Reviewerly/exHarmony huggingface dataset:
Prompt-Based Word-Level Information Injection BERT for Chinese Named Entity Recognition

Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, information retrieval, and text summarization, as it helps to organize the relevant information in a structured way. The current approaches to Chinese named entity recognition do not consider the category information of matched Chinese words, which limits their ability to capture the correlation between words. This makes Chinese NER more challenging than English NER, which already has well-defined word boundaries. To improve Chinese NER, it is necessary to develop new approaches that take into account category features of matched Chinese words, and the category information would help to effectively capture the relationship between words. This paper proposes a Prompt-based Word-level Information Injection BERT (PWII-BERT) to integrate prompt-guided lexicon information into a pre-trained language model. Specifically, we engineer a Word-level Information Injection Adapter (WIIA) through the original Transformer encoder and prompt-guided Transformer layers. Thus, the ability of PWII-BERT to explicitly obtain fine-grained character-to-word relevant information according to the category prompt is one of its key advantages. In experiments on four benchmark datasets, PWII-BERT outperforms the baselines, demonstrating the significance of fully utilizing the advantages of fusing the category information and lexicon feature to implement Chinese NER.
CROPro: a tool for automated cropping of prostate magnetic resonance images

PurposeTo bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images.ApproachCROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes (64 × 64, 128 × 128, and 256 × 256 pixels2), pixel spacing (0.2 × 0.2, 0.3 × 0.3, 0.4 × 0.4, and 0.5 × 0.5 mm2), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images (N = 1475) from the online available PI-CAI challenge were used to train (N = 1033), validate (N = 221), and test (N = 221) all models.ResultsAmong CNNs, SqueezeNet with stride cropping (image size: 128 × 128, pixel spacing: 0.2 × 0.2 mm2) achieved the best classification performance (0.678 ± 0.006). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm2) achieved the best performance (0.756 ± 0.009). Model performance depended on the cropped area, with optimal size generally larger with center cropping (∼40 cm2) than random/stride cropping (∼10 cm2).ConclusionWe found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.
Publishing locally private high-dimensional synthetic data efficiently

The existing researches on local differential privacy mainly focus on low-dimensional data. These may be faced with high computation cost or low data accuracy when handling high-dimensional data. LoPub published high-dimensional crowd-sourced data, whose precision decreases with the increase of attributes pairs. In this paper, PrivPJ, a method to preserve local differential privacy of published high-dimensional data, is proposed, which can capture a tradeoff between the availability of results and privacy preservation. In PrivPJ, we design mVAE, a multivariate joint distribution algorithm that can alleviate the effect of increasing attributes pairs on accuracy. Then the complete graph is obtained based on the distribution and Markov network, and a local-differential-private junction tree is built based on the graph. PrivPJ can publish a locally private synthetic dataset with the approximate joint distribution. PrivPJ satisfies local differential privacy, and performs well on computation cost, estimation accuracy and misclassification rate of the algorithm on high-dimensional datasets.
A Multi-agent Autonomous Formation Motion Planning Method for Car-Like Robots Based on the Leader-Follower Model

With the continuous application and development of unmanned technology in the military field, unmanned combat systems have gradually become an important force in modern warfare and even future warfare. The purpose of this paper is to study the coordinated formation movement planning of multiple unmanned wheeled combat vehicles under the background of focusing on the development of the future war situation. The typical formation framework of the leader-follower structure is used to combine quadratic programming and polynomial planning methods to realize the trajectory planning of formation members, and the decoupling planning is carried out based on the Frenet coordinate system to reduce the dimension and simplify the problem. The strategy of virtual vehicles guiding followers is proposed to ensure the stable driving of the formation. Finally, experiments are carried out on the Gazebo simulation platform and the real environment, which proves the effectiveness of the method.
ABISchain: Towards a Secure and Scalable Blockchain Using Swarm-based Pruning

Due to its features regarding immutability, transparency and traceability, blockchain has emerged as a promising solution in various application domains. However, despite its advantages, blockchain suffers from major drawbacks in terms of scalability and performance. This is due to blockchain’s ever-growing nature. Several research studies focus on optimizing blockchain by adopting new techniques and solutions such as pruning, sharding, SegWit and off chain storage solutions. However, so far, most of the suggested solutions entail massive technical burden, increase complexity and reduce transparency and security. In this work, after conducting a comparison among existing blockchain pruning solutions, we present a new intelligent pruning technique that only prunes spent transactions or previous versions of smart contracts. In the proposed approach, data are pruned according to data usage, allowing each participant to independently provide a data list that should be pruned. Our solution involves an intelligent selection process based on particle swarm optimization that uses node parameters to select the best list from all agents lists, this list will be adopted by all the blockchain network members. The approach is implemented in our ABISchain blockchain. ABIshchain pruning solution will reduce the storage requirements for nodes, increase the overall performance and security level as joining nodes will need only to fetch a pruned blockchain (which is more secure than joining blockchain network with a blockchain snapshots provided by other nodes).
To Compare the Attention Span of Down Syndrome Children with Healthy Children of Same Age Group

Introduction: Down syndrome is the most common congenital chromosomal disorder. In terms of genetic causes of mental impairment, this condition is the most prevalent. The typical characteristics of students with intellectual disability, such as down syndrome are limited attention span and motor skills. Attention is the first stage in information processing. It collects parts of the environmental information for later processing. However, there are dearth of studies which compares attention span of down syndrome children with healthy children of same age group. &#x0D; Aim: The aim of the study is to examine the attention span of down syndrome children and comparing their attention span with healthy children of same age group.&#x0D; Materials and methods: The present study had a cross-sectional comparative research design. 20 subjects were taken conveniently which included 10 down syndrome and 10 healthy children (8-14 years old). The attention span was measured by using Stroop color and word test and analyzed with independent t-test.&#x0D; Results: Independent t-test indicated a significant difference between the attention span of down syndrome and healthy children.&#x0D; Conclusion: This research evaluated that down syndrome children have less attention span than healthy children of same age group. Therefore, down syndrome children need more attention training.&#x0D; Keywords: Attention span, Down syndrome, healthy children, Comparison.
An automatic whole-slide hyperspectral imaging microscope

Whole slide imaging (WSI) is a common step used in histopathology to quickly digitize stained histological slides. Digital whole-slide images not only improve the efficiency of labeling but also open the door for computer-aided diagnosis, specifically machine learning-based methods. Hyperspectral imaging (HSI) is an imaging modality that captures data in various wavelengths, some beyond the range of visible lights. In this study, we developed and implemented an automated microscopy system that can acquire hyperspectral whole slide images (HWSI). The system is robust since it consists of parts that can be swapped and bought from different manufacturers. We used the automated system and built a database of 49 HWSI of thyroid cancer. The automatic whole-slide hyperspectral imaging microscope can have many potential applications in biological and medical areas.
Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons

The rapid development of cyber-physical systems in high-stakes safety-critical areas requires innovations in protecting them against malicious adversaries. Data-driven attack detection mechanisms based on deep learning (DL) have emerged as powerful tools to fulfil this need. However, it is well-known that adversarial attacks deceive DL models with specifically crafted perturbations added to clean data samples. This work combines cyber-physical system characteristics with DL to develop a hybrid attack detection system. Using knowledge from both physical dynamics and data, we defend against both cyber-physical attacks and adversarial attacks. This approach paves the way to use classical theories from the application domain to mitigate the deficiency of DL, complementing existing adversarial defence methods such as adversarial training. We implement our defence system for an autonomous vehicle platoon test-bed in a sophisticated simulator, where our approach doubles the detection F1 score and increases the minimum inter-vehicle distances compared to existing baselines. Hence, we greatly improve the safety and security of the target system against adversarially-masked cyber-physical attacks.
Morphosyntactic Features of Membuat ‘Make’ in the Light Verb Constructions of Indonesian

Light verb constructions (LVCs) are verbs that have an idiomatic-like sense that can be interpreted from their noun-part of construction. Based on the morphosyntactic presentation, the LVCs have been construed by using verbs and nouns. This study aimed to identify and describe the morphosyntactic features of LVCs in Indonesian, namely the membuat ‘make’ marker construction. The data had been retrieved from three reputable corpora, i.e., LCCI (Liepzig Corpora Collection-Indonesian), SEAlang (SEAlang Library Indonesian Text Corpus), and KBBI (Kamus Besar Bahasa Indonesia). The analysis has been done using immediate constituent analysis (ICA). The results provided the following three folds. First, the verb membuat ‘make’ in Indonesian LVCs tends to construct the ACTIVE type of LVCs. Second, the verb membuat ‘make’ in Indonesian LVCs tends to produce the TRANSITIVE type of LVCs. Third, the verb membuat ‘make’ in Indonesian LVCs tends to characterize LVCs as the non-valency-changing verb.
Praksis Literasi Digital dalam Pembelajaran Bahasa Inggris Abad 21

This study aims to determine the praxis of digital literacy carried out in the English learning process at Citra Kasih High School based on the global digital literacy competency framework developed by UNESCO. The method used in this research is descriptive qualitative to describe the practice of digital literacy in learning English at Citra Kasih High School. The results showed that there were seven areas of digital literacy competence that were applied in the English learning process at Citra Kasih High School, namely: 1) hardware and software; 2) information and data literacy; 3) communication and collaboration; 4) digital content creation; 5) security; 6) problem solving; 7) career-related competencies. In conclusion, the indicators of digital literacy skills initiated by UNESCO for the digital community, in general, can be used in the English learning process in particular. The seven digital literacy competency areas applied in learning English at Citra Kasih High School are implemented in preliminary activities, core activities, and closing activities.&#x0D; Keywords: Global Framework, Digital Literacy, Practice, UNESCO
Vocabulary Materials Based on Picture Media for Autistic Students

This study aims to develop image-based vocabulary material for autistic students at MTs Sunan Drajat Sugio Lamongan. This research is a Research and Development (R&amp;D) research. The subjects of this study were autistic students at MTs Sunan Drajat Sugio Lamongan. Researchers used three instruments to obtain data, namely interviews, questionnaires and observation checklists. The results showed that autistic students needed vocabulary material that contained many exciting pictures, varied activities, and attractive colors. The product of this research is vocabulary material entitled Picture Vocabulary Mastery for Autistic Students, which is suitable for the needs of autistic students. In conclusion, autistic students like vocabulary material and these products can help autistic students learn English, especially vocabulary.&#x0D; Keywords: Autistic Students, Picture Media, Vocabulary
Key–Value Pair Identification from Tables Using Multimodal Learning

Computer vision and optical character recognition techniques have rapidly advanced in order to accurately capture text and other features from paper documents. While state-of-the-art tools in these fields now yield high accuracy, analyzing their outputs requires more research. Since tables are common in such documents, a new pipeline, based on multimodal learning, is proposed to better extract key–value pairs from tables. Its performance is evaluated with a synthetically generated dataset with randomly generated tables and a dataset of mechanical part documents provided by SiliconExpert Technologies. Its performance is also compared with another state-of-the-art model built for similar tasks, LayoutLM. The proposed pipeline provides a fully automated, end-to-end scalable solution, beginning with image processing and computer vision components to a machine learning model that uses data from optical character recognition and natural language processing to make the final decisions. In the best configuration, the pipeline achieved a 96.26% accuracy on a large, synthetically generated training and test set. When comparing the proposed pipeline with LayoutLM, the proposed pipeline performed similarly on the synthetic dataset and better on the real dataset. These results show the potential of the multimodal approach in extracting key–value pairs from tables from real paper documents.
Digital Management Strategy of Natural Resource Archives Under Smart City Space-Time Big Data Platform

The data under the smart city spatio-temporal big data platform is very diverse, and there are many modern spatial and spatial databases in the archives management system related to natural resources. Big data can effectively improve the quality and classification of natural resource archives management (referred to as NRAM for convenience of description). However, the traditional NRAM method and informatization level can no longer meet the needs of the current NRAM, so people must continue to make efforts to digitize the natural resource archives. To this end, this paper analyzed the characteristics and problems of NRAM and then used the big data platform to make corresponding management adjustments to promote the development of NRAM. Under big data, the degree of management improvement and management efficiency were better than the original NRAM, and the degree of management improvement was 14% higher than the original NRAM. In short, both big data and artificial intelligence can improve the integrated management of natural resource archives.
Modal analysis of tethered UAV system under tension

In order to analyze the modal of the tethered UAV system, the motion of the UAV is treated as the boundary condition of the tether, and coupling dynamic model of the tethered UAV system is obtained. Considering the aerodynamic force of the tethered UAV system, the approximate expressions of the equilibrium tension and the equilibrium curvature of the tether are derived. On the basis of rebalancing analysis, the nonlinear equations of the tethered UAV system are linearized, and the normal natural frequencies and modes of the tethered cables are obtained. The influence of cable length and UAV load mass on the normal natural frequency and mode shape of mooring line is studied. It is found that with the increase of cable length and load mass, the forward second-order frequency of mooring method decreases continuously, and the normal second order frequency decreases more than the first order frequency. When the UAV’s load mass is fixed, the first normal mode shape of the mooring line changes little with the increase of the length of the mooring line. When the length of the cable is fixed, with the increase of the UAV load mass, the natural frequencies of the mooring method change little. When the wind excitation frequency of the system is close to the frequency of the system, the resonance of the system can be avoided by changing the length of the cable and load mass of the UAV to maintain the normal operation of the system. These studies provide a theoretical basis for the normal operation of the tethered UAV system.
A Multi-Directional Pixel-Swapping Approach (MPSA) for Entropy-Retained Reversible Data Hiding in Encrypted Images

Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme MPSA allows the data hider to hide two bits in every encrypted block. The existing reversible data-hiding schemes modify the encrypted image pixels leading to a compromise in image security. However, the proposed work complements the support of encrypted image security by maintaining the same entropy of the encrypted image in spite of hiding the data. Experimental results illustrate the competency of the proposed work accounting for various parameters, including embedding rate and computational time.
Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

The graph neural network (GNN) based approaches have attracted more and more attention in session-based recommendation tasks. However, most of the existing methods do not fully take advantage of context information in session when capturing user’s interest, and the research on context adaptation is even less. Furthermore, hypergraph has potential to express complex relations among items, but it has remained unexplored. Therefore, this paper proposes an adaptive context-embedded hypergraph convolutional network (AC-HCN) for session-based recommendation. At first, the data of sessions is constructed as session hypergraph. Then, the representation of each item in session hypergraph is learned using an adaptive context-embedded hypergraph convolution. In the convolution, different types of context information from both current item itself and the item’s neighborhoods are adaptively integrated into the representation updating of current item. Meanwhile, an adaptive transformation function is employed to effectively eliminate the effects of irrelevant items. Then, the learned item representations are combined with time interval embeddings and reversed position embeddings to fully reflect time interval information and sequential information between items in session. Finally, based on learned item representations in session, a soft attention mechanism is used to obtain user’s interest, and then a recommendation list is given. Extensive experiments on the real-world datasets show that the proposed model has significantly improvement compared with the state-of-arts methods.
Voicify Your UI

Nowadays, voice assistants help users complete tasks on the smartphone with voice commands, replacing traditional touchscreen interactions when such interactions are inhibited. However, the usability of those tools remains moderate due to the problems in understanding rich language variations in human commands, along with efficiency and comprehensibility issues. Therefore, we introduce Voicify, an Android virtual assistant that allows users to interact with on-screen elements in mobile apps through voice commands. Using a novel deep learning command parser, Voicify interprets human verbal input and performs matching with UI elements. In addition, the tool can directly open a specific feature from installed applications by fetching application code information to explore the set of in-app components. Our command parser achieved 90\% accuracy on the human command dataset. Furthermore, the direct feature invocation module achieves better feature coverage in comparison to Google Assistant. The user study demonstrates the usefulness of Voicify in real-world scenarios.
Edge Matching of Image with Large Viewpoint Variation Based on Relative Motion Direction of Edge Corners

Fast edge matching of images with large viewpoint change is a difficult task for present feature matching techniques. In this paper, an edge matching method based on relative motion direction of edge corners (RMDEC) is proposed, which maintains strong robustness even under large affine deformation. This method is composed of extracting corners from structural edges, establishing RMDEC sequence for each edge, and identify correspondence by investigating the corresponding edge corner pairs of the comparative edges. This approach is most applicable for image matching with high definition, strong structure, and large parallax. Its main purpose is to find the most matching edge pair between the comparison images and the pairwise jump point pairs used to generate the transformation matrix between images. To verify the performance of the proposed method, the traditional state-of-the-art feature matching techniques are used as the comparison, and the classical data sets in relevant literature used for image invariance verification are put into experiments. The results show that the proposed method shows the best performance in matching accuracy and time efficiency, especially for high-definition and strong structure images with large change of viewpoint.