Reviewerly/exHarmony huggingface dataset:
Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice.The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text.TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions.The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization.This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.
Semi-Supervised Gated Spectral Convolution on a Directed Signed Network

A complex network is a powerful tool that enables a complex system in the real world to be represented as a network structure. Due to the heterogeneous edges and nodes implying rich semantic information, network representation has received considerable attention in both research and industrial domain. Over the recent years, the graph convolutional network (GCN) has provided a novel approach for learning network embeddings. However, this primarily supports undirected unsigned networks; that is, it cannot be directly applied to directed signed networks because it is challenging to effectively depict the direction and signs of edges in such models. In this paper, we therefore propose a method for semi-supervised gated spectral convolution in directed signed networks. We first extend the concept of the GCN to directed signed networks, which not only preserves the advantages of the traditional GCN but also properly describes the significance of the directions and signs of the edges. We then innovatively define sign (label) propagation rules in directed signed networks, rendering the networks semi-supervised. Furthermore, we enhance the balance theory to constrain the process of sign propagation to obtain network embedding with better interpretability. To satisfy the needs of large-scale complex networks, we propose a gating mechanism to adaptively forget sign information, which significantly reduces the time-space complexity of the sign propagation process. Finally, we compare the proposed method with state-of-the-art baselines using four real-world data sets for the classical link sign prediction task. Experimental results demonstrate that the proposed method is competitive.
Determining How Social Media Affects Learning English: An Investigation of Mobile Applications Instagram and Snap Chat in TESOL Classroom

The purpose of this research paper was to explore the effects of social media in learning English speaking and reading skills.Although social media platforms like Facebook, Instagram, Twitter, and Snapchat, are mostly used socially and for marketing, they also have a significant impact on learning English.In determining how social media affects learning English, this paper will be focusing on mobile Apps; Snapchat and Instagram.The paper investigates how user attitude, experience, and perception influence the use of Instagram and Snapchat in learning the English language in a classroom.The research was done on different databases, including Academic Search Complete, Education Source, ERIC, Library, Information Science, and Technology Abstracts, and Professional Development Collection.Research shows that learners with social media accounts are less enthusiastic about learning English, even if it means recreational reading.This differs from the learners without user accounts whose attitude of learning English speaking skills is positive.This research paper concludes that, in comparison to Snapchat, Instagram is a more effective social media platform for engaging and learning of the English language.This contributes to a greater understanding of the English language and its culture while providing more practical knowledge and promoting interactions.However, more research is needed to determine the impact of Snapchat on learning English speaking skills.
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints

Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy.
Simulation of hepatic arteries and synthesis of 2D fluoroscopic Images for interventional imaging studies

Training of deep learning-based segmentation algorithms requires large datasets of annotated images. Obtaining large clinical datasets, particularly for interventional imaging, is difficult and annotating vessels manually in the images is very time-consuming. Simulated images can be used to supplement clinical datasets. We developed a technique for the simulation of realistic, anatomically- and physiologically-motivated hepatic artery trees and subsequent synthesis of fluoroscopic images. The proposed approach creates a network of main feeding arteries for each of the eight liver segments as defined by the Couinaud classification system. A constrained constructive optimization based approach was then used to connect a set of randomly generated endpoints within each segment to the corresponding feeding artery. Vessel curvature was created using cubic splines and the generated vasculature was inserted into the digital XCAT phantom. The simulated 2D fluoroscopic images were generated using ray tracing and included focal spot blur, detector blur and Poisson noise. The length ratio (1.1 ± 1.7) and two parameters from Murray’s law, branching angles (7.9 ± 7.7<sup>𝑜</sup> mean absolute difference from Murray’s law) and radius ratio (1.0 ± 0.1) of the generated vasculature were in accordance with values reported in literature (1.3, Murray’s law applied to branching angles, and 1.0 respectively). Simulated vasculature included main branches for each of the eight Couinaud segments, where 87% of all connected endpoints terminated in the same segment. Simulated 2D projection images were analyzed using a vessel phantom study with contrast-enhanced tubes (0.305-3.353 mm diameter). The normalized root mean squared difference between the measured and simulated vessel profiles averaged 3.5%. In conclusion, the proposed method provides realistic simulated fluoroscopic images of the liver vasculature and could prove useful for the training of machine learning based algorithms for vessel segmentation.
Plugging slurry (backfill) and surface cavity closure technology

The purpose of the study is to study mining and environmental emergencies associated with the formation of sinkholes of the earth’s surface over mining workings after the mass closure of coal mines and to develop a new method for eliminating the sinkhole. The study used a method of physical modeling, which investigated the influence of humidity on the deformation properties of burnt rocks used as a filler in the manufacture of plugging and filling suspensions, while establishing the dependence of the relative deformation of dump rocks on the load at their natural humidity and water saturation. The main results of the research are: a new method for eliminating dips in the earth’s surface over the inclined opening workings of the liquidated coal mines of the Eastern Donbass; formulations of plugging and filling suspensions used for layer-by-layer filling of sinkholes and providing non-shrink laying.
Guidance and surroundings awareness in outdoor handheld augmented reality

Handheld and wearable devices are becoming ubiquitous in our lives and augmented reality technology is stepping out of the laboratory environment and becoming ready to be used by anyone with portable devices. The success of augmented reality applications for pedestrians depends on different factors including a reliable guidance system and preventing risks. We show that different guidance systems can be supplementary to provide directions to a point of interest and offer clues that help the user find the augmented data when they get close to the location they have to visit. We tested the helpfulness of a map with the points of interest, an image preview of the next point of interest to visit, and an arrow showing the direction to it. The results show that the effectiveness of these guidance systems depend on the distance to the point of interest and the accuracy of the data obtained from the Global Positioning System. We also measured the total time that participants spent looking at the screen, as well as the perceived elapsed time as a measurement of real world dissociation. Finally, we discuss preliminary results to minimize the risk of accidents when using augmented reality applications in an outdoor urban environment.
Assessing Information Security Vulnerabilities and Threats to Implementing Security Mechanism and Security Policy Audit

In spite of the massive investment of money, time and efforts an organization devotes to growth and enhancement with continuous improvements of a sound information security strategy, the human factor is ultimately and eventually the one behind the keyboard.As a result, human beings remain the most vulnerable and weakest entity in the information security chain.A negligent and irresponsible in-house employee can be a threat even to the most breath-tight secure environment, an organization built to keep intruders away from unauthorized access.Information security is considered to be one of the most necessary and crucial issues of the field, with the rapid changes in the information technology that takes place every day and with the business models chasing it and trying to catch up, recently and for a while, it has become one of the most interesting fields to technology and business communities.
Real-Time Learner Classification Using Cognitive Score

Recommending and providing suitable learning materials to the learners according to their cognitive ability is important for effective learning. Assessing the cognitive load of a learner while studying a learning material can be helpful in assessing his/her intelligence and knowledge adapting abilities. This paper presents a real-time assessment method of the intelligence of students according to their instant learning skills. The proposed system can read the brain waves of students of different age groups at the time of learning and classify their instant learning skills using the cognitive score. Based on this, the learners are suggested suitable learning materials which maintain the learner in an overall state of optimal learning. The main issues concerning this approach are constructing cognitive state estimators from a multimodal array of physiological sensors and assessing initial baseline values, as well as changes in the baseline. These issues are discussed in a data processing block-wise structure. Synchronization of different data streams and feature extraction and formation of a cognitive state metric by classification/clustering of the feature sets are done. The results demonstrate the efficiency of using cognitive score in RTLCS in the identification of instant learning abilities of learners.
Dynamical-invariant-based holonomic quantum gates: Theory and experiment

Among existing approaches to holonomic quantum computing, the adiabatic holonomic quantum gates (HQGs) suffer errors due to decoherence, while the non-adiabatic HQGs either require additional Hilbert spaces or are difficult to scale. Here, we report a systematic, scalable approach based on dynamical invariants to realize HQGs without using additional Hilbert spaces. While presenting the theoretical framework of our approach, we design and experimentally evaluate single-qubit and two-qubits HQGs for the nuclear magnetic resonance system. The single-qubit gates acquire average fidelity 0.9972 by randomized benchmarking, and the controlled-NOT gate acquires fidelity 0.9782 by quantum process tomography. Our approach is also platform-independent, and thus may open a way to large-scale holonomic quantum computation.
Improved Touch-screen Inputting Using Sequence-level Prediction Generation

Recent years have witnessed the continuing growth of people’s dependence on touchscreen devices. As a result, input speed with the onscreen keyboard has become crucial to communication efficiency and user experience. In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME). Taken input efficiency as the optimization target, we proposed a neural end-to-end candidates generation solution to handle automatic correction, reordering, insertion, deletion as well as completion. Evaluation metrics are also discussed base on real use scenarios. For a more thorough comparison, we also provide a statistical strategy for mapping touch coordinate sequences to text input candidates. The proposed model and baselines are evaluated on a real-world dataset. The experiment (conducted on the PaddlePaddle deep learning platform1) shows that the proposed model outperforms the baselines.
Three-branch and Mutil-scale learning for Fine-grained Image Recognition (TBMSL-Net)

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years. But applying ILSVRC's annual champion directly to fine-grained visual categorization (FGVC) tasks does not achieve good performance. To FGVC tasks, the small inter-class variations and the large intra-class variations make it a challenging problem. Our attention object location module (AOLM) can predict the position of the object and attention part proposal module (APPM) can propose informative part regions without the need of bounding-box or part annotations. The obtained object images not only contain almost the entire structure of the object, but also contains more details, part images have many different scales and more fine-grained features, and the raw images contain the complete object. The three kinds of training images are supervised by our multi-branch network. Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales. Our approach can be trained end-to-end, while provides short inference time. Through the comprehensive experiments demonstrate that our approach can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets. Our code will be available at this https URL
A Two-Stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information

Traditional matrix factorization (MF) methods take a global view on the user-item rating matrix to conduct matrix decomposition for rating approximation. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. This article proposes a novel approach called two-stage rating prediction (TS-RP) to matrix clustering with implicit information. In the first stage, implicit feedback is used to discover the inherent structure of the user-item rating matrix by spectral clustering. In the second stage, we conduct rating prediction on the dense blocks of explicit information of user-item clusters discovered in the first stage. The proposed TS-RP approach can not only alleviate the data sparsity problem in recommendation but also increase the computation scalability. Experiments on the MovieLens-100K data set demonstrate that the proposed TS-RP approach performs better than most state-of-the-art methods of rating prediction based on MF in terms of recommendation accuracy and computation complexity.
A Fuzzy Logic Controller Approach for Controlling Heat Exchanger Temperature

This paper presents a fuzzy logic controller approach for controlling heat exchanger temperature. Fuzzy logic controller is an artificial intelligence-based controller. The fuzzy logic controller has been widely used for control applications in the industrial world. One of the tools used in the industrial world that requires accurate control is the heat exchanger. A heat exchanger is a device used to process the mixing of liquids that have different temperatures. In this case, temperature control becomes very important. Fuzzy logic control is applied to the heat exchanger so that the mixed fluid has a constant temperature. Fuzzy logic control models in this study are combined with neural network techniques. The fuzzy logic controller model is simulated in Matlab software. The results showed that the fuzzy logic controller was able to stabilize the temperature of the heat exchanger well.
SELWasm: A Code Protection Mechanism for WebAssembly

WebAssembly is a novel front-end programming technology that has been wildly supported by most web browsers. The high-level language can be compiled into WebAssembly binary code, and executed by the browser at almost native speed. With the help of WebAssembly, the complex business logic and algorithms can be integrated into the webpage and migrate the computation overhead to the browsers. However, due to the limit of browsers, it is very difficult for developers to ensure that the intellectual property embedded in the front-end code is well protected. Even though the WebAssembly code is a compiler-generated binary file, it is easy to obtain it from the website for reuse or reverse engineering. This paper proposed a new framework, SELWasm, to protect the WebAssembly code. In the framework, three major mechanisms are provided. The Environment Self-checking mechanism can protect the source code from illegally reusing by any unauthorized website. The Encryption&Decryption mechanism can protect the WebAssembly code from being distributed as plaintext. The Lazy-loading mechanism can improve the performance of page loading, mitigate the overhead introduced by the previous mechanisms. Comprehensive experiments are conducted to measure the effectiveness and efficiency of SELWasm. The results show that the behaviors considered from web attackers can be resisted and the overhead involved is negligible.
Linearly Self-Equivalent APN Permutations in Small Dimension

All almost perfect nonlinear (APN) permutations that we know to date admit a special kind of linear self-equivalence, i.e., there exists a permutation G in their CCZ-equivalence class and two linear permutations A and B, such that G °A = B °G. After providing a survey on the known APN functions with a focus on the existence of self-equivalences, we search for APN permutations in dimension 6, 7, and 8 that admit such a linear self-equivalence. In dimension six, we were able to conduct an exhaustive search and obtain that there is only one such APN permutation up to CCZ-equivalence. In dimensions 7 and 8, we performed an exhaustive search for all but a few classes of linear self-equivalences and we did not find any new APN permutation. As one interesting result in dimension 7, we obtain that all APN permutation polynomials with coefficients in \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> must be (up to CCZ-equivalence) monomial functions.
Digital economy and its role in accounting

The digital economy is one of the accelerating drivers for economic growth today. In this regard, the article discusses the concept of «Digital economy», the importance of accounting and its role in the digital economy; the need for further development of theoretical and methodological provisions of accounting, state projects that have been successfully implemented in the framework of the «Digital Kazakhstan» program regarding accounting; and the transformation of accounting activities in the digitalization of the economy. The main directions of accounting modernization in the digital economy and information society are considered. Digital literacy is the knowledge and ability of a person to use information and communication technologies in their daily and professional activities. changes to the accountant’s function, adding artificial intelligence to accounting operations, Robots, or programs that process data.
Ontology-Mediated Historical Data Modeling: Theoretical and Practical Tools for an Integrated Construction of the Past

Building upon the concepts of constructed past theory, this paper introduces the outcome of ontology-mediated data modeling developed by the authors within the last 15 years. Assuming that the past is something constructed through reflection of former times, one of our major concerns is guaranteeing the traceability of the construction process of an integrated historical discourse built from all available sources of information, regardless of their origin or nature. Therefore, by means of defining key concepts such as ‘unit of topography’ and ‘actor’, we created an information system for data gathering and exploitation and applied it to some experiences of construction of the past. When applied within the archaeological domain, the result is an archaeological information system interoperable with other sources of historical information. Its strength is that it ensures the traceability of the process from the beginning avoiding the introduction and repetition of errors within the system. Along with the main case example developed in this paper, we also summarize some other data modeling examples within the same conceptual framework.