Reviewerly/exHarmony huggingface dataset:
How privacy concerns and cultural differences affect public opinion during the COVID-19 pandemic: a case study

Purpose COVID-19, an infectious disease first identified in China, has resulted in an ongoing pandemic all over the world. Most of the countries have been experiencing a difficult period during the fighting of this pandemic. The purpose of this study is to explore the effect of privacy concerns and cultural differences on public opinion related to the pandemic. The authors conducted a comparative analysis of public opinion in the US and in China as a case study, in order to determine the results. Design/methodology/approach National policies on important issues faced during the COVID-19 pandemic in the US and in China were examined through a comparative analysis. The authors used text clustering and visualization to mine public opinion on two popular social media platforms, Twitter and Weibo. From the perspectives of concern for privacy and of national culture, this study combines qualitative and quantitative analysis to discover the acceptance level of national policies by the public in the two countries. Findings The anti-pandemic policies and measures of the US and China reflect the different characteristics of their respective political systems and national cultures. When considering the culture of the US, it is hard to establish and enforce a rigorous regulation on either mask wearing in public or home quarantine on the national level. The opinions of US people are diverse, regarding national COVID-19 policies, but they are rather unified on privacy issues. On the other hand, Chinese people show a high acceptance of national policies based on their mask-wearing customs and their culture of collectivism. Originality/value Prior studies have paid insufficient attention to the ways in which user privacy and cultural difference affect public opinion on national policies between the US and China. This case study that compares public opinion on current and topical issues which are closely bound up with public life shows originality, as it innovatively provides a cross-cultural perspective on the research of public opinion dissemination during emergencies by considering the ongoing COVID-19 pandemic.
Characterizing English Variation across Social Media Communities with BERT

Abstract Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups. We extend this type of study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. The specificity of different sense clusters to a community, combined with the specificity of a community’s unique word types, is used to identify cases where a social group’s language deviates from the norm. We validate our metrics using user-created glossaries and draw on sociolinguistic theories to connect language variation with trends in community behavior. We find that communities with highly distinctive language are medium-sized, and their loyal and highly engaged users interact in dense networks.
An ontology for reasoning over engineering textual data stored in FMEA spreadsheet tables

Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence, industry must find ways to extract the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make the classes and relations held in spreadsheet tables explicit. Ontologies offer a pathway because they use formal descriptions to define machine-interpretable definitions of shared concepts and relations between concepts. We illustrate this with two case studies on a failure modes and effects analysis (FMEA) table. Our examples demonstrate how the relationship between rows and columns in a table can be represented in logic for FMEA entries, thereby allowing the same ontology to ingest instance data from the IEC 60812:2006 FMEA Standard and a real industrial FMEA. We give relationships in the FMEA and asset hierarchy spreadsheets an explicit representation, so that OWL-DL reasoning can infer final failure effects at the system level from component failures. The prototype ontologies described in this paper are modular and aligned to a top level ontology, and hence can be applied to other use cases. Our contribution is showing that engineers can make data captured in commonly used spreadsheet tables machine readable using a FMEA ontology.
DHS

Data stream processing is a crucial computation task in data mining applications. The rigid and fixed data structures in existing solutions limit their accuracy, throughput, and generality in measurement tasks. We propose Dynamic Hierarchical Sketch (DHS), a sketch-based hybrid solution targeting these properties. During the online stream processing, DHS hashes items to buckets and organizes cells in each bucket dynamically; the size of all cells in a bucket is adjusted adaptively to the actual size and distribution of flows. Thus, memory is efficiently used to precisely record elephant flows and cover more mice flows. Implementation and evaluation show that DHS achieves high accuracy, high throughput, and high generality on five measurement tasks: flow size estimation, flow size distribution estimation, heavy hitter detection, heavy changer detection, and entropy estimation.
Neural-Answering Logical Queries on Knowledge Graphs

Logical queries constitute an important subset of questions posed in knowledge graph question answering systems. Yet, effectively answering logical queries on large knowledge graphs remains a highly challenging problem. Traditional subgraph matching based methods might suffer from the noise and incompleteness of the underlying knowledge graph, often with a prolonged online response time. Recently, an alternative type of method has emerged whose key idea is to embed knowledge graph entities and the query in an embedding space so that the embedding of answer entities is close to that of the query. Compared with subgraph matching based methods, it can better handle the noisy or missing information in knowledge graph, with a faster online response. Promising as it might be, several fundamental limitations still exist, including the linear transformation assumption for modeling relations and the inability to answer complex queries with multiple variable nodes. In this paper, we propose an embedding based method (NewLook) to address these limitations. Our proposed method offers three major advantages. First (Applicability), it supports four types of logical operations and can answer queries with multiple variable nodes. Second (Effectiveness), the proposed NewLook goes beyond the linear transformation assumption, and thus consistently outperforms the existing methods. Third (Efficiency), compared with subgraph matching based methods, NewLook is at least 3 times faster in answering the queries; compared with the existing embed-ding based methods, NewLook bears a comparable or even faster online response and offline training time.
Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode

The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements, but they can't accurately detect small objects and objects with obstructions.Therefore, we propose a helmet detection algorithm based on the attention mechanism (AT-YOLO).First of all, a channel attention module is added to the YOLOv3 backbone network, which can adaptively calibrate the channel features of the direction to improve the feature utilization, and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly, we use DIoU (Distance Intersection over Union) bounding box regression loss function, it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes, which makes the network more accurate in detecting small objects and faster in convergence.Finally, we explore the training strategy of the network model, which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%, and the detection speed can reach 27 fps.Compared with other existing methods, it has better performance in detection accuracy and speed.
ModuleNet: Knowledge-Inherited Neural Architecture Search

Although neural the architecture search (NAS) can bring improvement to deep models, it always neglects precious knowledge of existing models. The computation and time costing property in NAS also means that we should not start from scratch to search, but make every attempt to reuse the existing knowledge. In this article, we discuss what kind of knowledge in a model can and should be used for a new architecture design. Then, we propose a new NAS algorithm, namely, ModuleNet, which can fully inherit knowledge from the existing convolutional neural networks. To make full use of the existing models, we decompose existing models into different modules, which also keep their weights, consisting of a knowledge base. Then, we sample and search for a new architecture according to the knowledge base. Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macrospace by the NSGA-II algorithm without tuning parameters in these modules. Experiments show that our strategy can efficiently evaluate the performance of a new architecture even without tuning weights in convolutional layers. With the help of knowledge we inherited, our search results can always achieve better performance on various datasets (CIFAR10, CIFAR100, and ImageNet) over original architectures.
EchoVib: Exploring Voice Authentication via Unique Non-Linear Vibrations of Short Replayed Speech

Recent advances in speaker verification and speech processing technology have seen voice authentication being adopted on a wide scale in commercial applications like online banking and customer care support and on devices such as smartphones and IoT voice assistant systems. However, it has been shown that the current voice authentication systems can be ineffective against voice synthesis attacks that mimic a user's voice to high precision. In this work, we suggest a paradigm shift from the traditional voice authentication systems operating in the audio domain but susceptible to speech synthesis attacks (in the same audio domain). We leverage a motion sensor's capability to pick up phonatory vibrations, that can help to uniquely identify a user via voice signatures in the vibration domain. The user's speech is played/echoed back by a device's speaker for a short duration (hence our method is termed EchoVib) and the resulting non-linear phonatory vibrations are picked up by the motion sensor for speaker recognition. The uniqueness of the device's speaker and its accelerometer results in a device-specific fingerprint in response to the echoed speech. The use of the vibration domain and its non-linear relationship with audio allows EchoVib to resist the state-of-the-art voice synthesis attacks, shown to be successful in the audio domain.
Recommendation algorithm of probabilistic matrix factorization based on directed trust

This paper focuses on improving the performance of recommender systems by the use of social trust information. Probabilistic matrix factorization is a classic algorithm for recommender systems. However, both the rating matrix and trust matrix become sparser, which makes the recommended results inaccurate. We propose a hybrid method based on probabilistic matrix factorization and directed trust. First, we apply the probabilistic matrix factorization approach to break down the trust matrix. Thus, the potential preferences of users, considering trusters and trustees, are obtained. This approach alleviates the problem of the sparsity of the trust matrix. Second, to capture the trust relations among users, we modify undirected trust to directed trust, since a user has different preferences when he is treated as a truster or trustee. Last, the two algorithms are combined to predict ratings. Experiments involving two datasets show that the proposed algorithm is superior to existing benchmark algorithms.
A new active contour model driven by pre-fitting bias field estimation and clustering technique for image segmentation

Due to uneven illumination or limitations of imaging devices, intensity inhomogeneities are more or less present in images obtained by different imaging modes. This ubiquitous intensity inhomogeneity makes image segmentation more difficult. This paper proposes a new bias field model (KPBFE) based on pre-fitting bias field estimation to deal with intensity inhomogeneity in the image segmentation. A new function for computing bias field b is proposed with K-means++ clustering algorithm. The computation method of clustering center points takes into account the average value of the grayscale within the contour of the bias field estimation and outside the contour. Meanwhile, we use a variational level set function with arctan function and a new adaptive function τ to limit the magnitude of the data driver term. Since the computation of bias field estimation is completed before the iteration and there is no convolution operation in the process, the computing speed of the proposed model is greatly increased. Experiments results show that our model can effectively segment the images with intensity inhomogeneity. Compared with some classical models, our method also has faster computation speed, higher segmentation accuracy and better initial robustness.
Assessment of Blockchain Technology Application in the Improvement of Pharmaceutical Industry

Blockchain technology (BCT) has paved a way for new potentials of handling serious data privacy, integrity and security issues in healthcare. To curb the increasing challenges in healthcare industry, healthcare organizations need to apply blockchain technology to better improve patient safety and protect patients records from counterfeiting and fraud. The purpose of this research paper was to define BCT can assist in improving pharmaceutical industries in Saudi Arabia upon utilization of its application. This study adopted quantitative methods to gather the study data. Based on healthcare leaders perception and Internet connection, lack of cooperation, and economic inequality were found to be leading factors hindering the application of blockchain technology in the pharmaceutical industries, Saudi Arabia. Factors facilitating the application of blockchain technology in the pharmaceutical industries, Saudi Arabia were found as system robustness of BCT, increased data safety and decentralization, need for enhanced supply chain management and interoperability, and government laws and policies. Adopting interventions that are targeted to specific patient population medications, effective delivery systems, transit provider reimbursement far from intensity and volume of services towards value and quality was found to compromise the pre-existent challenges and real capacity in healthcare system. Although the relationship between implementation of blockchain technology and cost spending is negative in the short-term, in the long run, the relationship is positive Blockchain helps in managing multiple levels in a more secure way, reduces paper work and amplifies verification inefficiency.
Shortened Linear Codes Over Finite Fields

The puncturing and shortening techniques are two important approaches to constructing new linear codes from old ones. In the past 70 years, a lot of progress on the puncturing technique has been made, and many works on punctured linear codes have been done. Many families of linear codes with interesting parameters have been obtained with the puncturing technique. However, little research on the shortening technique has been done and there are only a handful references on shortened linear codes. The first objective of this paper is to prove some general theory for shortened linear codes. The second objective is to study some shortened codes of the Hamming codes, Simplex codes, some Reed-Muller codes, and ovoid codes. Eleven families of optimal shortened codes over finite fields are presented in this paper. As a byproduct, five infinite families of 2-designs are also constructed from some of the shortened codes presented in this paper.
Easy2Hard: Learning to Solve the Intractables From a Synthetic Dataset for Structure-Preserving Image Smoothing

Image smoothing is a prerequisite for many computer vision and graphics applications. In this article, we raise an intriguing question whether a dataset that semantically describes meaningful structures and unimportant details can facilitate a deep learning model to smooth complex natural images. To answer it, we generate ground-truth labels from easy samples by candidate generation and a screening test and synthesize hard samples in structure-preserving smoothing by blending intricate and multifarious details with the labels. To take full advantage of this dataset, we present a joint edge detection and structure-preserving image smoothing neural network (JESS-Net). Moreover, we propose the distinctive total variation loss as prior knowledge to narrow the gap between synthetic and real data. Experiments on different datasets and real images show clear improvements of our method over the state of the arts in terms of both the image cleanness and structure-preserving ability. Code and dataset are available at https://github.com/YidFeng/Easy2Hard.
Research on the construction of O2O teaching system of cross-cultural knowledge in College English based on MOOC

In order to improve the teaching effect of college English cross-cultural knowledge, with the support of recommendation algorithms, this paper builds a O2O teaching system of cross-cultural knowledge in college English based on MOOC, and provides service methods such as material resource learning, “smart grab-order” question answering, resource recommendation, LBS appointment teaching and offline teaching. Moreover, this paper constructs a learner and teacher interest model, introduces a suspicious degree update model into the interest model, uses a deep learning network algorithm to recommend composition resources, and uses a deep network adaptive learning algorithm to enhance the accuracy of composition resource recommendation. In addition, this paper constructs a system framework structure based on actual needs, and studies the realization process of each of its functional modules. Finally, this paper designs an experiment to evaluate the practical effect of O2O teaching system of cross-cultural knowledge in college English based on MOOC. The research results show that the system constructed in this paper meets the expected requirements.
The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.
Domain Invariant and Agnostic Adaptation

Domain adaptation addresses the prediction problem in which the source and target data are sampled from different but related probability distributions. The key problem here lies in properly matching the distributions and learning general feature representation for training the prediction model. In this article, we introduce a Domain Invariant and Agnostic Adaptation (DIAA) solution, which matches the source and target joint distributions, and simultaneously aligns the feature and domain label joint distribution to its marginal product. In particular, DIAA matches and aligns the distributions via a feature transformation, and compares the two kinds of distribution disparities uniformly under the Kullback–Leibler (KL) divergence. To approximate the two corresponding KL divergences from observed samples, we derive a linear-regression-like technique that fits linear models to different ratio functions under the quadratic loss. With the estimated KL divergences, learning the DIAA feature transformation is formulated as solving a Grassmannian minimization problem. Experiments on text and image classification tasks with varied nature demonstrate the success of our approach.
Towards Efficient and Strong Backward Private Searchable Encryption with Secure Enclaves

Dynamic searchable symmetric encryption (DSSE) can enable a cloud server to search and update over the encrypted data. Recently, forward and backward privacy in DSSE receive wide attention due to the rise in a number of emerging attacks exploiting the leakage in data update operations. Forward privacy ensures newly added data is not related to queries issued in the past, whilst backward privacy ensures previously deleted data is not revealed in the queries. Unfortunately, achieving strong forward and backward privacy, i.e., only revealing insertion timestamps of search results, requires the adoption of oblivious data structures, which incur heavy computation and communication overhead at both the client and server-side. In this paper, we resort to secure enclaves, aka Intel SGX, to tackle the above problem. Specifically, we propose Maiden, the first strong backward-private DSSE scheme without relying on ORAM. Our key idea is to keep track of the states of updates and the deletion information inside the secure enclave to prevent the leakage from the server. To speed up, we further leverage a compressed data structure to maintain a sketch of addition operations in the enclave to facilitate the fast generation of search tokens of non-deleted data. We conduct formal security analysis and perform comprehensive evaluations on both synthetic and real-world datasets. Our results confirm that Maiden outperforms the prior work.