Reviewerly/exHarmony huggingface dataset:
Cascaded Deep Neural Ranking Models in LinkedIn People Search

LinkedIn connects the world's professionals to make them more productive and successful. People Search plays an important role in fulfilling this goal by helping members find the most relevant and personalized results through a broad range of queries like names, job titles, skills, companies, locations, etc. It is one of the biggest search verticals at LinkedIn both in terms of engineering footprint and search traffic. In this paper, we present an overview of the People Search system, and discuss how we build and serve deep neural network (DNN) models, leveraging state-of-the-art deep natural language processing (NLP) techniques (e.g., convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT)). We describe our journey of applying deep neural ranking models to a real-life product, including the modeling and system bottleneck challenges, crucial design choices, and lessons learned along the way. We hope a story of our endeavors and successes will provide meaningful insights to other similar systems.
Moving target trajectory prediction based on Dropout-LSTM and Bayesian inference for long-time multi-satellite observation

Multi-satellite cooperation facilitates long-time and wide-area observation of moving targets and has received considerable attention. An accurate and real-time trajectory prediction of an observed target is the primary premise of multi-satellite cooperative observation that attempts to address the field-of-view switching problem for different satellites. However, the existing methods do not consider the non-linear and time-variant satellite observation process, and the interpretability requires improvement. Therefore, a trajectory prediction model is constructed with decoupled linear Gaussian and non-linear Gaussian parts, based on the analysis of the satellite observation process and the verification of the normal distribution of the observation error. Thereafter, a trajectory prediction method, comprising long short-term memory networks and Bayesian inference, is proposed. The proposed method not only enables the long short-term memory with dropout variational inference to automatically extract features and learn more complex spatiotemporal patterns, but also employs Bayesian inference to introduce the satellite orbit and attitude information as known variables and improve the spatiotemporal smoothness. Finally, a simulation system of moving target trajectory prediction is established, and the results indicate that our approach is superior in terms of the prediction accuracy and generalization properties, compared with the existing methods, particularly when the observed target is manoeuvring rapidly. Consequently, the proposed approach provides a technical basis for achieving long-time multi-satellite observation of the moving target.
HLISA

Automated browsers (web bots) are an invaluable tool for studying the web. However, research has shown that web bots can be distinguished from regular browsers and that they may be served different content as a consequence. This undermines their utility as a measurement tool. So far, three methods have been used to detect web bots: browser fingerprint, order of site traversal, and aspects of page interaction.
Attribute-Based Private Data Sharing With Script-Driven Programmable Ciphertext and Decentralized Key Management in Blockchain Internet of Things

In this article, we address the problem of secure sensitive data sharing for the specified recipients in Blockchain Internet of Things (BIoT). To do it, we present a cryptographic solution to meet the requirements of decentralization and convenience through key management and programmable ciphertext. First, we design a new ciphertext-policy decentralized-key attribute-based encryption (CP-DK-ABE) scheme. After the master secret key is shared into all full nodes in the form of threshold secret sharing, a decentralized multiparty computation protocol is used to generate the user’s private key in an interactive way. Meanwhile, the attribute subkeys associated with the private key can be reconstructed by obtaining a fragment from each of full nodes, so as to achieve the cooperative management of attribute key through all of full nodes. Furthermore, following the blockchain’s script system, we introduce five new opcodes to represent ciphertext in the programmable format. Such a mechanism provides flexible capability to represent the logical relationship of the access control policy among attribute subciphers in the CP-DK-ABE ciphertext by the scripting language. As a result, the processes of encryption and decryption are implemented entirely by the script interpreter on the blockchain node, thereby greatly improving the convenience of programming in BIoT devices. In addition, we prove that the proposed CP-DK-ABE scheme is key private and semantically secure for a limited number of corrupted full nodes under the decisional linear and bilinear Diffie–Hellman assumption, respectively.
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving

Moving object Detection (MOD) is a critical task in autonomous driving as moving agents around the ego-vehicle need to be accurately detected for safe trajectory planning. It also enables appearance agnostic detection of objects based on motion cues. There are geometric challenges like motion-parallax ambiguity which makes it a difficult problem. In this work, we aim to leverage the vehicle motion information and feed it into the model to have an adaptation mechanism based on ego-motion. The motivation is to enable the model to implicitly perform ego-motion compensation to improve performance. We convert the six degrees of freedom vehicle motion into a pixel-wise tensor which can be fed as input to the CNN model. The proposed model using Vehicle Motion Tensor (VMT) achieves an absolute improvement of 5.6% in mIoU over the baseline architecture. We also achieve state-of-the-art results on the public KITTI_MoSeg_Extended dataset even compared to methods which make use of LiDAR and additional input frames. Our model is also lightweight and runs at 85 fps on a TitanX GPU. Qualitative results are provided in https://youtu.be/ezbfjti-kTk.
Quantum-inspired complex convolutional neural networks

Quantum-inspired artificial neural network is an interesting research area, which combines quantum computing and deep learning. Several models of quantum-inspired neuron with real-valued weights have been proposed, and they were mainly used to build the three-layer feedforward neural networks. In this work, we improve the convolutional neural networks (CNNs) by utilizing the quantum-inspired way of data representation and convolutional operation. Specifically, we first improve the quantum-inspired neuron by exploiting the complex-valued weights, which have richer representational capacity and better non-linearity. Moreover, we extend the method implementing the quantum-inspired neurons to perform convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Here five specific types of QICNNs are proposed, which are different in the way of implementing the convolutional layers and fully connected layers. We establish the detail mathematical framework to implement the QICNNs. The performances of accuracy, convergence and robustness of the five QICNNs against the classical counterpart are tested using the MNIST and CIFAR-10 datasets. The results show that (1) the QICNN can achieve higher classification accuracy (up to 99.65%) than the classical CNN when using the MNIST dataset; (2) the QICNN has faster convergence speed, which means that QICNN can be trained easily than classical CNN when they have a similar number of parameters; (3) the QICNN has better robustness in the case of employing different way of weight initialization or rotating the input data. It is expected that our QICNNs can outperform the classical counterparts in more practical learning tasks.
Predictive machine learning for prescriptive applications: A coupled training–validating approach

In this research we propose a new method for training predictive machine learning models for prescriptive applications. This approach, which we refer to as coupled validation, is based on tweaking the validation step in the standard training–validating–testing scheme. Specifically, the coupled method considers the prescription loss as the objective for hyper-parameter calibration. This method allows for intelligent introduction of bias in the prediction stage to improve decision making at the prescriptive stage, and is generally applicable to most machine learning methods, including recently proposed hybrid prediction-stochastic-optimization techniques, and can be easily implemented without model-specific mathematical modeling. Several experiments with synthetic and real data demonstrate promising results in reducing the prescription costs in both deterministic and stochastic models. • Proposed coupled training-validating approach to train predictive learning models • Method considers prescription loss as the objective for hyper-parameter calibration. • The coupled approach is applicable to most predictive machine learning models. • Experiments with synthetic and real data demonstrate promising results.
Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false-positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography examinations from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.
Designing for place-making in XR: the process of the Co-Drive stops and its atlas

The study described in this paper shows how the concept of the social construction of space can support the design of the XR experience called "Co-Drive" by injecting acts of place-making along the design process. Co-Drive is a novel service that allows social virtual travelling by car between a driver of an automated vehicle and a remote passenger connected via virtual reality from home. In this paper, the focus is on the Co-Drive stops, the peculiar spaces where the driver and the remote passenger get to meet for the very first time, and how such spaces turn into places which are bounded by their physical features; the individual and social experiences; and the narratives drawn from those experiences.
An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu

During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NLP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language for communication in the world, covering millions of users around the globe. Although some techniques have been proposed for sentiment analysis in Roman Urdu, these techniques are limited to a specific domain or developed incorrectly due to the unavailability of language resources available for Roman Urdu. Therefore, in this article, we are proposing an unsupervised approach for sentiment analysis in Roman Urdu. First, the proposed model normalizes the text to overcome spelling variations of different words. After normalizing text, we have used Roman Urdu and English opinion lexicons to correctly identify users’ opinions from the text. We have also incorporated negation terms and stemming to assign polarities to each extracted opinion. Furthermore, our model assigns a score to each sentence on the basis of the polarities of extracted opinions and classifies each sentence as positive, negative, or neutral. In order to verify our approach, we have conducted experiments on two publicly available datasets for Roman Urdu and compared our approach with the existing model. Results have demonstrated that our approach outperforms existing models for sentiment analysis tasks in Roman Urdu. Furthermore, our approach does not suffer from domain dependency.
On Robustness and Bias Analysis of BERT-Based Relation Extraction

Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly understood. We do not know, for example, how excellent performance can lead to the perfection of generalization models. In this study, we analyze a fine-tuned BERT model from different perspectives using relation extraction. We also characterize the differences in generalization techniques according to our proposed improvements. From empirical experimentation, we find that BERT suffers a bottleneck in terms of robustness by way of randomizations, adversarial and counterfactual tests, and biases (i.e., selection and semantic). These findings highlight opportunities for future improvements. Our open-sourced testbed DiagnoseRE is available in https://github.com/zjunlp/DiagnoseRE .
Using Logical Architecture Models for Inter-Team Management of Distributed Agile Teams

Projects at large-scale have been adopting agile practices in order to optimize how a group of teams deliver software, however has facing issues when dealing with work items that are dependent on each other, need for well-defined interfaces and shared understanding of the existing knowledge. Systems thinking and modeling can have important contributions in managing a process with this nature. This article proposes a design framework, called logical architecture-based distributed agile team management (LADATM), that uses logical architecture models to serve as the basis for the delivery of work items to distributed agile teams, support the identification of boundaries, dependencies and coordination needs, and derive an agile team product backlog. By performing Design Science Research (DSR) cycles, this article introduces the constructs of the LADATM framework. Afterwards, it demonstrates - using a research project - how it provided a systematized approach for process mananagement decision support for agile teams based in requirements models and through shared understanding.
RoMA: A Method for Neural Network Robustness Measurement and Assessment

Neural network models have become the leading solution for a large variety of tasks, such as classification, natural language processing, and others. However, their reliability is heavily plagued by adversarial inputs: inputs generated by adding tiny perturbations to correctly-classified inputs, and for which the neural network produces erroneous results. In this paper, we present a new method called Robustness Measurement and Assessment (RoMA), which measures the robustness of a neural network model against such adversarial inputs. Specifically, RoMA determines the probability that a random input perturbation might cause misclassification. The method allows us to provide formal guarantees regarding the expected frequency of errors that a trained model will encounter after deployment. The type of robustness assessment afforded by RoMA is inspired by state-of-the-art certification practices, and could constitute an important step toward integrating neural networks in safety-critical systems.
Boundary delineation of reflux esophagitis lesions from endoscopic images using color and texture

Automatic assessment of medical images and endoscopic images in particular is an attractive research topic recent years. To achieve this goal, many tasks must be conducted for example lesions detection, segmentation and classification. In order to design suitable models for such tasks, it would be preferable to know at first: i) which characteristics that differentiate a lesion from a normal region; ii) how large is the boundary of these two regions that still allows to distinguish them. This paper presents an in-depth study of the role of color and texture features for delineation of boundary between a lesion region and a background region. To this end, from the groundtruth contour of a manually segmented lesion, we first expand two margins in two directions. We name inner margin in the lesion region and outer margin in the background region. We then extract color dependent features in different color spaces (HSV, RGB, Lab) and texture features (LBP, HOG, GLCM) on these two margins. Finally we deploy the Support Vector Machine (SVM) technique to classify two classes (lesion and non-lesion). Extensive experiments conducted on a dataset of endoscopic images answer to our aforementioned questions and give some suggestions for designing suitable models of lesion detection in the future.
Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS-TSK

Developing an accurate system to analyze breast cancer image data can give doctors an extra measure of confidence in assessing patients and can be used to scan all past scans in a clinic database to understand if any patients are at risk. Fuzzy Logic Systems are well suited to building knowledge bases and rule bases that can accurately approximate expert human knowledge, such as a doctor who routinely diagnoses breast cancer in patients. Genetic Algorithms boost the capability of a Fuzzy Logic System, especially when given a representative data set of the system, by using a subset of the data to learn the optimal membership functions and rule base of the Fuzzy Logic System. The combination of these two techniques can be utilized to develop a highly accurate approximator of cancer diagnosis.
Enabling Decentralized and Dynamic Data Integrity Verification for Secure Cloud Storage via T-Merkle Hash Tree Based Blockchain

Cloud storage provides elastic storage services for enterprises and individuals remotely. However, security problems such as data integrity are becoming a major obstacle. Recently, blockchain-based verification approaches have been extensively studied to get rid of a centralized third-party auditor. Most of these schemes suffer from poor scalability and low search efficiency and even fail to support data dynamic update operations on blockchain, which limits their large-scale and practical applications. In this work, we propose a blockchain-based dynamic data integrity verification scheme for cloud storage with T-Merkle hash tree. A decentralized scheme is proposed to eliminate the restrictions of previous centralized schemes. The data tags are generated by the technique of ZSS short signature and stored on blockchain. An improved verification method is designed to check the integrity of cloud data by transferring computation from a verifier to cloud server and blockchain. Furthermore, a storage structure called T-Merkle hash tree which is built based on T-tree and Merkle hash tree is designed to improve storage utilization of blockchain and support binary search on chain. Moreover, we achieve efficient and secure dynamic update operations on blockchain by an append-only manner. Besides, we extend our scheme to support batch verification to handle massive tasks simultaneously; thus, the efficiency is improved and communication cost is reduced. Finally, we implemented a prototype system based on Hyperledger Fabric to validate our scheme. Security analysis and performance studies show that the proposed scheme is secure and efficient.
A new instance density-based synthetic minority oversampling method for imbalanced classification problems

Many useful data pre-processing techniques have been developed to improve the performance of imbalanced classification problems. However, the results from the adoption of the several pre-processing methods indicated that the class imbalance is not the only reason for difficulties in learning in such a situation but the pre-processing for specific minority classes can improve classification performance more effectively. Thus, in this research, we present a new synthetic minority oversampling method based on instance density to enhance the performance of imbalanced classification problems. For this, two dimensional reduction techniques, i.e., principal component analysis (PCA) and factor analysis of mixed data (FAMD), are used to map the instances and k-nearest neighbor (kNN) method is used to classify minority class instances into four distinct types. Finally, through the comprehensive experiments on several imbalance datasets with different combination of subcategories, it is shown that the proposed method outperform the traditional oversampling methods.
COVID Contact Tracing App Technology Acceptance Among Students

The COVID-19 pandemic has shown the importance of effective contact tracing for saving lives. This quantitative study uses an extended technology acceptance model to investigate university-age students’ behavioral intention to use a contact-tracing app. This study shows that overall, respondents from this demographic are willing to adopt this technology. That being said, the behavioral intention to adopt the technology is influenced by perceived usefulness, which is influenced by the extent to which respondents worry about COVID-19. This study is among the first to investigate how culture and the threat of COVID-19 impact adoption intention.
Machine Learning Classification Based Techniques for Fraud Discovery in Credit Card Datasets

The frequency of credit card-based online payment frauds has increased rapidly in recent years, forcing banks and e-commerce companies to create automated fraud detection systems that perform mining on massive transaction logs. Machine learning appears to be one of the most promising techniques for detecting illegal transactions since it uses supervised binary classification algorithms appropriately trained using pre-screened sample datasets to differentiate between fraudulent and non-fraudulent cases. This study aims to concentrate on machine learning (ML) methods thereby proposing a credit card fraud discovery scheme to detect fraud. The ML techniques employed are Decision Tree (DT) and K-Nearest Neighbor (KNN) ML classification techniques. The performance outcomes of the two ML classification techniques are evaluated depending on accuracy, precision, specificity, recall, f1-score, and false-positive rate (FPR). The area under the ROC curve (AUC) of the receiver operating characteristics (ROC) curve was similarly drawn built on the confusion matrix for both classifiers. The two classification techniques were evaluated and compared using the performance metrics mentioned earlier and it was demonstrated that the KNN technique outperformed that of the DT with a greater ROC curve value of 91% for KNN and 86% for DT. It was concluded that KNN is considered a better ML classification technique that can be employed to discover credit card fraudulent activities.