Reviewerly/exHarmony huggingface dataset:
Semantic Annotation in Maritime Legal Case Texts Based on Co-training

In the era of artificial intelligence and big data, a large number of legal case texts have been accumulated in the process of law enforcement of marine rights protection. These case texts contain a lot of important information, such as the time, place, person, event, judgment body, judgment result and so on. The annotation of these semantic information is an important link in text analysis, mining and retrieval of sea-related cases. In this paper, a semantic annotation method based on collaborative training for maritime legal texts is proposed. The experimental results show that the method is correct and feasible.
Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationship Parser

The neural components in deep learning framework are crucial for the performance of many natural language processing tasks. So far there is no systematic work to investigate the influence of neural components on the performance of implicit discourse relation recognition. To address it, in this work we compare many different components and build two implicit discourse parsers base on the sequence and structure of sentence respectively. Experimental results show due to different linguistic features, the neural components have different effects in English and Chinese. Besides, our models achieve state-of-the-art performance on CoNLL-2016 English and Chinese datasets.
An interactive motion analysis framework for diagnosing and rectifying potential injuries caused through resistance training

With the rapid increase in individuals participating in resistance training activities, the number of injuries pertaining to these activities has also grown just as aggressively. Diagnosing the causes of injuries and discomfort requires a large amount of resources from highly experienced physiotherapists. In this paper, we propose a new framework to analyse and visualize movement patterns during performance of four major compound lifts. The analysis generated will be used to efficiently determine whether the exercises are being performed correctly, ensuring anatomy remains within its functional range of motion, in order to prevent strain or discomfort that may lead to injury.
Multiwavelength Neuromorphic Silicon Photonics

Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. AI is revolutionizing the healthcare industry with complex medical data analysis, actualizing self-driving cars, and beating humans at strategy games such as Go. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Over the last six years, this compute power has doubled every 3.5 months. Traditional CPUs, GPUs and even neuromorphic electronics (IBM TrueNorth [1] and Google TPU [2]) have improved both energy efficiency and speed enhancement for learning (inference) tasks. However, electronic architectures face fundamental limits as Moore's law is slowing down. Furthermore, moving data electronically on metal wires has fundamental bandwidth and energy efficiency limitations, thus remaining a critical challenge facing deep learning hardware accelerators [3].
Quadratic Discriminant Analysis Based on Graphical Lasso for Activity Recognition

New sensing technology are widely used in ambient and wearable sensors for human activity recognition. In this paper, we apply quadratic discriminant analysis based on graphical lasso (QDAGL) to the Opportunity activity recognition dataset. The Opportunity activity recognition dataset is a versatile human activity recognition dataset recorded in a sensor-rich environment. Quadratic discriminant analysis is a simple nonlinear discriminant analysis based on maximum likelihood estimation. Graphical lasso can estimate a covariance matrix and sparse inverse covariance matrix remarkably fast, using a coordinate descent algorithm for a single lasso problem. Because of the high dimension feature of the Opportunity dataset, the accuracy and speed of Quadratic discriminant analysis for it are not well. In this paper, we use graphical lasso to estimate the covariance matrix and inverse covariance matrix on the Opportunity activity recognition dataset. The experiments demonstrate that QDAGL can perform better and faster than QDA.
Book Information Retrieval Method Research Based on Word Sense Disambiguation

In this paper, formal description and data extraction of Book information is the core problem to solve the user query and information access. Along with the information resources diversification and rapid expansion, the existing description and extraction method had some defects, for example recall rate accuracy rate. In order to solve these problems, a new information retrieval method based on word sense disambiguation is provided in this paper, according to the features of book information, the domain concept set and the domain knowledge set is redefined. Based on it, construction process of the domain concept set and domain knowledge set is given, including domain concept automatic extraction, domain knowledge sets automatic construction and the similarity algorithm description. The experimental results show that the proposed method has higher recall and precision at this paper than the existing method, and has feasibility and good validity.
An Active Learning Framework Based on Query-By-Committee for Sentiment Analysis

Social networks are the main resources to obtain information about people's opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. However, it cost too much time to label the abundantly unlabeled data. To dramatically reduce annotation cost, this paper creatively apply an Active learning framework for Sentiment analysis called Active learning for Sentiment analysis (AL4SA) to naturally integrate active learning and machine learning into a single framework. First, AL4SA train a committee with multiple weak learners. Then, committee members are used to select the most informative samples from the unlabeled data by the Vote entropy strategy and enhance the model's performance by incorporating newly samples in each iteration. Our experiments were compared with baseline machine learning methods such as KNN, Naïve Bayes, Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM) on the hotel review corpus, the result shows that the cost of the labeled data can be cut by at least half and the performance is optimal than those baseline machine learning methods due to the most informative samples were selected by committee members.
High-accuracy disparity acquisition using the phase-shift method and the fringe pattern stereo matching network

For disparity information acquisition tasks, phase-shift profilometry can achieve high disparity accuracy, but it is a stereo matching technique based on phase unwrapping. The phase unwrapping depends on the fringe pattern of multifrequency. Recent work also shows that deep learning can obtain disparity from a stereo pair of images, but it is difficult to obtain high accuracy. To tackle these problems, we propose a stereo matching method of obtaining high-accuracy disparity using a combination of the fringe pattern stereo matching network and the phase-shift method. First, the coarse disparity is obtained by using the fringe pattern stereo matching network, and then the wrapped phase map obtained by the 3 steps phase-shift method is used to optimize the coarse disparity to obtain a high-accuracy disparity map. This method rectifies the left and right fringe patterns in advance and eliminates the influence of the calibration parameters without loss of generality. It also avoids the challenge of phase unwrapping compared with phase-shift profilometry. We prove that the sinusoidal fringe pattern stereo matching can obtain a better coarse disparity effect than the stereo matching of the texture image, especially in the textureless area. Experiments show that high-precision disparity can be obtained with only three frames of high-frequency fringe patterns.
Blind Forgery Detection in Digital Images: An Analysis

In today’s world, the usage of images in digital forms have become common in various fields for information exchange because of the development in handy digital devices like cameras and mobiles. Checking the authenticity of the image has become necessary as there could be unethical modifications possible with the images using several image editing tools and software that are easily available over the Internet, which leaves no clues of modification to the viewer. Detection of blind or passive forgery becomes more difficult as we do not have any prior knowledge of the forged image. This paper gives a detailed analysis of various blind forgery techniques and detailed classification of several forgery detection methods. It also gives a comparative analysis of different blind forgery detection methods with respect to different parameters that will give clear idea to the researcher about the performance of forgery detection methods used currently and give directions to future scope of improvement over the existing methods.
Impact of Sharing Algorithms for Cloud Services Management

In this paper will be presented a classification of sharing algorithms dedicated to secure services management in Cloud Computing. Especially will be described an impact of sharing processes in service protocols, realised in the Cloud. Service management procedures can be used at different levels, so the impact of service management processes will be analysed at all of them. Also will be presented the impact of service management and cloud data sharing protocols, on decision-making processes in management-support systems, using linguistic and biometric threshold schemes.
Novel Method to Detect Multiple Cloning in Targeted Image Invariant to Rotation

Digital Media plays a vital role in our society. Digital image is one of the most important parts of digital media. There are various cases filed due to an image tampering on social networking websites. To detect tampering in an image, various techniques are available, but still these techniques have drawbacks. These techniques are not able to detect certain type of tampering in an image, like multiple region duplication with rotation. In this paper, the proposed system uses the approach, which is block based. In block based approach an image is divided into overlapping blocks, for the betterment of results the block is then divided diagonally into four subblocks. Feature vectors are then calculated using Zernike Moments as this is invariant to Rotation and it is insensitive to image noise. Thus the proposed approach is going to be crucial in digital image tamper detection in the upcoming era of digital media.
Towards Plug and Use Functionality for Autonomous Buildings

Existing building automation solutions suffer from the drawback of causing high engineering, commissioning, and installation efforts. Furthermore, the existing approaches are error-prone and require a lot of manual interaction between the involved parties. This article presents the results of an analysis for moving towards autonomous plug&use functionality by exploiting external data sources merged and maintained in a central information model, e.g., device information obtained via the scanning of NFC or QR tags, functional planning information, network topology information, wiring plan information, etc. In this context, the autonomous plug&use functionality aims at fully automating the commissioning process of devices of a building automation system by deriving input parameters from external data sources thereby minimizing manual user efforts.
Association Relationship Analyses of Stylistic Syntactic Structures

Exploring linguistic features and characteristics helps better understand natural language. Recently, there have been many studies on the internal relationships of linguistic features, such as collocation of morphemes, words, or phrases. Although they have drawn many useful conclusions, some summarized linguistic rules lack physical verification of large-scale data. Due to the development of machine learning theories, we are now able to use computer technologies to process massive corpus automatically. In this paper, we reveal a new methodology to conduct linguistic research, in which machine learning algorithms help extract the syntactic structures and mine their intrinsic relationships. Not only the association of parts of speech (POS), but also the positive and negative correlations of syntactic structures, linear and nonlinear correlation are considered, which have not been well studied before. Combined with the linguistic theory, detailed analyses show that the association between parts of speech and syntactic structures mined by machine learning method has an excellent stylistic explanatory effect.
Research on Speech Recognition Acceleration Technology Based on Embedded Platform

This paper implements the latest speech recognition technique and improves it so that it can be used on embedded mobile devices. In order to implement the speech recognition system, I build the two-layer Long Short-Term Memory networks (LSTMs) using the TensorFlow framework and train the model by using the Mel Frequency Cepstral Coefficients (MFCCs) vector extracted from the wave files. In order to speeds up the model, this paper innovatively applies the pruning method to the LSTMs by pruning the weights of each cell and regularizing the weight matrix of regression layer. After pruning, I retrain the model to preserve the accuracy. Finally, this new model discussed in this paper successfully improves the performance of the original model without reducing the precision of the model. It reduces the computational complexity and speeds up the speech recognition process.
Acceleration of Target Detection Based on Forced Knowledge Distillation

Abstract In recent years, deep learning has achieved outstanding results on many problems such as computer vision, natural language processing and so on. The research of network model compression and acceleration can make the network model run efficiently on resource-constrained devices by greatly reducing the amount of computation of the network when the performance of the network decreases slightly. At present, knowledge distillation has achieved good results in classification tasks, but it shows strong limitations in the face of more complex tasks like detection. In this work, we propose a forced knowledge distillation framework, which focuses on improving the ability of feature extraction in detection. The whole model can be compact and fast without losing much accuracy. We use PASCAL VOC, KITTI and MSCOCO data sets to make a comprehensive evaluation. The results show that the forced knowledge distillation framework can fully learn the knowledge of teacher networks and achieve better detection results in smaller student networks.
Association Rules Mining of Silk Relics Database With the RCFP-Growth Algorithm

The running speed of traditional auxiliary pattern design system which can extract association rules from silk relics database with Apriori algorithm is very low. In this paper, a rare common frequent pattern growth (RCFP-growth) algorithm is proposed to extract the association rules from silk relics database which has the obvious characteristics of imbalance, big data and specific mining target by the user in the auxiliary pattern design system. It contributes to the obvious reduction of the memory and time costs for association rules mining in the system compared to the Apriori algorithm and frequent pattern growth (FP-growth) algorithm. Some pattern designs with RCFP-growth algorithm are shown.
A simple authentication-based optical security method

Optical security has attracted much attention in recent years, and much research work has been done to establish various optical security systems. It has been further found that when optical authentication is introduced into optical encryption systems, system security can be enhanced. Hence, authentication-based optical security has been widely studied. However, much previous work needs to use relatively complicated optical setups or algorithms to establish authentication-based optical security systems. In this paper, a simple method is presented by using direct wave propagation to generate a compressed phase-only mask as ciphertext, and the input image is compressed before the encoding. Results and analyses demonstrate that the proposed method is feasible and effective for authentication-based optical security. It is expected that the method presented can provide a promising approach for effectively enriching authentication-based optical security area.
A Characteristic Distill Arithmetic of Region Growing Method Based On Fuzzy Connectedness

Abstract To solve the problem that the traditional region growing algorithm cannot deal well with the fuzziness between the damage area and the background, and the segmentation result is sensititive to the initial seed pixel, a region growing method based on fuzzy connectedness is proposed. The method takes fuzzy connectedness as the similarity measure between the initial pixel and the growing pixel, which keeps the fuzziness among the pixels well. A growing crition integrated gray- fluctuation control with USAN area is proposed to fit the gray fluctuation in the damage area and get edge position precisely. The experiment results show the validation of this method.
On a Novel Algorithm for Digital Resource Recommender Systems

By increasing the use of users from online resource websites, we need a promising smart recommendation system for high-volume data which has been provided by high-speed service. A lot of research has been done to improve this trend in recent years. Undoubtedly, machine learning has played a main role and has created growing trend in proposer systems evolution. We can point to hybrid recommender systems and content based and collaborative filtering. Different data structures may be used in these methods and we intend to present a new method based on a tree data structure called ckd-tree. We compare this algorithm to different models with different data sets and we got that this algorithm provides a better result for a large system with massive data sets in comparison with other methods. This can be more valuable and also it would be an alternative to common methods like kd-tree.
Chronological Classification of Ancient Paintings of Mogao Grottoes Using Convolutional Neural Networks

Mogao Grottoes are known as one of the three famous ancient Buddhist sculptural sites of China, which contain some of finest Buddhist paintings spanning a period of 1,000 years. Chronological classification of the ancient Buddhist paintings of Mogao Grottoes can help archaeologists and culture researchers to study the humanities, customs and economy of the corresponding eras. In this paper, this paper first perform an initial study on the effect of three state-of-the-art convolutional neural network based methods (AlexNet, VGG and ResNet) on chronological classification of paintings of Mogao Grottoes, and then propose a new network by replacing the last average pooling layer of ResNet-50 with a sequential layers. Experiments demonstrate that our method achieves higher classification accuracy than the three models and two existing chronological classification methods.
Effectual Hand Posture Recognition using Block Level DCT-Diagonal-HOG Features

Hand gestures have become one of the most common tools for human computer interaction and their automatic recognition has attained key interest of many researchers in recent years. This paper proposes an image based approach for hand posture recognition using multiclass support vector machine. The image feature is obtained by combining the diagonal, dct (discrete cosine transform) and hog (histogram of oriented gradients) descriptors and the feature dimensionality is further reduced by pca (principal component analysis). The proposed method has been tested on two different publicly available hand posture datasets and achieved good recognition results.
FPGA Based Acceleration of Security Algorithm Using Co-design Approach for WSN Applications

The optimum security in wireless sensor network (WSN) is an emerged research area. A cryptographic algorithm is a technique for securing the data collected and transmitted in wireless sensor network. Number of cryptographic algorithms are available but they differ from each other in terms of key size, data size, security complexity and processing time. Tiny Encryption Algorithm (TEA) known as a lightweight and competent security algorithm, which will be appropriate for WSN applications. This paper presents TEA encryption or decryption algorithm implementation as a custom component based on ALTERA FPGA with cyclone IV E device. With this customization, the design of TEA is further accelerated with parallelism, while adding as custom instruction. The paper aims to compare the custom codesign approach i.e. custom instruction approach and full software approach with NIOS II processor. The obtained result shows that the performance of proposed custom codesign aspect outperforms the software only implementation in terms of execution clock cycles and power consumption.
An Efficient Method for Boundary Points Detection Based on Data Expression

Clustering analysis is the core technology of data mining. However, massive data analysis makes it difficult for traditional clustering algorithms to have better clustering effects. At present, a more effective way to solve such problems is to detect the cluster boundary by means of the boundary point detection algorithm, and then combine it with the clustering algorithm. In this paper, we propose a clustering boundary points detection algorithm based on data representation. It does not need to understand the dimension and the meaning of data. It is only necessary to construct the expression matrix W through a specific algorithm and then we can recognize the boundary points of the data set through computing the number of zero and negative components of W, which helps us capture the inner structure of data about clustered data. Meanwhile, our algorithm overcomes the dimensional disaster of traditional algorithms on high-dimensional data sets. In addition, the algorithm has a high recall rate and accuracy on both synthetic data sets and real data sets.
Deviation Analysis for Approximate Maximum Likelihood Localization Algorithms

Approximate maximum likelihood localization Algorithm (AML) based on TDOA put forward by Yiu-Tong Chan estimates the tag accurate position through establishing a maximum likelihood function and solving its extreme value. However, if the hyperbolas of the TDOA equations have two intersections, AML will get two solutions that fit the equations. In this case, the solution chosen with the AML suggestion may not be optimal. Through simulation, we prove that AML may choose non-optimal solution when hyperbolas have two intersections. Further, this article analyzes the cause of appearing two solutions and the areas where exist two solutions. From the simulations and analysis, deviation areas are related to the sensors' arranging positions. In order to avoid deviations caused by not selecting the optimal solution, some suggestions for sensors application arrangement are given.