Reviewerly/exHarmony huggingface dataset:
Multi-hop Question Answering Model Based on Chinese Open Domain Knowledge Graph

Existing question answering systems based on Chinese knowledge graph mainly model the single-hop problem, that is, the answer can be obtained by querying a single triplet. In this paper, a multi-hop question answering system of Chinese open domain knowledge graph is modeled, which mainly includes a named entity recognition module and a question classification module based on ERNIE model, a multi-hop relationship prediction module based on seq2seq model, and an entity linking module based on multi-feature fusion. The multi-hop question answering in CKBQA field is realized by pipeline. The experimental results show that the F1-score of the system is 0.62 on CCKS2021 dataset, and the experimental results are competitive.It should be noted that the Chinese examples in the text have been translated into English descriptions.
A Learning-Based Multi-Type Noise Suppressing Method for Remote Sensing Images

Remote sensing images (RSIs) play an important role in a wide range of applications. However, they are frequently contaminated by multiple kinds of noises and existing methods are mostly applied to suppressing single noise type and performs poorly for various noises. To deal with above deficiencies, we propose a learning-based multi-type noise suppressing method (MNSM). Firstly, “Parallel” denoising approach is utilized to obtain partially denoised images that supply sufficient information for the subsequent fusion task. Mean-while, the noise recognition net identifies noise type and adjusts the brightness of every partially denoised image, realizing the adaptivity for different noises. The fusion net lastly merges these images to acquire one clean image. Experimental results show that this approach obtains higher peak-signal-to-noise ratio (PSNR) than existing methods.
Research on Sentimental Tendency Classification of Chinese Internet Memes Based on Self-Learning Decision Level Fusion Model

The analysis of the sentimental tendency of Internet memes has broad practical application prospects in politics, economy, society, humanities and other fields. The research on classifying Internet memes using artificial intelligence methods has attracted more and more researchers' attention. As a multimodal expression, Internet memes can be divided into two parts: images and texts embedded in images. Currently, the main research object in this field is the Internet meme of English text. This paper establishes a data set consisting of 8000 Chinese Internet meme pictures, and a self-learning decision level multimodal fusion model based on Bert and resnet50 network models is proposed to classify the data set into three categories according to sentimental tendency: positive, neutral and negative. The model's accuracy is 0.79, and the F1 score is 0.76. The above experiments preliminarily verify the feasibility of using artificial intelligence methods to classify Chinese Internet memes. In addition, this paper also proposes a new evaluation index, which is more suitable for the effect evaluation of non-discrete classification tasks such as sentimental tendency classification.
Holistic Multimodal Interaction and Design

This paper addresses the lack of an HCI framework that examines multimodal experiences in a holistic way to help design interactions where modalities are properly integrated. Such a holistic approach is important as new emerging technologies and experiences are being introduced and used frequently. The resulting multimodal experiences present new requirements and challenges which are not managed by existing HCI frameworks. Through a literature review, we propose a set of principles that define the Holistic Multimodal Interaction and Design (HMID) framework. We perform a user study as the initial evaluation of the effectiveness of this framework. The study findings showed the potential value of HMID and suggested improvements to its guiding principles.
Research on the Application of Information Technology in Chinese Distance Multimedia Teaching

With the integration of Internet technology and educational activities, it is increasingly urgent to integrate information technology into distance multimedia teaching. In order to effectively improve the quality of Chinese distance multimedia teaching, it is necessary to take the new context of intelligent teaching as the basis, to focus on enhancing and improving the learning experience of students, and to give full play to the advantages of multimedia teaching, the project of Chinese distance multimedia teaching is reconstructed actively. This paper analyzes the advantages of the application of information technology in Chinese distance multimedia teaching, and puts forward some relevant suggestions.
Subsystem for Intelligent Systems’ Users Training

The article proposes a method for addressing the issue of training both end users and developers of intelligent systems. Each intelligent system would be supplemented with a module that serves as an intelligent training subsystem, with the aim of instructing both end users and developers of the main system in the fundamentals of using, operating, and developing the system. As a foundation for the implementation of this approach, it is proposed to use an Open Semantic Technology for Intelligent Systems (OSTIS Technology).
Multimodal Transformer for Automatic 3D Annotation and Object Detection

Despite a growing number of datasets being collected for training 3D object detection models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To automate the annotation and facilitate the production of various customized datasets, we propose an end-to-end multimodal transformer (MTrans) autolabeler, which leverages both LiDAR scans and images to generate precise 3D box annotations from weak 2D bounding boxes. To alleviate the pervasive sparsity problem that hinders existing autolabelers, MTrans densifies the sparse point clouds by generating new 3D points based on 2D image information. With a multi-task design, MTrans segments the foreground/background, densifies LiDAR point clouds, and regresses 3D boxes simultaneously. Experimental results verify the effectiveness of the MTrans for improving the quality of the generated labels. By enriching the sparse point clouds, our method achieves 4.48% and 4.03% better 3D AP on KITTI moderate and hard samples, respectively, versus the state-of-the-art autolabeler. MTrans can also be extended to improve the accuracy for 3D object detection, resulting in a remarkable 89.45% AP on KITTI hard samples. Codes are at https://github.com/Cliu2/MTrans .
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over 31.10% ROC score.
Fast Disparity Estimation from a Single Compressed Light Field Measurement

The abundant spatial and angular information from light fields has allowed the development of multiple disparity estimation approaches. However, the acquisition of light fields requires high storage and processing cost, limiting the use of this technology in practical applications. To overcome these drawbacks, the compressive sensing (CS) theory has allowed the development of optical architectures to acquire a single coded light field measurement. This measurement is decoded using an optimization algorithm or deep neural network that requires high computational costs. The traditional approach for disparity estimation from compressed light fields requires first recovering the entire light field and then a post-processing step, thus requiring long times. In contrast, this work proposes a fast disparity estimation from a single compressed measurement by omitting the recovery step required in traditional approaches. Specifically, we propose to jointly optimize an optical architecture for acquiring a single coded light field snapshot and a convolutional neural network (CNN) for estimating the disparity maps. Experimentally, the proposed method estimates disparity maps comparable with those obtained from light fields reconstructed using deep learning approaches. Furthermore, the proposed method is 20 times faster in training and inference than the best method that estimates the disparity from reconstructed light fields.
Human activity recognition system using smartphone based on machine learning algorithms

Human Activity Recognition System aims to capture the state of the user with respect to the external/heterogeneous environment in order to analyze the human health conditions with the help of the numerous sensors attached to the different body parts of the human 3. The first hand-held mobile was developed in the year 1979 and from that year to 2011 it is surveyed that around 80% of the world population is now using the Smartphone and from that year it is been observed that there is been a tremendous increase/up gradation in the field of technology and size also and in this futuristic world of technology. As we can see now a days Smartphone are playing a key role and are used to collect the data of human activities and which is further used to monitor the health of the person. Prior to collect the human activity data we use to attach the sensors to the different body parts like chest, hands, thighs, wrist etc. In this paper, with the help of the m ach in e learn in g algorithms and the data mining techniques we are going to analyze the posture by keeping in mind about the attributes which are going to take part. We are using six of the attributes with the help of these attributes we are going to predict the conditions which are as follows: Sitting, Standing, Walking, Walking Upstairs, Walking downstairs and Laying.
Multilayers Physical Authentication and NoSQL PRESENT algorithm for Data Center

The server room is one area in the data center that has an important role. It is necessary to enforce an access restriction to prevent unauthorized parties from entering the room. Authentication can be done by implementing layered access control to secure the server room. The addition of this layer corresponds to the latest developments regarding the fulfillment of 3 factors for high level security. Multilayer authentication that meets the knowledge factor uses a unique Personal Identification Number (PIN), the ownership factor uses Radio Frequency Identification (RFID) and the biometric factor uses Local Binary Pattern for facial recognition. The prototype result has average 1,1275s to proccess PIN, Biometric authentication in average 2,4362s. Face recognition accuracy rate of 100% using the Equal Error Rate (EER) method and avoid face spoofing attack.
Image Sensing with Nonlinear Optical Neural Networks

We demonstrate a multilayer optical-neural-network image encoder realized using optoelectronic, optical-to-optical nonlinear activations. We show that nonlinear preprocessing in the optical domain can enable machine-vision systems that operate faster and more sensitively.
Development of Android-Based Tourism Information System Prototype at Purwodadi Botanical Garden

Abstract Purwodadi Botanical Gardens is one of the dry lowland plant conservation. The number of tourist attractions and a large area of the botanical gardens raise two main problems among visitors regarding information about the Purwodadi Botanical Garden, especially the system information. This research and development aim to solve this Purwodadi Botanical Garden information system by making a tourism information system prototype that can be access by using the tourist gadget everywhere and anywhere. The system development contains information about the facilities map in Purwodadi Botanical Garden. There are two types of data used in this research development namely primary (the coordinates point of sites and facilities) and secondary data (about the site function). The primary data collect by using the application named GPS essential, and the secondary data obtained from related agencies named LIPI. Figma used for the prototype system development by the researcher. The next existing application prototype can be developed further by adding features and menus according to the needs and changes that occur every time. Researcher hopes that with the development of this prototype, the management can follow up in terms of developing a tourism information system at Purwodadi Botanical Gardens.
Fast Model-based Policy Search for Universal Policy Networks

Adapting an agent’s behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal policy networks partially address this issue by enabling the storage of multiple policies trained in simulation on a wide range of dynamic/latent factors, efficiently identifying the most appropriate policy for a given environment remains a challenge. In this work, we propose a Gaussian Process-based prior learned in simulation, that captures the likely performance of a policy when transferred to a previously unseen environment. We integrate this prior with a Bayesian Optimisation-based policy search process to improve the efficiency of identifying the most appropriate policy from the universal policy network. We empirically evaluate our approach in a range of continuous and discrete control environments, and show that it outperforms other competing baselines.
The Cybersecurity Risks of Using Internet of Things (IoT) and Surveys of End-Users and Providers Within the Domiciliary Care Sector

This paper examines the home care sector through the lens of the Internet of Things (IoT). Because IoT technology contains a large number of security risks, the paper analyzes cybersecurity threats in home care automation and conducts an analysis of these risks. In addition, two surveys were conducted, one for end-users and the other for home care providers, both of which provided valuable information and outcomes that could be used to analyze attitudes within the target group. The results of this study can also be used to create a strategy for entering the IoT security industry.
On the Validity of the Two Raster Sequences Distance Transform Algorithm

This paper examines the validity of the two raster sequences distance transform algorithm, originally given by Rosenfeld and Pfaltz for the distance $$d_4$$ , then extended to the weighted distances by Montanari and Borgefors. We show that the convergence in two passes does not hold for all chamfer masks, and we prove that the chamfer norm condition is a sufficient condition of validity for the algorithm.
Optimized Quantum Generative Adversarial Networks for Distribution Loading

Loading data efficiently from classical memories to quantum computers is a key challenge in the current era of quantum computing. Approximate techniques, including Generative Adversarial Networks (qGANs), were proposed in literature to reduce the depth of data loading circuits. Tuning a qGAN to balance accuracy and training time is a hard task, that becomes paramount when target distributions are multivariate. Thanks to our tuning of the hyper-parameters and of the optimizer, the Kolmogorov–Smirnov statistic was reduced of 43 – 64% with respect to the state of the art. The ability to reach optima is non-trivially affected by the starting point of the search algorithm. It also becomes manifest, after our testing campaign, that a gap arises between the training accuracies achieved by nearly-optimal and non-optimal runs. We finally point out that the Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer does not provide the same accuracy as Adam AMSGRAD in our conditions, therefore calling for new advancements to support scaling capability of qGANs.
Analysis of Intelligent English Learning System Based on Intelligent Data Collection Technology

Artificial intelligence emerges as a brand-new scientific and technological breakthrough, mainly studying the intelligent system of human activities, and using artificial means to research intelligent technology. Combined with the Internet, the utilization of artificial intelligence technology by college English teachers, as well as the development of auxiliary teaching systems, can well improve the demerits of present college English teaching methods, like the obsolete teaching model, the rigid learning method, and the impact of teaching the quality improvement is very effective. The speed of information technology development in today’s society is very rapid, and there are more and more researches on English courses, which can create a new field for the educational environment. In this case, this article is guided by the fusion of information technology and curriculum. It is hoped that through this research, the effectiveness of college English teaching can become strengthened, and the teaching model can be more efficient and faster. This article takes the utilization of artificial intelligence technology in college English class as the premise, from the perspective of current educators to educate students, conducts technical research, through the use of the HAIES system, combined with artificial intelligence technologies such as the comprehending of natural language, learning by machines, and intelligent search. Study the conditions for the realization of the system to carry out the intelligent setting of the college English teaching system, and apply the designed intelligent teaching system to actual teaching. Finally, through data integration analysis on the figures provided by the intelligent scheme, relevant conclusions can be drawn.
On Enhancing Low Bit-Rate Performance of an Image Codec Using Deep Learning-Based Nonlinear Processing

In recent research, convolutional neural networks (CNNs) that can perform image super-resolution and denoising significantly better than the traditional model-based methods have been reported. This chapter investigates the application of such CNN architectures to improve the rate-distortion performance of a standard image codec. In particular, we propose a network architecture for restoration of images coded by, first, sub-sampling the codec input for rate reduction. Experimental results show that the system investigated in this chapter not only outperforms JPEG coding without sub-sampling and JPEG coding with sub-sampling enhanced by various coding artifact reduction methods but is also competitive with a recently reported fully CNN-based image coding system which requires more complex end-to-end training.
Testing of Forest and Land Fire Prevention Patrol Information System

Ministry of Environment and Forestry ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> oEF) established forest and land fire prevention patrol teams. The team uses the Forest and Land Fire Prevention Patrol Information System (SIPP karhutla) to support their patrol activities. The web version of SIPP karhutla is used by the managerial level in patrol activities. This study aims to perform functional and usability testing of the SIPP Karhutla website. Black box testing was conducted before usability testing. Metrics that are measured in usability testing are error and efficiency. All scenarios in black box testing succeed except the filter data patrol feature in the analysis menu. The result of usability testing shows that the task success metric is above 75%, except for the patrol menu scenario. In efficiency measurement, there are still scenarios in which the respondent or the respondent does not complete and thinks that the scenario is done but is not. Problems found in the testing produce improvement recommendations in user interface design.
Interpretable Decisions Trees via Human-in-the-Loop-Learning

Interactive machine learning (IML) enables models that incorporate human expertise because the human collaborates in the building of the learned model. Moreover, the expert driving the learning (human-in-the-loop-learning) can steer the learning objective, not only for accuracy, but perhaps for discrimination or characterisation rules, where isolating one class is the primary objective. Moreover, the interaction enables humans to explore and gain insights into the dataset, and to validate the learned models. This requires transparency and interpretable classifiers. The importance and fundamental relevance of understandable classification has recently been emphasised across numerous applications under the banner of explainable artificial intelligence. We use parallel coordinates to design an IML system that visualises decision trees with interpretable splits beyond plain parallel axis splits. Moreover, we show that discrimination and characterisation rules are also well communicated using parallel coordinates. We confirm the merits of our approach by reporting results from a large usability study.
Parameter optimization algorithm for quantum particle swarm-based i-vector identification systems

For the noise robustness problem in i-vector: Based on the theoretical principle of i-vector speaker recognition system, the extraction principle and scoring calculation method of i-vector and the process of channel compensation algorithm based on PLDA (Probabilistic Linear Discriminant Analysis) with PLDA model are studied. The matching principle is studied. A statistical averaging i-vector extraction algorithm based on speech fragmentation is proposed to extract more robust i-vector features by weakening the statistical parameters of bad speech fragments to improve the recognition performance of the system. After that, the i-vector system is designed to improve the recognition performance of the i-vector.l Then, a Quantum Particle Swarm Optimization is designed to optimize the parameters of the i-vector recognition system to avoid the degradation of the system performance caused by artificial empirical values. Experimental analysis shows that the proposed algorithm has improved performance over the traditional i-vector recognition algorithm, especially in the case of noise interference, and has better recognition performance