. Meanwhile, we take advantage of the clustering result to provide self-supervised information to guide the above processes. Finally, we attempt to preserve the local structure of the data, such that the data embedding becomes more precise and the model performance can be further improved. Extensive experimental results on several publicly available datasets clearly demonstrate the effectiveness of our method, compared with the state-of-the-arts.
Multi-Device Nutrition Control

Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human-computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users.
AB-ResUNet+: Improving Multiple Cardiovascular Structure Segmentation from Computed Tomography Angiography Images

Accurate segmentation of cardiovascular structures plays an important role in many clinical applications. Recently, fully convolutional networks (FCNs), led by the UNet architecture, have significantly improved the accuracy and speed of semantic segmentation tasks, greatly improving medical segmentation and analysis tasks. The UNet architecture makes heavy use of contextual information. However, useful channel features are not fully exploited. In this work, we present an improved UNet architecture that exploits residual learning, squeeze and excitation operations, Atrous Spatial Pyramid Pooling (ASPP), and the attention mechanism for accurate and effective segmentation of complex cardiovascular structures and name it AB-ResUNet+. The channel attention block is inserted into the skip connection to optimize the coding ability of each layer. The ASPP block is located at the bottom of the network and acts as a bridge between the encoder and decoder. This increases the field of view of the filters and allows them to include a wider context. The proposed AB-ResUNet+ is evaluated on eleven datasets of different cardiovascular structures, including coronary sinus (CS), descending aorta (DA), inferior vena cava (IVC), left atrial appendage (LAA), left atrial wall (LAW), papillary muscle (PM), posterior mitral leaflet (PML), proximal ascending aorta (PAA), pulmonary aorta (PA), right ventricular wall (RVW), and superior vena cava (SVC). Our experimental evaluations show that the proposed AB-ResUNet+ significantly outperforms the UNet, ResUNet, and ResUNet++ architecture by achieving higher values in terms of Dice coefficient and mIoU.