Reviewerly/exHarmony huggingface dataset:
Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

In general, physicians make a preliminary diagnosis based on patients' admission narratives and admission conditions, largely depending on their experiences and professional knowledge. An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians, particularly in the shortage of medical resources. Despite its great value, little work has been conducted on this diagnosis method. Thus, in this study, we propose a fusion model that integrates the semantic and symptom features contained in the clinical text. The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network. The symptom concepts, recognized from the input text, are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases. Finally, two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code. Model training and evaluation are performed on a public clinical dataset. The results show that both fusion strategies achieved a promising performance, in which the best performance obtained a top-3 accuracy of 0.7412.
What makes you unique?

This paper proposes a uniqueness Shapley measure to compare the extent to which different variables are able to identify a subject. Revealing the value of a variable on subject t shrinks the set of possible subjects that t could be. The extent of the shrinkage depends on which other variables have also been revealed. We use Shapley value to combine all of the reductions in log cardinality due to revealing a variable after some subset of the other variables has been revealed. This uniqueness Shapley measure can be aggregated over subjects where it becomes a weighted sum of conditional entropies. Aggregation over subsets of subjects can address questions like how identifying is age for people of a given zip code. Such aggregates have a corresponding expression in terms of cross entropies. We use uniqueness Shapley to investigate the differential effects of revealing variables from the North Carolina voter registration rolls and in identifying anomalous solar flares. An enormous speedup (approaching 2000 fold in one example) is obtained by using the all dimension trees of Moore and Lee (1998) to store the cardinalities we need.
Role of cybersecurity and Blockchain in battlefield of things

The Internet of Things is an essential component in the growth of an ecosystem that enables quick and precise judgments to be made for communication on the battleground. The usage of the battlefield of things (BoT) is, however, subject to several restrictions for a variety of reasons. There is a potential for instances of replay, data manipulation, breaches of privacy, and other similar occurrences. As a direct result of this, the implementation of a security mechanism to protect the communication that occurs within BoT has turned into an absolute requirement. To this aim, we propose a blockchain‐based solution that is both safe and private for use in communications inside the BoT ecosystem. In addition, research is conducted on the benefits of integrating blockchain technology and cybersecurity into BoT application implementations. This work elaborates on the importance of integrating cybersecurity and blockchain‐based tools, techniques and methodologies for BoT.
Mineralogical and sulfur isotopic characteristics of Archean greenstone belt‐hosted gold mineralization at the Tau deposit of the Mupane gold mine, Botswana

Abstract The Mupane gold deposit, which is one of the numerous gold occurrences in the Tati Greenstone Belt in the northeastern part of Botswana, consists of four orebodies, namely Tau, Tawana, Kwena, and Tholo deposits. The present research, which focuses on the genesis of the Tau deposit, was based on ore petrography, mineral chemistry of sulfides, and sulfur isotope data. Mineralogical characteristics of the host rocks indicate that banded iron formation at the Tau deposit includes iron oxides (magnetite), carbonates (siderite and ankerite), silicates (chlorite and amphibole), and sulfides (arsenopyrite and pyrrhotite). The deposit features arsenopyrite‐rich zones associated with biotite‐chlorite veins, which are indicative of the precipitation of arsenopyrite concomitant with potassic alteration. The replacement of magnetite by pyrrhotite in some samples suggests that sulfidation was likely the dominant gold precipitation mechanism because it is considered to have destabilized gold‐thiocomplexes in the ore‐forming fluids. Based on textural relationships and chemical composition, arsenopyrite is interpreted to reflect two generations. Arsenopyrite 1 is possibly early in origin, sieve textured with abundant inclusions of pyrrhotite. Arsenopyrite 1 was then overgrown by late arsenopyrite 2 with no porous textures and rare inclusions of pyrrhotite. Gold mineralization was initiated by focused fluid flow and sulfidation of the oxide facies banded iron formation, leading to an epigenetic gold mineralization. The mineralogical assemblages, textures, and mineral chemistry data at the Tau gold deposit revealed two‐stage gold mineralizations commencing with the deposition of invisible gold in arsenopyrite 1 followed by the later formation of native gold during hydrothermal alteration and post‐depositional recrystallization of arsenopyrite 1. Laser ablation inductively coupled plasma mass spectrometric analysis of arsenopyrite from the Tau deposit revealed that the hydrothermal event responsible for the formation of late native gold also affected the distribution of other trace elements within the grains as evidenced by varying trace elements contents in arsenopyrite 1 and arsenopyrite 2. The range of δ 34 S of gold‐bearing assemblages from the Tau deposit is restricted from +1.6 to +3.9‰, which is typical of Archean orogenic gold deposits and indicates that overall reduced hydrothermal conditions prevailed during the gold mineralization process at the Tau deposit. The results from this study suggest that gold mineralization involved multi‐processes such as sulfidation, metamorphism, deformation, hydrothermal alteration, and gold remobilization.
Configuration Path Control

Reinforcement learning methods often produce brittle policies — policies that perform well during training, but generalize poorly beyond their direct training experience, thus becoming unstable under small disturbances. To address this issue, we propose a method for stabilizing a control policy in the space of configuration paths. It is applied post-training and relies purely on the data produced during training, as well as on an instantaneous control-matrix estimation. The approach is evaluated empirically on a planar bipedal walker subjected to a variety of perturbations. The control policies obtained via reinforcement learning are compared against their stabilized counterparts. Across different experiments, we find two- to four-fold increase in stability, when measured in terms of the perturbation amplitudes. We also provide a zero-dynamics interpretation of our approach.
An outlier-robust smoothness-based graph learning approach

Graph learning (GL) is a tool for finding direct relationships between the nodes of a network, and hence, inferring the graph topology from the data. Recently, many GL algorithms have been proposed in the field of graph signal processing, which are based on smoothness of the graph signals on the learned graph. However, although it is possible for the input graph signals to be contaminated by outliers, for example due to sensor failures or temporary faulty information records, existing techniques are very vulnerable to outliers. So, the goal is to infer a graph topology to be, as much as possible, insensitive to this kind of data corruptions. To this aim, due to the sparse nature of outlier data, we propose a new approach for robustifying GL algorithms by incorporating L1-norm or squared L1-norm terms into the objective function of smoothness based GL methods, yielding to a non-convex minimization problem. A novel iterative minimization method is introduced to solve the resulting non-convex problem. Moreover, the convergence of the algorithm is established despite of its non-convex nature. In simulations, the high performance of the proposed algorithm is demonstrated in presence of a considerably large amount of outliers.
Avoiding Research Tribal Wars Using Taxonomies

Every scientific domain benefits from a unified understanding and categorization of terms. This article highlights lessons learned from several years of taxonomy and terminology research in a cybersecurity domain.
Cohesive Subgraph Discovery Over Uncertain Bipartite Graphs

In this article, we propose the <inline-formula><tex-math notation="LaTeX">$(\alpha,\beta,\eta)$</tex-math></inline-formula> -core model, which is the first cohesive subgraph model on uncertain bipartite graphs. To capture the uncertainty of relationships/edges, <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -degree is adopted to measure the vertex engagement level, which is the largest integer <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> such that the probability of a vertex having at least <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> neighbors is not less than <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> . Given degree constraints <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> , and a probability threshold <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> , the <inline-formula><tex-math notation="LaTeX">$(\alpha,\beta,\eta)$</tex-math></inline-formula> -core requires that each vertex on the upper or lower level have <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -degree no less than <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> , respectively. An <inline-formula><tex-math notation="LaTeX">$(\alpha,\beta,\eta)$</tex-math></inline-formula> -core can be obtained by iteratively removing the vertices with <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -degrees below the degree constraints. Apart from the online computation algorithm, we propose a probability-aware index to strike a balance between time and space costs. To efficiently build such an index, we design a top-down index construction algorithm to allow computation sharing. Then, we show how to parallelize our query algorithms and index construction algorithms. In addition, we study community search on uncertain bipartite graphs by adopting the <inline-formula><tex-math notation="LaTeX">$(\alpha,\beta,\eta)$</tex-math></inline-formula> -core model. Extensive experiments are conducted on 13 datasets to validate the efficiency and effectiveness of our proposed techniques.
Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE 2 O 3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE 2 O 3 content. The content distribution map of RE 2 O 3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE 2 O 3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible.
A visually meaningful image encryption scheme based on a 5D chaotic map and deep learning

The VMIE techniques follow two steps: pre-encryption and embedding. In the first phase of the proposed method, the 5D chaotic map and zig-zag transformation are used for encryption, which provides higher security. In the second phase, the U-Net architecture-based encoder is implemented to hide the encrypted image in the reference image. Furthermore, an efficient decoder is designed to extract the encrypted image from the reference image. The exquisiteness of deep learning in image embedding is that it hides an image into the same size reference image without degrading the image quality. Moreover, to validate the proposed method, some standard security and image quality-related parameters are analysed. The results in terms of image security and quality parameters compared to the existing VMIE reveal that the proposed method is highly secure and efficient for imperceptible image communication and better image quality at the receiver's end.
Weakly supervised prototype topic model with discriminative seed words: modifying the category prior by self-exploring supervised signals

Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words of categories, known as seed words. The recent generative dataless methods construct document-specific category priors by using seed word occurrences only; however, such category priors often contain very limited and even noisy supervised signals. To remedy this problem, in this paper, we propose a novel formulation of category prior. First, for each document, we consider its label membership degree by not only counting seed word occurrences, but also using a novel prototype scheme, which captures pseudo-nearest neighboring categories. Second, for each label, we consider its frequency prior knowledge of the corpus, which is also a discriminative knowledge for classification. By incorporating the proposed category prior into the previous generative dataless method, we suggest a novel generative dataless method, namely Weakly Supervised Prototype Topic Model. The experimental results on real-world datasets demonstrate that Wsptm outperforms the existing baseline methods.
Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry

In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which seriously misleads consumers’ purchasing decisions and undermines consumer trust. Until now, it has been a challenging task to accurately detect the “water army”, who could easily alter their behaviors or writing styles. The focus of this paper is on some collusive clues between members of the same social platform to propose a new graph model to detect the “water army”. First is the extraction of six kinds of user collusive relationships from two aspects: user content and user behavior. Further, the use of three aggregation methods on such collusive relationships generates a user collusive relationship factor (CRF), which is then used as the edge weight value in our graph-based water army detection model. In the combination of the graph grouping method and evaluation rules on candidate subgraphs, the graph model effectively detects multiple collusive groups automatically. The experimental results based on the Mafengwo platform show that the CRF generated from the coefficient of variation (CV) method demonstrates the best performance in detecting collusive groups, which provides some practical reference for the detection of “water armies” in an e-commerce environment.
Teachers Perceptions on Early Childhood’s Traffic and Life Safety Education Program Using VR

The objective of this study is to verify the preventive effect of daily safety education on children by analyzing its influence on the improvement in their understanding, perceptions, and attitudes toward safety, when virtual reality (VR) is used. The use of VR-based tangible safety education is increasing. Thus, the effectiveness of VR stability education should be verified. This study was conducted for three months by selecting infants aged 4–5 years and their homeroom teachers enrolled in early childhood education institutions. After completing daily safety education using VR, questionnaires prepared by the teacher, comprising interviews and Q&amp;A with the infants, were analyzed. Descriptive statistics, F-test, ANOVA, and multiple regression were used for data analysis. It was found that (a) the preference for audiovisual and VR as effective teaching methods in early childhood educational institutions was high; (b) safety education using VR showed a positive correlation with children’s traffic safety perceptions and attitudes; and (c) immersion was found to be a significant factor influencing the perception and attitude of safety education. These findings suggest that various methods to implement and utilize safety education programs using VR should be explored. Finally, VR safety education improved preventive attitudes toward the safety of children.
Fast Adaptive Binarization of QR Code Images for Automatic Sorting in Logistics Systems

With the development of technology, QR codes play an important role in information exchange. In order to work out the problem of uneven illumination in automatic sorting in logistics systems, an adaptive method in binarization is presented. The proposed method defines the block windows’ size adaptively for local binarization based on the traits of the QR code. It takes advantage of integral images to calculate the sum of gray values in a block. The method can binarize the QR code with high quality and speed under uneven illumination. Compared with several existing algorithms, it is shown that the proposed method is more effective. The experimental results validate that the proposed method has a higher recognition accuracy and is more efficient in binarization.
A Unified Lyapunov Framework for Finite-Sample Analysis of Reinforcement Learning Algorithms

Reinforcement learning (RL) is a paradigm where an agent learns to accomplish tasks by interacting with the environment, similar to how humans learn. RL is therefore viewed as a promising approach to achieve artificial intelligence, as evidenced by the remarkable empirical successes. However, many RL algorithms are theoretically not well-understood, especially in the setting where function approximation and off-policy sampling are employed. My thesis [1] aims at developing thorough theoretical understanding to the performance of various RL algorithms through finite-sample analysis. Since most of the RL algorithms are essentially stochastic approximation (SA) algorithms for solving variants of the Bellman equation, the first part of thesis is dedicated to the analysis of general SA involving a contraction operator, and under Markovian noise. We develop a Lyapunov approach where we construct a novel Lyapunov function called the generaled Moreau envelope. The results on SA enable us to establish finite-sample bounds of various RL algorithms in the tabular setting (cf. Part II of the thesis) and when using function approximation (cf. Part III of the thesis), which in turn provide theoretical insights to several important problems in the RL community, such as the efficiency of bootstrapping, the bias-variance trade-off in off-policy learning, and the stability of off-policy control. The main body of this document provides an overview of the contributions of my thesis.
SCDNet: Self-Calibrating Depth Network with Soft-Edge Reconstruction for Low-Light Image Enhancement

Captured low-light images typically suffer from low brightness, low contrast, and blurred details due to the scattering and absorption of light and limited lighting. To deal with these issues, we propose a self-calibrating depth network with soft-edge reconstruction for low-light image enhancement. Concretely, we first employ the soft edge reconstruction module to reconstruct the soft edge of the input image and extract the texture and detail information of the image. Afterward, we explore the convergence properties of each input via the self-calibration module to significantly improve the computational effectiveness of the method and gradually correct the inputs at each subsequent level. Finally, the low-light image is iteratively enhanced by an iterative light enhancement curve to obtain a high-quality image. Extensive experiments demonstrate that our SCDNet visually enhances the brightness and contrast, restores the actual color, and makes the image more in line with the characteristics of the human eye vision system. Meanwhile, our SCDNet outperforms the compared methods in some qualitative and quantitative metrics.
Motion Sickness during Roll Motion: VR HMD View versus Monitor View

The purpose of this study was to investigate the effect of two views on motion sickness caused by low-frequency roll motion in the laboratory. Fifteen healthy male subjects participated in the study and were exposed to 30 min of 0.25 Hz roll oscillation at an angle of rotation (±5°). Subjects sat on a rigid seat with one of two visual scenes each session: (i) viewing 360° videos through virtual reality (VR) head-mounted display (HMD) device and (ii) reading articles on a monitor in a closed cabin. Ratings of motion sickness were obtained at 1 min intervals. The mean illness ratings of subjects for all visual conditions increased over the 30 min exposure to motion. There was significantly less sickness in the HMD condition than in the monitor condition. The findings suggest a beneficial effect of the HMD view on the severity of sickness. However, the HMD view had no effect on the sickness experienced by those vulnerable to sickness caused by exposure to motion or use of VR. It was concluded that the visual activity had a significant influence on motion sickness induced by 0.25 Hz roll oscillation with an angle of rotation (±5°), and the applications of VR could be implemented to further reduce motion sickness.