Reviewerly/exHarmony huggingface dataset:
Study on Tibetan Word Vector based on Word2vec

This paper uses Word2vec to study Tibetan word vector. Word2vec is optimized by two methods: Hierarchical Softmax and Negative Sampling in CBOW and Skip-gram models. Through the training of neural network, the words in Tibetan sentences are converted into vector form. Word2vec transforms the Tibetan text content processing into a simple vector space operation, calculates the similarity in the vector space, and then obtains the semantic similarity of the text, providing an accurate word vector for the training of the language model.
Stable-Ordered Models for Propositional Theories with Order Operators

The stable-model semantics has been generalised from logic programs to arbitrary theories. We explore a further generalisation and consider sequences of atoms as models instead of sets. The language is extended by suitable order operators to access this additional information. We recently introduced an extension of classical logic by a single order operator with a temporal interpretation for activity reasoning. The logic envisaged here is a nonmonotonic version thereof. Our definition of what we call stable-ordered models is based on the stable-model semantics for theories due to Ferraris and Lifschitz with the necessary changes. Compared to related nonmonotonic versions of temporal logics, our approach is less costly as checking model existence remains at the second level of the polynomial hierarchy. We demonstrate versatile applications from activity reasoning, combinatorial testing, debugging concurrent programs, and digital forensics.
High-Dimensional Energy-Time Entanglement up to 6 Qubits per Photon through Biphoton Frequency Comb

We demonstrate high-dimensional entanglement with ≈ 6 qubits per photon via mode-locked biphoton frequency comb. Hong-Ou-Mandel quantum revival is observed with 61 time-bins and 99.8% visibility.
Protecting data in an age of cloud and virtualisation

In a challenging business environment, IT budgets are increasingly tight. A firm today needs to be able to do more with the resources already available if it is to effectively compete with its rivals. Some IT departments are even asked to ‘do more with less’ – increasing the capabilities of the departments while reducing their costs.
Object recognition, identification and classification for intelligent surveillance and reconnaissance platforms

We research solutions to enable intelligence, surveillance and reconnaissance by means of near-real-time target recognition, identification and classification. The cloud services, intelligence, surveillance, target acquisition and reconnaissance are of importance for the C<sup>4+</sup>iSTAR systems. These platforms are expected to ensure high-level cognitive autonomy to accomplish complex missions and tasks in rapidly-changing adverse environments. We research and apply deep learning concepts and algorithms to enable high-confidence awareness and advance situational analysis. We examine engineering solutions for object identification and classification. Our findings ensure sufficient level of fidelity on the object recognition and classification likelihood with high identification probability, processing latency on low-power ARM CPUs, and, integration capabilities. Advanced concepts on moving target recognition and object classification using fly data are researched with low-fidelity experimental substantiations. We modify the YOLOv3 object detection method to detect bounding boxes for arbitrary orientation angle, angle of view and corner shapes which are of importance in aerial applications. Our results demonstrate adequate detection capability while maintaining fast computational performance of the original YOLOv3 architecture. The proposed algorithms and computing schemes are supported by codes in C++.
Life Long Across All Four Seasons Scene Understanding

Recently, automatous driving is grabbing everyone's attention. The technical partition of the automatic vehicle driving is scene understanding, driving policy-making and motion control. First and foremost, the scene understanding plays an imperative role. Meanwhile, the scene understanding breaks down into four branches, which are free space scene semantic segmentation, lane detection, moving objects detection and moving objects tracking. Main challenges of all four challenging branches are unrestricted open vocabulary and diverse scenes. This paper has proposed a new theory which combines the improved fully convolutional network (iFCN) and the transfer learning across all four seasons to alleviate all the challenges mentioned above. The proposed iFCN changes the traditional structure of FCN in the pooling layer and replaces the learning rate with a self-adapted and much more reasonable one, which generates a significant improvement on the scene understanding assignment. The global prior representation of transfer learning across all four scenes is to avoid training entire network from scratch with random initialization, which makes the portability of the network improved quite a lot. Experiment results indicate that the proposed theory achieves the state-of-the-art mean IoU on the challenging GAC R&D environment perception benchmark with the value of 86.2%, which gains 34.67% improvement compared with conventional work.
Thinking and Exploration on the Teaching of the Course of "College Computer Foundation" under the Mode of "Internet Plus"

At present, information technology is in the rapid development stage.The development of information technology has made the Internet technology widely used, has also been applied in the network teaching platform, and has gradually formed the "Internet Plus" mode. The "Internet Plus" mode has been applied to the teaching of the course of "College Computer Foundation",at this stage, some teachers have made a full study of the "Internet Plus" mode,it is hoped that this mode can be better applied to the teaching of the course of "College Computer Foundation", so that the teaching quality and teaching efficiency of "College Computer Foundation" can be improved significantly.
Enhanced Saliency Prediction via Free Energy Principle

Saliency prediction can be treated as the activity of human brain. Most saliency prediction methods employ features to determine the contrast of an image area relative to its surroundings. However, only few studies have investigated how human brain activities affect saliency prediction. In this paper, we propose an enhanced saliency prediction model via free energy principle. A new AR-RTV model, which combines the relative total variation (RTV) structure extractor with autoregressive (AR) operator, is firstly utilized to decompose an original image into the predictable component and the surprise component. Then, we adopt the local entropy of ‘surprise’ map and the gradient magnitude (GM) map to estimate the component saliency maps-sub-saliency respectively. Finally, inspired by visual error sensitivity, a saliency augment operator is designed to enhance the final saliency combined two sub-saliency maps. Experimental results on two benchmark databases demonstrate the superior performance of the proposed method compared to eleven state-of-the-art algorithms.
Adaptive underwater image recovery using polarimetric imaging

Underwater image generally suffers from color degradation and poor visibility due to the attenuation and scattering of the propagated light. To restore the real environment of the underwater, most of previous methods only consider the scattering in the polarimetric image recovery model. To compensate for the absorption in different wavelengths, a simple but effective underwater image enhancement method that based on the adaptive polarimetric image recovery model is proposed in this paper. In order to achieve automatic estimation of model parameters, the backscattered light estimation is based on the quad-tree decomposition to extract the fully backscattered area automatically. The experimental results demonstrate that the visual quality of underwater images are efficiently enhanced.
S-DWF: An Integrated Schema for Secure Digital Image Watermarking

The recent advancement in the field of information and communication technologies requires potential security solutions to provide efficient information protection. The proposed study conceptualizes a secure digital watermarking framework also abbreviated as S-DWF which performs an ‘image dithering’ form of decomposition followed by a key-based watermarking procedure. The proposed S-DWF implemented in a numerical computing platform with respect to the addition of different noisy level and also checked for performance assessment. The outcome of the study shows that the proposed S-DWF attains better information protection in terms of Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER) even in the presence of any noisy attack as compared to the existing baseline. It also balances the trade-off between computational effort required and the visual and perceptual quality of the retrieved watermark image signal information.
Breaking CNN-Based Steganalysis

With the rapid development of deep learning, a lot of CNN-based steganalyzers have emerged. This kind of steganalyzer uses statistical learning to investigate the properties caused by steganography, which is the most efficient approaches for breaking information hiding. However, we find a vulnerability of CNN-based steganalyzer that it can be defeated by dual operations. In this paper, we propose an easy yet effective algorithm to perturb the stego images against neural network, which can evade CNN-based steganalyzer with high probabilities. We elaborated on the theoretical basis of the method we proposed and proved the feasibility of this method through experiments.
Imbalanced Data Classification Algorithm Based on Integrated Sampling and Ensemble Learning

In order to alleviate the impact of imbalanced data on support vector machine (SVM), an integrated hybrid sampling imbalanced data classification method is proposed. First, the imbalance rate of imbalanced data is reduced by the ADASYN-NCL (Adaptive Synthetic Sampling Technique—Domain Cleanup Rule Downsampling Method) hybrid sampling method. Then, the AdaBoost algorithm framework is used to give different weight adjustments to the misclassification of minority and majority classes, and selectively integrate several classifiers to obtain better classification. Finally, use the 10 sets of imbalanced data in the KEEL database as test objects, and F-value and G-mean are used as evaluation indicators to verify the performance of the classification algorithm. The experimental results show that the classification algorithm has certain advantages for the classification effect of imbalanced data sets.
A Public Auditing Scheme with Data Recovery

In orderWu, Tsu-Yang to solve theChen, Chien-Ming integrity verificationWang, King-Hang of outsourced fileWu, Jimmy Ming-Tai in cloud, we basedPan, Jeng-Shyang on the concept of regenerating code (RC) to propose a new public auditing scheme with data recovery in this paper. With the advantage of RC, our scheme provides a (k, n) recovery functionality, i.e., only encoded data blocks from any k cloud servers, user can recover the original data files, where n denotes the total number of cloud servers and $$k\le n$$ .
A New Comprehensive Database for Hand Gesture Recognition

At present, limited hand gesture databases are made available for reference and research, and these databases are small in scale with limited variants of gestures. In response to this problem, we have established a large-scale dynamic gesture database called LR-DHG database. The sensors for collecting data include Leap Motion and RealSense. The data formats include video frame, depth images, color images, and hand-joint point coordinates. In this paper, we describe in detail the recording work of this database, and use subjective evaluation methods to conduct preliminary tests on it, resulting in a higher recognition rate. This database provides more valuable reference and research data for human–computer interaction.
Design and Implementation of Ontology Knowledge Base of Endemic Genera of Seed Plants in Yunnan Province

There is complex terrain, various soil and climate in Yunnan Province. The unique natural condition produces the rich resources of the species. The spermatophyte endemic genus resource is the most quantity of China. It is essential to protect and use Yunnan spermatophyte endemic genus resource reasonably. According to Yunnan spermatophyte endemic genus resource various data formats, deficient integration, low-efficiency intelligent retrieval, lack of individualized information service mode and more problems, This paper builds Yunnan spermatophyte endemic genus ontology knowledge base. This ontology knowledge base is the solution of Yunnan spermatophyte endemic genus resource using technical barrier, it laid the foundation to build Yunnan spermatophyte endemic genus area terminology information retrieval system.
An LSTM-Based Topic Flow Pattern Learning Algorithm and Its Application in Deceptive Review Detection

Great progress has been made in detecting deceptive reviews based on traditional machine learning. However, existing classification methods for detecting deceptive reviews mainly focus on combining word-embedding with the traditional machine learning algorithm, while neglecting the latent semantic meaning and its temporal relation of the topic in a review. In this paper, we propose a deceptive review detection model based on the learning of topic flow pattern. First, this paper analyzes the topic of reviews and considers the temporal relations of topics simultaneously to construct discriminative characteristics of reviews. Second, the Long Short-Term Memory (LSTM) neural network is used to classify reviews that contain information on topic series. Experimental results on three domains of datasets show that our proposed method is superior to benchmark methods.
Extending $$\mathcal {ALC}$$ with the Power-Set Construct

We continue our exploration of the relationships between Description Logics and Set Theory, which started with the definition of the description logic $$\mathcal {ALC}^\varOmega $$ . We develop a set-theoretic translation of the description logic $$\mathcal {ALC}^\varOmega $$ in the set theory $$\varOmega $$ , exploiting a technique originally proposed for translating normal modal and polymodal logics into $$\varOmega $$ . We first define a set-theoretic translation of $$\mathcal {ALC}$$ based on Schild's correspondence with polymodal logics. Then we propose a translation of the fragment $$ \mathcal {LC}^{\varOmega } $$ of $$\mathcal {ALC}^\varOmega $$ without roles and individual names. In this—simple—case the power-set concept is mapped, as expected, to the set-theoretic power-set, making clearer the real nature of the power-set concept in $$\mathcal {ALC}^\varOmega $$ . Finally, we encode the whole language of $$\mathcal {ALC}^\varOmega $$ into its fragment without roles, showing that such a fragment is as expressive as $$\mathcal {ALC}^\varOmega $$ . The encoding provides, as a by-product, a set-theoretic translation of $$\mathcal {ALC}^\varOmega $$ into the theory $$\varOmega $$ , which can be used as basis for extending other, more expressive, DLs with the power-set construct.
Building a Blockchain Simulation using the Idris Programming Language

The primary aim of this work is to create a program simulating a private distributed blockchain using the functional programming language Idris. This simulation is implemented such that a rock-paper-scissors game can be played between any two users of the blockchain via the use of smart contracts. Our motivation is to assert, using relevant features of Idris, that such an implementation possesses some of the accepted properties of blockchains. This paper first presents some differences between our implementation and most real-world blockchains. Next the Idris language and some of its features are discussed, focusing on how the language is used to implement the simulation. Finally, the advantages and disadvantages of utilizing Idris instead of an imperative programming language are examined.
Touchless Recognition of Hand Gesture Digits and English Characters Using Convolutional Neural Networks

Computer technology has changed the way humans define success. Gesture recognition is a new and intuitive interaction method, as it does not require any mechanical interface for interaction. The model proposed takes input from the user using an infrared emitter and a web camera and this is used to generate a 28 $$\,\times \,$$ 28 image contains the drawn character. This is passed through a convolutional neural network which detects the character drawn. The benefits include reliability and cost-effective nature of the proposed system. Such an approach can help people with disabilities.
A self-similarity based method for medical image resolution enhancement

Medical imaging quality is limited by physical, technology and economical considerations. In the practical application, bilinear or bicubic interpolation are performed to enhance the resolution of medical image. However, these interpolation methods will lead to undesirable jagged artifacts and blur details, which would affect the performance of post-processing such as segmentation and registration. Example learning based single image super-resolution (SR) is an economical way to recover the high-resolution (HR) images from a low-resolution (LR) image. The learning based methods are to add the high frequency information borrowed from the learning data to the input image by referencing the similarities between the input data and the learning examples. Due to the incompatibility between the examples and the testing image, this paper proposes a novel multiscale rotational similarity based single image SR method, in which the learning examples are generated from the LR image itself. The motivation of the proposed method is that the small patches in medical image tends to repeat themselves across different scale or different angle. With the intensive investigation of the similarity redundancy in the multiscale and multiangular levels, the relationship between the patches of HR image and that of LR images will be estimated. Subsequently, the HR images will be generated from a single LR image in a gradual magnification framework. Meanwhile, a nonlocal prior regularization terms will be utilized to enhance the accuracy of reconstruction. Some experiments are performed to indicate the feasibility and effectiveness of the proposed algorithm.
Modified Histogram Segmentation Bi-Histogram Equalization

In the application of image processing, contrast enhancement is a major step. Conventional methods which are studied in contrast enhancement such as Histogram Equalization (HE) have not satisfactory results on many different low-contrast images and they also cannot automatically handle different images. These problems result of specifying parameters manually in order to produce high contrast images. In this paper, Modified Histogram Segmentation Bi-Histogram Equalization (MHSBHE) is proposed. In this study, histogram is modified before segmentation to improve the input image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. MHSBHE avoids over-enhancement and generates images with natural improvement. Simulation results show that in terms of visual assessment, peak signal-to-noise (PSNR), average information content (entropy) and Absolute Mean Brightness Error (AMBE) the proposed method has better results compared to literature methods. The proposed method enhances the natural appearance of images especially in no static range images and the improved image is helpful in generation of the consumer electronic.
Blockchain-Based Autonomous Peer to Peer Information Interaction System in Financial Audit

The current distributed energy Internet financial audit has problems in information sharing and lacks an efficient peer-to-peer security transmission strategy. Facing the problems of financial auditing in distributed energy internet, we construct an autonomous peer-to-peer information interaction system combining with blockchain and named data network, and research on data acquisition and secure transmission of this system. For data acquisition, a data identification parsing module based on blockchain is designed, and the corresponding data is obtained by parsing the data identifier. For secure transmission, this paper designs a blockchain-based encryption and Decryption module to ensure the secure transmission of data in the system environment and the controllability of data access by encrypting and decrypting data assets. This paper builds an experimental environment based on the campus network, and performs functional testing and verification on the two modules.
External Hierarchical Archive Based Differential Evolution

Evolutionary Algorithms (EAs) haveMeng, Zhenyu become muchPan, Jeng-Shyang popular in tacklingLi, Xiaoqing kinds of complex optimization problems nowadays, and Differential Evolution (DE) is one of the most popular EAs for real-parameter numerical optimization problems. Here in this paper, we mainly focus on an external hierarchical archive based DE algorithm. The external hierarchical archive in the mutation strategy of DE algorithm can further improve the diversity of trial vectors and the depth information extracted from the hierarchical archive can achieve a better perception of the landscape of objective function, both of which consequently help this new DE variant secure an overall better optimization performance. Commonly used benchmark functions are employed here in verifying the overall performance and experiment results show that the new algorithm is competitive with other state-of-the-art DE variants.
Modular Algorithm Testbed Suite (MATS): an open architecture for automatic target recognition

Here we discuss an open software architecture to develop, test, and evaluate machine learning algorithms for target detection and classification. This architecture, known as the Modular Algorithm Testbed Suite (MATS), aids developers by defining interfaces for various portions of the automatic target detection processing chain. There are several key advantages to this approach. First, with “plug and play” modules for detection, feature extraction, and classification, developers can mix and match different approaches and focus on particular portions of the processing chain that yield the most performance benefit. Second, since some portions of the processing chain may be more agnostic to the sensor data type than others, e.g. target features may change but the pattern classification approach is the same, MATS enables quick ATR development for similar data types. Finally, since developers can insert "black boxes" into the ATR processing chain, MATS allows for independent blind testing of algorithms without compromising intellectual property. In this paper, we will discuss the MATS architecture and review several case studies where MATS enabled rapid demonstration and transition of ATR algorithms to Navy mine countermeasure (MCM) post-mission analysis software.