Reviewerly/exHarmony huggingface dataset:
Digital-Reported Outcome from Medical Notes of Schizophrenia and Bipolar Patients Using Hierarchical BERT

Patient-reported (PRO) and clinician-reported (CRO) outcomes are assessment instruments that are completed by patients and trained healthcare professionals, respectively. A PRO is a report of the direct experience of the patient with a given disease condition. A CRO is an assessment of the condition of the patient by the healthcare provider. PROs may not be accessible to all patients, especially those suffering from severe disease conditions. CROs are time-consuming and therefore administered infrequently. In the present study, we introduce a new form of assessment, the digital-reported outcome (DRO), which is automatically derived from the medical notes of the patient. DROs have a low overhead and can be generated at each patient’s visit to complement other outcome-assessment instruments and enhance clinical decision support by identifying at-risk patients. In this study, a DRO is developed to evaluate the functional impairment in the daily activities of two cohorts of patients suffering from bipolar disorder and schizophrenia. The input of the DRO is a single medical note from the electronic medical record of the patient. This note is submitted to a hierarchical bidirectional encoder representations from transformers (BERT) model. First, a sentence-level embedding is produced for each sentence in the note using a token-level attention mechanism. Second, an embedding for the entire note is constructed using a sentence-level attention mechanism. Third, the final embedding is classified using a feed-forward neural network. The model is trained to classify patients into moderate or severe functioning impairment levels according to the general assessment of functioning (GAF) scale, a CRO instrument for the assessment of the impact of mental illness on the daily activities of the patient. The DRO is validated using medical notes that were labeled by multiple healthcare providers from different healthcare institutions. The results indicate that a general DRO is able to classify patients from the two cohorts according to the two functioning impairment levels (severe versus moderate) prior to the onset of disease with an AUC of 76%. Disease-specific DROs are only applicable after the onset of the disease and produced AUCs of nearly 85%. The methodology introduced in the present paper is practical and can support the automated monitoring of the severity of the functioning impairment of bipolar and schizophrenia patients. Extending the proposed DRO to other psychiatric conditions and types of impairments is the subject of ongoing research work.
Mining the English application learning patterns of college students based on time series clustering

As a convenient learning tool in the We Media era, mobile apps have been paid more and more attention by college students because of their accompanying timeliness and practicality. With the increasing number of English learning apps, many such apps provide college students with new ways to obtain learning resources and diversified learning modes. The related research in the field of mobile-assisted language learning at home and abroad has developed over nearly 20 years, basically following the route from theory to application in practice, but there have been few process studies on learners’ individual language skill learning behaviors based on mobile platform data. In this study, the time series clustering method was adopted, and the learning behavior of college students in an English vocabulary learning app in China was selected for data mining. Firstly, taking the “single-day memorization amount” as the measurement index, the memorization records of college students in the whole use cycle were extracted and processed into trajectory data, and the KmL algorithm was used to cluster the trajectory of the memorization amount in the time series. According to the intra-class average trajectory, the characteristics of learning behavior changes among the different college students are summarized, and two learning modes are depicted. Secondly, through the experimental analysis, it was found that adopting the English learning model three weeks before an exam can effectively stimulate college students and improve their willingness to learn and continue using the app.
Email Etiquette: A Pragmatic Analysis

This essay studies email etiquette, which is probably the most practical application of pragmatic theories. It deduces some common strategies for writing email from basic pragmatic theories and principles.
Evaluation Study on the Comprehensive Benefits of the "Three Olds" Renovation in Tianhe District of Guangzhou City

In order to study the innovative initiatives of the "Three Olds" transformation in the pilot areas in Guangdong Province, this paper attempts to take the evaluation of the benefits of the "Three Olds" transformation as a new paradigm, and firstly, to evaluate the comprehensive benefits of the "Three Olds" transformation in terms of indexes and indicators. This paper attempts to take the "three olds" transformation benefit evaluation as a new paradigm, firstly, analysing the content and structure of the "three olds" transformation comprehensive benefit evaluation index model, and at the same time, using the principal component analysis and hierarchical analysis, interpreting the mechanism of the urban "three olds" transformation comprehensive benefit evaluation, and providing a new benefit evaluation model for the government in the "three olds" transformation. It also explains the mechanism of comprehensive benefit evaluation of the "three olds" transformation and provides new reference suggestions for the government's benefit evaluation mode in the "three olds" transformation.
Stabilization analysis of fractional-order nonlinear permanent magnet synchronous motor model via interval type-2 fuzzy memory-based fault-tolerant control scheme

The main objective of this study is to improve the convergence rate performance and analyze the stability properties of the FOPMSM model considering the load-torque external disturbances and actuator faults. Due to the complex nonlinearity, the presented FONPMSM model in the d-q frame is approximated by an IT-2 T-S fuzzy modeling technique. Besides, the fuzzy memory-based FTC is designed to eliminate the typical characteristics of chaotic behaviors and stabilize the proposed nonlinear model even if load torque disturbances, actuator faults in the controller, and time delays occur. Further, by employing the fractional order-based fuzzy LKF, some sufficient conditions are carried out in terms of LMIs to guarantee the asymptotic stability conditions, and simultaneously, disturbance reduction is confirmed. And then, the desired control gain matrices are determined from solvable LMIs, which can help to enhance the system stability performance. Finally, the numerical simulation of T-S fuzzy-based FOPMSM model is given to validate the applicability and efficiency of the proposed controller.
Glimpse and focus: Global and local-scale graph convolution network for skeleton-based action recognition

In the 3D skeleton-based action recognition task, learning rich spatial and temporal motion patterns from body joints are two foundational yet under-explored problems. In this paper, we propose two methods for improving these problems: (I) a novel glimpse-focus action recognition strategy that captures multi-range pose features from the whole body and key body parts jointly; (II) a powerful temporal feature extractor JD-TC that enriches trajectory features by inferring different inter-frame correlations for different joints. By coupling these two proposals, we develop a powerful skeleton-based action recognition system that extracts rich pose and trajectory features from a skeleton sequence and outperforms previous state-of-the-art methods on three large-scale datasets.
IVMS: An immersive virtual meteorological sandbox based on WYSIWYG

A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics (IA). We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data. This innovative visual method enables users to interact directly with data through non-contact aerial gestures (NCAG). Referring to the “What you see is what you get” concept in scientific visualization, we have proposed a novel approach for visual exploration of meteorological data that aims to immerse users in the analysis process. We hope this approach can inspire immersive visualization techniques for other types of geographic data as well. Finally, we conducted a user questionnaire to evaluate our system and work. The evaluation results demonstrate that our system effectively reduces cognitive burden, alleviates mental workload, and enhances users’ retention of analysis findings.
A new modified DES algorithm based on the development of binary encryption functions

One of the most commonly used symmetric encryption methods is the Data Encryption Standard (DES), but it has a number of flaws. For instance, the encryption method only employs two functions (XOR), acts on one bit, and only allows for a finite number of combinations of data. To solve these issues, a new modified DES algorithm is presented in this study. The typical encryption DES algorithm of 16 rounds has been extended in the key space and the plaintext (using multiple keys) by presenting a new operation (Xo) replace the present XOR operation to increase complexity. In contrast to DES, which only employs one key, the research approach uses three keys other than the standard DES one. The input key for encryption and decryption processes is the first key. while other three keys are used to determine the table numbers (2, 4, 6, 8, 10, and 12), which have values ranging from 0 to 4096. When compared to the famous DES and other modified algorithms, the complexity analysis of the suggested method reveals it is the most difficult. As a result, the attacker must make at least (4.7594738544661044544894020894748E+173) attempts to break the key and get the clear message. This indicates that the well-known DES security level will be raised by the suggested DES algorithm, depending on the new Xo function.
Robust target area search using sets of probabilities

Target area search in the standard Bayesian information-theoretic formulation consists of a repetitive cycle of sensing, recursive Bayesian estimation and motion control. The paper formulates the problem of target area search in the framework of imprecise probability theory using probability sets. The rationale is that the measurement model parameters, such as the probability of detection or the probability of false alarm, are rarely known as precise values. Instead, we adopt probability intervals to express beliefs. The paper formulates the Bayes-like state estimation equations as well as the reward function for motion control. The reward function is based on an uncertainty measure which takes into account both randomness and epistemic uncertainty.
Binary multi-view clustering with spectral embedding

Techniques for large-scale multi-view clustering have attracted increasing attention in recent years. To explore complementary and consensus information across multiple views, mapping multi-view data into a hamming space for binary multi-view learning is highly effective. However, for binary multi-view clustering, it is difficult to adopt the local manifold structure of original data into the consensus representation. Therefore, a novel algorithm named Binary Multi-View Clustering With Spectral Embedding (BMVC_SE) is proposed in this study. This algorithm unifies binary consensus representation learning and multi-view spectral learning into a joint framework. Specifically, this algorithm exploits the shareable and individual information across multi-view data to learn a binary consensus representation. Furthermore, spectral rotation technique is employed to embed manifold information from original multi-view data into binary consensus representation. Note that a multi-view GPI algorithm is proposed in this study to obtain the better local manifold information. We also provide an effective optimization method to solve the existing method. Extensive experimental results show that our method outperforms some state-of-the-art methods.
Analysis of factors inhibiting the customer engagement of eHealth in India: Modeling the barriers using ISM-Fuzzy MICMAC analysis

The current study aims to evaluate and measure the relationship between the customer engagement barriers influencing the adoption of eHealth in India. Previous studies have shown that low levels of engagement can lead to worse health outcomes, so this study is an expanded version of those findings to highlight those challenges.For this, the study followed three phases of research: factor identification through a review of the literature; expert opinion for selecting key and pertinent factors for the study; and application of interpretive structural modelling approach to capture the proper association between various factors. Using fuzzy-MICMAC analysis, the factors were divided into independent, dependent, autonomous and linkage categories.A model is created in this study that shows the relationship between different barriers that aid the Indian healthcare industry in better implementation and also Indian citizens in better adoption of eHealth services. According to the research and derived model, some of the biggest obstacles in the eHealth are need of product portfolio, Lack of Customer support and Low first call resolution rate.This study contributes to the body of knowledge by offering novel perspective into the types of hierarchical relationships that exist among barriers. These insights will be valuable to academicians and practitioners interested in India's healthcare market and its strategic expansion.
Face identity and expression consistency for game character face swapping

Customizing the appearance of game characters according to individual preferences is an important application in the gaming industry. The traditional solutions such as manual editing within the game engine require much time and some professional experience. To address this problem, this paper proposes a novel face-swapping framework to swap real faces onto game characters while maintaining the art style of the game. This approach helps players speed up the character customization process, as they can input any face and quickly see the results of swapping it onto the character. Specifically, our framework extracts a more robust identity embedding from compound face identity models. Then the identity embedding is sent into AdaIN for feature fusion and changes the identity attribute for the output of the decoder. To maintain the expression consistency between the swapped and target faces, we utilize a novel expression embedding loss which effectively constrains the fine-grain expression similarity. To reduce the cross-domain gap between human and game faces, we also construct a game face dataset and propose to use fine-tuning to improve the image quality of cross-domain face swapping. Extensive qualitative and quantitative experiments indicate that our method achieves leading swapping results in both pure natural human faces and game faces datasets.
The application of the sensemaking perspective for the examination of employees’ behavioural responses to the HR chatbot

Abstract Background and Purpose The emergence of new technologies affects different business areas, including HR activities. Employee communication is an HR activity that can be automated with the application of chatbots. Besides the numerous advantages these artificial entities offer, the challenges generated by them also need to be considered. One such challenge is user acceptance, which plays a substantial role in the implementation. This study aims to explore employees’ behavioural responses to HR chatbots. Thus, we applied the sensemaking perspective, according to which new, unknown phenomena induce the need for interpretation in individuals that simultaneously shape individuals’ behaviour toward the phenomenon. Methodology For data collection, we conducted semi-structured interviews that were analysed with interpretive phenomenological analysis (IPA). The sample consisted of 6 HR professionals and ten general users. The study took place at the Hungarian organisations of a multinational company. Results The analysis revealed that subjects who attributed positive meanings to the HR chatbot typically engaged in actions that aligned with organisational expectations, i.e., using the digital assistant became part of their routine. Interestingly, the closer HR professionals are to technology in their work activities, the more positively they perceive it. However, having ambivalent feelings towards technology resulted in occasional use or avoidance, while experiencing negative feelings led to the rejection of use. Conclusion We explored the different meanings employees attributed to the HR bot and their actions directed towards it both in the initial and transitional technology adoption phase. The results contribute to understanding how an HR chatbot can be successfully implemented in an organisation.
Scheduling coflows for minimizing the total weighted completion time in heterogeneous parallel networks

Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem encountered in large data centers is a challenging NP-hard problem. Many previous studies on coflow scheduling mainly focus on the single-core model. However, with the growth of data centers, this single-core model is no longer sufficient. This paper addresses the coflow scheduling problem within heterogeneous parallel networks, which feature an architecture consisting of multiple network cores running in parallel. In this paper, two polynomial-time approximation algorithms are developed for the flow-level scheduling problem and the coflow-level scheduling problem in heterogeneous parallel networks, respectively. For the flow-level scheduling problem, the proposed algorithm achieves an approximation ratio of O(log⁡m/log⁡log⁡m) when all coflows are released at arbitrary times, where m represents the number of network cores. On the other hand, in the coflow-level scheduling problem, the proposed algorithm achieves an approximation ratio of O(m(log⁡m/log⁡log⁡m)2) when all coflows are released at arbitrary times. Moreover, we propose a heuristic algorithm for the flow-level scheduling problem. Simulation results using synthetic traffic traces validate the performance of our algorithms and show improvements over the previous algorithm.
Creating New Mathematics by Schoolchildren

Abstract The paper discusses an example of an educational project in modern mathematics in which school students create mathematics that is new to them. The mathematical results produced by the students in the theory of definability also have an “absolute” novelty, i.e., are the basis for professional publications. The described course was based on recent results of this article’s authors in the theory of definability. The new results were obtained by a group of 10 schoolchildren from different regions of Russia.
The Work of a Mathematician As a Prefiguring of Mastering Mathematics by Students: The Role of Experiments

Abstract This paper considers an approach to mathematical education adequate to the task of developing mathematics and its applications in the 21th century. This approach is based on improving the efficiency of the educational process by maintaining the motivation of students of various categories. The basis for the formation of motivation is, on the one hand, independent design; invention of mathematical objects, methods of action, and models of the world around us; and the discovery of facts of mathematical reality and, on the other hand, solving of new, unexpected, and feasible tasks for the student. The student’s work is similar to the work of a mathematician–researcher and programmer. The possibilities of research work in educational mathematics are significantly expanded due to computer-based intramathematic experiments. Debugging a computer program is a special kind of mathematical experiment.
Primary School Mathematics in the Context of Digitalization

Abstract We consider the content and methods of teaching mathematics in primary school in the context of digitalization. The necessity of a significant updating of the curriculum is shown. In particular, this implies the use of digital tools as an object of study and a way of significantly improving the content and effectiveness of learning mathematics to prepare children for the future life and work.
A Hardware Accelerator for Standard Convolution and Depthwise Convolution

In this paper, a CNN hardware accelerator for standard convolution and depthwise convolution is proposed. The accelerator can support two different data flow modes. A computation array composed of DSP is designed to support the parallel strategy of input channel and output channel for standard convolution efficiently. A Membank architecture is designed to make the computation array more efficient for the parallel strategy of input feature map and kernel for depthwise convolution. The accelerator can accelerate EfficientNet on Xilinx Alveo U280 at the system clock of 300MHz and the DSP clock of 600MHz. The results show that the accelerator can achieve 1.14× throughput and 1.12× throughput/DSP compared with the latest FPGA-based accelerator on the same data center accelerator card.
An On-Chip Superconducting Quantum Transponder

Transmitting signals from one port to different ports at low temperature is necessary in superconducting quantum test system. An on-chip superconducting quantum transponder (SQT) is designed. The SQT has the function of routing signals from one port to multiple ports. Routing signals from one port to different ports can be realized through the impedance match between SQUIDs and output ports. In addition, due to the non-reciprocity of SQT, the isolation between sources and receivers can be achieved. The dual function of “transmit” and “isolate” may play an important role in superconducting quantum computing.
Post-training Quantization or Quantization-aware Training? That is the Question

Quantization has been demonstrated to be one of the most effective model compression solutions that can potentially be adapted to support large models on a resource-constrained edge device while maintaining a minimal power budget. There are two forms of quantization: post-training quantization (PTQ) and quantization-aware training (QAT). The former starts from a trained model with floating-point computation and then gets quantized afterward, while the latter compensates for the quantization-related errors by training the neural network using the quantized version in the forward pass during training. Though QAT is able to produce accuracy benefits, it suffers from a long training process and less flexibility during deployment. Traditionally, researchers usually make the one-time bold decision between QAT and PTQ depending on the quantized bit-width and hardware requirement. In this work, we observed that even though the hardware cost is approximately the same for various quantization schemes, the sensitivity to training for each quantized layer is different. This leads to that certain scheme requires QAT more than others. We argue that it is necessary to look into this dimension by measuring the accuracy difference for each layer under QAT and PTQ conditions. In this paper, we introduce a methodology to provide a systematic and explainable way to quantity the tradeoffs between the quantization forms. This is especially beneficial for evaluating a layer-wise mixed-precision quantization (MPQ) scheme, where different bit-widths across are allowed and the search space is enormous.