Reviewerly/exHarmony huggingface dataset:
Exact Query in Multi-version Key Encrypted Database via Bloom Filters

As people become more privacy conscious, the security requirements of databases are increasing, and thus the technology of encrypted databases is developing rapidly. Although many theoretical results have been produced in the academic community, only a few examples have been operated in the enterprise community, mainly due to the imbalance in the trade-off between the efficiency and security of encrypted databases. In this paper, we focus on an example of the encrypted database, the multi-version key encrypted database, which loses the unique indexing function and has excessive exact query time overhead in achieving security. In order to solve this problem and fully optimize the database, we systematically analyze the implementation principle of the exact query algorithm of this database to reduce the redundant operations in the original query process, and draw on Blind Seer's idea of index design, use Bloom filters to construct unique indexes for this encrypted database, optimize the exact query process, and finally achieve high efficiency and security at the cost of tolerable space overhead and false alarm rate. Theoretical analysis shows that the new scheme is not less secure than the original scheme, the time complexity is reduced, and it is compatible with other operations; experimental results show that the efficiency of the exact query has been effectively improved.
Parallelize Accelerated Triangle Counting Using Bit-Wise on GPU

Triangle counting is a graph algorithm that calculates the number of triangles in a graph, the number of triangles is a key metric for a large number of graph algorithms. Traditional triangle counting algorithms are divided into vertex-iterator and edge-iterator when traversing the graph. As the scale of graph data grows, the use of CPU with other architectural platforms for triangle counting has become mainstream. Our accelerating method proposes an algorithm for triangle counting on a single machine GPU, and performs a two-dimensional partition algorithm for large-scale graph data, in order to ensure that large graph data can be correctly loaded into GPU memory and the independence of each partition to obtain the right result. According to the high concurrency of GPU, a bit-wise operation intersection algorithm is proposed. We experiment with our algorithm to verify that our method effectively speeds up triangle counting algorithm on a single-machine GPU.
Knowledge Graph Completion with Fused Factual and Commonsense Information

Knowledge graph contains a large number of factual triples, but it is still inevitably incomplete. Previous commonsense-based knowledge graph completion models used concepts to replace entities in triplets to generate high-quality negative sampling and link prediction from the perspective of joint facts and commonsense. However, they did not consider the importance of concepts and their correlation with relationships, resulting in a lot of noise and ample room for improvement. To address this problem, we designed commonsense for knowledge graph completion and filtered the commonsense knowledge based on the analytic hierarchy process. The obtained commonsense can further improve the quality of negative samples and the effectiveness of link prediction. Experimental results on four datasets of the knowledge graph completion (KGC) task show that our method can improve the performance of the original knowledge graph embedding (KGE) model.
Empowering Chinese Hypernym-Hyponym Relation Extraction Leveraging Entity Description and Attribute Information

Hypernym-hyponym relations play a crucial role in various entity-based natural language processing applications. Most previous hypernym-hyponym relation extraction approaches used pattern-based, encyclopedia-based, and clustering-based methods; however, the performance of these approaches in Chinese texts is not ideal. In this paper, we introduce an entity description and attribute information-based approach for extracting Chinese hypernym-hyponym relations. First, we mine entity descriptions and attribute information from a Chinese encyclopedia, followed by applying a filtering strategy to gather candidate entities from plain Chinese texts. Following this, we develop a neural network extraction model that integrates entity descriptions and attribute information to identify hypernym-hyponym relations among the collected Chinese candidate entities. In this model, bidirectional gated recurrent units (Bi-GRU) are utilized to extract Chinese hypernym-hyponym semantic information from entity descriptions, while an attention model captures Chinese hypernym-hyponym semantic information from attribute information. We implement the proposed approach on four real-world Chinese datasets, demonstrating its practicality and superiority to compared approaches concerning evaluation metrics. This paper contributes to enhancing the accuracy of hypernym-hyponym relation extraction in Chinese texts.
X-ray Prohibited Items Recognition Based on Improved YOLOv5

Safety inspections play a crucial role in maintaining social stability and protecting the safety of public life and property. The X-ray security detector is an important scanning device in the security inspection of public transportation and express packages. However, current intelligent detection algorithms face problems such as a limited dataset of prohibited items, the imbalanced distribution of categories, variable target postures, and varying target scales, leading to the occurrence of false positives and missed detections. We propose an improved X-ray prohibited item recognition algorithm, named YOLOv5s-DAB. A deformable convolution module is designed to address the characteristics of different scales and postures of the same prohibited item in different samples. Besides, a multi-scale feature enhancement module SA-ASPP based on attention mechanism is designed, which can handle the problem of overlapping occlusion of multi-scale contraband. Experimental results in the real X-ray prohibited items dataset demonstrate that our model outperforms state-of-the-art methods in terms of detection accuracy.
Interactive Model and Application of Joint Knowledge Base Question Answering and Semantic Matching

Driven by the new generation of information technologies such as the Internet of Things and mobile communications, smart education is an important strategy for future educational development, and knowledge-based question answering is one of the important methods to help smart education development. But it is only used to answer students' questions. In this regard, on the basis of satisfying question answering, it is important to use questions and corresponding answers to support teaching, implement the educational concept of "student-centered, teacher-led", and carry out relevant research. First, a knowledge-based complex question answering model (KB-CQA) is proposed, and an unsupervised retriever is designed with the help of SBERT and k-dimensional trees to construct a training set that meets the new problems from the original training set, which improves retrieval speed and model accuracy; Second, the information of student and model interaction is matched with classroom teaching content based on BERT semantics, and teachers can adjust the teaching content according to the matching results. Experimental results on the CQA show that the model has achieved certain improvements in indicators such as Macro F1 and Micro F1; the specific results of semantic matching allow teachers to objectively understand the teaching situation and then make informed and targeted changes to the teaching expressions. The goal of improving teaching quality can be achieved through the simultaneous improvement of "students' learning" and "teachers' teaching".
Combines Contrastive Learning and Primary Capsule Encoder for Target Sentiment Classification

Target Sentiment Classification (TSC) aims to judge the sentiment polarity of the specific target appearing in a sentence. Most of the existing TSC algorithms use Recurrent Neural Network (RNN) to encode and model sentences, which can mine the semantic features of sentences, but there are still some shortcomings. RNN cannot fully capture long-distance semantic information, nor can it perform parallel processing calculations. At present, some research attempts to solve the problems of RNN as an encoder, but the generalization ability and prediction ability of these models are low, and there are certain limitations. In view of the above problems, this paper proposes a PC-SCL model that combines contrastive learning and primary capsule encoder. The primary capsule encoder network is designed to extract the hidden state of the word vector of the embedding layer, which can parallel calculate and fully extract and integrate the sentiment features between context and target. In addition, the model uses supervised contrastive learning, which enables the model to extract feature representations more accurately, and improves the generalization and prediction capabilities of the model. The model is tested on three general datasets, and the experimental results prove the effectiveness of the proposed model.
Will Data Sharing Scheme Based on Blockchain and Weighted Attribute-Based Encryption

Traditional testator-written wills can easily be lost, easily be tampered with, and forged when kept by the testator himself. The appearance of electronic wills provides convenience for will retention. However, centralized storage has risks such as malicious attacks and single point of failure. Due to the advantages of decentralization, immutable and traceable, applications based on blockchain can realize the sharing of will data but faces the problem of privacy leakage in the sharing. In this paper, we propose a weighted attribute-based encryption scheme to achieve access control of will data in blockchain while reducing the complexity of access policy. The Traceable Radix Tree index structure is designed to improve the query efficiency of users while supporting the quick query of wills' historical versions in reverse chronological order to ensure the legal validity of wills. The supervision chain, which is regulated through supervision institutions, is introduced to store the query records of users on the blockchain and prevent the privacy disclosure of users during the query process. Finally, the effectiveness of the proposed scheme is proved by relevant experiments.
The forensic information identification based on machine learning algorithms

Investigating crimes in selected areas, studying their occurrence and appropriate graphical visualization may be an important supportive information for police units. Nowadays, the available IT tools can improve the work of law enforcement agencies. For this purpose, in this work, we propose an exploration approach which allows for analyzing crime events that are recorded in the city of Baltimore, Maryland, USA. Based on the collected data, seven types of offenses are recorded. First, they are analyzed in terms of time verifying whether any type of event depends on the day of the week or a period of the day. Then the distribution of the crimes is examined by spatial clusterization and kernel density estimation methods. As a result of the analysis, it is shown when and where the citizens of the considered city are subject to the highest crime rate.
NU$^{2}$P-Based Reversible Data Hiding in the Multi-Histogram Modification Framework

This letter aims to propose an advanced predictor called NU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P in the multiple histogram modification (MHM)-based framework by combining deep learning techniques that take the local characteristics of pixels into account. NU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P is firstly designed for reversible data hiding by incorporating U <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P (an improved version of U <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -Net) equipped with convolutional block attention module (CBAM). The purpose of U <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P is to integrate the feature maps with different sizes and receptive fields through U-Net-like residual U-blocks (RSUs), which can make full of strong correlations between adjacent pixels while reducing the computational cost by means of the pooling layers of RSUs. CBAM pays different attention to feature maps and elements of feature maps from the perspectives of channel and spatial attention, thereby helping NU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P further enhance the prediction performance. In the MHM-based framework, for multiple categories generated using fuzzy C-means with multiple deliberately-designed features, NU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> P is conductive to constructing a sharp prediction error histogram (PEH) for each category and the improved discrete particle swarm optimization without significantly increasing the computational cost is used to adaptively select the optimal bins for each PEH. The experimental results show that the proposed method significantly outperforms several state-of-the-art RDH methods in terms of image quality and payload.
Reactive and Predictive Control Scheme for Evasive Maneuvers in Aerial Robots

A theoretical control solution to improve the navigation performance of an aerial vehicle, focusing on a quadcopter vehicle, is presented in this article. The main control purpose is to endow the aerial vehicle with predictive and reactive properties (evasive maneuvers) to avoid collisions with dynamic objects (or other aerial agents) that may be in direct collision with it. The proposed control architecture is formed by three modules: a predictor algorithm, an upper level command generator, and a lower level drone controller. The trajectory predictor algorithm gives a horizon of possible future positions where an object could collide with the aerial vehicle. These predictions are sent to the second module, which, based on an artificial particle field, generates velocity commands to avoid possible collisions. These commands are then sent to the drone controller, which was designed using the Lyapunov formalism to guarantee an agile and stable response to the aggressive nature of the problem. The overall architecture was validated and tested in real time with different scenarios and using a cyber-physical twin framework, showing good capabilities to anticipate (predictive properties) and avoid (reactive properties) imminent collisions.
LAC-RRT: Constrained Rapidly-Exploring Random Tree With Configuration Transfer Models for Motion Planning

Motion planning is challenging for robotic manipulators. Achieving high efficiency and generalization while considering various constraints simultaneously can be difficult. One common solution is to use iterative projection techniques to acquire feasible manipulator configurations and then search for a valid path to reach the goal state based on those configurations. However, such iterative techniques tend to be computationally expensive and time-consuming. The problem becomes more serious if equality constraints are involved, due to their narrower solution space. In this paper, we propose the Learning-Assisted Constrained Rapidly-Exploring Random Tree (LAC-RRT) algorithm, which employs self-supervised learning to train a model that can directly convert any sampled configuration to a new and valid configuration using feature values constrained by the imposed equality constraints, avoiding the need for iterative optimizations. Unlike other learning-based motion planning techniques, which typically solve the problem by building the constraint manifold based on a fixed set of constraints, LAC-RRT permits better generalization by allowing the equality constraints to be specified at run time. The experimental results show that the proposed LAC-RRT surpasses other approaches in most cases. Specifically, LAC-RRT can significantly reduce computation time by 80-90% for acquiring valid configurations and performing motion planning.
Hiformer: Sequence Modeling Networks With Hierarchical Attention Mechanisms

The attention-based encoder-decoder structure, such as the Transformer, has achieved state-of-the-art performance on various sequence modeling tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , machine translation (MT) and automatic speech recognition (ASR), benefited from the superior capability of layer-wise self-attention mechanism in the encoder/decoder to access long-distance contextual information. Recently, analysis on the Transformer layers has shown that different levels of information, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , phoneme level, word level and semantic level, are represented at different layers. Effectively integrating information from various levels is important for structured prediction. However, the self-attention in the conventional Transformer structure only focuses on intra-layer integration, and does not explicitly model inter-layer information relationships. Also, attention across the encoder and decoder (cross-coder) only focuses on the top encoder layer but ignores the intermediate layers. In this paper, we propose a sequence modeling structure equipped with a hierarchical attention mechanism, named Hiformer, that can consider the inter-layer and cross-coder hierarchical information to improve structured prediction performance. Extensive experiments conducted on both MT and ASR tasks demonstrate the effectiveness of the proposed Hiformer model.
Pronunciation Dictionary-Free Multilingual Speech Synthesis Using Learned Phonetic Representations

This paper presents a multilingual speech synthesis approach that leverages learned phonetic representations to eliminate the need for pronunciation dictionaries in target languages. The learned phonetic representations consist of unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR). To extract UPRs, a pre-trained wav2vec 2.0 model is utilized, while a language-independent automatic speech recognition (LI-ASR) model with a connectionist temporal classification (CTC) loss is employed to derive segment-level SPRs from the speech data of target languages. An acoustic model using UPRs and SPRs as intermediate representations is then designed, comprising a UPR predictor, an SPR predictor, and a representation-to-mel-spectrogram (RTM) converter. The two predictors generate UPRs and SPRs from texts, respectively. The RTM converter first combines UPRs with SPRs using a Transformer-based encoder, and then feeds the merged representations into a decoder to produce mel-spectrograms. Considering the difficulty of collecting large training corpora for all languages in multilingual speech synthesis, the parameters of both the two predictors and the RTM converter can be pre-trained on non-target languages to further improve model performance. Experimental results on six target languages demonstrate that our method outperformed the approaches directly predicting mel-spectrograms from character or phoneme sequences, and pre-training the acoustic model using a multilingual corpus further improved the performance of synthetic speech.
Outdoor Trash Detection in Natural Environment Using a Deep Learning Model

Trash production and disposal have emerged as serious issues for underdeveloped nations as their populations have swelled. As manual classification can be both time-consuming and potentially dangerous, therefore, nowadays, it is increasingly being replaced by automated methods. Recent advances in AI and deep learning have allowed for significant advancements in trash detection and classification systems. Due to the lack of a comprehensive trash detection dataset tailored to Bangladesh, we set out to collect data that would accurately portray the complexity of Bangladesh’s scenario while also incorporating openlittermap. In this study, we employ a deep learning model known as YOLOv5. Several variants of the YOLOv5 model are used and assessed with both the freshly minted dataset and the already existing benchmark datasets. Simulation results indicate that the finetuned YOLOv5 model outperforms existing models in terms of mean average precision (mAP) and F1-score. On the Bangladeshi dataset, the model shows an mAP of 34.3% and an F1-score of 43.7%. The mAP and F1-score provide a holistic evaluation of YOLOv5’s object recognition accuracy, localization, and precision-recall balance. By incorporating the additional data from openlittermap into the new dataset, the mAP is increased to 45.4%. In addition, for some variants of YOLOv5, the suggested model produces greater mAP than the current literature on both the TACO and PlastOpol datasets. The model also achieves an mAP of 84.4% and an F1-score of 78.2% in single-class detection experiments with the newly created dataset. This is because concentrating on just one class helps eliminate class ambiguity, improves localization accuracy, and mitigates class imbalance.
MSM-VC: High-Fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-Scale Style Modeling

In addition to conveying the linguistic content from source speech to converted speech, maintaining the speaking style of source speech also plays an important role in the voice conversion (VC) task, which is essential in many scenarios with highly expressive source speech, such as dubbing and data augmentation. Previous work generally took explicit prosodic features or fixed-length style embedding extracted from source speech to model the speaking style of source speech, which is insufficient to achieve comprehensive style modeling and target speaker timbre preservation. Inspired by the style's multi-scale nature of human speech, a multi-scale style modeling method for the VC task, referred to as MSM-VC, is proposed in this paper. MSM-VC models the speaking style of source speech from different levels, i.e., global, local, and frame levels. To effectively convey the speaking style and meanwhile prevent timbre leakage from source speech to converted speech, each level's style is modeled by specific representation. Specifically, prosodic features, pre-trained ASR model's bottleneck features, and features extracted by a model trained with a self-supervised strategy are adopted to model the frame, local, and global-level styles, respectively. Besides, to balance the performance of source style modeling and target speaker timbre preservation, an explicit constraint module consisting of a pre-trained speech emotion recognition model and a speaker classifier is introduced to MSM-VC. This explicit constraint module also makes it possible to simulate the style transfer inference process during the training to improve the disentanglement ability and alleviate the mismatch between training and inference. Experiments performed on the highly expressive speech corpus demonstrate that MSM-VC is superior to the state-of-the-art VC methods for modeling source speech style while maintaining good speech quality and speaker similarity. Furthermore, ablation analysis indicates the indispensable of every style level's modeling and the effectiveness of each module.