Reviewerly/exHarmony huggingface dataset:
Object Detection and Tracking Turret based on Cascade Classifiers and Single Shot Detectors

The involvement of embedded systems and computer vision is increasing day by day in various segments of consumer market like industrial automation, traffic monitoring, medical imaging, modern appliance market, augmented reality systems, etc. These technologies are bound to make new developments in the domain of commercial and home security surveillance. Our project aims to make contributions to the domain of video surveillance by making use of embedded computer vision systems. Our implementation, built around the Raspberry Pi 4 SBC aims to utilize computer vision techniques like motion detection, face recognition, object detection, etc to segment the region of interest from the captured video footage. This technique is superior as compared to traditional surveillance systems as it requires minimum human interaction and intervention at the control room of such security systems. The proposed system is capable of sensing suspicious events like detection of an unknown face in the captured video or motion detection/object detection in a closed section of a building. Moreover, with the help of the turret mechanism built using servo motors, the camera integrated in the system is capable of having 360° rotation and can track a detected face or object of interest within its range. Apart from automated tracking, the system can also be manually controlled by the operator.
Application of Artificial Intelligence Technology in Pathological Image Analysis of Breast Tissue

Abstract Breast histopathological examination usually relies on experienced pathologists. In recent years, artificial intelligence technology has been applied to digital pathological images to help doctors diagnose breast tumor. This paper mainly introduces the research progress of artificial intelligence technology in pathological classification and histological grade of breast cancer. In view of the simplification of the pathological image analysis object of breast tissue, a multimodal breast cancer aided pathological diagnosis model is proposed. The proposed model combines the patient’s immunohistochemical diagnosis data and pathological image data with artificial intelligence to analyze and diagnose. To achieve a deeper evaluation of the patient’s condition on the basis of simple tumor screening, optimize the clinical treatment plan to make it more targeted. Discuss the main problems faced by artificial intelligence technology in the field of breast pathological image analysis, and prospect its development prospects.
Detection of Wrong Disease Information Using Knowledge-Based Embedding and Attention

International Classification of Diseases (ICD) code has always been an important component in electronic health record (EHR). The coding errors in ICD have an extremely negative effect on the subsequent analysis using EHR. Due to some diseases been viewed as a stigma, doctors, despite having made the right diagnosis and prescribed the right drugs, would choose some diseases that symptom similarity instead of the real diseases to help patients, such as using febrile convulsions instead of epilepsy. In order to detect the wrong disease information in EHR, in this paper, we propose a method using the structured information of medications to correct the code assignments. This approach is novel and useful because patients' medications must be carefully prescribed without any bias. Specifically, we employ the Knowledge-based Embedding to help medications to get better representation and the Self-Attention Mechanism to capture the relations between medications in our proposed model. We conduct experiments on a real-world dataset, which comprises more than 300,000 medical records of over 40,000 patients. The experimental results achieve 0.972 in the AUC score, which outperforms the baseline methods and has good interpretability.
Design of Blockchain Aggregator for Benefit of Rural Workers using I.E Techniques

Rural workers today are facing unemployment issues due to unorganized work sector in the construction domain. Local contractors hire these workers on a first-come-first-serve daily wage basis until they fulfill their capacity. This method has been observed to be ineffective as it depends on the demographic of the location. This paper exhibits an implement of a Blockchain network, which ensures the supply chain visibility thus, seamlessly connecting all stakeholders of the supply chain network who are a part of the Blockchain ecosystem. This paper also exhibits the use of Mathematical metrics such as the cosine similarity index which helps us determine the closeness of two or more tensors and Operations Research which help us optimize the allocation of jobs. These computations are performed with the help of the smart contracts, which are incorporated in the Ethereum Blockchain network. The transactions are later verified, and blocks are created. This approach will incorporate a trust factor in the Blockchain transactions and tackle the growing concern of unemployment in the country.
Practice research of classroom teaching system based on Kinect

With the rapid development of network information technology, the traditional teaching methods have also ushered in a variety of development models. The natural interactive teaching assistant system based on network has been paid much attention to by all walks of life. This paper introduces Kinect-based motion-sensing technology into classroom teaching and realizes the teaching function by establishing gesture recognition. This method has the characteristics of novel form, flexible method, and low cost, which can effectively improve the teaching quality and has a wide application prospect. This new auxiliary teaching mode is not only flexible in teaching methods, but also easy for the interaction between teachers and students.
Mathematical methods for planning energy-efficient motion path of the manipulator anthropomorphic robot for the typical obstacles

Abstract Anthropomorphic robots (AR) have a problem of low work duration, that negatively affects their functioning efficiency in different environments. The article discusses the optimization of its manipulators energy efficiency when performing operations in the working area with an obstacle to reduce the energy consumption of robots. Increasing the useful life of the anthropomorphic robot on one cycle of charging is the purpose of the study. This happens due to reducing the energy consumption of manipulators with excessive mobility when operating in the working area with a typical obstacle through the planning of energy-efficient travel path. The article shows the results of the method development for planning the path of the AR manipulator in the working area with a typical obstacle in the shape of a sphere or a parallelepiped. The method for solving kinematics problems based on a geometric approach for kinematic chains consisting of rotational joints using the Denavit-Hartenberg representation is presented in the paper. The computational complexity of the proposed solutions is analized, the efficiency of the developed methods and algorithms is evaluated. #COMESYSO1120
Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms

Simulating a large set of medical images with variability in anatomical representation and image appearance has the potential to provide solutions for addressing the scarcity of properly annotated data in medical image analysis research. However, due to the complexity of modeling the imaging procedure and lack of accuracy and flexibility in anatomical models, available solutions in this area are limited. In this paper, we investigate the feasibility of simulating diversified cardiac magnetic resonance (CMR) images on virtual male and female subjects of the eXtended Cardiac and Torso phantoms (XCAT) with variable anatomical representation. Taking advantage of the flexibility of the XCAT phantoms, we create virtual subjects comprising different body sizes, heart volumes, and orientations to account for natural variability among patients. To resemble inherent image quality and contrast variability in data, we vary acquisition parameters together with MR tissue properties to simulate diverse-looking images. The database includes 3240 CMR images of 30 male and 30 female subjects. To assess the usefulness of such data, we train a segmentation model with the simulated images and fine-tune it on a small subset of real data. Our experiment results show that we can reduce the number of real data by almost 80\(\%\) while retaining the accuracy of the prediction using models pre-trained on simulated images, as well as achieve a better performance in terms of generalization to varying contrast. Thus, our simulated database serves as a promising solution to address the current challenges in medical imaging and could aid the inclusion of automated solutions in clinical routines.
Comparative Study of Digital Signal Processing Techniques for Tuning an Acoustic Guitar

This paper presents the result of a comparative study between different methodologies for tuning an acoustic guitar. In order to tune a guitar, determining the fundamental frequency of the note being played is crucial. Various digital signal processing techniques have been employed to analyse the sounds of musical instruments such as guitar, piano, etc. With the number of techniques already available, it becomes imperative to determine the optimal technique. In this study, the sounds of guitar strings are recorded and stored as WAV files, that act as input to the tuning program, implemented on Python and MATLAB. Fast Fourier Transform and Spectral Density Estimation are the two techniques that have been compared. The tuning process by both the methods has been explained in detail and the results of both the methods have been presented in a tabular format for comparing the accuracy of each method. The method with the results closer to the real values of the fundamental frequency is regarded as the better method.
Constructing 3-dimensional 5G coverage map for real-time airborne missions

With recent deployments of the fifth generation (5G) network and advances in unmanned aerial vehicles, diverse airborne missions that provide high resolution aerial imagery in real-time are possible. However, a reliable inference of 3D cellular coverage is required to provide seamless imagery. As a part of intercontinental 5G testbed activities within Korea-EU 5G Project (PriMO-5G), we construct 3D 5G coverage map manually, to present insights regarding 3D coverage and how to construct its map efficiently. We then devise algorithms for constructing 3D coverage map simultaneously with real-time airborne missions in a cost-effective manner.
Randomized continuous frames in time-frequency analysis

Recently, a Monte Carlo approach was proposed for processing highly redundant continuous frames. In this paper, we present and analyze applications of this new theory. The computational complexity of the Monte Carlo method relies on the continuous frame being so-called linear volume discretizable (LVD). The LVD property means that the number of samples in the coefficient space required by the Monte Carlo method is proportional to the resolution of the discrete signal. We show in this paper that the continuous wavelet transform (CWT) and the localizing time-frequency transform (LTFT) are LVD. The LTFT is a time-frequency representation based on a 3D time-frequency space with a richer class of time-frequency atoms than classical time-frequency transforms like the short time Fourier transform (STFT) and the CWT. Our analysis proves that performing signal processing with the LTFT has the same asymptotic complexity as signal processing with the STFT and CWT (based on FFT), even though the coefficient space of the LTFT is higher dimensional.
Latent Space Clustering via Dual Discriminator GAN

In recent years, deep generative models have achieved remarkable success in unsupervised learning tasks. Generative Adversarial Network (GAN) is one of the most popular generative models, which learns powerful latent representations, and hence is potential to improve clustering performance. We propose a new method termed CD2GAN for latent space Clustering via dual discriminator GAN (D2GAN) with an inverse network. In the proposed method, the continuous vector sampled from a Gaussian distribution is cascaded with the one-hot vector and then fed into the generator to better capture the categorical information. An inverse network is also introduced to map data into the separable latent space and a semi-supervised strategy is adopted to accelerate and stabilize the training process. What's more, the final clustering labels can be obtained by the cross-entropy minimization operation rather than by applying the traditional clustering methods like K-means. Extensive experiments are conducted on several real-world datasets. And the results demonstrate that our method outperforms both the GAN-based clustering methods and the traditional clustering methods.
Towards eXplainable AI in Text Features Engineering for Concept Recognition

The rapid and pervasive development of methods from Artificial Intelligence (AI) affects our everyday life. Its application improves the users’ experience of many daily tasks. Despite the enhancements provided, such approaches have a substantial limitation in the shortfall of people’s trust connected with their lack of explainability. In natural language understanding (NLU) and processing (NLP), a fundamental objective is to support human interactions using sense-making of the language for communication. Such methods try to comprehend and reproduce the self-evident processes of human communication. This applies either in receiving speech signals or in extracting relevant information from a text. Furthermore, the pervasiveness of AI methods in the workplace and on the free time demands a sustainable and verified support of users’ trust, as a natural condition for their acceptance. The objective of this work is to introduce a framework for the calculation and selection of understandable text features. Such features can increase the confidence placed into adopted NLP solutions. The following work outlines the Text Feature Framework and its text features, based on statistical information coming from a general text corpus. The showcase experiment uses those features to verify them on the concept recognition task. The results shows their capability to explain a model and its predictions. The resulting concept recognition models are competitive with other methods existing in the literature. It has the definitive advantage of being able to externalize the supporting evidence for a choice of concept identification.
Attention with Long-Term Interval-Based Gated Recurrent Units for Modeling Sequential User Behaviors

Recommendations based on sequential User behaviors have become more and more common. Traditional methods depend on the premise of Markov processes and consider user behavior sequences as interests. However, they usually ignore the mining and representation of implicit features. Recently, recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences and consider the dynamics of user behaviors. In order to better locate user preference, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions. In the network, we propose a time interval-based GRU architecture to better capture long-term preferences and short-term intents when encoding user actions rather than the original GRU. And a specially matrix-form attention function is designed to learn weights of both long-term preferences and short-term user intents automatically. Experimental results on two well-known public datasets show that the proposed ALI-GRU achieves significant improvement than state-of-the-art RNN-based methods.
3DSENet: 3D Spatial Attention Region Ensemble Network for Real-time 3D Hand Pose Estimation

The data representations of existing depth-based 3D hand pose estimation methods include depth images, volumetric representation and point cloud. Compared with the other two modalities, point cloud can directly represent the spatial information and avoid the quantization error caused by the voxelization. Furthermore, considering both the accuracy and the real time performance, we design a novel 3D deep network for point cloud, named 3D spatial attention region ensemble network (3DSENet), with smaller model, less training time and higher frame rate. Taking point cloud as input, our proposed 3DSENet is able to capture local structure information and makes fully use of the physical constraints in fingers with the guidance of an initial hand pose. The experimental results on three public datasets demonstrate that our approach helps improve the final accuracy and achieves comparable performance with state-of the-art methods.
Unsupervised Arabic dialect segmentation for machine translation

Abstract Resource-limited and morphologically rich languages pose many challenges to natural language processing tasks. Their highly inflected surface forms inflate the vocabulary size and increase sparsity in an already scarce data situation. In this article, we present an unsupervised learning approach to vocabulary reduction through morphological segmentation. We demonstrate its value in the context of machine translation for dialectal Arabic (DA), the primarily spoken, orthographically unstandardized, morphologically rich and yet resource poor variants of Standard Arabic. Our approach exploits the existence of monolingual and parallel data. We show comparable performance to state-of-the-art supervised methods for DA segmentation.
Detection of anomalous episodes in urban Ozone maps

Abstract In addition to classification and regression, outlier detection has emerged as a relevant activity in deep learning. In comparison with previous approaches where the original features of the examples were used for separating the examples with high dissimilarity from the rest of the examples, deep learning can automatically extract useful features from raw data, thus removing the need for most of the feature engineering efforts usually required with classical machine learning approaches. This requires training the deep learning algorithm with labels identifying the examples or with numerical values. Although outlier detection in deep learning has been usually undertaken by training the algorithm with categorical labels—classifier—, it can also be performed by using the algorithm as regressor. Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables about air quality. The measurements of these sensors can be treated individually—as time series—or collectively. Collectively, a variable monitored by a network of sensors can be transformed into a map. Maps can be used as images in machine learning algorithms—including computer vision algorithms—for outlier detection. The identification of anomalous episodes in air quality monitoring networks allows later processing this time period with finer‐grained scientific packages involving fluid dynamic and chemical evolution software, or the identification of malfunction stations. In this work, a Convolutional Neural Network is trained—as a regressor—using as input Ozone‐urban images generated from the Air Quality Monitoring Network of Madrid (Spain). The learned features are processed by Density‐based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for identifying anomalous maps. Comparisons with other deep learning architectures are undertaken, for instance, autoencoders—undercomplete and denoizing—for learning salient features of the maps and later to use as input of DBSCAN. The proposed approach is able efficiently find maps with local anomalies compared to other approaches based on raw images or latent features extracted with autoencoders architectures with DBSCAN.
Design of Cultural Creative Product Interaction System Based on VR

In order to improve the intelligence and fidelity of cultural and creative product design, the method of cultural and creative product interaction system based on VR technology is proposed, and the virtual reality(VR) design of cultural creative product is carried out by image processing. the 3D geometric model of the cultural creative product is modeled with Multigen Creator software, the visual simulation of the cultural creative product is carried out, and the polygon modeling of the cultural creative product is carried out. A graphic instance set of cultural and creative products is constructed in Lynx Prime, the VR rendering and 3D modeling of the interactive system of cultural and creative products are realized by using the real-time scene simulation rendering software Vega Prime. The virtual reality visual simulation of cultural and creative products is realized. The software of intelligent management system of cultural and creative products is developed under embedded Linux, and the database model of interactive system of cultural and creative products is constructed according to OpenGL graphics library. The simulation results show that the interactive system of cultural and creative products designed in this paper has a good virtual modeling effect of cultural and creative products. The design of cultural and creative products fidelity is good; the ability of information interaction is strong.
Reward-Modulated Adversarial Topic Modeling

Neural topic models have attracted much attention for their high efficiencies in training, in which, the methods based on variational auto-encoder capture approximative distributions of data, and those based on Generative Adversarial Net (GAN) are able to capture an accurate posterior distribution. However, the existing GAN-based neural topic model fails to model the document-topic distribution of input samples, making it difficult to get the representations of data in the latent topic space for downstream tasks. Moreover, to utilize the topics discovered by these topic models, it is time-consuming to manually interpret the meaning of topics, label the generated topics, and filter out interested topics. To address these limitations, we propose a Reward-Modulated Adversarial Topic Model (RMATM). By integrating a topic predictor and a reward function in GAN, our RMATM can capture the document-topic distribution and discover interested topics according to topic-related seed words. Furthermore, benefit from the reward function using topic-related seed words as weak supervision, RMATM is able to classify unlabeled documents. Extensive experiments on four benchmark corpora have well validated the effectiveness of RMATM.
Innovation and Development of English Higher Education in the Era of “Internet +”

The rapid development of information technology has brought great influence on people’s daily life. Internet, Internet of things, big data and artificial intelligence technology are constantly changing the world. Among them, the higher education industry is deeply influenced by the concept of “Internet +”. The introduction of Internet technology has completely broken the traditional educational concept and enriched the teaching and teaching means of teachers, and it has been increasingly welcomed by universities and teachers. This paper will deeply analyze the current situation of the integration of “Internet +” and college English higher education and then summarize the current situation and problems of English higher education in the era of “Internet +”. Finally, based on the existing Internet technology platform and teaching concept, this paper will explore effective ways to implement English higher education in the era of Internet plus and then guide the teaching practice of English education.