Reviewerly/exHarmony huggingface dataset:
Sparse Variational Autoencoder-Based Interpretable Bimodal Word Embeddings

Word embedding is a basic task in the field of natural language processing, which is widely applied to a variety of tasks. In spite of delivering the semantic information, there is no meaningful explanation for specific dimension of the word embedding. As such, research is ongoing to explore the interpretability of word embeddings and thus improve their performance in downstream tasks. Current interpretable word embedding models, however, merely focus on the textual information of the words instead of the multimodalities. In line with the cognition principle, we establish a sparse variational autoencoder, which exploits both the textual and visual information to generate interpretable word embedding. Experiments are conducted to verify the interpretability of word embeddings together with their performance in downstream tasks. Comparing to the state-of-arts, experimental results indicate that the proposed interpretable word embeddings not only effectively increase the interpretability, but also obtain better result in most downstream tasks.
Multi-feature fusion model of English semantic vocabulary based on machine learning

In order to improve the expression and detection ability of English vocabulary features, a multi-feature fusion model of English vocabulary semantic features based on machine learning is proposed, and a multi-feature fusion and recognition method of English vocabulary semantic features based on machine learning is proposed. By using time domain feature analysis, the dynamic word meaning distribution model of multidimensional features of English vocabulary semantic features is established, the disturbance identification of multidimensional features of English vocabulary semantic features is carried out by combining the joint estimation method of semantics and corpus parameters, the feature decomposition model of multidimensional features of English vocabulary semantic features is constructed by using the dynamic word meaning distribution modulation method, the fuzzy understanding method of binary semantics is adopted, the associated semantic ambiguity is resolved, and the machine self- organizing learning method is adopted. English semantic text fusion and target language retrieval are carried out, and a statistical information analysis model of multidimensional features of English vocabulary semantic features is established. Through information clustering and source language autocorrelation detection, the fusion and feature recognition of multidimensional parameter features of English vocabulary semantic features are realized. The test results show that the accuracy of multidimensional feature recognition of English vocabulary semantic features by this method is high, the semantic expression ability of English vocabulary is improved through the fusion of English vocabulary semantic features, and the output signal-to-noise ratio is high.
Military Text Triplet Extraction Model Combining ERNIE and Conditional Normalization

There are a large number of information resources that can be analyzed and mined in the existing military information system. Extracting the key knowledge from these resources is of great significance for knowledge response and reasoning. Aiming at the problems of error propagation and relationship overlap in samples in the process of military text triplet extraction, this paper proposes a triplet extraction model combining Ernie and conditional normalization, which introduces Ernie knowledge enhanced pre training extraction model, and uses “semi pointer semi Annotation” structure and conditional layer norm to make the model have better expression ability. The experimental results show that on the manually labeled military news data set, the extraction effect of the model is significantly better than the traditional pipeline extraction model and the joint extraction model based on a single pre training model, and the F1 value reaches 81.23%.
Effective Deep Learning Algorithms for Personalized Healthcare Services

The main motive to present this chapter is to provide effective deep learning algorithms for personalized healthcare services and to make the reader understand the importance of deep learning in the field of healthcare. We realize that acquiring ability and significant viewpoints from nuanced, high-dimensional, and heterogeneous biomedical information keeps on being a significant test in medical care change. Electronic wellbeing reports, imaging, omics, sensor information, and text are instances of nuanced, heterogeneous, gravely explained, and generally unstructured information that have arisen in contemporary biomedical science. As feature engineering is needed in traditional data mining approach to extract efficient and more scalable features from data, and after that predicting and clustering the models make it more challenging in these steps, especially in case of complicated data, the latest advancements in deep learning in the field of healthcare give effective calculations to get start to finish models from complex information. In the coming days, it is believed that deep learning will have a major role in converting big and complex biomedical data into improved human health. However, we also notice limitations and the need for improved method creation and implementations, especially in terms of domain experts’ and citizen scientists’ comprehension. To close the gap between deep learning models and human interpretability, we propose designing holistic and practical interpretable algorithms.
Adaptive and Lightweight Network Traffic Classification for Edge Devices

Network traffic classification (NTC) is essential to network measurement, management, and security. Recently, Artificial Intelligence (AI)-based NTCs have demonstrated its accuracy in identifying data traffic. However, existing AI-based NTCs require inputs of lengthy data payload with padding, which can lead to complex designs of NTC models that are especially challenging to networking edge devices, e.g., Wi-Fi routers, etc. To address this issue, an adaptive and lightweight NTC framework is proposed in this paper. In specific, an input feature contribution extraction scheme is developed to weigh each input feature based on both the significance and the uniqueness of the corresponding feature. The optimal set of input features are determined to minimize the complexity of a targeting AI-based NTC while maintaining high performance in classification. Moreover, an autonomous update scheme is proposed to detect the changes in feature contribution and process updates. Evaluations on two fundamental AI-based NTC models, i.e., MLP and CNN, demonstrated that the proposed scheme can significantly reduce the input features and accelerate NTC models by one to two magnitudes while maintaining high accuracy. The proposed autonomous update scheme can accurately detect a change in feature contributions and update the NTC models to sustain the high accuracy.
A New Segmentation Method to Preserve the Underlying Image Features: U-Net with Multi-scale Pooling

Due to the low quality of ultrasound images, irregular changes of the bladder, and effects of other organs or tissues, it is difficult for doctors to diagnose and analyze bladder diseases quickly and precisely. In this paper, a segmentation method with U-Net as the basic network framework has been proposed to alleviate these challenges. In the original U-Net, it will still cause information loss after convolution for many times. Therefore, the Multi-scale Pooling sub-module has been proposed to retain more underlying information in our method. Aiming at the problem with pool data sets, we used a method of elastic distortion combined with random affine transformation to achieve data augmentation. In addition, Dice and Cross-Entropy loss were combined as the loss function in this study. This can effectively solve the problems of Dice gradient instability and Cross-Entropy loss not paying enough attention to target regions. The experimental results showed that the method proposed in this paper has certain advantages in the accuracy of segmentation results and detail preservation. The accuracy is 0.0256 higher than that of the original method.
Cognitive Computing and Decision-Making of Traffic Intersection Based on Rule Set

The decision-making of autonomous vehicles at intersections is of great significance to a safe drive. And it is also popular research content at the moment. In this paper, cognitive computing is integrated into decision-making with the rule-based algorithm to transform environmental information into behavioral results. By establishing the database of traffic signs and rules, the YOLOv5 algorithm is used to recognize traffic signs and combine the rules into rule sets. Based on the Belief Rule Base (BRB) and the Evidential Reasoning (ER) algorithm, the information in the rule set is reasoned and fused. The traffic environment at the intersection is cognitively computed through the rule set. The BRB algorithm assigns weights to each rule and the parameters in the rule which conveniently activated different rules according to the weight calculation. The ER algorithm calculates the belief of each result according to the activated rules. We complete the decision-making of the autonomous vehicle at the intersection through our proposed cognitive model.
Visualized Interaction Method of Mechanical Arm Based on Augmented Reality

A visualized interactive application of a mechanical arm based on augmented reality is designed and developed by using Unity 3D software and Vuforia development kit. The visual application design scheme based on augmented reality is proposed, and the technical process is specifically explained. The key technology of the visual interaction of the mechanical arm is achieved by constructing the three-dimensional modeling of the mechanical arm, realizing the augmented reality function of this application and setting the virtual button for human-computer interaction. The feasibility of realizing the visual interaction of the mechanical arm by using Unity 3D and Vuforia SDK and the interaction method based on virtual buttons is verified through the display of augmented reality effects and functional testing of the application on the mobile terminal.
A Federated Learning-Based Light-Weight Privacy-Preserving Framework for Smart Healthcare Systems

Smart healthcare systems have been widely applied in the fields of intelligent healthcare, self-monitoring, diagnosis, and emergency. In recent years, there have been growing concerns regarding the privacy of the data collected from the users of the smart healthcare systems. This chapter proposes a light-weight federated learning framework based on multi-key homomorphic encryption for deploying predictive models trained on patient data distributed across multiple healthcare institutions without exchanging them. Two predictive models based on the proposed framework are deployed for in-house mortality prediction from patient data and COVID-19 detection from chest x-ray images. Performance evaluation of these models with standard datasets and comparative analyses show that the proposed models are superior to state-of-the-art approaches. The proposed framework and the models are potential solutions to improve the quality of healthcare across multiple healthcare institutions, protecting the sensitive patient data and ensuring personalization of healthcare.
Modal approach to quantum temporal imaging

We consider the problem of quantum temporal imaging in the case where the time lens is implemented by a Sum Frequency Generation nonlinear process, in particular when the device is operated close to 100% conversion efficiency. In the general case where the time lens also presents a finite aperture and a non-perfect phase-matching the relevant figures of merit, as for example the temporal resolution, do not have an explicit expression. As a consequence, the performances of imaging scheme are difficult to assess. Having a reliable estimation of these figures of merits is particularly important because they can be significantly different from the regime of low conversion efficiency usually considered in classical temporal imaging. We show that this problem can be approached in terms of the eigenmodes of the imaging scheme and we show how its relevant figures of merit can be extracted from the modal description of the imaging scheme. As a consequence we obtain criteria allowing to design imaging schemes with close to unity efficiencies.
The importance of using a virtual reference service in the process of independent learning

The article announces the capabilities of libraries in the modern era. Thus, it is emphasized that today most of the library resources are adapted to the online mode, which makes it easier to access the database and special collections via a computer. Nowadays readers can use the personal account of the library without even leaving home. But this does not mean that remote access will reduce the demand for librarian support. Traditionally, it is natural for a user in need of expert assistance to feel the need for support even remotely. Therefore, it is advisable to create a modern virtual (or digital) reference service in conjunction with the traditional information service. Along the way, the author explains some of the new services in the library system. A virtual reference is a type of service that allows librarians and users to communicate in real time via the Internet, email, chat, or instant messaging. Improving all of these capabilities is the hottest topic in libraries today.
Enhanced Efficient YOLOv3-tiny for Object Detection

Lightweight object detection models have great application prospects in resource-restricted scenarios such as mobile and embedded devices, and have been a hot topic in the computer vision community. However, most existing lightweight object detection methods show poor detection accuracy. In this study, we put forward a lightweight objection detection model named Enhanced-YOLOv3-tiny to improve the detection accuracy and reduce the model complexity at the same time. In Enhanced-YOLOv3-tiny, we propose a new backbone named GhostDarkNet based on DarkNet53 and Ghost Module for decreasing the model parameters, which helps to get more representative features compared with YOLOv3-tiny. Furthermore, we put forward a new Multiscale Head, which adds three more heads and includes Ghost Module in each head to fuse multi-scale features. Experiments on the Priority Research Application dataset from the real scenes in driving show that the proposed Enhanced-YOLOv3-tiny outperforms the state-of-the-art YOLOv3-tiny by 8.4% improvement in AP metric and decreases parameters from 8.8M to 3.9M, demonstrating the application potentials of our proposed method in resource-constrained scenarios.
Learning with Less Labels in Digital Pathology Via Scribble Supervision from Natural Images

A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via transfer learning from the natural image domain (NI), where the annotation cost is considerably cheaper. Cross-domain transfer learning from NI to DP is shown to be successful via class labels [1]. One potential weakness of relying on class labels is the lack of spatial information, which can be obtained from spatial labels such as full pixel-wise segmentation labels and scribble labels. We demonstrate that scribble labels from NI domain can boost the performance of DP models on two cancer classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer dataset). Furthermore, we show that models trained with scribble labels yield the same performance boost as full pixel-wise segmentation labels despite being significantly easier and faster to collect.
Acquisition and Comparison of Classification Algorithms in Electrooculogram Signals

In recent years there has been considerable advances in the development of assistive technologies to enable the Person with Disabilities (PwD) better participate in society and interact. One way to allow PwD interaction is using interfaces based on the electrical potentials that our body presents, called biopotentials. The acquisition and the classification of these are crucial steps in the development of these technologies. In this work, a setup to acquire electrooculogram (EOG) signals was developed to track eye movements for a minimally invasive computer/game interface. This work includes setup and creation of a dataset, feature extraction of the collected signals and the implementation of classification algorithms. Three machine learning algorithms were designed to classify the EOG signals based on its characteristics: Softmax Regression, Gaussian Discriminant Analysis (GDA) and K-Nearest Neighbors (KNN). Comparing the algorithms classification performances, KNN presented the best results with 88.9% overall accuracy.
Attribute-Based Inner-Product Functional Encryption from Standard Lattices

Attribute-based inner-product functional encryption (ABIPFE), which combines the notions of attribute-based encryption (ABE) and inner-product functional encryption (IPFE), allows users to get the desired inner product from the encrypted data if a specified access policy is satisfied. ABIPFE is so useful in applications where users need to use IPFE to achieve some desired inner-product computations, while requiring fine-grained access control to the data, that many researchers are working on designing ABIPFE schemes with different features. However, existing ABIPFE schemes are based on the conventional number-theoretic assumptions, which cannot resist quantum attacks. This motivates us to construct the first lattice-based (key-policy) ABIPFE scheme that supports any policy function of polynomial depth. Technically, we adapt an existing matrix embeddings technique developed by Brakerski and Vaikuntanathan, in conjunction with the IPFE framework of Agrawal \textit{et al.}. In terms of performance, we show that our scheme has short secret keys, where the size of keys only depends on the depth of the policy functions. We also formally prove that our scheme is selectively secure in the standard model based on the learning with errors (LWE) problem.
Applications of a Quantum Linear System Algorithm to Linear MIMO Detections

A linear system can be solved more efficiently by quantum computing.However, previously known quantum algorithms provide only a quantum state as the solution; consequently, we cannot obtain the value of each component of the solution.We propose a method to extract the component values of the solution, and we present an application to linear multiple-input multiple-output (MIMO) detections.In the proposed algorithm, we demonstrate a concrete method that applies a quantum linear system algorithm (QLSA) when the components of a solution have binary variables, quaternary variables, or roots of a complex number.Whereas the conventional method requires an additional process to read out the values of the components, the proposed algorithm does not need any post-procedure.Instead, our method uses a QLSA iteratively, and the number of uses is logarithmic in the size of the linear system.Thus, our method maintains the runtime with the quantum advantage, but the conventional approach increases the runtime significantly.Furthermore, the application of the proposed method shows that quantum computing can collaborate with communication systems for large-scale MIMO systems.
Simple heuristics for scheduling apache airflow: A case study at PT. X

In the big data era, there will be a lot of data that needs to be handled properly for making well-informed business decisions. As a result, the data needs to get updated regularly for relevant analysis. Apache Airflow is commonly utilized to schedule the data update by running the query through sequences of DAG tasks. Unfortunately, scheduling DAG tasks is categorized as NP-Hard problem which is difficult to obtain the global optimum solution. This paper shows a simple heuristic algorithm to solve the subset of DAG tasks at PT.XYZ using 2 computers (virtual machines). This is a special case of Pm|prec|Cmax problem where there are 2 computers are being used. Before constructing the algorithm, CPM (Critical Path Method) is used as the baseline to get the most optimal solution. A Knapsack problem is then used to add additional tasks for virtual machine #1, and a heuristic for virtual machine #2 as a compromise to balance the cost and time of completion. The characteristics of this simple algorithm is discussed with numerical examples/experiments.
Semantic Similarity Calculation based on Adaptive Semi-supervised Method

Semantic similarity calculation is a key problem in natural language processing tasks. Supervised language models have been widely used in semantic similarity calculation. However, when a small part of the labeled data is used for training, the accuracy of the supervised model will be greatly reduced. An important issue is how to effectively use labeled and unlabeled data to build models. Thus, we propose an adaptive semi-supervised semantic similarity calculation method that combines the learning content of the supervised model Siamese LSTM with the topic information of the unsupervised LDA topic model through an attention layer. Inspired by active learning, we apply an adaptive mechanism to actively optimize model parameters. We apply this method to standard semantic datasets for ablation experiments. The experiment results demonstrate that the proposed method considerably outperforms other state-of-the-art methods.
Landslide Prediction using CNN with Data Modeling and Feature Reduction using Median Pooling

Landslides are one of the most severe and pernicious natural phenomena which occur on Earth. They particularly occur in sloped regions where the integrity of the topsoil of the land has been compromised. The reasons for this are multifaceted, depending on the lithography of the region in question. A landslide can be triggered by intense inundation, cloudburst, earthquakes, deforestation, which at times can be a consequence of urbanization, amongst other factors. They have a detrimental effect on the geography, local economy of the region they occur in and cause immense monetary loss as well. There is a need for timely detection of landslides for establishing correct preventive measures and technological advancements, especially in the field of deep learning are being used for the same. In this paper we present an approach based on Convolutional Neural Networks, using median pooling for feature reduction applied on a dataset with highly inclusive features like vegetation index, water-based index, temperature wind speed, precipitation etc. The architecture of the models is discussed and the preprocessing of features is done as well. We finish with a discussion on model performance and possible improvements.
Credit rating modeling of small and micro enterprises based on fuzzy analytic hierarchy process

The credit rating index of the traditional model is low, and the rating effect decreases. This paper designs the credit rating modeling method of small and micro enterprises based on fuzzy analytic hierarchy process and the research significance is analyzed. The overall idea of enterprise credit rating design is adjusted. The implementation process of credit rating indicators for small and micro enterprises is formulated. It is verified that the credit rating effect of the new model is better and has great promotion value through the comparative experiment.