Reviewerly/exHarmony huggingface dataset:
Human detection algorithm based on I-Unet

Recently, with the development of intelligent search and rescue system, humans trapped in disaster detection plays an increasingly crucial role in our daily life, and a sea of related technologies have been proposed. However, most of these algorithms have a low accuracy rate in detecting persons in sophisticated backgrounds. In this paper, we come up with a human detection algorithm based on the improved I-Unet to solve the challenge of low people-recognition rate in complex disaster scenarios. This paper replaces traditional convolution with dilated convolution to extract more features from images. In the meanwhile, we add batch normalization after convolution to speed up the training process. To address the problem of class imbalance, this paper uses the focal loss function to prevent confusion between the human category and other confusing types. Experimental results show that this algorithm applied in human detection is effective in complex scenes, and more accurate than the Unet-based method.
A Point Cloud Optimization Algorithm Based on Spatial Geometry Relationship and Siamese Network

This paper presents a robust point cloud optimization algorithm based on spatial geometry relationship and Siamese Network. The proposed algorithm is designed to be improve the integrity and accuracy of stereo matching. In order to approach the prior corresponding pixels in image pairs, an epipolar line constraint is employed to fix the effective matching range in searching images. Then a Siamese Network is utilized to calculate the similarity of matching templates between reference image and searching images to approach the optimal matching pixels. At last the corresponding pixel pairs are used to compute the coordinate of object points by the Space Intersection method. Comparison studies and experimental results prove the high integrity and accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.
A robust video steganography algorithm for H.264/AVC based on secret sharing

This paper presents a robust video steganography algorithm based on secret sharing to prevent the loss of video information that may be encountered in bad channel transmission. To improve the robustness of the secret message, we split the embedded message into sub-secrets by -threshold secret sharing. We select the sub-macroblock size of the inter-prediction process in the P frame as the embedding position. By establishing the sub-macroblock size and secret message mapping rules, the sub-secret is embedded in the sub-macroblock size of inter prediction process by chaos sequence selection mapping rules. When we restore the secret message, we can extract the sub-secrets to recover the secret message. The experimental results show that when the loss rate is 20%, 40%, and 60%, the survival rate of the proposed algorithm is 94.40%, 87.80%, and 73.26% respectively. We can still extract some secret messages when the loss rate reaches 80%. The experiments prove that the proposed algorithm can significantly enhance the robustness of secret messages. It can effectively address the serious loss of information that may be encountered in video transmission and prevent the loss of secret messages.
A Convolutional Neural Network Acceleration Method Based on 1-D Fast Fourier Transform

This paper introduces FFT1d-Conv, a method to accelerate convolution operations in convolutional neural networks. Convolution is one of the most computationally intensive operations in these networks, and optimizing it is crucial for improving their performance. This article explains three primary methods for convolution acceleration: time-domain, frequency-domain, and hardware acceleration. FFT1d-Conv, the new method proposed here, utilizes a unique one-dimensional fast Fourier transform algorithm that avoids the high computational cost of n-dimensional fast Fourier transform, making it more efficient. The method comprises four steps: dimensionality reduction transformation, queued convolution, dimensionality enhancement transformation, and boundary reshaping. Experimental results demonstrate that FFT1d-Conv can enhance the calculation speed by up to 63% compared to PyTorch Conv2d algorithm and is 15% faster than the conventional FFT-Conv algorithm. Furthermore, FFT1d-Conv is more efficient for large-batch and large-size convolutions, exhibiting greater adaptability to wide parameter changes. The paper concludes by outlining potential future research directions, such as designing higher-performance dimensionality reduction and reshaping transformation matrices and exploring multidimensional convolutions.
An entity relation extraction method based on the fusion of contextual information

With the growth of information technology, numerous entity relation extraction methods have developed. However, current studies primarily emphasize effective entity recognition, disregarding the significance of local information about entities in text on relation extraction and only acknowledging the presence of a singular relationship. To address this, a proposed entity relation extraction method based on the fusion of contextual information integrates Bi-RNN to further extract sentence vectors encoded by BERT.The model combining local information about entities with context, facilitating multi-relation classification through a biaffine classifier. Additionally, the model reduces the dimensionality of fused information to capture more effective information. Negative sampling is also introduced to enhance generalization capabilities. The model outperforms existing works in multiple public datasets.
A Conformer-Based Hashing Method for Large-Scale Image Retrieval

Image retrieval is an important task in computer vision. Among various image retrieval approaches, deep hashing methods map input images to binary codes for nearest neighbor search, achieving advantages in both search time and space. In recent years, most deep hashing methods have adopted convolutional neural networks (CNNs) as the backbone network. CNNs can effectively extract local information, but perform poorly in capturing global information, resulting in decreased accuracy of image retrieval. The latest visual transformers can capture global features well, but blur out the details of local features. To solve the problem, this paper proposes a Conformer-based deep hashing architecture, called ConHash, which employs a CNN and a Transformer in parallel to maximize the preservation of both local and global features. Specifically, a new encoding optimization loss, called entropy-balanced loss, was proposed to increase the entropy of the hash codes and bring them closer to the ideal uniform distribution. Comprehensive experiments were carried out on Canadian Institute For Advanced Research (CIFAR-10) and ImageNet datasets. The results show that the proposed approach achieved state-of-the-art performance. Compared to the latest deep hashing methods, our approach achieved a 4.3% and 8.7% average improvement of mean average precision (mAP) at different bit lengths. This proves the superiority of our method.
STFormer3D: Spatio-Temporal Transformer Based 3D Object Detection for Intelligent Driving

This paper proposes a novel solution to the problem of efficiently detecting 3D objects in point clouds. By leveraging Convolutional Neural Networks (CNNs) and Transformer Networks, our method combines the strengths of both networks in feature extraction and long-range contextual information. To improve the detection performance under occlusion conditions, we propose a temporal fusion module to fuse the features of the current frame and the previous frame together. At the same time, we use BiFPN to effectively aggregate features of different scales.
Graph Quaternion-Valued Attention Networks for Node Classification

Node classification is a prominent graph-based task and various Graph neural networks (GNNs) models have been applied for solving it. In this paper, we introduce a novel GNN architecture for node classification called Graph Quaternion-Valued Attention Networks (GQAT), which enhances the original graph attention networks by replacing the vector multiplication in self-attention with quaternion vector multiplication.
Towards Effective Crowd-Assisted Similarity Retrieval of Large Cursive Chinese Calligraphic Character Images

Chinese calligraphy is the art of handwriting which draws a lot of attention for its beauty and elegance. People can easily access and enjoy these priceless calligraphy works through the Internet as more and more ancient Chinese calligraphic scripts are digitalized. Despite some research on shape-based retrieval, it is still a great challenge to accurately retrieve the cursive Chinese calligraphy character image(CCI) due to the randomness and complexity of the shape. The paper proposes an effective and efficient crowd-assisted retrieval method of the CCIs which includes three supporting techniques: 1) a NRP- based similarity measure to represent calligraphic character shapes by their contour points extracted from the CCIs; 2) a Hybrid- Distance-Tree(HD-Tree)-based high-dimensional indexing scheme to boost the retrieval performance; and 3) a crowdsourcing-based human verification scheme to refine the result CCIs. Our extensive experiments have demonstrated the satisfactory performance of our proposed retrieval and indexing schemes, respectively.
Scaled Fenwick Trees

A novel data structure that enables the storage and retrieval of linear array numeric data with logarithmic time complexity updates, range sums, and rescaling is introduced and studied. Computing sums of ranges of arrays of numbers is a common computational problem encountered in data compression, coding, machine learning, computational vision, and finance, among other fields. Efficient data structures enabling log n updates of the underlying data (including range updates), queries of sums over ranges, and searches for ranges with a given sum have been extensively studied (n being the length of the array). Two solutions to this problem are well-known: Fenwick trees (also known as Binary Indexed Trees) and Segment Trees. The new data structure extends the capabilities for the first time to further enable multiplying (rescaling) ranges of the underlying data by a scalar as well in log n. Scaling by 0 can be enabled, with the effect that subsequent updates may take (log n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> time. The new data structure introduced here consists of a pair of interacting Fenwick tree-like structures, one of which holds the unscaled values and the other of which holds the scalars. Experimental results demonstrating performance improvements for the multiplication operation on arrays from a few dozen to over 30 million data points are discussed. This research was done as part of Ajna Labs in the course of developing a decentralized finance protocol. It enables an efficient on-chain encoding and processing of an order book-like data structure used to manage lending, interest, and collateral.
New Algorithm 1: Binary Direct Feedback Alignment for Fully Connected Layer

There were many algorithms to substitute the back-propagation (BP) in the deep neural network (DNN) training. However, they could not become popular because their training accuracy and the computational efficiency were worse than BP. One of them was direct feedback alignment (DFA), but it showed low training performance especially for the convolutional neural network (CNN). In this chapter, we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training. To improve the training stability, we also suggest the feedback weight initialization method by analyzing the patterns of the fixed random matrices in the DFA. Finally, we propose the new training algorithm, binary direct feedback alignment (BDFA) to minimize the computational cost while maintaining the training accuracy compared with the DFA. In our experiments, we use the CIFAR-10 and CIFAR-100 datasets to simulate the CNN learning from the scratch and apply the BDFA to the online learning based object tracking application to examine the training in the small dataset environment. Our proposed algorithms show better performance than conventional BP in both two different training tasks especially when the dataset is small.
Aspect-Level Semantic and Syntactic Reinforcement for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task whose main target is to identify the sentiment polarity associated with a given aspect in a sentence. Its main challenge is aspect feature extraction of sentences. For the problem of insufficient aspect-level information extraction, such as long dependencies problems, we propose a novel model that enhances aspect information from semantic and syntactic perspectives to address this issue. In terms of semantics, we provide a mechanism to actively mask aspect words and enable the model to capture specific properties in context. In terms of syntax, we advance a new aspect weight graph that can directly connect aspect words with syntactically related words. Furthermore, we integrate semantic extraction and syntactic analysis methods to enhance the informative representation of aspect-level words. We evaluate the effectiveness of our method on publicly available datasets. Experimental results show that our model achieves excellent results on the ABSA tasks.
An Integrated Platform for Synchronous and Auto-Tagged Data Collection From Diverse Sensors

The advances achieved in mobile and wearable devices that can collect different data including biosignals have allowed medical professionals and researchers to move from cure-oriented to prevention-oriented healthcare. This paper describes the design and implementation of an integrated platform for synchronous data collection and tagging from diverse sensors. We aim to provide researchers with a flexible and easy-to-use platform that can accommodate various types of sensors. Unlike previous implementations, this platform is focused on data acquisition in a research experimental context and effective management of the different requirements of such experiments. We also provide two use cases where the platform was used for experiment setting and management as well as automatic data tagging and collection from various sensors.
Incentive-compatible Intelligence Collaboration Analysis Framework Based on Blockchain and Evolutionary Game

Collaborative intelligence data analysis can effectively dig out valuable information from massive data to support decision-making. Ensuring data credibility in collaboration under the background of big data is a crucial problem to be solved. In addition, the low willingness of analysts to share results in low efficiency of collaborative tasks. To solve the above problems, this paper proposes a blockchain-based intelligence collaborative analysis and sharing framework, ICASChain, which contains a highly trusted intelligence collaboration mechanism and a dynamically adjustable incentive mechanism. Based on Evolutionary Game Theory, this paper analyzes the evolutionary stability strategy of intelligence sharing willingness of analysts by constructing a replication dynamic equation. We use a smart contract incentive algorithm including ICASToken to dynamically regulate them to increase the proportion of users participating in intelligence sharing. Finally, the influencing factors of information sharing and the incentive effect of the incentive model are discussed through simulation experiments. The simulation results show that the proposed ICASChain has essential application value in improving the analysts’ willingness to collaborate and share intelligence and the correctness of the analysis results.
Attention-Based Knowledge-aware Multi-Interest Intelligent Model for Sequential Recommendation

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user’s next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user’s multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence’s structure and user’s interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user’s multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study.
Ultrannel: Ultrasound based Covert Communication Channel

The covert channel is a popular method of secure data transmissions due to its stealthiness. In this paper, we propose a new type of covert channel model, Ultrannel. It allows two adjacent but air-gapped smart devices stealthily transfer data via ultrasonic waves. Unlike the former air-gap covert channels, Ultrannel modulates sensitive data on ultrasonic carriers to achieve inaudibility and does not require ultrasonic transducers or speakers.We first investigate the inverse piezoelectric effect of the capacitor and the nonlinearity of the microphone. Then we analyze how a capacitor can generate an acoustic signal. By leveraging the physical phenomenon of the capacitor and microphone, we establish a covert communication channel, with the capacitor as the signal sender and the microphone as the receiver. By transmitting a string of numbers, we validate this covert channel on common smart devices, a smart lamp, and a smartphone. The modulated sensitive data can be successfully demodulated and recovered. We evaluate the quality of the covert channel with different metrics. The experiment results demonstrate that Ultrannel can covertly transmit data between two air-gapped smart devices with 95% accuracy at a transmission speed of 1Kbps.
Enhancing Heterogeneous Graph-based Short Text Topic Learning

Short texts generated on social networks have become a widespread format of information. Multiple applications require a semantic understanding of text content, so deriving coherent topics from short texts is vital. However, traditional topic models lack sufficient word co-occurrence information limited by the sparsity of short text. The development of word embedding technology facilitates us to use the semantic and syntactic information in words for short text research. Word vectors consider the characteristics of words in various contexts when calculating the correlation among words, which is appropriate for short text topic learning. Therefore, this paper proposes a novel topic learning method called E-ShoTT, which combines Heterogeneous Graphs with prior semantic knowledge derived from word embeddings. E-ShoTT guides the extraction of phrase instances by constructing a heterogeneous graph regarding parts of speech while increasing the probability that semantically related words share the same topic assignment during topic sampling. Experiment results on two public benchmark datasets show E-ShoTT outperforms other methods in terms of topic coherence, cluster and classification accuracy.
A differential privacy based prototypical network for medical data learning

The use of machine learning models in numerous computing domains is becoming increasingly widespread, and the number of machine learning models used in diverse fields is growing. The security issues of machine learning models are getting increasingly important as a result of continual in-depth study for medical data learning. This research provides a training model based on differential privacy that blends differential privacy with a prototypical network for medical data learning. We verify the performance of the proposed method on the open-source Omniglot dataset and a synthetic dataset from Dermnet website and Kaggle, and results demonstrate that our method achieves privacy protection during medical data learning without a big drop in classification accuracy.
Introduction

Artificial intelligence (AI) is already essential in the current smart machines. For example, smartphones now utilize AI-based face recognition for user authentication. Their AI voice assistant can improve the user’s convenience because it can remove the user’s manual device operation. Still, AI applications used in commercial products highly depend on the cloud server. However, communication with the cloud can cause not only the delay due to unreliable latency of the communication channel but also a privacy problem because it transfers user data regardless of the user’s intent. Many deep neural network (DNN) accelerators provided on-device inference to solve this problem. However, they give limited applications with the lower accuracy because it just repeats pre-determined task based on pre-trained DNN. The user environment cannot be easily trained during the pre-training, and the inference processor shows poor performance if it is placed in an unexpected situation. Finally, the passive behavior of AI processors prevents providing user-friendly AI services. To make AI more interactive, demand for on-chip training has been increased. On-chip training can not only compensate for accuracy degradation but also give personalization functionality by tuning global knowledge to be optimized to the user-specific datasets. In addition, it can divide the workload of the server through the distributed learning. The distributed learning can protect even the privacy during the dataset collection so that it can easily scale up the DNN training. Even though the on-chip DNN training can have many advantages, the realization of training at the micro-AI systems seems challenging. Specifically, edge/mobile devices must handle training with their limited computing resources and battery capacity. Since slow training cannot actively adapt itself to the ever-changing environment, training should be performed reasonably quickly and efficiently.
HNPU-V1: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-Point and Active Bit-Precision Searching

This chapter presents HNPU, which is an energy-efficient DNN training processor by adopting algorithm–hardware co-design. The HNPU supports stochastic dynamic fixed-point representation and layer-wise adaptive precision searching unit for low-bit-precision training. It additionally utilizes slice-level reconfigurability and sparsity to maximize its efficiency in both DNN inference and training. Adaptive bandwidth reconfigurable accumulation network enables reconfigurable DNN allocation and maintains its high core utilization even in various bit-precision conditions. Fabricated in a 28-nm process, the HNPU accomplished at least 5.9 × higher energy-efficiency and 2.5 × higher area efficiency in actual DNN training compared with the previous state-of-the-art on-chip learning processors.