Reviewerly/exHarmony huggingface dataset:
Image Deep Steganography Detection based on Knowledge Distillation in Teacher-Student Network

The current hot deep steganography can hide confidential information of an image into a carrier image of the same size. It also has the characteristics of a high steganography rate and is often used for private communication within specific organizations. However, the existing deep steganography detection network is complex and time-consuming. Thus, rapid steganography detection for massive image information is urgently needed. In this study, a typical distillation algorithm is applied to the existing classical deep steganography detection network (Ye-Net and Yedroudj-Net). We constructed a student network, taking Ye-Net and Yedroudj-Net as teacher networks respectively. Appropriate parameters T and α are also selected in accordance with the effect of knowledge distillation. With these parameters, a fast training model of deep steganography detection is established. The proposed deep steganography detection network is compared with Ye-Net and Yedroudj-Net through experiments. Three spatial steganography algorithms (Wow,S-uniward and Hill) and different steganography rates are used. The experimental results show that the proposed network can shorten the training and average detection time by about 70% and 24% when the detection accuracy is slightly lower. Overall, compared with Ye-Net and Yedroudj-Net, the proposed method can achieve fast detection of deep steganography. And code will be available at: https://github.com/hhfshiqi/KD-Ye-net
Let the Robot Speak! AI-Generated Speech and Freedom of Expression

Up until very recently, AI-generated, or more precisely, machine learning (ML)-generated content was still in the realm of sci-fi. A recent series of important inventions gave AI the power of creation: Variational Autoencoders (VAEs) in 2013, Generative Adversarial Networks (GANs) in 2014, and Generative Pre-trained Transformers (GPT) in 2017. Synthetic products based on generative ML are useful in diverse fields of application. For example, generative ML can be used for the synthetic resuscitation of a dead actor, or a deceased loved one. Can ML be a source of speech that is protected by the right to freedom of expression in Article 10 ECHR? In contrast to a tool, such as a pen or a typewriter, ML can be such a decisive element in the generative process, that speech is no longer (indisputably) attributable to a human speaker. Is speech generated by a machine protected by the right to freedom of expression in Article 10 ECHR? I first discuss if ML-generated utterances fall within the protective scope of freedom of expression (Article 10(1) ECHR). After concluding that this is the case, I look at specific complexities raised by ML-generated content in terms of limitations to freedom of expression (Article 10(2) ECHR). The first set of potential limitations that I explore are those following from copyright, data protection, privacy and confidentiality law. Some types of ML-generated content could potentially circumvent these limitations. Second, I study how new types of content generated by ML can create normative grey areas where the boundaries of constitutionally protected and unprotected speech are not always easy to draw. In this context, I discuss two types of ML-generated content: virtual child pornography and fake news/disinformation. Third, I argue that the nuances of Article 10 ECHR are not easily captured in an automated filter and I discuss the potential implications of the arms race between automated filters and ML-generated content.
Agricultural super docking Traceability System of Fresh Agricultural Products

In response to the national call of “food safety”, this paper introduces edge computing and block chain technology to design the agricultural super docking traceability system for fresh agricultural products. The system employs two-dimensional code, camera, temperature and humidity sensors and other terminal equipment to collect real-time data, including the detection data of fresh agricultural products, the information of processing and packaging fresh agricultural products, the status of fresh agricultural products during transportation. Edge computing technology is used to process edge information, alleviate the network transmission congestion and network delay of the traceability system. Blockchain technology is used to implement the safe storage tamper-resistant of data. Our research also carried out a traceability system construction of fresh agricultural products for a supermarket in Yucheng city.
Improving the Robustness and Accuracy of Intelligent Clinical Text Processing: Producing Noise for Data Augmentation

Artificial Intelligence (AI) systems are attracting increasing interest in the medical domain due to their ability to learn complicated tasks that require human intelligence and expert knowledge. AI systems that utilize high-performance Natural Language Processing (NLP) models have achieved state-of-the-art results on a wide variety of clinical text processing benchmarks. They have even outperformed human accuracy on some tasks. However, performance evaluation of such AI systems have been limited to accuracy measures on curated and clean benchmark datasets that may not properly reflect how robustly these systems can operate in real-world situations. In order to address this challenge, we introduced and implemented a wide variety of perturbation methods that simulate different types of noise and variability in clinical text data. While noisy samples produced by these perturbation methods can often be understood by humans, they may cause AI systems to make erroneous decisions. Conducting extensive experiments on several clinical text processing tasks, we evaluated the robustness of high-performance NLP models against various types of character-level and word-level noise. The results revealed that the NLP models’ performance degrades when the input contains small amounts of noise. Furthermore, as a data augmentation strategy, we used noisy samples produced by the perturbation methods to further fine-tune the NLP models. The experimental results showed that further fine-tuning on noisy samples can improve the robustness and predictive accuracy of clinical NLP systems. This study takes a significant step towards exposing vulnerabilities of AI models utilized in clinical text processing systems. It also shows the effectiveness of training on noisy data to enhance the robustness and generalization abilities. The proposed perturbation methods can be used in performance evaluation tests to assess how robustly clinical NLP models can operate on noisy data, in real-world settings. Moreover, they can be utilized to produce noisy training samples in data augmentation scenarios.
A Text Generation Model that Maintains the Order of Words, Topics, and Parts of Speech via Their Embedding Representations and Neural Language Models

Our goal is to generate coherent text accurately in terms of their semantic information and syntactic structure. Embedding methods and neural language models are indispensable in generating coherent text as they learn semantic information, and syntactic structure, respectively, and they are indispensable methods for generating coherent text. We focus here on parts of speech (POS) (e.g. noun, verb, preposition, etc.) so as to enhance these models, and allow us to generate truly coherent text more efficiently than is possible by using any of them in isolation. This leads us to derive Words and Topics and POS 2 Vec (WTP2Vec) as an embedding method, and Structure Aware Unified Language Model (SAUL) as a neural language model. Experiments show that our approach enhances previous models and generates coherent and semantically valid text with natural syntactic structure.
New Year Pictures Stylization Algorithm Based on Multi-feature Data Fusion

In recent years, Internet technology has developed rapidly. With the development of the Internet, data has been created and accumulated at an unprecedented speed. In order to effectively extract the effective information contained in the massive data and provide users with suggestions and references based on this information, the new year picture stylization algorithm based on multi-data fusion was born and has attracted more and more attention from all walks of life, and there is still a lot of room for growth and research. value. New Year pictures represent the fusion of my country's classical literature and traditional art, are very representative of Chinese traditional culture, and are one of the important treasures of intangible cultural heritage. The main research content of this paper is the study of the New Year picture stylization algorithm based on multi-feature data fusion. This paper discusses the classic algorithm and particle filter algorithm based on the multi-feature data fusion, and studies the New Year picture stylization algorithm based on the multi-feature data fusion. The final result of the research shows that the average center distance of the multi-feature fusion algorithm is 7.5, the error standard value is 3.2, and the overlap rate is 86%. Compared with other algorithms, the center distance average and standard error deviation of the multi-feature fusion algorithm is the smallest, indicating that the multi-feature data fusion algorithm has a higher success rate and accuracy rate.
Sign Language Annotation Algorithm Based on BGRU-CTC

Sign language annotation provides accurate supervised training data for sign language recognition, improves the accuracy of sign language recognition, solving intelligent communication problems between hard of hearing people and listening society. However, the traditional sign language annotation uses manual annotation, which is time-consuming and laborious. Therefore, we propose an automatic sign language annotation algorithm based on Bi-Gated Recurrent Unit(BGRU) and Connectionist Temporal Classification(CTC). This method first extracts the sign language feature sequence through resnet50 and then encodes the sequence through BGRU based on the extracted sign language feature sequence. Learn the relationship between the input sequence and output sequence through CTC. We evaluate the performance of the algorithm on the CSL. The word error rate is 0.3% lower than the state of art methods, which verifies the effectiveness.
Density Peaks Clustering Algorithm for Large-scale Data Based on Divide-and-Conquer Strategy

Density peaks clustering algorithm is a simple but effective clustering method, which requires fewer parameters and iteration, and can determine the number of clusters. But this algorithm has high complexity of time and space which makes it is unsuitable to cluster large-scale data. So that this paper proposes a density peaks clustering algorithm based on the divide-and-conquer strategy. Firstly, divides the large-scale data into a series of data blocks consistent with the original data distribution, then performs density peaks clustering on a randomly selected block to get the clustering center of the data block. Since the data block has the same distribution with the original large-scale data, the clustering center can be used as the clustering center of the original data. Finally, allocates the remaining data blocks to the corresponding clustering center to obtain the final clustering result. Experimental results on real and synthetic datasets verify the effectiveness of the proposed algorithm.
Structured Fusion Attention Network for Image Super-Resolution Reconstruction

To improve the extraction ability of image features, reduce the complexity of model parameters, and enhance the reconstruction effect of image super-resolution (SR), a structured fusion attention network (SFAN) is proposed.Firstly, the deep convolution method is used to extract shallow features from lowresolution images, and different residual attention modules are considered to improve the structured residual of the encoder to extract more image features.Secondly, the features output by the encoder are refined, and the spatial attention module and the channel attention module are reorganized according to an improved fusion attention method to provide better input features for PixelShuffle, thus achieving the effect of reconstructing the decoder.Finally, through adding low-frequency inputs and network predictions, the input image is directly interpolated into the target, thereby accelerating the convergence of the network's highfrequency residual and improving the image reconstruction effect.Under the condition of reconstruction magnification of ×2, ×3, ×4, SFAN is compared with some of the most advanced SR networks in the public data sets of Set5, Set14, BSD100, Urban100 and Manga 109.The experimental results show that SFAN has the best PSNR and SSIM values with low model parameters, thus proving that SFAN can achieve a good balance between the performance of SR and the complexity of parameters.
Remote Sensing Image Object Detection Algorithm Based on Receptive Field Enhancement

Small object detection algorithm is always a difficult research topic in remote sensing images. A remote sensing image object detection algorithm which is based on receptive field enhancement is proposed in this paper. Firstly, the backbone network is the SSD net. In order to form the feature map pyramid, the receptive field enhanced convolution module is added in feature extraction. Then, the feature maps of the low-level and the high-level are spliced, so he output feature map retains the rich context information. Because it contains the information in the low-level feature map and the high-level feature map, which are location information and semantic information, respectively. Finally, based on the idea of dilated convolution, the receptive field enhancement module is designed to make the receptive field of the feature map larger. And the feature information is filtered to reduce the aliasing effect when the feature map is spliced. After the experimental result on remote sensing image dataset NWPU vhr-10, the average detection accuracy of the algorithm is 90%, which improves the object detection accuracy, especially the detection accuracy of the small objects.
A Summary of the Workshop on Pilot Implementation for Testing Human-Work Interaction Designs

Pilot implementations are field tests of properly engineered, yet unfinished, systems. By exposing systems to their intended environment, pilot implementations emphasize realism and collect real-use feedback for system finalization. While practitioners recognize pilot implementations as a means of testing the fit between a system and its use environment, pilot implementations have received less attention from researchers in human-computer interaction. The workshop on pilot implementation for testing human-work interaction designs aimed to create research interest in pilot implementation and to provide a forum for discussing and maturing such research. The seven workshop papers included in this post-proceedings volume span a variety of angles on pilot implementation. They contribute valuable insights but also leave open questions. Collectively, they provide illustrative case studies and inspiration for further research.
Application and Prospect of VR Technology in the Treatment of Autism

Research significance: The traditional technical means in the treatment of autism, most of the biological orientation therapy, applied behavior analysis training, and game orientation, these methods are low targeted, time-consuming, and biological orientation therapy with drug treatment will affect the physical and mental health of children. With the continuous progress of science and technology, virtual reality technology better solves the problem of being limited by the environment and avoids the harm of children to the greatest extent. For example, it can cultivate children's emotional cognitive ability in the virtual world and reduce their self-stimulation behaviors. Objective: To explore the application status of virtual reality technology in the treatment of autism, including social skills, motor skills, attention training, and daily life, and further expand in detail, and finally point out the current shortcomings of virtual reality technology and prospects for the future. Research methods: Literature on the application of virtual reality technology in the treatment of autism in recent years was searched through literature navigation. The children's motor skills, social interaction, daily life, and casual attention were compared with virtual reality technology. Studying knot: theory: virtual reality technology has developed rapidly, in the treatment of autism has the great advantage over traditional technology, can let the children with autism in the case of not hurt to complete treatment, experience in a virtual environment close to the real thing, through the virtual reality technology to help patients bridge in the virtual world and real world. However, virtual reality technology still has a lot of development space but also has the current stage of the deficiency, how to ensure that children do not indulge in the virtual world, escape from the real world.
Research on the method of knowledge base question answering

Knowledge base question answering is a way of communication between computer and human in the form of natural language, which is a branch of artificial intelligence. In recent years, knowledge base question answering has become a hot research and application direction in academia and industry. At present, question answering algorithms mainly include template-based approach, semantic analysis approach, deep learning approach. Many scholars have continuously improved existing algorithms for obtaining better accuracy. For giving people an overall understanding of the research significance, knowledge base and algorithm of question answering, in this study we mainly review three classical algorithms of knowledge base question answering, and make a comprehensive comparison of these three algorithms. We identified the deficiencies and challenges of knowledge base question answering. we propose future development directions for the principle of matching questions and answers to construct query sentences, deep learning models, and inherent features of language.
Complex-Valued Signal Capsule Network for Automatic Modulation Classification

Most current deep learning (DL) based AMC methods often require a large number of labeled samples to drive itself optimization study, so as to achieve a more superior performance. However, for many AMC tasks with a small amount of data, the AMC method based on deep learning still has many challenges, which makes the deep neural network model trained on a small dataset lack effective migration effect, leading to the failure to achieve better performance effect in the testing process. In this paper, we propose a novel AMC method based on Complex-valued Signal Capsule Network (CSCN). Compared with other DL based methods, the proposed CSCN can achieve a better performance under the condition of small dataset and low SNR on the well-known deepsig dataset RML2016.10a and RML2016.10b.
Multi-task Learning Based on Multiple Data Sources for Cancer Detection

Histopathologic cancer detection aims to detect cancer by identifying metastatic tissue in histopathologic scans. Compared with manual detection, machine learning based methods can eliminate the influence of subjective factors, thereby improving the accuracy and efficiency of cancer detection. Existing methods either focus on the improvement of network structure or add prior knowledge to the model. However, both methods are task related, thus failing to apply them to other medical image classification datasets. We propose a multi-task learning method based on multiple data sources. During training, the model can learn the shared knowledge of each task, thereby improving the generalization performance. In this way, the proposed model can partly alleviate the data sparsity problem, improving the performance on low-resource benchmarks. We apply this method to the PCam dataset, which improves the recall rate by 0.6% and reduces the misdiagnosis rate by 0.3%.
Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

In this study, we aim to detect in social media texts written in Hebrew girls who are suspected of being anorexic.We constructed a dataset containing 100 blog posts written by females who are probably anorexic, and 100 blog posts written by females who are likely to be non-anorexic.The construction of this dataset was supervised and approved by an international expert on anorexia.We tested several text classification (TC) methods, using various feature sets (content-based and style-based), five machine learning (ML) methods, three RNN models, four BERT models, three basic preprocessing methods, three feature filtering methods, and parameter tuning.Several insights were found as follows.A set of 50-word n-grams (mostly word unigrams) given by an expert was found as a good basic detector.A heuristic process based on the random forest ML method has overcome a combinatorial explosion and led to significant improvement over a baseline result at a level of P = .01.Application of an iterative process that tests combinations of ''k out of n '' where n < n (n is the number of feature sets) lead to a result of 90.63%, using a combination of 300 features from ten feature sets.
Classification Using Random Forest on Imbalanced Credit Card Transaction Data

The aim of the paper is to perform an optimum solution of imbalance classification problem on a real-life scenario like a fraudulent transaction data. The credit card fraudulent datasets are significantly skewed because the number of genuine financial transactions is higher than the number of fraudulent transactions. The term imbalanced dataset refers to a skewed distribution of data objects in which the instances in classes are not evenly distributed. As the distribution of known classes is biased or skewed, instances of one of the two classes are higher than the other. The main objective of this research is to improve testing accuracy in imbalance classification problem. For this purpose, a combination of Random Forest (RF) classifier and repeated stratified k-fold, grid search cross-validation, Synthetic Minority Oversampling Technique (SMOTE), and Random Under-Sampling (RUS) are applied to perform classification. From the experimental results, it is reported that the RF with grid search cross-validation provides the maximum performance in classification accuracy on a highly imbalanced credit card transaction data.