Reviewerly/exHarmony huggingface dataset:
Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation

Human peripheral blood leukocytes' classification is important for diagnosing blood diseases. Many microscopic leukocyte image automatic detection methods are proposed. In recent years, convolutional neural networks (CNNs) are applied to microscopic leukocyte image automatic classification. But when a CNN is used for microscopic leukocyte image classification, the dataset's scarcity and imbalance will lead to low classification accuracy. To improve classification accuracy, a data augmentation method is proposed, and a resampling method is adopted when using a CNN method.First, a deep CNN model for microscopic leukocyte image classification is designed. Then, a new data augmentation method based on feature concentration is proposed to enrich the dataset and overcome the problem of dataset scarcity. To make the CNN model focus on the leukocyte region, many images are generated by putting a segmented leukocyte into images with different microscopic surroundings using an image processing method. Finally, taking the imbalance of the five kinds of leukocytes in the dataset into consideration, a resampling method is adopted. The resampling method iteratively feeds the leukocyte images with a low proportion to the CNN model within an epoch to ensure that images of each of the five kinds of leukocytes are represented in relatively equal numbers in each batch.The experimental results demonstrate that the proposed classification method can achieve 97.6% average testing accuracy. Classification precision for the five kinds of leukocytes is above 93.4%, while sensitivity is above 92.5%. Both the proposed data augmentation and the resampling methods improve classification accuracy.A human peripheral blood leukocyte classification method based on a CNN and data augmentation is proposed. The problem of dataset scarcity is solved by the proposed data augmentation method, and the dataset imbalance is solved by a resampling method.
Recognizing Nested Named Entity Based on the Neural Network Boundary Assembling Model

The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.
Homomorphic Encryption for Finite Automata

We describe a somewhat homomorphic GSW-like encryption scheme, natively encrypting matrices rather than just single elements. This scheme offers much better performance than existing homomorphic encryption schemes for evaluating encrypted (nondeterministic) finite automata (NFAs). Differently from GSW, we do not know how to reduce the security of this scheme from LWE, instead we reduce it from a stronger assumption, that can be thought of as an inhomogeneous variant of the NTRU assumption. This assumption (that we term iNTRU) may be useful and interesting in its own right, and we examine a few of its properties. We also examine methods to encode regular expressions as NFAs, and in particular explore a new optimization problem, motivated by our application to encrypted NFA evaluation. In this problem, we seek to minimize the number of states in an NFA for a given expression, subject to the constraint on the ambiguity of the NFA.
Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling

This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELMALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELMALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
Diversity measure as a new drift detection method in data streaming

Data stream mining is an important research topic that has received increasing attention due to its use in a wide range of applications, such as sensor networks, banking, and telecommunication. A serious and challenging problem affecting data stream mining is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Several concept drift detection methods have been proposed, however; they either suffer from a high cost in terms of memory or run time or they are not fast enough in terms of detection speed. In this work, we propose a method, called diversity measure as a new drift detection method (DMDDM), which reacts rapidly to concept drift in less time and with less memory consumption. The proposed method combines one of the diversity measures, disagreement measure, known from static learning in streaming scenarios with the Page-Hinkley test and uses these calculations to detect drifts. The proposed method has been experimentally compared with ten drift detection methods in different drift scenarios using several datasets. The experiment results show that the proposed method is capable of detecting concept drifts faster than most of the compared methods with minimal consumption in terms of memory and run time.
Online Intelligent Calibration of Cameras and LiDARs for Autonomous Driving Systems

The online extrinsic parameters calibration among multiple sensors for data fusion has been a crucial task in autonomous driving. This paper proposes a new online, robust pipeline of automatic extrinsic parameters calibration between monocular RGB cameras and LiDARs for autonomous driving applications. The novelties of this work includes threefold: (1) an automatic full pipeline of the calibration is developed for the autonomous driving system; (2) an initialization stage is developed for calibration without the initial value of extrinsic parameters; (3) a combination of depth estimation and edge detection is used to improve the calibration accuracy and robustness. We implement the proposed calibration algorithm on an autonomous driving platform, a BYD Qin electric sedan. Both the simulation and experiment results verify the online and robust performance of the proposed calibration pipeline.
Strong approximate Markov blanket and its application on filter-based feature selection

In feature selection problems, strong relevant features may be misjudged as redundant by the approximate Markov blanket. To avoid this, a new concept called strong approximate Markov blanket is proposed. It is theoretically proved that no strong relevant feature will be misjudged as redundant by the proposed concept. To reduce computation time, we propose the concept of modified strong approximate Markov blanket, which still performs better than the approximate Markov blanket in avoiding misjudgment of strong relevant features. A new filter-based feature selection method that is applicable to high-dimensional datasets is further developed. It first groups features to remove redundant features, and then uses a sequential forward selection method to remove irrelevant features. Numerical results on four benchmark and seven real datasets suggest that it is a competitive feature selection method with high classification accuracy, moderate number of selected features, and above-average robustness.
Secure data transmission and detection of anti-forensic attacks in cloud environment using MECC and DLMNN

Anti-forensics is a set of techniques and measures adopted by an attacker aimed at compromising the digital investigation process in a computational environment. Cloud computing, which is an environment providing on demand resources to users, is susceptible to anti-forensic attacks. An anti-forensic attacker in the cloud can influence the cloud forensic process and tamper with evidences, causing damage to the investigation. Though some solutions have been proposed against anti-forensic attacks in cloud, there is a need to secure the evidences while in transit as well as in storage. In this work, we propose efficient algorithms for secure data (evidence) transmission and early detection of Anti-Forensic Attack (AFA). First, the data packets are compressed using a B-tree Huffman Encoding (BHE) algorithm; next, the packet marking technique is implemented to secure the IP address of the sender. For securely sending the data, we propose the Modified Elliptic curve cryptography (MECC) algorithm which encrypts the data packets and transmits it to a receiver. At the receiver side, the training is done using a Deep Learning Modified Neural Network (DLMNN) classifier, which tests the received data packet IP-address. Based on the IP-address of the sender, DLMNN identifies whether the received packet is an packet attacked or a non-attacked one. After the identification of the data packets, the decryption and de-compression of non-attacked data packets are done to obtain the original information. The original evidence information is further analyzed for investigation purposes. Experimental results shown by the proposed method are weighed against the prevailing techniques for performace comparison.
Security-enhanced chaotic communications with optical temporal encryption based on phase modulation and phase-to-intensity conversion

We propose and numerically demonstrate a security-enhanced chaotic communication system by introducing optical temporal encryption (OTE) into the modulated chaotic carrier (chaos + message).In the proposed scheme, the message is firstly embedded into the original chaotic carrier generated by a conventional external-cavity semiconductor laser (ECSL), and before being transmitted to the receiver end, the modulated chaotic carrier propagates through an OTE module that consists of one phase modulator driven by a secret sinusoidal signal and one dispersive component.Our numerical results indicate that, as a direct result of the spectral expansion effect of the sinusoidal phase modulation and the phaseto-intensity conversion effect of the dispersive component, the original chaotic carrier can be encrypted as an uncorrelated chaotic signal with a flat spectrum and an efficiently-suppressed time delay signature, this greatly enhances the privacy of the modulated chaotic carrier.Moreover, comparing with the conventional ECSL-based chaotic communication systems without OTE, the proposed scheme not only shows significantly higher security against attacks including direct linear filtering and synchronization utilization, but also provide additional physical key space to further enhance the system security.In addition, by making use of the transmission dispersion for decryption, the proposed encryption scheme supports dispersion-compensation-free secure fiber communication, and it also supports centralized encryption/decryption in wavelength division multiplexing secure chaotic communication systems.The proposed scheme provides a novel plug-and-play encryption method for implementation in high-security chaotic communication systems.
Research on SQL Injection Attack and Prevention Technology Based on Web

This SQL injection attack is one of the common means for hackers to attack database. With the development of B/S mode application development, more and more programmers use this mode to write applications. However, due to the uneven level and experience of programmers, a considerable number of programmers do not judge the legitimacy of user input data when writing code, which makes the application security risks. Users can submit a database query code and get some data they want to know according to the results of the program. SQL injection attack belongs to one of the means of database security attack. It can be effectively protected by database security protection technology. This paper introduces the principle of SQL injection, the main form of SQL injection attack, the types of injection attack, and how to prevent SQL injection. Discussed and illustrated with examples.
Depth Estimation From Light Field Using Graph-Based Structure-Aware Analysis

Existing light field depth map estimation approaches only utilize partial angular views in occlusion areas and local spatial dependencies in the optimization. This paper proposes a novel two-stage light field depth estimation method via graph spectral analysis to exploit the complete correlations and dependencies within angular patches and spatial images. The initial depth map estimation leverages the undirected graph to jointly consider occluded and unoccluded views within each angular patch. The estimated depth minimizes the structural incoherence of its corresponding angular patch with the focused one by evaluating the highest graph frequency component. Subsequently, depth map refinement optimizes the initial depth map with the color consistency and smoothness formulated by weighted adjacency matrix. The structural constraints are efficiently employed using low-pass graph filtering with Chebyshev polynomial approximation. Experimental results demonstrate that the proposed method improves the depth map estimation, especially in the edge regions.
Visual Relationship Embedding Network for Image Paragraph Generation

Image paragraph generation aims to produce a complete description of a given image. This task is more challenging than image captioning, which only generates one sentence to describe the entire image. Traditional paragraph generation methods usually produce paragraph descriptions based on individual regions that are detected by a Region Proposal Network (RPN). However, relationships among visual objects are either ignored or utilized in an implicit manner in previous work. In this paper, we attempt to explore more visual information through a novel paragraph generation network that explicitly incorporates visual relationship semantics when producing descriptions. First, a novel Relation Pair Generative Adversarial Network (RP-GAN) is designed to locate regions that may cover subjective or objective elements. Then, their relationships are inferred through an attention-based network. Finally, the visual features and relationship semantics of valid relation pairs are taken as inputs by a Long Short-Term Memory (LSTM) network for generating sentences. The experimental results show that by explicitly utilizing the predicted relationship information, our proposed method obtains more accurate and informative paragraph descriptions than previous methods.
Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control

This article describes a method for real-time integrated motion planning and control aimed at autonomous vehicles. Our method leverages feedback control, positive invariant sets, and equilibrium trajectories of the closed-loop system to produce and track trajectories that are collision-free with guarantees according to the vehicle model. Our method jointly steers the vehicle to a target region and controls the velocity while satisfying constraints associated with future motion of surrounding obstacles. We develop a receding-horizon implementation of the control policy and verify the method in both a simulated road scenario and an experimental validation using a scaled mobile robot with car-like dynamics using only onboard sensing. The results show that our method generates dynamically feasible and safe (i.e., collision-free) trajectories in real time, and indicate that the proposed planner is robust to sensing and mapping errors.
Text-Guided Neural Network Training for Image Recognition in Natural Scenes and Medicine

Convolutional neural networks (CNNs) are widely recognized as the foundation for machine vision systems. The conventional rule of teaching CNNs to understand images requires training images with human annotated labels, without any additional instructions. In this article, we look into a new scope and explore the guidance from text for neural network training. We present two versions of attention mechanisms to facilitate interactions between visual and semantic information and encourage CNNs to effectively distill visual features by leveraging semantic features. In contrast to dedicated text-image joint embedding methods, our method realizes asynchronous training and inference behavior: a trained model can classify images, irrespective of the text availability. This characteristic substantially improves the model scalability to multiple (multimodal) vision tasks. We also apply the proposed method onto medical imaging, which learns from richer clinical knowledge and achieves attention-based interpretable decision-making. With comprehensive validation on two natural and two medical datasets, we demonstrate that our method can effectively make use of semantic knowledge to improve CNN performance. Our method performs substantial improvement on medical image datasets. Meanwhile, it achieves promising performance for multi-label image classification and caption-image retrieval as well as excellent performance for phrase-based and multi-object localization on public benchmarks.
Enriching linguistic descriptions of data: A framework for composite protoforms

One of the current limitations of fuzzy linguistic descriptions of data is the lack of diversity of protoforms that can be used to linguistically summarize data. Despite an important effort in providing protoforms with improved semantics that are applicable to time series data or specific application domains, type-I and type-II fuzzy quantified sentences are still predominant in the literature. In this context, we propose a different approach for defining new types of protoforms. Instead of understanding protoforms as individual primitives, our proposal draws inspiration from Rhetorical Structure Theory to provide a framework that allows to define new types of complex protoforms based on semantic relations among simpler protoforms. Based on this framework, we propose an initial taxonomy of relations among protoforms and provide an illustrative use case based on real data and evaluated by human users.
Optimal Autonomous Driving Through Deep Imitation Learning and Neuroevolution

Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicle.
A multi-task transfer learning method with dictionary learning

Transfer learning is a problem that samples are generated from more than one domains, which focuses on transferring knowledge from source tasks to target tasks. A variety of methodologies are proposed for transfer learning. And a number of them concentrate on the inner relationship among each domain while some pay more attention to knowledge transfer. In this paper, based on the hinge loss and SVM, a new dictionary learning with multi-task transfer learning method(DMTTL) is proposed. The dictionary learning method is utilized to learn sparse representation of the given samples. Moreover, a regularization term for two dictionaries are exploited so that the similarity of samples can be well determined. Besides, a new optimization method based on alternate convex search is proposed with convergence analysis, which indicates that the DMTTL is a reasonable approach. After that, the comparison of DMTTL with the state-of-the-art approaches manifests the feasibility and the competitive performance for multi-task classification problem. And the statistic results show that the proposed method outperforms the previous methods.
Train-O-Matic: Supervised Word Sense Disambiguation with no (manual) effort

Word Sense Disambiguation (WSD) is the task of associating the correct meaning with a word in a given context. WSD provides explicit semantic information that is beneficial to several downstream applications, such as question answering, semantic parsing and hypernym extraction. Unfortunately, WSD suffers from the well-known knowledge acquisition bottleneck problem: it is very expensive, in terms of both time and money, to acquire semantic annotations for a large number of sentences. To address this blocking issue we present Train-O-Matic, a knowledge-based and language-independent approach that is able to provide millions of training instances annotated automatically with word meanings. The approach is fully automatic, i.e., no human intervention is required, and the only type of human knowledge used is a task-independent WordNet-like resource. Moreover, as the sense distribution in the training set is pivotal to boosting the performance of WSD systems, we also present two unsupervised and language-independent methods that automatically induce a sense distribution when given a simple corpus of sentences. We show that, when the learned distributions are taken into account for generating the training sets, the performance of supervised methods is further enhanced. Experiments have proven that Train-O-Matic on its own, and also coupled with word sense distribution learning methods, lead a supervised system to achieve state-of-the-art performance consistently across gold standard datasets and languages. Importantly, we show how our sense distribution learning techniques aid Train-O-Matic to scale well over domains, without any extra human effort. To encourage future research, we release all the training sets in 5 different languages and the sense distributions for each domain of SemEval-13 and SemEval-15 at http://trainomatic.org.