Reviewerly/exHarmony huggingface dataset:
An Improved U-Net for Nuclei Semantic Segmentation

Semantic Segmentation is a pixel-level image processing method, which can automatically divide the object area from the image and identify the category of the object. Since the classical U-Net often misidentifies the blurred targets in the process of cell nuclei segmentation, in this work we address the task of nuclei semantic segmentation with an improved U-Net and make three main contributions that are experimentally shown to have substantial practical merits. First, we combine batch normalization and ReLU activating function after each convolution layer. Second, we further propose a hybrid loss function to reduce the over-segmentation in a non-nuclei image. Third, we introduce a residual path to make the encoder features and the decoder features get the same depth before concatenation. Our proposed approach has achieved better results at 93% Dice coefficient and nearly 90% Jaccard similarity coefficient which surpasses the original U-Net on a dataset of 670 images and approximately 29500 cell nuclei published in the Data Science Bowl in 2018.
One-Shot Relation Learning for Knowledge Graphs via Neighborhood Aggregation and Paths Encoding

The relation learning between two entities is an essential task in knowledge graph (KG) completion that has received much attention recently. Previous work almost exclusively focused on relations widely seen in the original KGs, which means that enough training data are available for modeling. However, long-tail relations that only show in a few triples are actually much more common in practical KGs. Without sufficiently large training data, the performance of existing models on predicting long-tail relations drops impressively. This work aims to predict the relation under a challenging setting where only one instance is available for training. We propose a path-based one-shot relation prediction framework, which can extract neighborhood information of an entity based on the relation query attention mechanism to learn transferable knowledge among the same relation. Simultaneously, to reduce the impact of long-tail entities on relation prediction, we selectively fuse path information between entity pairs as auxiliary information of relation features. Experiments in three one-shot relation learning datasets show that our proposed framework substantially outperforms existing models on one-shot link prediction and relation prediction.
Face Manipulation Detection Based on Supervised Multi-Feature Fusion Attention Network

Nowadays, faces in videos can be easily replaced with the development of deep learning, and these manipulated videos are realistic and cannot be distinguished by human eyes. Some people maliciously use the technology to attack others, especially celebrities and politicians, causing destructive social impacts. Therefore, it is imperative to design an accurate method for detecting face manipulation. However, most of the existing methods adopt single convolutional neural network as the feature extraction module, causing the extracted features to be inconsistent with the human visual mechanism. Moreover, the rich details and semantic information cannot be reflected with single feature, limiting the detection performance. Therefore, this paper tackles the above problems by proposing a novel face manipulation detection method based on a supervised multi-feature fusion attention network (SMFAN). Specifically, the capsule network is used for face manipulation detection, and the SMFAN is added to the original capsule network to extract details of the fake face image. Further, the focal loss is used to realize hard example mining. Finally, the experimental results on the public dataset FaceForensics++ show that the proposed method has better performance.
Hardware Design of SHA-3 for PQC Classic McEliece

As the computing power of quantum computers continues to improve, the security of the mathematical problems on current-used cryptographic algorithm are facing more and more serious challenge. It is necessary to formulate the standard of post-quantum cryptographic algorithm. Classic McEliece is one of the 7 candidates entering the Round 3 of NIST PQC contest. In the decryption module of Classic McEliece, H module uses the SHA-3 algorithm. As a new generation of hash algorithm, SHA-3 algorithm uses the Keccak sponge function, which has the advantages of higher speed, higher throughput and stronger security. Keccak truly realizes the processing of input information of any length, and can also generate hash values of any length. In the future, SHA-3 will be applied to more fields, so the high-performance and flexible implementation of the SHA-3 is especially important. This paper analyzed the four SHA-3 algorithms, and integrated the four algorithm standards into one implementation for Classic McEliece. The Xilinx Zynq-7000 series FPGA is chosen as the implementation and verification platform, and the performance are compared with the algorithm implementation of Keccak official team.
Redacting Sensitive Information from the Data

Redaction of personal, confidential and sensitive information from documents is becoming increasingly important for individuals and organizations. In past years, there have been many well-publicized cases of data leaks from various popular companies. When the data contains sensitive information, these leaks pose a serious threat. To protect and conceal sensitive information, many companies have policies and laws about processing and sanitizing sensitive information in business documents.The traditional approach of manually finding and matching millions of words and then redacting is slow and error-prone. This paper examines different models to automate the identification and redaction of personal and sensitive information contained within the documents using named entity recognition. Sensitive entities example person’s name, bank account details or Aadhaar numbers targeted for redaction, are recognized based on the file’s content, providing users with an interactive approach to redact the documents by changing selected sensitive terms.
Handwritten English Character and Digit Recognition

In today's world, one of the most sought-after technologies is a handwritten character recognition system. It has the potential to solve a wide range of issues and bring about radical change in our lives. We used Convolutional Neural Networks (CNNs) to recognize handwritten English capital letters and digits in this research. We improved a previously developed CNN architecture by adjusting hyperparameters and minimizing the model's overfitting. The MNIST digit dataset is used to evaluate the experiments, which are then compared to different methods. On the MNIST dataset, 99.47 percent test accuracy was attained, which is superior to other approaches. The research was then expanded upon by the addition of a new dataset for recognizing English capital letters. 98.94 percent accuracy was achieved on this extended dataset.
BlindAid - Android-based Mobile Application Guide for Visually Challenged People

Millions of people across the world are affected by visual impairments. The proposed application is designed for visually impaired people consists of three components which are face recognition with emotion, obstacle identification through distance measurement, extract and convey critical information like text, labels, and currency details. Most works reported in the literature depends on the availability of specific hardware. The proposed proof of concept prototype aims to make visually impaired people more independent and carry out tasks conveniently and safely.
Automatic Parameter Tuning for Big Data Pipelines with Deep Reinforcement Learning

Tuning big data frameworks is a very important task to get the best performance for a given application. However, these frameworks are rarely used individually, they generally constitute a pipeline, each having a different role. This makes tuning big data pipelines an important yet difficult task given the size of the search space. Moreover, we have to consider the interaction between these frameworks when tuning the configuration parameters of the big data pipeline. A trade-off is then required to achieve the best end-to-end performance. Machine learning based methods have shown great success in automatic tuning systems, but they rely on a large number of high quality learning examples that are rather difficult to obtain. In this context, we propose to use a deep reinforcement learning algorithm, namely Twin Delayed Deep Deterministic Policy Gradient, TD3, to tune a fraud detection big data pipeline. We show through the conducted experiments that the TD3 agent improves the overall performance of the pipeline by up to 63% with only 200 training steps, outperforming the random search on the high-dimensional search space.
PSO Based Path Planning and Dynamic Obstacle Avoidance in CG Space of a 10 DOF Rover

Dynamic path planning is a core research content for intelligent robots. This NP-hard problem is complex enough for an algorithm to plan a realistic path for a velocity-bound point robot in a dynamic environment. This paper presents a new CG-Space based real-time dynamic path planning method for finding the obstacle-free path for 10 DOF rocker-bogie type wheeled mobile robot (Rover) traversing over a 3D uneven terrain with dynamic and static obstacles. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A swarm intelligence-based Particle Swarm Optimization (PSO) method has been modified to plan an optimum collision-free path over CG-Space of Rover with dynamic obstacles. Dynamic replanning using a modified penalty in the PSO objective function can handle the randomly moving obstacles of varying sizes and shapes in real-time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles. This 3D dynamic replanning study will benefit the cooperative sensing in multiple Rover systems and target tracking in cluttered environments.
More Efficient Secure Matrix Multiplication for Unbalanced Recommender Systems

With recent advances in homomorphic encryption (HE), it becomes feasible to run non-interactive machine learning (ML) algorithms on encrypted data without decryption. In this work, we propose novel encoding methods to pack matrix in a compact way and more efficient methods to perform matrix multiplication on homomorphically encrypted data, leading to a speed boost of <inline-formula><tex-math notation="LaTeX">$1.5\times - 20\times$</tex-math></inline-formula> for slim rectangular matrix multiplication compared with state-of-the-art. Moreover, we integrate our optimized secure matrix arithmetic with the MPI distributed computing framework, achieving scalable parallel secure matrix computation. Equipped with the optimized matrix multiplication, we propose <i>uSCORE</i> , a privacy-preserving cross-domain recommendation system for the unbalanced scenario, where a big data owner provides recommendation as a service to a client who has less data and computation power. Our design delegates most of the computation to the service provider, and has a low communication cost, which previous works failed to achieve. For a client who has 16 million user-item pairs to update, it only needs about 3 minutes (in the LAN setting) to prepare the encrypted data. The server can finish the update process on the encrypted data in less than half an hour, effectively reducing the client's test error from 0.72 to 0.62.
Raspberry Pi based Robot for Drawing Patterns on Ovoid Objects

This paper proposes a Raspberry Pi based embedded system, consisting of a 3D printed custom frame, stepper motors, servomotor and robotic arm, which can be controlled using an intuitive Python graphical interface in order to draw precise patterns on ovoid objects. This robot is able to assist humans in their artistic behavior, by performing accurate drawing tasks with the aid of automation. For the proof of concept, we define 3 models, with various levels of complexity, which are automatically drawn on an egg-shaped object. The proposed drawbot solution proves to be cost-effective, reliable and precise.
Fractional stochastic configuration networks-based nonstationary time series prediction and confidence interval estimation

Time series prediction is an important topic in the field of data analytics for real industrial production. However, the time series from real system usually has strong nonstationarity, which affects the generalization ability of the prediction model. An improved forecasting technique, named as fractional stochastic configuration networks (FSCN), is proposed for the prediction of nonstationary time series. FSCN is built on the basis of traditional stochastic configuration network by introducing fractional differential operator. The fractional differential technique avoids the challenge of infinite variance caused by modeling nonstationary series and the over-difference problem caused by traditional integer order difference. First, several data analysis methods are introduced to find the tendency, periodicity and probability density distribution characteristics hidden in the raw industrial data. Hurst exponent is calculated to determine the order of fractional difference to eliminate the nonstationarity of the raw data. Then FSCN network is constructed to model and forecast the sequential data. An explicit prediction uncertainty is derived to provide the confidence interval for the FSCN prediction. The proposed method is tested on a nonstationary time series benchmark dataset and a real cooling system. The experiment result demonstrates that it has a good potential prediction performance compared with several traditional prediction methods.
Limiting sensitive values in an anonymized table while reducing information loss via <i>p</i>‐proportion

Abstract The ‐proportion model bounds the proportion of sensitive values of a sensitive attribute in each equivalence class of an anonymized database table in order to limit the ability of a user to link an individual or entity to a sensitive value in that table. Nonsensitive values are not subject to any such constraints, which reduces the amount of anonymization needed to meet the requirements of this model. This leads to less information loss in an anonymized table. Anonymization is performed using an extension of the Mondrian algorithm that incorporates categorical attributes. Known as the adapted Mondrian algorithm, it generalizes a value of a categorical attribute to a set. Existing algorithms, by comparison, replace one value of a predefined hierarchy by another. The ‐proportion model is compared against the ( )‐anonymity model using both the progressive local recoding and (adapted) Mondrian algorithms. Experiments demonstrate the advantage of ‐proportion and Mondrian over ( )‐anonymity and progressive local recoding in terms of reduced information loss, measured using the normalized certainty penalty, discernibility metric, and classification metric.
Face Image Generation for Illustration by WGAN-GP Using Landmark Information

With the spread of social networking services, face images for illustration are being used in a variety of situations. Attempts have been made to create illustration face images using adversarial generation networks, but the quality of the images has not been sufficient. It would be much easier to generate face images for illustrations if they could be generated by simply specifying the shape and expression of the face. Also, if images can be generated using landmark information, which is the location of the eyes, nose, and mouth of a face, it will be possible to capture and learn the features of the face. Therefore, in this paper, we propose a method to generate face images for illustration using landmark information. Our method can learn the location of landmarks and produce high quality images on creation of illustration face images.
Discovering the interplay between defensive avoidance and continued use intention of anti-malware software among experienced home users: A moderated mediation model

Dismissing or disregarding security notifications (defensive avoidance) may lead to inactive use of anti-malware, compromising the effective protection of home computers. Nevertheless, few studies have investigated the cognitive and behavioral mechanisms initiated by defensive avoidance in users. Motivated thus, we propose a moderated mediation model that depicts users' cognitive and behavioral mechanisms initiated by defensive avoidance, as well as the moderation role of perceived vulnerability and user involvement. Our results show that perceived vulnerability and user involvement may moderate a biased cognitive mediation process between defensive avoidance and future behavioral intention.
Ontology concept semantic similarity matching based on Ant Colony Optimization algorithm

Semantic closeness between ontology concepts is of great importance in various applications like information retrieval, text summarization, sentiment analysis, etc. These applications relying on domain ontologies required a computable measure of relationships among ontology concepts. A variety of techniques like semantic distance, information content, characteristics or features of terms, etc., are available to measure semantic similarity among concepts. In this research work, a method based on distance is proposed using Ant Colony Optimization to computing the shortest path, which will use layers to calculate semantic similarity. ACO is a probabilistic multi-agent technique based on the behavior of real ants. Evaluation and a comparison of proposed methods against related processes in a common framework deploy higher semantic similarity among concepts.
Controllable software licensing system for sub-licensing

The digital rights management is mainly about license management. Take software licensing as an example. A software license can be assigned to a user, a group of users, or an organization. To protect the copyright against software piracy, a software license is usually bound to the IP of the device or the identity of user. When a user wants to use a software product lawfully on multiple devices he owns, he has to purchase multiple licenses. We consider a scenario of software licensing where upon receiving a software license, the user can legally create a bounded number of licenses for his own devices. Our system is based on a novel chameleon function which is featured with n-time trapdoors. By using this feature, we can construct a secure software licensing system, called “controllable n-time software licensing system”. We formally prove that our software licenses are unforgeable, thanks to the collision resistance and indistinguishability feature of the proposed chameleon hash. We also provide the experimental results to demonstrate the computational efficiency of our system.
Balanced hierarchical max margin matrix factorization for recommendation system

Abstract Matrix factorization (MF) is one of the most important regression analysis methods used in recommendation systems. Max margin matrix factorization (MMMF) is a variant of MF which transforms the regression analysis problem into a single multi‐class classification problem, and then learns a multi‐class max margin classifier to achieve to a better error rate. One drawback of multi‐class MMMF is its bias towards class with small sample size. Therefore, hierarchical MMMF (HMF) which uses some two‐class MMMF problems in a hierarchical manner for multi‐class classification was proposed. Each two‐class MMMF of HMF is learned on the basis of thresholded training data which is too imbalanced for some two‐class MMMFs. Meanwhile, all training data is used in each two‐class MMMF. In the test phase of HMF, an imbalanced tree is used to estimate rating. Each node of this tree is a learned two‐class MMMF. In this paper, we propose a balanced HMF, which constructs a balanced tree with minimum depth. Each node of this tree is a learned two‐class MMMF on the basis of a part of data which is selected such that to be more balanced than that of the traditional HMF. Moreover, each part of data in our proposed balanced HMF does not have overlap with all previous parts of data. Therefore, the overall training data used in each step of balanced HMF is smaller than that of in the traditional HMF. Experimental results on real datasets show that training time, test time and error rate of our proposed balanced HMF is better than those of the traditional HMF.
Automatic SNOMED CT coding of Chinese clinical terms via attention-based semantic matching

A considerable amount of meaningful information is routinely recorded in Chinese clinical data in text format, referred to as Chinese clinical terms. The lack of coding is a major difficulty hindering the application of clinical terms. SNOMED CT is a widely used and comprehensive clinical health care terminology collection because of its coverage, granularity, clinical orientation, and logical underpinning. It is useful and efficient for automatically assigning SNOMED CT codes to Chinese clinical terms, but it still faces several problems. Current cross-language clinical term matching studies rely on external resources, such as machine translation and rule-based methods. Semantic matching methods have achieved strong performance on text matching, but few studies have been done on cross-language clinical term matching. We present an effective attention-based semantic matching algorithm to automatically cross-language code Chinese clinical terms with SNOMED CT.Firstly, BERT was used to turn the input into word embedding. Then, the word embeddings were encoded through a BiLSTM with self-attention to focus on capturing distant relationships among words with different weights depending on their contribution to semantic matching. Then, decomposable attention was used to make semantic matching trivially parallelizable to speed up calculation. Finally, fully connected layers and a sigmoid were utilized to output matching results.The 29,960 manually coded Chinese clinical terms, 30,040 unmatched Chinese clinical terms and SNOMED CT codes were collected to evaluate the proposed method. Compared with the existing semantic matching method, the proposed approach achieves state-of-the-art results demonstrating the effectiveness of the method with an accuracy of 0.905, a precision of 0.856, a recall of 0.518, and an F-measure of 0.645. The proposed Chinese-English bilingual term mapping, Chinese character-level and word-level encoder, English word-level encoder, BERT model, and attention mechanism performed better than other methods.The proposed automatic SNOMED CT coding approach of Chinese clinical terms via attention-based semantic matching can improve the performance of automated SNOMED CT code assignment for Chinese clinical terms and improve the efficiency of the code assignment.