Reviewerly/exHarmony huggingface dataset:
Construction of Interactive Platform for Audio Reading From the Perspective of All Media Based on Vue Framework

“All-media“ is the result of the deep integration of various media under the support of information and network technology. With the advent of the era of all-media, all-effective media, all-member media, holographic media and full-scale media are gradually formed, and life is more intelligent and convenient. With technology empowerment, reading content is getting richer and richer, and ‘‘ Audio Reading’’ has developed as both a carrier and platform for content, and a medium to connect content dissemination and recipients. This paper develops an interactive platform for Audio Reading based on the progressive framework Vue, which simplifies the event-driven user interface; based on the modular idea, it designs the specific module composition of the system interface and interaction, and builds a reader based on FBReader to meet the needs of audio reading in the era of allmedia.
Prediction of Ultrasound Kidney Imaging Using Convolution Neural Networks

Deep learning is now being used in the medical field to detect ailments such as cancer, diabetes, kidney disease, etc. Kidney disease has become a major public health concern around the globe. If CKD is not diagnosed at an early stage, it can lead to loss of kidney function and which require costly treatments like dialysis and a kidney transplant. Using CNN, an automatic model has been developed to predict chronic renal disease. The major goal of this study is to predict CKD and non-CKD patients. Convolutional neural networks are used to classify chronic renal disease in this proposed system, and the batch prediction method is evaluated for CKD prediction. The precision with which renal disease can be predicted is 95%, and the accuracy for the classification of CKD ultrasound images using CNN is 80%.
Analysis of API Architecture: A Detailed Report

Web services provided by various companies in the form of various APIs (Application Programming Interfaces) have increased to a very large extent in recent years. Service Oriented Architectures (SOA) and a message exchange protocol SOAP (Simple Object Access Protocol) have become prominent methods to serve the data exchange process for web services. But Recent reports state that a large number of companies are migrating from SOAP-based architecture to Representational State Transfer or REST. REST architecture is a resourceoriented architecture where resources are the entities of the system. The APIs are implemented around these resources. Also, REST architecture is stateless architecture as the client and server do not store. RESTful services return resources in some representation format. REST is a way by which a company provides a method to use its services that is provided by them exclusively. Various websites that were SOAP oriented went to REST implementation to simplify and modernize their operations. It was found that APIs were using simple HTTP verbs with no headers. REST web APIs are developed using HTTP protocol. Therefore, it makes use of all the HTTP properties like HTTP verbs, HTTP responses, header information, etc. The work presented here elaborates on the analysis of APIs using HTTP protocols and how the APIs are validated.
Epithelial Tissue Classification using Pre-Trained Deep Convolutional Neural Networks

Machine learning models can analyze medical images in a more objective and complete manner since they are algorithmic and automated. Many research efforts have explored machine learning’s potential in the detection, classification, segmentation, and other difficulties associated with different illness states. To deal with the deficiency of available data for deep learning model training, a popular solution has been to use transfer learning (TL). In particular, the ImageNet pre-trained models have been one of the most prominent applications of TL. In this study, we use transfer learning to categorize several Epithelial tissues (e.g., nervous tissues, blood, etc.), each of which is made up of cells that have a common ancestry, a comparable cellular structure, and a shared function. As our pre-trained model in Transfer Learning, we have mostly concentrated on the Xception network, a widely used CNN architecture. Along with the suggested model, some more Transfer Learning architectures like VGG16, InceptionV3, and ResNet50 were also tried. The proposed Xception model achieved an accuracy of 88%, making it the most reliable option.
Design of Semantic Segmentation Algorithm to Classify Forged Pixels

Anyone can use editing software to rotate, scale, or compress photos without permission this creates the challenges of authenticity and integrity. In this paper, a deep learning-based algorithm is proposed. In this paper semantic segmentation is used to classify forged pixels. In this algorithm, ninety-one layers are used. This model contains five blocks of encoder and decoder which contain convolution, ReLU, and pooling layers. The copy moves and splicing image CASIA dataset is used. The model is trained with sixty percent of training images and forty percent of testing images are used. This model is trained on NVIDIA 1070 GPU for 1810 minutes with 33292 iterations. This model produces an average accuracy of 98%. The model is compared with other methods, and it shows better accuracy than others.
Scenario Modelling of Country’s Region Development with Artificial Intelligence Support

The paper describes the modern control (governance, management) science approach to the socioeconomic development of a country’s region (province, subject, municipality) in the context of public administration. Proven methods for strategic goal setting, scenario and economic modelling with artificial intelligence support based on opportunity assessing and system optimisation are presented. The region is given as an integrated object for control. Strategic planning uses hierarchy analysis methods. The experience of scenario modelling, evaluation, and selection of priority scenarios of the region’s socioeconomic development with estimating the current economic indicators of the region is represented. The deep learning approach and big data analysis help to verify and automatically create scenario models under team members’ control. The proposed methodology has a significant approbation in the real sector of the economy.
Using Kullback Leibler Divergence to Perform Modulation Recognition on a TorchSig Dataset

This paper discusses a novel approach to modulation recognition (classification) of RF signals based on Kullback Leibler divergence calculations. The classification systems use small amounts of known (labeled) signals from each source to help determine the class of an unknown (test) signal. This method performs significantly better than a previous method that utilizes mutual information and may be suitable in scenarios where labeled data is unavailable in significant quantities. Testing is performed on clean signals as well as signals that are impaired with white Gaussian noise and wireless channel effects.
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning

Zero-Shot Learning (ZSL) is an extreme form of transfer learning that aims at learning from a few “seen classes” to have an understanding about the “unseen classes” in the wild. Given a dataset in ZSL research, most existing works use a predetermined, disjoint set of seen-unseen classes to evaluate their methods. These seen (training) classes might be sub-optimal for ZSL methods to appreciate the diversity and rarity of an object domain. Inspired by strategies like active learning, it is intuitive that intelligently selecting the training classes can improve ZSL performance. In this work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I) which, given an attribute-based dataset, can intelligently yield the most suitable “seen classes” for training ZSL models. DiRaC-I has two main goals – constructing a diversified set of seed classes, and using them to initialize a visual-semantic mining algorithm for acquiring the classes capturing both diversity and rarity in the object domain adequately. These classes can then be used as “seen classes” to train ZSL models for image classification. We simulate a real-world scenario where visual samples of novel object classes in the wild are available to neither DiRaC-I nor the ZSL models during training and conducted extensive experiments on two benchmark data sets for zero-shot image classification — CUB and SUN. Our results demonstrate DiRaC-I helps ZSL models to achieve significant classification accuracy improvements – specifically, up to 8% for CUB and up to 5% for SUN dataset. Additionally, while recognizing classes exhibiting rare attributes we also observe a performance boost for ZSL models, which is up to 10% and 7% for CUB and SUN datasets, respectively.
A Survey on 3D Hand Detection and Tracking Algorithms for Human Computer Interfacing

3D hand detection and tracking algorithms has increased research interests in computer vision, pattern recognition, and human-computer interfacing. It is greatly inspired by the emerging technologies like RGBD camera, depth sensors and processing architecture. Therefore, this paper presents a survey on recent works on 3D hand detection and tracking and their applications as a natural user interface to control the computer with hand movements and gestures. It examines the literature in terms of 1) 3D hand capturing techniques used like RGBD cameras, depth sensors, 2) processing with different image processing and computer vision algorithms and their hardware implementation 3) and applications in human computer interfacing for realization of the system. While the emphasis is on 3D mouse and keyboard, the related findings and future challenges are also discussed for practitioners.
Keyphrase Generation for Abstracts of the Russian-Language Scientific Articles

In this paper, we attempted to adapt various well-known algorithms for keyword selection to a very specific text corpus containing abstracts of Russian academic papers from the mathematical and computer science domain. We faced several challenges including the lack of research in the field of keyword extraction for Russian, the absence of large text corpora of academic abstracts, and the insufficient length of the abstracts. Keywords are often found in the full text of the paper and can simply be highlighted, whereas abstracts may not include keywords in an explicit form. At the same time, it is abstracts that are usually in the public domain, so automatic selection of keywords from them would significantly facilitate the process of searching for papers. Moreover, an automatic keyword selection would be useful even for papers for which keywords were already specified by the authors. During the study, we found that authors often use unique keywords for their papers. This complicates their systematization on a given topic. For visualizing the results, we have created a web resource keyphrases.mca.nsu.ru, where young/beginning scholars can form an approximate list of keywords for their first research paper.
Prompt-based for Low-Resource Tibetan Text Classification

Text classification is a critical and foundational task in Tibetan natural language processing, it plays a crucial role in various applications, such as sentiment analysis and information extraction. However, the limited availability of annotated data poses a significant challenge to Tibetan natural language processing. This paper proposes a prompt learning-based method for low-resource Tibetan text classification to overcome this challenge. This method utilizes pre-trained language models to learn text representation and generation capabilities on a large-scale unsupervised Tibetan corpus, enabling few-shot Tibetan text classification. Experimental results demonstrate that the proposed method significantly improves the performance of Tibetan text classification in low-resource scenarios. This work provides a new research idea and method for low-resource language processing, such as Tibetan natural language processing. Hopefully, it will inspire subsequent work on low-resource language processing.
Aerial online mapping on‐board system by real‐time object detection for UGV path generation in unstructured outdoor environments

Abstract An optimal path provides efficient operation of unmanned ground vehicles (UGVs) for many kinds of tasks such as transportation, exploration, surveillance, and search and rescue in unstructured areas that include various unexpected obstacles. Various onboard sensors such as LiDAR, radar, sonar, and cameras are used to detect obstacles around the UGVs. However, their range of view is often limited by movable obstacles or barriers, resulting in inefficient path generation. Here, we present the aerial online mapping system to generate an efficient path for a UGV on a two‐dimensional map. The map is updated by projecting obstacles detected in the aerial images taken by an unmanned aerial vehicle through an object detector based on a conventional convolutional neural network. The proposed system is implemented in real‐time by a skid steering ground vehicle and a quadcopter with relatively small, low‐cost embedded systems. The frameworks and each module of the systems are given in detail to evaluate the performance. The system is also demonstrated in unstructured outdoor environments such as in a football field and a park with unreliable communication links. The results show that the aerial online mapping is effective in path generation for autonomous UGVs in real environments.
Improving semantic coverage of data-to-text generation model using dynamic memory networks

Abstract This paper proposes a sequence-to-sequence model for data-to-text generation, called DM-NLG, to generate a natural language text from structured nonlinguistic input. Specifically, by adding a dynamic memory module to the attention-based sequence-to-sequence model, it can store the information that leads to generate previous output words and use it to generate the next word. In this way, the decoder part of the model is aware of all previous decisions, and as a result, the generation of duplicate words or incomplete semantic concepts is prevented. To improve the generated sentences quality by the DM-NLG decoder, a postprocessing step is performed using the pretrained language models. To prove the effectiveness of the DM-NLG model, we performed experiments on five different datasets and observed that our proposed model is able to reduce the slot error rate rate by 50% and improve the BLEU by 10%, compared to the state-of-the-art models.
Research on disturbing white‐box cryptography algorithm based on local differential privacy

Abstract White‐box cryptography protects the secret key extraction from software implementations of cryptographic primitives. The principle is to white‐box the key by using obfuscation and hide the key in the whole encryption process to ensure that the key cannot be traced and extracted. The idea of adding internal perturbed terms and additional random equations is given by Bringer et al. to implement white‐box encryption, which has been attacked due to the defect in structure. In this paper, we propose a new secure white‐box encryption method by combining the random perturbation mechanism of local differential privacy protection and adding internal interference term, which can protect the privacy of the intermediate secret key more securely. The further disrupted cipher can also obtain the correct result by voting without increasing the number of parallel systems, and also gives the practical white‐box representation to AEw/oS. Compared with the white‐box implementation proposed by Bringer et al., the memory space of the new white‐box scheme will be 44 bytes more, but in terms of security, it can resist traditional attacks and Mulder's attacks.
Non-imaging method for angle measurement based on an axicon

A new non-imaging angle measurement method based on an axicon is proposed in this paper. This method uses an axicon that can form a complex light spot with a clear edge and rich feature information after total internal reflection and refraction on the sloped face compared with convergent lenses, which can only form a blurry edge spot. According to the high sensitivity of the beam transformation of the axicon to the incident angle, light spot images with obvious feature variation can be easily obtained to achieve angle measurement with high accuracy. The method based on an axicon can meet the application requirements of multiple angle measurement ranges, and the structure is simple and compact. In the small-angle measurement experiment combined with a telescope module, the measurement resolution can reach 1 '' , the mean absolute error is 0.0010°, and the relative error is within 0.25% in the measurement range of 1.6°.
Intellectual Property Protection, Financial Innovation and Corporate Innovation: Evidence from a Quasi-Natural Experiment in China

Using China’s staggered intellectual property pilot and demonstration city (IPPDC) policy and the difference-in-differences method, this study assesses the effect of intellectual property (IP) protection on corporate innovation. The policy significantly stimulates corporate innovation investment and quality due to enhanced IP administrative enforcement after policy implementation. Financial innovation mitigates information asymmetry and negative spillovers, promoting treated firms’ technological development. By substituting high-failure-tolerance institutional investors, IP pledge financing helps treated firms to fund innovation activities. This study reveals the IP protection effect on corporate innovation, providing insights for emerging economies to formulate public policies and finance systems to achieve innovation-driven development.
Blockchain-Based Platform for Smart Tracking and Tracing the Pharmaceutical Drug Supply Chain

Every nation is presently addressing the threat posed by the sale of counterfeit medications. It is a growing global issue that has a significant effect on lower middle-income and lower income countries. According to current estimates from the WHO, one in ten of the medications circulating in low- and middle-income nations are either subpar or fake. According to the National Drug Survey 2014–2016, carried out by the National Institute of Biologics, Ministry of Health &amp; Family Welfare, counterfeit or substandard drugs make up about 3% of all pharmaceuticals in India. There is an urgent need for increased visibility and traceability within the supply chain due to the growing threat of counterfeit medications entering it and, in particular, making it into customers' hands.
Unsupervised real image super-resolution via knowledge distillation network

Super-resolution convolutional neural networks recently have demonstrated high-quality restoration for single images. Despite existing methods have achieved remarkable performance based on synthetic datasets, the performance is poor on real-world or natural data. To address this issue, zero-shot super-resolution (ZSSR) has been proposed for adaptive learning. However, ZSSR is unable to keep the simulated degradation process consistent with the degradation kernel of the real degradation process. Furthermore, the learned mapping of ZSSR is different from the desired mapping. In this paper, an unsupervised image super-resolution via knowledge distillation network (USRKDN) is proposed. Specifically, the proposed degradation module generates an image-specific degradation kernel and corresponding degenerated images. Moreover, the knowledge distillation module is proposed to solve the issue that the mapping cannot be completely equivalent, which transfers the learned map by knowledge distillation. The full convolution module is also explored to help the reconstruction of information. Extensive experimental results on synthetic and real datasets demonstrate the effectiveness of USRKDN. In addition, USRKDN is proven to be good at reconstructing image details in real scenes, which provides an effective method for generating information learning tasks with fewer samples.
Single image super-resolution by cascading parallel-structure units through a deep-shallow CNN

Nowadays, single image super-resolution based on deep learning algorithms is reaching state-of-the-art performance. Nevertheless, most frameworks suffer from needing huge sample set and powerful computing hardware for training. Therefore, this paper presents Cascading Parallel-structure units through a Deep-Shallow (CPDS) CNN which provides satisfactory results not only with small sample set but also low computational cost with the goal of fully using the network design capabilities. In the proposed deep-shallow CNN, an approximated image is reconstructed via the lightweight shallow network, and the missing high-frequency details are retrieved by its complementary deep network. Regarding this, deep CNN is designed based on cascading parallel-structure units in the whole parts of the model to reduce the depth of the model and the number of network parameters. Besides, in the proposed cascading strategy, feature disappearance is restricted by concatenating and fusing features. Based on the parallel-structure of deep CNN, the initial features are firstly extracted using two parallel feature extractor units. Moreover, deep CNN applies a multi-scale fashion to detect and extract coarse-to-fine features simultaneously. Finally, the strategy of combining upsampling methods of deconvolution layer and pixelshuffle is adopted to ensure the high-resolution image reconstruction is accurately validated. Experimental results demonstrate the proposed end-to-end approach outperforms most state-of-the-art methods in terms of training specifications, evaluation metrics, and visual performance.
High-order interaction feature selection for classification learning: A robust knowledge metric perspective

Feature selection is an important learning task in data mining and knowledge discovery. Nevertheless, the fuzziness, uncertainty, and noise presented by the data greatly complicate the construction of learning models. Moreover, most works focus on exploring low-order correlations between variables using low-dimensional mutual information, without paying attention to high-order interaction for multiple variables, resulting in the loss of some potentially important dependency information. Driven by these two issues, a robust knowledge metric approach is invented to perceive and excavate the latent information hidden in interaction. In this study, firstly, a robust fuzzy granularity space is constructed from different granular structures induced by different features, and the robust fuzzy uncertainty measures (RFUMs) are successively devised. Then, RFUMs are used to measure pair-wise, three-order, and even higher-order interaction dependencies among features. Further, a constrained high-order interaction evaluation function inspired by the N-gram language model is formulated, and a corresponding high-order interaction feature selection algorithm with RFUMs (HIFS-RFUMs) is designed. Next, comparative experiments with seven representative algorithms on twenty datasets illustrate its effectiveness. In addition, ablation experiments are conducted on the high-order interaction feature selection algorithm with fuzzy uncertainty measures (HIFS-FUMs) and the relative reduction algorithm with RFUMs (R2-RFUMs), which demonstrate the robustness of the metric and the effectiveness for mining high-order interactive features, respectively.