Reviewerly/exHarmony huggingface dataset:
An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

Aiming at the problems of low target image resolution, insufficient target feature extraction, low detection accuracy and poor real time in remote engineering vehicle detection, an improved CenterNet target detection model is proposed in this paper. Firstly, EfficientNet-B0 with Efficient Channel Attention (ECA) module is used as the basic network, which increases the quality and speed of feature extraction and reduces the number of model parameters. Then, the proposed Adaptive Fusion Bidirectional Feature Pyramid Network (AF-BiFPN) module is applied to fuse the features of different feature layers. Furthermore, the feature information of engineering vehicle targets is added by making full use of the high-level semantic and low-level fine-grained feature information of the target, which overcomes the problem that the original CenterNet network did not perform well in small target detection and improve the detection accuracy of the network. Finally, the tag coding strategy and bounding box regression method of CenterNet are optimized by introducing positioning quality loss. The accuracy of target prediction is increased by joint prediction of center position and target size. Experimental results show that the mean Average Precision (mAP) of the improved CenterNet model is 94.74% on the engineering vehicle dataset, and the detection rate is 29 FPS. Compared with the original CenterNet model based on ResNet-18, the detection accuracy of this model is improved by 16.29%, the detection speed is increased by 9 FPS, and the memory usage is reduced by 43 MB. Compared with YOLOv3 and YOLOv4, the mAP of this model is improved by 19.9% and 5.61% respectively. The proposed method can detect engineering vehicles more quickly and accurately in far distance. It has obvious advantages in target detection compared with traditional methods.
Application of Computer Network Multimedia Technology in Vocal Music Teaching Mode

Nowadays, with the continuous development of the times, multimedia technology(MT) is rapidly infiltrating people's lives, and people are paying more and more attention to multimedia teaching, which has been well applied in vocal music(VM) teaching. Since VM is a highly practical and relatively abstract subject, it is necessary to organically combine theory with practice. In addition, nowadays people have higher and higher requirements for music aesthetics, and the enrollment scale of VM majors is increasing, making traditional teaching methods no longer able to meet the needs of vocal talents. Because the network MT can well solve the problems in the traditional classroom and make up for the shortcomings, it has played an important role in improving classroom efficiency, improving the quality of teaching and teaching effects. At the same time, the use of modern methods for VM teaching can also provide teachers with more convenience. Therefore, under this background, it is necessary to combine modern education methods with VM teaching mode(VMTM) to realize the transformation of VMTM. This article uses questionnaire survey method and data analysis method to make use of the advantages of computer network MT to make VM teaching simpler and more intuitive, so as to achieve better VM teaching effects. According to the survey results, most interviewees believe that the application of computer network MT in the VMTM has certain effects and brings a lot of vitality to the development of the course.
Experimental Comparison of Metaheuristics for Feature Selection in Machine Learning in the Medical Context

We explore in this paper the use of metaheuristics to select features from a dataset in order to improve the prediction performance of models build with different machine learning methods. To this end, we compare the performances of 5 learning methods: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM) and Random Forest (RF) on 4 heterogeneous datasets in the number of data and features, for different feature selection methods (metaheuristics or statistical filters). The results obtained show that feature selection by improving a metaheuristic derived from the genetic algorithm leads to much better performances no matter the learning method used compared to without feature selection on the same dataset.
SMITH: A Self-supervised Downstream-Aware Framework for Missing Testing Data Handling

Missing values in testing data has been a notorious problem in machine learning community since it can heavily deteriorate the performance of downstream model learned from complete data without any precaution. To better perform the prediction task with this kind of downstream model, we must impute the missing value first. Therefore, the imputation quality and how to utilize the knowledge provided by the pre-trained and fixed downstream model are the keys to address this problem. In this paper, we aim to address this problem and focus on models learned from tabular data. We present a novel Self-supervised downstream-aware framework for MIssing Testing data Handling (SMITH), which consists of a transformer-based imputation model and a downstream label estimation algorithm. The former can be replaced by any existing imputation model of interest with additional performance gain acquired in comparison with that of their original design. By advancing two self-supervised tasks and the knowledge from the prediction of the downstream model to guide the learning of our transformer-based imputation model, our SMITH framework performs favorably against state-of-the-art methods under several benchmarking datasets.
Using Pseudo-Labelled Data for Zero-Shot Text Classification

Existing Zero-Shot Learning (ZSL) techniques for text classification typically assign a label to a piece of text by building a matching model to capture the semantic similarity between the text and the label descriptor. This is expensive at inference time as it requires the text paired with every label to be passed forward through the matching model. The existing approaches to alleviate this issue are based on exact-word matching between the label surface names and an unlabelled target-domain corpus to get pseudo-labelled data for model training, making them difficult to generalise to ZS classification in multiple domains, In this paper, we propose an approach called P-ZSC to leverage pseudo-labelled data for zero-shot text classification. Our approach generates the pseudo-labelled data through a matching algorithm between the unlabelled target-domain corpus and the label vocabularies that consist of in-domain relevant phrases via expansion from label names. By evaluating our approach on several benchmarking datasets from a variety of domains, the results show that our system substantially outperforms the baseline systems especially in datasets whose classes are imbalanced.
Design of non-contact IC card intelligent water meter

The non-contact IC card intelligence water meter is a new type of automated water meter system. It has a very good prospect of application. This thesis take the high-performance embedded microprocessor as the core, and use the technology of Radio Frequency Identification (RFID), proceeded the designs of non-contact IC card intelligence water meter system.
Developing a VR Tool to Support Repeat Pattern Design Learning

Abstract Virtual Reality (VR) applications have been progressively adopted in design industry and education, and are often associated with increased engagement, creativity, and spatial awareness skills. This study investigates the development and use of a bespoke VR application in textiles and fashion design education, designed to support the teaching and learning of repeat pattern design principles and techniques, transposing the limitations of traditional monitor displays and image editor software. Aiming at identifying potential benefits for students and educators, we have surveyed and observed students who explored the application to visualize their design outputs, applying their pattern designs onto real-size virtual objects and environments. Our findings suggests that VR tools have a positive effect in both learning and design process, allowing students to identify design shortcomings and technical issues, as well as fostering self-evaluation and reflection on their work. Moreover, although findings on spatial awareness are inconclusive, they indicate that the use of the VR application to estimate final dimensions of repeat patterns allows students to identify and correct patterns that have been inaccurately designed.
Enabling Collaborative Diagnosis through Novel Distributed Learning System with Autonomy

Multi-institutional collaboration in health care is a promising way to improve small-scale institutions’ competitiveness and provide patients with efficient diagnoses. However, the rising concern about data privacy within these institutions hinders such collaboration. We propose a collaborative diagnosis system that makes this cooperation feasible and efficient through a novel distributed learning system with autonomy and qualification. This collaborative learning system gives institutions the autonomy to determine their learning algorithms, algorithmic parameters, and attendance, and develops a corresponding qualification mechanism to ensure high-quality synthesized results. Our theoretical results demonstrate that the proposed system can achieve almost optimal generalization error bounds in the framework of statistical learning theory. Our numerical experiments, including toy simulations and analyses of two real-world datasets verify the theoretical assertions and show the feasibility and power of the proposed collaborative diagnosis system.
How a quantum computer could accurately solve a hydrogen-air combustion model

A quantum circuit method for modeling steady state behavior of homogeneous hydrogen-air combustion is presented.
Research on online teaching operation mode of postgraduate courses

In order to effectively improve the online teaching effect of graduate courses, it is very important to plan the online teaching operation mode with the full help of information technology. This paper analyzes the characteristics and existing problems of postgraduate online teaching. In view of the characteristics and existing problems of online teaching, according to the teaching experience and the characteristics of postgraduate courses, this paper designs the teaching operation mode from the three aspects of teaching, auxiliary teaching and interactive platform, and puts forward the methods of developing online teaching from the three aspects of classroom teaching, course management and course assessment. This paper discusses the process of online teaching in detail, which provides a reference for the efficient online teaching of graduate courses.
Interactive Design of Assistant Education Software Application for Autistic Children

This paper proposes the guidance of methodology on mobile terminal platforms for the assistant education application of autistic children. With the objective of the assistant intervention treatment of autistic children, this paper designs an application to be used in the assistant education of autistic children according to the user demand of the education of families with autistic children with the contents of cognition training, behavior training, and attention training in combination with ways of game and psychological counseling. The design of application gives full play to the multimedia characteristics of game, sound, and picture of mobile platform, and provides the interactive design, color design, and page design applicable for autistic children to use in combination with the professional courses and special needs of autistic children, to explore the application and design strategy of mobile platform software in the assistant education of autistic children.
Introducing Speaker Vectors for Child Speech Synthesis in Neural Vocoders

Speech synthesis and sound quality conversion technologies using neural networks have garnered attention in recent years. Neural vocoders can synthesize high-quality speech that is indistinguishable from human speech. However, conventional studies using neural vocoders have mainly synthesized adult speech and evaluated its quality, whereas speech synthesis by children is yet to be sufficiently investigated. This study examined speech synthesis in children using a neural vocoder aiming to improve the quality of synthesized speech. Multiple speaker vectors were introduced as the proposed method, and its effectiveness was confirmed through the conduction of objective and subjective evaluation experiments on the speech quality. Consequently, the experimental results that the effectiveness of the proposed method under certain conditions.
Development of Data Mining Models Based on Features Ranks Voting (FRV)

Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based on the feature ranks and voting algorithm. The FRV comprises of three different proposed techniques to select the minimum best feature set, the forward voting technique to select the best high ranks features, the backward voting technique, which drops the low ranks features (low importance feature), and the third technique merges the outputs from the forward and backward techniques to maximize the robustness of the selected features set. Different data mining models were built using obtained selected features sets from applying the proposed FVR on different datasets; to evaluate the success behavior of the proposed FRV. The high performance of these data mining models reflects the success of the proposed FRV algorithm. The FRV performance is compared with other features selection algorithms. It successes to develop data mining models for the Hungarian CAD dataset with Acc. of 96.8%, and with Acc. of 96% for the Z-Alizadeh Sani CAD dataset compared with 83.94% and 92.56% respectively in [48].
User Feedback Design in AI-Driven Mood Tracker Mobile Apps

AI-driven civilian healthcare systems can offer personalized mental health care to more people. When users provide feedback to the system, they can influence the results of the AI models and improve their user experiences over time. In this study, we investigate feedback designs in mood tracker apps and identify feedback implementation opportunities through the lens of user experience and user interface design. We reviewed the feedback designs on popular mood tracker apps and discovered that the feedback mechanisms were not regularly implemented in all stages of tracking moods. To explore feedback design opportunities in line with user actions, user mindsets, and user emotions, a journey map was developed to visualize the interaction with a mood tracker app from the user perspective. Based on the journey map, a mood tracker app prototype was created for the purpose of user testing. The preliminary user testing results validated some feedback design recommendations that highlight the human-centered design approach of crafting user experiences that leverage AI.
FLiB: Fair Link Prediction in Bipartite Network

Graph neural networks have become a popular modeling choice in many real-world applications like social networks, recommender systems, molecular science. GNNs have been shown to exhibit greater bias compared to other ML models trained on i.i.d data, and as they are applied to many socially-consequential use-cases, it becomes imperative for the model results and learned representations to be fair. Real-world applications of GNNs involve learning over heterogeneous networks with several nodes and edge types. We show that various kinds of nodes in a heterogeneous network can pick bias from a particular node type and remain non-trivial to debias using standard fairness algorithms. We propose a novel framework- Fair Link Prediction in Bipartite Networks (FLiB) that ensures fair link prediction while learning fair representations for all types of nodes with respect to the sensitive attribute of one of the node type. We further propose S-FLiB, which effectively mitigates bias at the subgroup level by regularising model predictions for subgroups defined over problem-specific grouping criteria.
Anthropomorphic Perceptions of Simple Text-Based Interfaces

Anthropomorphism, or the perception of humanlike qualities in something that is not human, can have great influence on one's ability to interpret and predict the behavior of an object or agent, such as a computer. The objective of this study was to consider how even small differences in interface design can influence the way people understand and work with a computer interface. Specifically, whether changing the dialogue of a simple text-based interface makes it seem more humanlike, useful, and trustworthy as well as whether these perceptions are due to the interface design itself or influenced by one's inherent tendency to anthropomorphize. This was tested by having participants use a simple text search interface to look up answers to various questions. There were two versions of the interface: Anthropomorphic (e.g., used humanlike grammar and referred to itself as 'I' and 'me') and non-Anthropomorphic (e.g., simple output without sentences or reference to itself). Results showed the Anthropomorphic interface was perceived as more humanlike and inherent tendency to anthropomorphize influenced perceptions of humanlikeness, mental workload, and performance. This study showed that small changes can influence perceptions of computers, even simple ones. It also highlighted the importance of one's inherent tendency to anthropomorphize. Implications for research and design are discussed.
LBP–Bilateral Based Feature Fusion for Breast Cancer Diagnosis

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%.
A New 32-Bit Color Image Encryption Method Based on Multiple 1d Chaotic Maps and the Four Square Cipher

In this paper, we introduce an improved 32-bit color image encryption method using Four 1D chaotic maps that are Logistic map, Chebyshev map, Tent map, and Sine map. these maps were used to fill the four matrices of our encryption system with unique integers that are between 0 and 255. Each matrix has a size of arranged in a square. The four channels of a 32-bit color image are represented by these matrices (red, green, blue, and alpha). The upper-left and lower-right matrices are used to encrypt respectively the red and the alpha channels. The upper-right and lower-left matrices are used to encrypt respectively the green and the blue channels. For the confusion section, we apply the Arnold Cat Map transformation to shuffle all the pixels and change their position. Several safety criteria were used to evaluate the performance of our algorithm against common attacks and have shown excellent results.
Knowledge Graph-Enhanced Sampling for Conversational Recommendation System

The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimation of user preferences based on sparse and noisy historical data. Conversational Recommendation System(CRS) uses the interactive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems. However, due to the lack of contextual information modeling, the existing CRS models are unable to deal with the exploitation and exploration(E&E) problem well, resulting in the heavy burden on users. To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling(KGenSam). KGenSam integrates the dynamic graph of user interaction data with the external knowledge into one heterogeneous Knowledge Graph(KG) as the contextual information environment. Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem. Experimental results on two real-world datasets demonstrate the superiority of KGenSam with significant improvements over state-of-the-art methods.
High Rank Self-Organising Maps for Image Fingerprinting

Image fingerprinting is the act of generating a unique digest for an image. Unlike cryptographical hashing, slight differences in the input to the hashing function do not create significant differences in the digest. This property makes image fingerprinting useful in identifying near-duplicates of an input image. This paper describes a novel technique for generating an image fingerprint using Self-Organising Maps (SOM) with ranks higher than 2. The method is compared to a selection of more traditional fingerprinting algorithms and against a further variation on the proposed technique using a more conventional rank 2 Self-Organising Map.