Reviewerly/exHarmony huggingface dataset:
A Feature-enhanced Model for Chinese Medical Text Classification Based on Improved BERT and Feature Fusion

With the development of internet hospitals and natural language processing technology, medical text classification based on machine learning gains increasing attentions. In this paper, we propose a feature-enhanced Chinese text classification model for intelligent medical consultation, which combines char-word level tokenization and feature fusion. Specifically, we expand the dictionary of BERT and improve its segmentation mechanism to make full use advantages of different segmentation mechanisms and pre-trained models. In addition, we select 243 common diseases and symptoms from different departments as manual features, and apply the attention mechanism, CNN with multiple kernel sizes and max-pooling operation to capture potential correlations and dependencies between patients' self-statement and manual features. In order to verify the effectiveness of the proposed model, we carry out many simulation experiments. Experimental results show that compared to other typical text classification models, the proposed model achieves better classification performance with accuracy of 96.87% and Fl-score of 96.75%. In addition, the proposed model improves the accuracies of most departments by 1% as compared to the Google BERT.
The Measurement Model

We have already seen, in Chap. 2 , that modelsModel play an important role in the advancement of knowledge. Nowadays, every scientific theory and every technical activity are based on modelsModel that describe them in logical and mathematical terms.
Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques

In the last decades, the prevalence of noncommunicable diseases has significantly increased, causing millions of deaths worldwide. Among them, cardiovascular diseases (CVDs) have become a global health problem due to the high rates of mortality and morbidity. This issue is exacerbated in patients with type 1 diabetes (T1D) since they have a higher risk of developing CVD and death. Machine Learning (ML) methods have reached remarkable results in both industry and academia. In the clinical setting, these methods are promising to extract clinical knowledge and help to prevent acute events caused by CVDs. This paper aims to identify clusters of patients with different CVD risks and interpret the clinical variables that play a significant role in CVD risk. Towards that end, unsupervised and interpretable ML techniques were considered, in particular k-prototypes and the agglomerative hierarchical clustering technique with Gower's distance. We used a dataset that collects information from 677 adult Danish patients with T1D. Experimental results showed that albuminuria, smoking, and gender are crucial for identifying T1D patients at different risks of suffering CVD. Our paper contributes to the identification of CVD risk factors for T1D patients, paving the way for clinical decision-making. This identification of individuals at higher CVD risk would allow physicians to make early interventions and adequate treatments to prevent the onset of diseases.
A Tonal Re-patterning Framework for Modeling Anaañ Nominal Constructions

It is observed that the Anaañ language (ISO 693-3: nic; Ethnologue: ANW) has a predictable tonal re-patterning sequence when nominal elements associate in a nominal construction. This Chapter develops a tonal pattern recognition framework that exploits a two-tape finite state machine, and supervised learning, for tonal pattern recognition in Anaañ nominal constructions. A subsequential transducer is constructed and implemented using Python—a multi-paradigm programming language, to prove the feasibility of the transducer. To make the design generic and replicable across language domains, we allow for the manipulation of most of the linguistic details using a template-based approach that allows the specification of the language requirement as text files instead of pure hard-coding. Inputs to the transducer were: a machine-readable Anaañ (prototype) lexicon (in UTF-8 encoding format) and a set of re-patterning rules. The maximum number of output sequence combinations generated by the design however depends on the lexicon entries and includes all possible combinations of the lexicon entries, n, with the number of words combined per sequence, k (i.e., $${}^nC_k$$ ). A system framework is finally proposed to consume the tonal repatterning process and learn unseen word patterns. Implementation of the learning module is however expected in a future paper. The framework is sure to serve as byproduct to related natural language processing tasks, and as pedagogical material for educational and research purposes.
DataAttest: A Framework to Attest Off-Chain Data Authenticity

In recent years, more and more applications are built on blockchains for boosting security, while keeping interaction with the off-chain world. These applications usually leverage blockchain oracles to access off-chain data. However, they are still threatened by the weakness of data authenticity as the external data source system may be attacked and provide forged and malicious data. In this paper, we propose DataAttest, a framework based on remote attestation to guarantee the authenticity of off-chain data. DataAttest first instruments the data source system for collecting runtime traces. Then, it measures the static code and runtime traces in TEE to construct a Data Authenticity Proof (DAP). Finally, blockchain oracles verify the DAP to guarantee that the integrity of the specific data source system is undamaged and the provided data is actually obtained from that system. In addition, DataAttest also adopts two optimization strategies to accelerate DAP verification and minimize the overhead brought by DAP construction. We implement DataAttest and evaluate it on a real-world system. The experimental results show that DataAttest can effectively check data authenticity and the overhead is relatively low.
Mobile Robot Path Planning in 2D space: A survey

For decades, the path planning method has evolved at a fast speed ever and in a great amount. This paper briefly summarizes the existing path planning algorithm of mobile robots in a 2D plane. We classify path planning algorithms into four categories, namely classical methods, bionic methods, artificial intelligence methods, and hybrids methods, and introduce representative algorithms of each category respectively. A thorough review and classification of existing path planning algorithms are provided, which is beneficial for beginners in mobile robotics research. We demonstrate principal ideas for each type of path planning algorithm and provide discussions of the feasibility of real-world application for practitioners.
Guidelines for Developing Bots for GitHub

Projects on GitHub rely on the automation provided by software development bots. Nevertheless, the presence of bots can be annoying and disruptive to the community. Backed by multiple studies with practitioners, this article provides guidelines for developing and maintaining software bots.
Extended Res-UNet with Hierarchical Inner-Modules for Liver Tumor Segmentation from CT Volumes

Liver cancer is the second most common cause of cancer death and the sixth most frequent cancer in the world. Multi-Detector Computed Tomography (MDCT) images are now widely used to manifest liver tumors’ shape and volume, but it is hard for a doctor to realize such information at once. To support automatic MDCT analyzing work, we propose an extended residual U-Net with the novel hierarchical inner-module (HIM) and skip-connected hierarchical inner-modules (SHIMs). The HIM and SHIMs realize fine-grained feature extraction by separating the feature map into groups by channels, and using a set of small inner filter groups to extract detailed features from these groups. The inner filter group made up of one convolutional layer and one attention layer are connected hierarchically to redistribute the model’s attention on different feature groups. We evaluate the proposed method using 3DIRCADb dataset including 22 CT volumes where 16 volumes have tumors in the liver. The Dice values of liver and tumor segmentation are 0.931 and 0.792. The tumor segmentation result is better than the state-of-the-art method, showing strong ability in tumor feature extraction.
Disentangled Feature Learning for Semi-supervised Person Re-identification

Semi-supervised learning has become more and more popular in person re-identification because acquiring annotations is time-consuming and cumbersome. In this paper, we design a Disentangled Feature Learning (DFL) model based on an encoder-decoder mode by making use of less labeled data together with lots of unlabeled data. Specifically, a pair of horizontally flipped images are fed into the encoder module to decompose identity and structure features. Then different decomposed features are combined to reconstruct images in the decoder module. Multi-view consistent constraints are proposed for the further learning of feature disentanglement in the image-view, feature-view and feature-vector-view, respectively. Moreover, we separate the identity encoders into a teacher encoder and a student encoder for the stability of training and better performance. Extensive experiments on four ReID datasets demonstrate the effectiveness of our DFL model in the low proportion of the labeled data.
An Analytical Study Based on Computer-Assisted Language Teaching

Computer-assisted instruction (CAI for short) has brought a whole new impact and revolution to the modern concept of education. The advantages of computer technology in the teaching of mathematics are obvious, as it can attract students’ attention and develop their linguistic thinking skills. As a modern teaching technology, CAI can be correctly and reasonably applied to the teaching work of various subjects, both as a requirement of information-based teaching and quality education. Language is a very basic and instrumental subject. How to realize the transformation of language teaching to quality education is a problem that every language teacher should think deeply about. This paper will explore how to improve the vividness and interactivity of the teaching process and put forward their own opinions in the light of the characteristics of language teaching, combined with CAI technology.
Depth Completion via A Dual-Fusion Method

Depth completion technology based on multi-level feature fusion (MF) strategy has recently achieved remarkable success. However, the existing MF-based methods treat RGB features and depth map features equally when performing modal fusion but ignore the difference in semantic richness and sparsity between them, which leads to the results generated by these methods overfitting the shape of RGB and harm to the accuracy of depth value. To address this problem, we proposed a novel dual fusion (DF) strategy for MF-based depth completion, which can prevent overfitting by weakening the influence of RGB features on the generated results through two fusion stages. The entire DF framework consists of two multi-level fusion modules. The first fusion module performs a simple fusion of RGB features and depth features, while the second fusion module enriches the sparse image representation with the previously obtained fused features. Besides, we utilize non-local sparse attention to solve the problem that ordinary convolution is not capable of expressing depth map features enough. We test our approach on the outdoor KITTI test set and achieve the state-of-the-art (SOTA) performance in RMSE. Extensive experiments on the indoor NYUv2 dataset and KITTI validation set further demonstrate that our approach outperforms existing MF-based methods.
A multidimensional time series data on wireless network to improve the detection performance of unsupervised learning algorithms

In recent years, only with developments of cellular networking technologies as well as the implementation of 4G LTE networks, the skills and options provided to end-users had expanded to a level that seems to be limitless. Smart phone users who travel by public transportation rely on high-speed internet infrastructure and expect to have a stable, greater connectivity for the length of their journey. Because of the high requirements placed on wireless networks and the regular changes in the fundamental radio channel, customers frequently encounter rapid and surprising alterations in the connectivity reliability of their connections. The old traditional process of evaluating and inspecting infrastructure cellular technology should be no longer sufficient to fulfill the demands of the manufacturing industry. Consequently, an accurate mobile network cellular irregularity diagnostic method is required to monitor for problems that lead and to improving the effectiveness of device construction and operation. It is estimated that there are thousands of mistakes and exclusions in the current single-dimensional abnormality identification technique, and also that the present multifunctional abnormality diagnostic technique has a poor detection accuracy on multifunctional time series analysis. Multidimensional statistics depending on network service device abnormality classification optimization approach described in this work is predicated on machine learning that use limited of information to support in the instruction of tremendous unmonitored techniques, thereby enhancing the detection accuracy of unsupervised classification techniques. This paper conducts experimental studies to validate the functionality of the optimization techniques, and it shows a significant progress over the four generally used unmonitored techniques, that can greatly enhance the abnormality detection performance of the existing techniques, according to the authors.
VIRTUAL RAPPORT WITH EXPRESSIONS OF GRATITUDE IN RESPONDING ONLINE REVIEWS

The development of web-based technologies has increased the popularity of an online type of word-of-mouth, known as electronic word-of-mouth (eWOM). With the proliferation of online platforms, online reviews as eWOM have significantly influenced consumers’ purchase decisions in various industries. Given that eWOM can be immensely influential in businesses, engaging in digital platforms is crucial in rapport building with customers. Responding to online reviews can have a positive impact on both customer relationships and online reputation management. Expressing gratitude can create rapport and build trust with customers as a sense of appreciation can be kindled. Building on Spencer-Oatey’s Rapport Management Model, this study examines expressing gratitude strategies in the hotel responses to positive online reviews on TripAdvisor. Using purposeful sampling, particularly intensity sampling, the hotel responses were collected from Malaysian five-, four- and three-star hotels, which are situated in popular tourist destinations. The study findings show that four-star hotels accounted for the highest frequency of expressing gratitude strategies, followed by five- and three-star hotels. The five- and four-star hotels tend to vary their expressing gratitude strategies. It was also found that there was a significant relationship between the hotel star rating categories, the strategies of gratitude expression and lexical choice in those strategies. This study will establish an understanding of digital rapport management strategies from a linguistic perspective.
Spatial-Temporal Separable Attention for Video Action Recognition

Convolutional neural networks (CNNs) have been proved as a efficient method for various of visual recognition tasks. However, it is more difficult for CNNs to capture long-range spatial-temporal cues in dynamic videos than in static images. Recent nonlocal neural networks attempt to overcome this problem by a self-attention mechanism, where pair-wise affinities for all the spatial-temporal positions are calculated. However, this introduces a substantial computational burden. In this paper, we propose a spatial-temporal separable attention module (STSAM) to reduce the computational complexity. The experimental results, based on the Kinetics 400 benchmark, show that our model achieves better performance but introduces less extra FLOPs than nonlocal neural networks.
On Quasi-Inversion of Quantum Channels in 2 and in Higher Dimensions

We review the concept of the quasi-inverse of qubit channels and of higher dimensional channels. Quasi-inverse is a channel which when concatenaded to the original channel, increases its average fidelity in an optimal way. For qubit channels, we fully characterize the quasi-inverse, while for higher dimensional channels, we prove general theorems and provide bounds for the increased average fidelity. Nevertheless, explicit examples are given when exact quasi-inverses can be found.
Federated Transfer Learning for Energy Efficient Privacy-preserving Medical Image Classification

The deep convolutional neural networks are widely used in medical image classification tasks. In some cases, they have outperformed physicians and achieved significant results. Unlike natural images, medical image dataset are very hard to collect, because they are protected by the privacy regulations to preserve patient's anonymity and requires a great deal of professional expertise to label them. However, because of the easier access and availability of high-performance computational resources, leveraging deep neural networks to detect diseases is becoming increasingly popular and common practice among healthcare researchers. As a result, considerable amount of energy is consumed to find an optimal and effective solution, which has a huge impact on our environment and contributes to global warming to some level.To address these challenges and reduce the carbon footprint caused by the deep learning practitioners, we attempted to combine the advantages of both federated learning and transfer learning for the medical image classification task in our study. Our findings suggest that federated transfer learning could be an useful technique to minimize computational costs and energy efficient, while maintaining privacy and addressing the problem of data scarcity. Moreover, this approach can be applied to solve other healthcare related tasks.
Low Area and Low Power Threshold Implementation Design Technique for AES S-Box

Threshold implementation (TI) is a promising countermeasure against side-channel attacks (SCA) in the presence of glitches. However, the hardware implementation of TI in S-box needs a large number of D flip-flops to synchronize intermediate signals, which results in a large silicon area and power consumption overhead. In this brief, we present the low area and low power TI design technique for advanced encryption standard (AES) S-box. In the proposed approach, instead of using D flip-flops, low-cost synchronization circuits such as customized tri-state XOR gates, tri-state buffers, and D latches are efficiently adopted with critical path replica (CPR) circuits. As a result, the proposed TI S-box implementation with 28nm CMOS process shows up to 33.7% area and 44.3% power savings. The security of the proposed TI AES S-box against side-channel attacks is also verified with test vector leakage assessment (TVLA) tests.
Research on Multi-modal Corpus Construction Technology in ELAN-Based Communicative Competence Cultivation

With the widespread use of computer technology and the rapid development of Internet technology, corpus research has moved from ‘Corpus 3.0’ to ‘Corpus 4.0’.A multimodal corpus is a corpus in which multiple information such as audio, video and text corpora are integrated and can be processed, retrieved and statistically related by the researcher in a multimodal manner. In order to improve the effectiveness of multimodal corpus in the cultivation of intercultural communication skills, this paper has carried out the conceptual structure design of corpus storage, logical structure design of corpus storage and ELAN-based corpus labelling technology, solving the key technical problems in the construction of multimodal corpus, laying the foundation for the construction of multimodal corpus system, realizing more complex multimodal corpus retrieval, and serving the cultivation of intercultural communication talents.
Mix-up Consistent Cross Representations for Data-Efficient Reinforcement Learning

Deep reinforcement learning (RL) has achieved re-markable performance in sequential decision-making problems. However, it is a challenge for deep RL methods to extract task-relevant semantic information when interacting with limited data from the environment. In this paper, we propose Mix-up Consistent Cross Representations (MCCR), a novel self-supervised auxiliary task, which aims to improve data efficiency and encourage representation prediction. Specifically, we calculate the contrastive loss between low-dimensional and high-dimensional representations of different state observations to boost the mutual information between states, thus improving data efficiency. Furthermore, we employ a mixed strategy to generate intermediate samples, increasing data diversity and the smoothness of representations prediction in nearby timesteps. Experimental results show that MCCR achieves competitive results over the state-of-the-art approaches for complex control tasks in DeepMind Control Suite, notably improving the ability of pretrained encoders to generalize to unseen tasks.
Automatic Segregation of Limited Wastes through Tiny YOLOv3 Algorithm

To maintain a clean environment, proper waste management must be put into effect. This maintains a safe and healthy environment by preventing surface waterways and land regions contamination, reducing air pollution, and suppressing flooding and disease outbreaks. Using innovative technology can effectively help enhance waste management. In this paper, the researchers developed a device that segregates limited wastes through the application of tiny YOLOv3. The researchers were able to detect, classify, and segregate limited biodegradable wastes from non-biodegradable wastes using a pre-trained model of Tiny YOLOv3 on a Raspberry Pi 4B. Based on the collected data, the system produced macro-averaged precision, recall, and F1 score values for detection and classification of 100%, 88%, and 93%, respectively. Additionally, it had a detection and classification accuracy of 88% overall and a waste segregation accuracy of 100%.
Efficient and Collusion Resistant Multi-party Private Set Intersection Protocols for Large Participants and Small Sets Setting

Multi-party private set intersection (MP-PSI) allows m participants with private sets to calculate their intersection without revealing any information, which is quite different from the traditional two-party PSI. Although some efficient MP-PSI protocols already exist, the efficiency of these protocols decreases significantly in the scenarios with the increasing number of participants for the small sets setting. In this paper, we propose efficient MP-PSI protocols based on one-round two-party key agreement embedding with unconditional zero sharing technique and prove secure by resisting up to $$m-2$$ parties collision in the semi-honest model and $$m-1$$ parties collision in the malicious model. Compared with the current efficient MP-PSI, our protocol performs better in terms of the communication round, communication cost and the running time in small sets setting. Extensive experiments show that as the number of participants m increases from 5 to 50 and the upbound of the set size n scales from $$2^8$$ to $$2^{10}$$ , the running time of our MP-PSI protocol is faster than those of the most efficient MP-PSI protocol [14] and the recent work [4]. Take the set size at $$2^{7}$$ for example, as the number of the participants increases from 10 to 50, our protocol only requires 4–26 $$\times $$ less running time and 8–11 $$\times $$ less communication cost than those of [14], respectively. We conclude that our protocols are practical solutions suitable for the scenarios in small set setting with increasing participants.