Reviewerly/exHarmony huggingface dataset:
Verb Raising and the Construction Mechanism of Synthetic Compounds

Synthetic compounds have received considerable research attention in linguistics, since they can provide important implications to explore the reciprocal relationship between syntax and morphology. It is suggested that synthetic compounds are generated by argument structure. For instance, the zhǐzhāng ‘paper’ and jī ‘machine’ in zhǐzhāng-fěnsuì-jī ‘paper shredder’ can be analyzed as the arguments of the verb fěnsuì ‘shred’. Previous research on incorporation and adjunction analyses has demonstrated the syntactic-semantic relations in synthetic compounds but failed to explain the asymmetry between the actual linear order and the underlying argument structure. The current paper proposes and illustrates the analysis method of verb raising. The synthetic compounds are headed by a nominal affix -n, which selects a verb phrase as its complement leftwards. Then the [+affix] feature of -n triggers the verb to raise to the -n position to generate the surface linear order. This approach provides a solution to analyze Chinese synthetic compounds and can be supported by relevant facts in other languages such as English and Japanese.
A Quantitative Study on the Low-Degree Adverb “Shaowei” – A Stylistic Perspective

Based on large-scale corpora, this study examines the stylistic characteristics of the low-degree adverb “shaowei” (稍微‘slightly’) from three aspects: stylistic distribution, colligation and semantic prosody. It has been found that: 1. The proportion of “shaowei” in spoken Chinese is higher than that in written Chinese, but the distribution of “shaowei” in different sub-genres varies considerably. 2. The proportion of colligation of “shaowei + verb phrase” is higher than that of “shaowei + adjective phrase”. In terms of sub-genres, “shaowei” inclines to modify verbs in Translation Works and tends to modify adjectives in CCTV News. 3. “Shaowei” has a positive semantic prosody in both written and spoken Chinese, but it is quite different from the dictionary which says “shaowei” can only collocate with commendatory words. To be specific, “shaowei” shows a neutral semantic prosody in Practical Writing and Learned and a negative one in CCTV News.
DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy.
Fine-Grained Classification via Hierarchical Feature Covariance Attention Module

Fine-Grained Visual Classification (FGVC) has consistently been challenging in various domains, such as aviation and animal breeds. It is mainly due to the FGVC’s criteria that differ with a considerably small range or subtle pattern differences. In the deep convolutional neural network, the covariance between feature maps positively affects the selection of features to learn discriminative regions automatically. In this study, we propose a method for a fine-grained classification model by inserting an attention module that uses covariance characteristics. Specifically, we introduce a feature map attention module (FCA) to extract the feature map between convolution blocks, constituting the existing classification model. The FCA module then applies the corresponding value of the covariance matrix to the channel to focus on the salient area. We demonstrate the need for fine-grained classification in a hierarchical manner by focusing on the diverse scale representation. Additionally, we implemented two ablation studies to show how each suggested strategy affects classification performance. Our experiments are conducted on three datasets, CUB-200-2011, Stanford Cars, and FGVC-Aircraft, primarily used for fine-grained classification tasks. Our method outperforms the state-of-the-art models by a margin of 0.4%, 1.1%, and 1.4%.
Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.
SoC Reconfigurable Architecture for Implementing Software Trained Recurrent Neural Networks on FPGA

Recurrent neural networks (RNNs) are used extensively in time series data applications. Modern RNNs consist of three layer types: recurrent, Fully-Connected (FC), and attention. This paper introduces the design, acceleration, implementation, and verification of a complete reconfigurable RNN using a system-on-chip approach on an FPGA. This design is suitable for small-scale projects and Internet of Things (IoT) end devices as the design utilizes a small number of hardware resources compared to previous configurable architectures. The proposed reconfigurable architecture consists of three layers. The first layer is a Python software layer that contains a function serving as the architecture’s user interface. The output of the python function is three binary files containing the RNN architecture description and trained parameters. The embedded software layer implemented on an on-chip ARM microcontroller is the second layer of that architecture. This layer reads the first layer output files and configures the hardware layer with the required configuration and parameters to execute each layer in the RNN. The hardware layer consists of two Intellectual Properties (IPs) with different configurations. The Recurrent Layer Hardware IP implements the recurrent layer using either Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) as basic building blocks, while the ATTENTION/FC IP implements the attention layer and the FC layer. The proposed design allows the implementation of a recurrent layer on an FPGA with variable input and a hidden vector length of up to 100 elements for each vector. It also supports implementing an attention layer with up to 64 input vectors and a maximum vector length of 100 items. The FC layers can be configured to support a maximum value of 256 for the input vector length and the number of neurons in each layer. The hardware design of the recurrent layer achieves a maximum performance of 1.958 and 2.479 GOPS for the GRU and LSTM models, respectively. The maximum performance of the attention and FC layers is 2.641 GOPS and 634.3 MOPS, respectively. The hardware design works at a maximum frequency of 100 MHz.
Multilevel Subgranting by Power of Attorney and OAuth Authorization Server in Cyber–Physical Systems

Many Cyber-Physical Systems are today semiautonomous and powerful enough to perform advanced tasks on their own. This means they can also act as representatives of people or devices that have given them an order. However, traditional access control policies and delegation models do not meet industrial requirements such as support for letting autonomous CPS devices act on their own with certified credentials under the sub authorization by subcontractors, without the need for a separate account per device. In this paper, we analyze and compare power of attorney, proxy signature by warrant, and OAuth to identify the strengths and challenges of each. Based on the comparison, we propose an OAuth grant type based on the power of attorney and inspired by the concept of proxy signature by warrant. Power of Attorney is a generic and self-contained document that a principal signs and directs to an agent, thereby providing it the power to execute actions on behalf of the principal for a predefined time, even if it is offline. One key advantage of the power of attorney is that it can support effective sub-granting on several levels to support industrial scenarios where resource owners bring in authorized contractors that can in their turn authorize and bring in several devices without incurring management overhead to the resource owner. A proof-of-concept and performance evaluation of the proposed model is presented using an industrial use-case scenario with multi-level authorization.
A Dependency Structure Annotation for Modality in Chinese News Articles

This paper reports an effort to annotate modality in Chinese news articles. We introduce the annotation scheme for the modality that employs a dependency structure, which is based on Meagan Vigus (2019) and “UMR Aspect, Modal Strength, and Polarity Annotation Guidelines”. We test the cross-linguistic adaptability of the schema we adopt, present the preliminary results of the first pass, and analyze the types of disagreement.
Aspect-Level Sentiment Classification Based on Auto-Adaptive Model Transfer

Aspect-level sentiment classification (ASC) is a fine-grained sentiment analysis task that involves detecting the sentiment polarity of a specific opinion target in a given sentence. Despite the popularity of deep learning methods, the limited availability of ASC training data has resulted in the suboptimal performance of neural network models. To address this issue, researchers have proposed transferring resource-rich document-level sentiment knowledge to ASC tasks using model transfer and shared parameters. However, these methods have neglected the impact of differences in the training data for different tasks on the shared layer parameters. Therefore, this paper proposes a transfer method that automatically adapts to task and domain differences—Auto-adaptive Model Transfer (AAMT). This method considers adding specific disturbance variables on shared layer parameters to subdivide task differences to further obtain more accurate and effective transfer knowledge. In addition, on this basis, we introduce an attention mechanism to combine aspect and sentence features to better capture the sentiment vocabulary associated with a given target. A series of experiments on three public datasets show that our proposed method can solve the problem of insufficient training data for ASC and achieve good results.
A Framework for Dictionary Development: Building Domain Dictionary for Legal Field

The domain dictionary is a hotspot of research in natural language processing. Constructing a dictionary effectively for a specific field enables more precise labeling and classification of words. The development of legal domain dictionaries contributes to the advancement of artificial intelligence research. Despite extensive research on domain dictionaries, pre-trained models with high transcendental knowledge are underutilized, and legal domain dictionaries are scarce. To address these issues, this article is the first to employ the masked language model (MLM) to construct the legal domain dictionary. We verify the performance and effectiveness of the dictionary we construct. It will be available to the public to provide possible directions for future research. To facilitate related research in the community and benefit other researchers, we make our resource in this work publicly available on: https://github.com/GDUFS-Strawberry/Legal-Dictionary .
Extraction and Application of Verb Event Structure Based on Grammatical Knowledge-Base of Contemporary Chinese(GKB)

Although the semantic features of words are hidden deep-seated features, they can generally be shown through the grammatical combination of words. This paper mainly relies on the descriptions of some grammatical features of verbs in GKB, and then extracts the event structure of verbs by features’ stacking and filtering. Finally, the authors use those features to construct semantic knowledge base of verbs. Experiments show that the event structure knowledge is useful for machine translation and semantic role labeling.
Building a Semantically Annotated Corpus of Chinese Directional Complements

In this study, we build a semantically annotated corpus of Chinese directional complements, conduct Chinese linguistic analysis using them, and propose an application plan for NLP and Teaching Chinese as a Second Language (TCSL). This study particularly focuses on the four directional complements shang, qilai, xiaqu, and xialai, which function as grammatical markers. This study utilizes a semantically annotated corpus for Chinese linguistic analysis. We classify the directional complements according to semantic features, quantitatively analyze the classified semantic types, and conduct a network analysis. The semantically annotated corpus from this study can be utilized in various fields in the future. It can be used to improve the performance of word sense disambiguation in the field of NLP. It will also be actively used for TCSL. If our corpus is properly utilized, it will be possible to provide learners and instructors with a variety of essential information on directional complements.
RoBERTa: An Efficient Dating Method of Ancient Chinese Texts

To address the dating problem of ancient Chinese texts, this paper proposes to model the text with RoBERTa(Robustly Optimized BERT Pretraining Approach) model, fully learn the contextual information of the text and combine with the ancient Chinese pre-training model for training. The experiments demonstrated the effectiveness of RoBERTa model for the chronological classification of ancient Chinese, and the accuracy of the classification reaches 93.98%. Our work can subsequently help researchers of ancient Chinese to perform automatic dating of ancient Chinese.
A Preliminary Quantitative Investigation of Chinese Monosyndetic Coordinators

Despite extensive discussion in the traditional linguistic literature, few quantitative data regarding the phenomenon of coordination are known. The present study hence offers an exploratory yet comprehensive quantitative study of the Chinese coordinators using the GSDSimp treebank in the Universal Dependencies (UD) project. The results are many-folded: (1) At the micro level, hé is the most frequent overt coordinator in Written Chinese, followed by jí, yǔ, bìng, huò. Gēn is extremely infrequent despite its reported commonality in the literature. (2) At the macro level, measured by the lexical richness indicator of entropy, Chinese has a rather large inventory of coordinators, at least compared with English. (3) For the syntactic category of coordinands, NP and VP coordinations constitute two largest parts and each have dedicated markers. By contrast, the coordinations of other categories are rather infrequent, mostly without dedicated markers. Specifically, adpositional and sentential coordinations are rarely found in Chinese. The present study also sheds some light on the nature of coordination.
DiveNet: Dive Action Localization and Physical Pose Parameter Extraction for High Performance Training

The tremendous progress of deep convolution neural networks has shown promising results on the classification of various sports activities.However, the accurate localization of a particular sports event or activity in a continuous video stream is still a challenging problem.The accurate detection of sports actions enables the comparison of different performances, objectively.In this work, we propose the DiveNet action localization module to detect the springboard diving sports action in an unconstrained environment.We used Temporal Convolution Network (TCN) over a backbone feature extractor to localize diving actions, with low latency.We estimate the divers center of mass (COM) trajectory and the peak dive height using the temporal demarcations provided by the action localization step via the projectile motion formula.In addition, we train a DiveNet pose regression network, which extends the Unipose architecture with direct physical parameter estimation, i.e COM and 2D joint keypoints.We propose a new homography computation method between the diving motion plane and the image-view for each dive.This enables the representation of physical parameters in metric scale, without any calibration.We release the first publicly available diving sports video dataset, recorded at 60 Hz with a static camera setup for different springboard heights.DiveNet action localization achieves an accuracy of 95% with a single frame latency (< 25 ms).The DiveNet pose regression model shows competitive results around 70% PCK on different diving pose datasets.We achieve COM accuracy of 6 pixels, dive peak height sensitivity of 20 cm and mean joint angle errors around 10 degrees.
The Construction of Grammatical Synonym Resources of Disyllabic Verbs in Modern Chinese

At present, there are about two shortcomings in the resources of Chinese synonyms. One is that there are no synonym resources specifically for verbs. The other is that scholars usually pay too much attention to the conceptual meaning of synonyms and ignore the grammatical meaning of synonyms. These two shortcomings may lead to inapplicability when applying synonym resources. Therefore, we are oriented toward verbs, consider conceptual and grammatical meanings, and construct grammatical synonym resources for verbs. Firstly, we formulated the Grammatical Synonym Annotation specification for Modern Chinese Disyllabic Verbs. Secondly, we constructed a collection of modern Chinese disyllabic verbs depending on the current research results. Finally, we took the annotation specification as the standard, referred to the related resources and the performance of verbs in the corpus, and built the grammatical synonym resources for modern Chinese disyllabic verbs. After forming the annotation team, it took us fourteen months to complete the annotation task. This paper provides high-quality data for natural language processing and dramatically improves the accuracy of sentence-level retelling.
A pointwise optimal subset selection strategy assisted by shape functions in digital image correlation algorithm

In the practical application of the digital image correlation (DIC) technique, the local imperfection of the speckle pattern and complex unknown deformation usually make it conflicting to choose an appropriate subset size for each individual sampling point on the tested specimen surface. In this paper, a pointwise shape functions-assisted subset selection (SFSS) strategy for DIC was proposed with the aid of the so-called deformation deviation function. Firstly, a WBSSD correlation criterion was proposed by introducing the Butterworth window function to the classic SSD correlation function, so that the subset size could be continuously adjusted in subpixel level. In addition, the deformation deviation function was taken as an indicator to present the matching degree of the affine shape function in the subset. In this basis, an initial traversing-fine targeting procedure was designed to determine the optimal subset size for each individual sampling point by quantitatively evaluating the deformation deviations in a given range of subset sizes. Subsequently, the feasibility and effectiveness of the newly-built SFSS strategy were verified by a series of synthetic speckle images, implying that the presented strategy could enhance the measurement accuracy and precision of DIC algorithm by selecting an appropriate subset size control parameter in WBSSD criterion for each sampling point on the specimen surface. Comparing the proposed SFSS method with the traditional SSD-based correlation algorithm as well as WSSD and WZNSSD algorithms in DIC calculations, it was further demonstrated that the proposed SFSS strategy could improve the measurement performance of DIC, even in the condition of larger complex deformation cases.