Reviewerly/exHarmony huggingface dataset:
Measurement Invariance Investigation for Performance of Deep Learning Architectures

Models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Multi-class classification is a classification task where each image is assigned to one and only one label. Confusion matrix, Precision, Recall, and F1 Score are popular performance metrics. The common sense is that the performance of any architecture is dependent on the sizes of data set. The goodness of the architecture of deep learning models for different data sets is critical issue. This paper implements Pearson correlation coefficient and the multivariate linear regression method to assess the accuracy of deep learning architectures with five performance indicators. The five performance indicators are training loss rate, robustness, training time, number of model parameters and computation complexity. There are five image datasets used to test four deep learning models: Alexnet, GoogLeNet, ResNet, MobileNet to obtain the values of accuracy and other five indicators. The most important contribution of the article is to show that the accuracy indicator related to training loss rate and training time indicators are not dependent on the selection of the data group. According to the definition of Measurement Invariance (MI), the measurement invariance is demonstrated by the linear regression analysis and inner product of the unit normal vector of the linear regression planes.
Tackling Solitary Entities for Few-Shot Knowledge Graph Completion

Few-Shot Knowledge Graph Completion (FSKGC) aims to predict new facts for relations with only a few observed instances in Knowledge Graph. Existing FSKGC models mostly tackle this problem by devising an effective graph encoder to enhance entity representations with features from their directed neighbors. However, due to the sparsity and entity diversity of large-scale KG, these approaches fail to generate reliable embeddings for solitary entities, which only have an extremely limited number of neighbors in KG. In this paper, we attempt to mitigate this issue by modeling semantic correlations between entities within an FSKGC task and propose our model YANA (You Are Not Alone). Specifically, YANA introduces four novel abstract relations to represent inner- and cross- pair entity correlations and construct a Local Pattern Graph (LPG) from the entities. Based on LPG, YANA devises a Highway R-GCN to capture hidden dependencies of entities. Moreover, a query-aware gating mechanism is proposed to combine topology signals from LPG and semantic information learned from entity's directed neighbors with a heterogeneous graph attention network. Experiments show that YANA outperforms the prevailing FSKGC models on two datasets, and the ablation studies prove the effectiveness of Local Pattern Graph design.
Encrypted TLS Traffic Classification on Cloud Platforms

Nowadays, encryption technology has been widely used to protect user privacy. With the explosive growth of mobile Internet, encrypted TLS traffic rises sharply and occupies a great share of current Internet traffic. In reality, the classification of encrypted TLS traffic on cloud platforms brings a new challenge to traditional encrypted traffic classification methods, because some information such as certificates in the TLS flows is no longer effective. In this paper, we apply deep learning technology to the problem of encrypted TLS traffic classification on cloud platforms, and propose NeuTic, which takes the packet sequence of each TLS flow as the input, and effectively classifies raw TLS flows generated by many “cloud” applications. Our approach is able to automatically capture the long-range dependencies between elements in the packet sequences for robust and accurate encrypted TLS traffic classification. In NeuTic, we first convert each TLS flow into three attribute sequences. Then, we train a multi-application traffic classification model using our newly designed deep learning model. Finally, we use the well-trained classification model to classify new incoming TLS flows. We conduct comprehensive experiments on real-world application traces covering multiple “cloud” applications from three different companies. In addition, we compare our experimental results of NeuTic with two deep learning-based methods for encrypted traffic classification. NeuTic outperforms the state-of-the-art approaches in classification accuracy.
An Attribute-Based Keyword Search Scheme for Multiple Data Owners in Cloud-Assisted Industrial Internet of Things

The cloud-assisted industrial Internet of Things (IIoT) architecture can sustain highly available computation and massive storage services for modern industrial systems. When data owners store IIoT data to remote cloud platforms, the data security will face tough challenges. Cryptographic technologies endow an ability to guarantee data confidentiality. However, traditional encryption techniques make data access control and data searching malfunctioning. Recently emerging attribute-based keyword search (ABKS) primitive achieves fine-grained access control and effective data searching over ciphertexts. However, existing ABKS schemes only consider single data owner scenarios and may be an inappropriate choice for IIoT applications, where there exists multiple data owners for an integrated industrial system. Directly extending state-of-the-art single owner schemes to ones for multiowner environment will impose a complicated key management issue. We present an ABKS scheme for multiowners in the cloud-assisted IIoT architecture. By designing a novel master key generation and private key aggregation mechanism with desired communication overheads, our scheme eliminates the complex key management issue in the multiowner model. Formal security proof demonstrates that our scheme is secure against the cloud server. Experimental evaluations also demonstrate its correctness and practicality.
Deep Generative Networks Coupled With Evidential Reasoning for Dynamic User Preferences Using Short Texts

Seeking an efficient solution for the problem of dynamic user preferences on social networks is challenging because the input data are <i>short texts</i> and user preferences usually <i>change</i> over time. This work proposes a novel framework that tackles these challenges based on deep neural networks and the Dempster-Shafer theory of evidence. The framework consists of three primary phases: (1) learning the hidden space of user texts; (2) word generation and mass inference; and (3) mass combination and keyword extraction. In the first phase, user texts are grouped into small batches according to timestamps. Each batch is used for separately training two types of neural networks, the Variational Autoencoder (VAE) and the Generative Adversarial Network (GAN). In the second phase, the generators in the trained VAE and GAN work independently as two <i>experts</i> to generate bunches of tokens for modeling user preferences. Each bunch is considered as one piece of evidence, and is transformed into the so-called mass function in Dempster-Shafer theory by maximum a posterior estimation. In the final phase, Dempster&#x2019;s rule of combination is utilized for fusing the two independent pieces of evidence into an overall mass. This mass is used for extracting top keywords to form the user preferences within a specific time span. The experiments on short text datasets verified that the proposed method outperforms baseline models on many evaluation metrics. Additionally, the output of the proposed framework could be used for visualization, which is useful in many practical applications.
OPERA: Optional Dimensional Privacy-Preserving Data Aggregation for Smart Healthcare Systems

Massive multidimensional health data collected from Internet of Things (IoT) devices are driving a new era of smart health, and with it come privacy concerns. Privacy-preserving data aggregation (PDA) is a proven solution providing statistics while hiding raw data. However, existing PDA schemes ignore the willingness of data owners to share, so data owners may refuse to share data. To increase their willingness to contribute data, we propose an OPtional dimEnsional pRivacy-preserving data Aggregation scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(OPERA)</i> to provide data contributors with options on sharing dimensions while keeping their choices and data private. OPERA uses selection vectors to represent the decisions of users and count participants dimensionally and achieves data privacy and utility based on a multisecret sharing method and symmetric homomorphic cryptography. Analyses show that in OPERA, the probability of adversaries breaching privacy is less than 4.68e-97. Performance evaluations demonstrate that OPERA is outstanding in computation and practical in communication.
Graph-Aware Deep Fusion Networks for Online Spam Review Detection

Product reviews on e-commerce platforms play a critical role in shaping users’ purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect “spam reviews” either focus on sophisticated feature engineering with traditional classification models or rely on tuning neural networks with aggregated features. In this article, we develop a novel graph-based model, namely, graph-aware deep fusion networks (GDFNs) that use information from relevant metadata (review text, features of users, and items) and relational data (network) to capture the semantic information from their complex heterogeneous interactions via graph convolutional networks (GCNs). Besides, GDFN also uses a novel fusion technique to synthesize low- and high-order interactions with propagated information across multiple review-related subgraphs. Extensive experiments on publicly available datasets show that our proposed model is effective and outperforms several strong state-of-the-art baselines.
Clustering of Short Texts Based on Dynamic Adjustment for Contrastive Learning

Faced with the large amount of unlabeled short text data appearing on the Internet, it is necessary to categorize them using clustering that can divide text into several clusters based on similarity degree of text semantics.Recently, combining clustering with contrastive learning has been the focus of clustering research.Due to the excellent representation learning ability of contrastive learning, clustering achieves better results on short texts that are high-dimensional and sparse.However, contrastive learning pays more attention to general feature representation at the instance-level and ignores the semanticlevel correlation of data belonging to same cluster in clustering.The inconsistent training objectives of contrastive learning and clustering lead to lower confidence of clustering results and sparse cluster space.To improve this problem, we propose a clustering method based on Dynamic Adjustment for Contrastive Learning (DACL).The method smoothly transitions loss weight of model from contrastive learning to clustering during training and filters negative samples in contrastive learning by the pseudo-labels generated by clustering.To demonstrate the effectiveness of the method, DACL is compared with eight short text clustering models on eight datasets.The results show that we achieve considerable performance improvements on most datasets compared to state-of-the-art short text clustering methods.In addition, The effectiveness of loss smooth transition and negative filtering is proved by ablation experiments.
Trajectory estimation of ultrasound images based on convolutional neural network

Estimating ultrasound probe position and consequently its image position is challenging in image registration and reconstruction concept. On the other hand, in the last few years, the convolutional neural network has had a huge impact on the image processing concept. In this study, we proposed a new network modality. The proposed model attempts to employ the benefits of both the densely connected network and FlowNet for estimating the position information of the ultrasound images. Furthermore, for reducing implementation costs, the inertial measurement unit was utilized. Definition of the images to the networks' input relies on stack manner. Evaluation of the network's results was completed in three stages: in the first stage, the comparison is made between proposed network performance with two different image sequences, whereby the proposed model has better performance with a stack of three sequences; in the second stage, the network performance was compared with the conventional method, whereby results indicate better performance, especially in rotation angels; and finally, in the third stage, we attempted to answer how is that the network performance if instead of inertial measurement unit, transformation matrix computed with conventional feature extracting methods. According to the acquired results—utilizing conventional methods with three and five sequence networks performance reduces the amount of absolute mean square error in comparison to stage two results. Especially the amount of this reduction is significant in Euler angels' estimation. However, the network has better performance while the transformation matrix is computed with IMU's information.
Data rescue: saving environmental data from extinction

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.
ScienceQA: a novel resource for question answering on scholarly articles

Machine Reading Comprehension (MRC) of a document is a challenging problem that requires discourse-level understanding. Information extraction from scholarly articles nowadays is a critical use case for researchers to understand the underlying research quickly and move forward, especially in this age of infodemic. MRC on research articles can also provide helpful information to the reviewers and editors. However, the main bottleneck in building such models is the availability of human-annotated data. In this paper, firstly, we introduce a dataset to facilitate question answering (QA) on scientific articles. We prepare the dataset in a semi-automated fashion having more than 100k human-annotated context-question-answer triples. Secondly, we implement one baseline QA model based on Bidirectional Encoder Representations from Transformers (BERT). Additionally, we implement two models: the first one is based on Science BERT (SciBERT), and the second is the combination of SciBERT and Bi-Directional Attention Flow (Bi-DAF). The best model (i.e., SciBERT) obtains an F1 score of 75.46%. Our dataset is novel, and our work opens up a new avenue for scholarly document processing research by providing a benchmark QA dataset and standard baseline. We make our dataset and codes available here at https://github.com/TanikSaikh/Scientific-Question-Answering.
Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

In English education, British and American literature is a new type of course. The teaching of British and American literature has also undergone many reforms. In the practice of teaching reform, artificial intelligence- (AI-) assisted teaching such as machine learning (ML) has a long history. The performance is continuously improved by studying the mechanism of computer simulation of the human brain learning British and American literature. Then, computer intelligence can be realized. Based on this, this paper mainly discusses two aspects. One is the sentiment tendency analysis method based on the sentiment dictionary, and the other is the sentiment tendency analysis method based on ML. It mainly introduces the judgment of different emotional tendencies by the sentiment analysis model, which is an automatic review analysis and ensemble classification approach. The improvement of sentiment analysis improves the recognition range of text sentiment words in British and American literature teaching to optimize the process of text analysis. Its main feature is that the sentiment analysis of text directly acts on the tendency of words, with fine granularity and accurate analysis. Finally, it is concluded that the maximum value of the algorithm proposed here is 0.9, which has higher accuracy than the maximum value of 0.81 of other analysis models. The results indicate that the integrated classification model combining British and American literature teaching with the dimensions hidden Markov model has relatively reasonable text analysis and high sentiment classification accuracy. In terms of British and American literature teaching, using ML algorithms can effectively help teachers teach British and American literature through sentiment analysis.
Construction and Management of Intelligent Campus Based on Student Privacy Protection under the Background of Artificial Intelligence and Internet of Things

With the coming of the era of artificial intelligence development and the mobile Internet of things, privacy security will become more and more prominent. As the contemporary college students have gradually developed into one of the main forces of the next generation of network information technology and intelligent terminals of Internet mobile communication, the problem of privacy and protection of college students in the era of artificial intelligence and the Internet of Things has naturally become a practical problem urgently solved by colleges and universities. This paper studies the application of “artificial intelligence + Internet of Things” (AIoT) in the construction and management of intelligent campus for student privacy protection. Firstly, some basic concepts and theories related to artificial intelligence and the Internet of Things are summarized, and the basic technical framework of “Artificial intelligence + Internet of Things” (AIoT) is preliminarily explained and analyzed. Secondly, relevant simulation experiments are carried out, and data support and help are provided for subsequent solutions and management methods by analyzing the simulation results. Through the investigation and analysis of the intelligent campus construction and management based on student privacy protection under the background of artificial intelligence and the Internet of Things, the final research results show that the background of artificial intelligence and the Internet of Things has a great role in promoting the function of student privacy protection, so this research has an important guiding role for student privacy protection in the context of artificial intelligence and the Internet of Things.
A Deep Neural Network Accelerator using Residue Arithmetic in a Hybrid Optoelectronic System

The acceleration of Deep Neural Networks (DNNs) has attracted much attention in research. Many critical real-time applications benefit from DNN accelerators but are limited by their compute-intensive nature. This work introduces an accelerator for Convolutional Neural Network (CNN) , based on a hybrid optoelectronic computing architecture and residue number system (RNS) . The RNS reduces the optical critical path and lowers the power requirements. In addition, the wavelength division multiplexing (WDM) allows high-speed operation at the system level by enabling high-level parallelism. The proposed RNS compute modules use one-hot encoding, and thus enable fast switching between the electrical and optical domains. We propose a new architecture that combines residue electrical adders and optical multipliers as the matrix-vector multiplication unit. Moreover, we enhance the implementation of different CNN computational kernels using WDM-enabled RNS based integrated photonics. The area and power efficiency of the proposed accelerator are 0.39 TOPS/s/mm 2 and 3.22 TOPS/s/W, respectively. In terms of computation capability, the proposed chip is 12.7× and 4.02× better than other optical implementation and memristor implementation, respectively. Our experimental evaluation using DNN benchmarks illustrates that our architecture can perform on average more than 72 times faster than GPU under the same power budget.
Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei

The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively.The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches.The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.