Reviewerly/exHarmony huggingface dataset:
Research on Intelligent Voice Question Answering System for Simulation Training of Thermal Power Plant

When students participate in thermal power plant simulation training, the amount of training knowledge is large, students have many questions and poor learning autonomy, teachers have the characteristics of poor real-time performance and low efficiency. In order to improve the teaching quality of simulation training, an intelligent question and answer scheme for simulation training in thermal power plant is proposed. This paper analyzes the work flow of the intelligent question answering system, uses Microsoft Speech SDK as the secondary software development kit, carries on the speech interaction, and uses the forward maximum matching algorithm to deal with the questions raised by the students. By combining the TF-IDF method based on vector space model and the similarity calculation method based on semantics, the similarity value of the problem is calculated and supplemented by the associative function to realize the complete matching. The answers are presented to the students in the form of voice and video presentations. Based on the knowledge base of simulation training in thermal power plant, an intelligent question answering system for thermal power plant is studied based on VC++6.0, which provides a new tutoring scheme for simulation practice teaching in thermal power plant.
A development process for component software with crosscutting and dynamic features

Traditional development processes fail to be applied effectively to the development of component software. Some features in the design need to be (un)plugged dynamically to software systems and their implementations may require to crosscut the code of components. This is often not possible in component systems due to the nature of used components (black box, signed or encrypted components). In this paper we propose an appropriate process for the development of component software with explicit support for crosscutting and dynamic features. The proposed process covers all software lifecycle activities, follows a top-down approach and stimulates the reuse of pre-existing components. In the proposal, crosscutting and dynamic features are identified earlier in the process and modeled separately using aspect-orientation concepts for reusablity and maintainability purposes. The identification of crosscutting and dynamic features at early stages allows making proper design decisions to improve software modularity and reusability, manage feature conflicts and ensure better maintainability of component software. The proposed process is validated through modeling a case study.
A Non-Photorealistic Approach to Night Image Enhancement Using Human JND

Night image enhancement is a challenging image enhancement problem due to its extremely low lighting condition and high dynamic range. Traditional methods such as gamma correction mostly try to keep the overall image lighting regime while increasing the exposure level. In this paper we propose a new non-photorealistic approach for this problem. The goal is to enhance each possible image detail regardless of the overall lighting condition, so as to see every possible image detail. In this approach, we first convert the image RGB color space to HSV color space and enhance only the V channel; Then we compute the background level for each pixel and locate all the edge points via human just-noticeable-difference (JND). Then we enhance all these edge points so that their background levels are adjusted to the range which is visually comfortable to humans. In the following step, we propagate these scaling factors to the rest of the non-edge points through least-squares optimization. We finally use the enhanced V channel and convert back to the RGB color space. Experimental results show that the propose approach, though not strictly photo-realistic, can achieve significant enhancement results entailing the most image details when compared with other typical methods.
The Design Concept of Intelligent Guide Cane

Some people are congenial blindness or visually impaired while the others are caused by acquired factors. One reason that results in poor vision or blindness is age. As people get old, the eyesight of people is destined to deteriorate. Normally, people with poor vision can only live in a familiar environment to ensure their own safety. The mobility of people with impaired vision is restricted and they may live in inconvenient life. Therefore, in this study, we aim to provide a design concept of novel intelligent guide cane to enhance the mobility of visually impaired people. The proposed design is to add some functions to the original guide cane and connect the intelligent guide cane with hand-held device. Since the hand-held device that is widely used in today and has a considerable amount of computing, the proposed design can act as an auxiliary machine similar to guide dogs or better than guide dogs. The proposed design aims to provide the functions such as route planning, navigation, obstacle detection, and object recognition. With the proposed design, the mobility of visually impaired people can be greatly improved.
Role of Filters in Speckle Reduction in Medical Ultrasound Images- A Comparative Study

To diagnose and predict complex disorders in human body, various Medical Imaging Techniques are used. Widely accepted technique among them is the Ultrasound imaging modality, because of its low cost and noninvasive nature. But the images produced by ultrasound scanning are of low quality and amenable to faster degradation due to the presence of speckle noise. This led to various studies for effectively removing speckle noise from ultrasound images. In this paper, an endeavor is made for a comparative analysis of chosen set of post filtering methods for Speckle reduction, VIZ Anisotropic Diffusion, Wavelet, Adaptive Median Filter, Hybrid Algorithm, Modified Fourier Transform and Sparse Code Shrinkage using ICA. The different methods are tested on a collection of ultrasound images and their performance evaluated with the Normalized Cross Correlation metric (NCC), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Universal Quality Index (UQI), Edge Preservation Index (EPI) and Structural Similarity Index (SSI). Further relative execution time of different approaches are also analyzed. On analysis of the values of different metrics and execution time, Wavelet Based Hybrid Thresholding is found to outperform the other filters considered.
Centrality Heuristics for Exact Model Counting

Model counting is the archetypical #P-complete problem consisting of determining the number of satisfying truth assignments of a given propositional formula. In this short paper, we empirically investigate the potential of employing graph centrality measures as a basis of search heuristics in the context of exact model counting. In particular, we integrate centrality-based heuristics into the search-based exact model counter sharpSAT. Our experiments show that employing centrality information significantly improves the empirical performance of sharpSAT, and also allows for simplifying the search heuristics compared to the current default heuristics of the model counter. In particular, we show that the VSIDS heuristic, which is an integral search heuristic employed in essentially all state-of-the-art conflict-driven clause learning Boolean satisfiability solvers, appears to be of very limited use in the context of model counting.
Combining Word and Character Vector Representation on Neural Machine Translation

This paper describes combinations of word vector representation and character vector representation in English-Indonesian neural machine translation (NMT). Six configurations of NMT models were built with different input vector representations: word-based, combination of word and character representation using bidirectional LSTM (bi-LSTM), combination of word and character representation using CNN, combination of word and character representation by combining bi-LSTM and CNN by three different vector operations: addition, pointwise multiplication, and averaging. The experiment results showed that NMT models with concatenation of word and character representation obtained BLEU score higher than baseline model, ranging from 9.14 points to 11.65 points, for all models that combining both word and character representation, except the model that combining word and character representation using both bi-LSTM and CNN by addition operation. The highest BLEU score achieved was 42.48 compared to the 30.83 of the baseline model.
Improving Performance in Hadoop Cluster Over Cloud Computing Environment Using Hold &amp; Release Mechanism

With the increasing use of cloud applications, data generation from the application is also increases which has become an important issue for the cloud that is known as a big data problem. So there is a need of technology to manage the big data on the cloud efficiently. Apache Hadoop is an efficient solution for handling big data because it is an open source technology that stores the big data in a distributed manner over the cluster of heterogeneous systems, which provides a reliable storage as well as processing to the big data over the cloud. In this paper to improve the performance of MapReduce in heterogeneous or shared environments, a data holding and releasing mechanism is proposed through which we can hold the map output at the block level and perform shuffling at the map end so the shuffle bytes has been reduced, which will improve the overall mapreduce performance over the cloud.
Ship target detection algorithm based on improved deep learning

Faster R-CNN algorithm is a deep learning network model based on regional suggestion network, which is applied to the field of target detection and recognition. Extracting a small number of pixels in the original image and generating a downscaled image can improve the detection efficiency of Faster R-CNN. Scene semantic narrowing is be directed at specific types of regions and geographical locations in the image. By performing targeted analysis on these regions, it can be interpreted more carefully, and the image is better applied to Faster R-CNN. The deep convolutional network with the narrowing function of the theme is studied. The main factors that affect the understanding of image content accumulated by human visual cognitive experience and computer vision research are summarized into various themes, and the appropriate theme is selected as a narrow sub-network according to the task. Realize the optimization of the Faster R-CNN algorithm by implementing a collaborative deep network with clear functions in the overall black box and subnet. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.
Deterministic hierarchical joint remote state preparation using partially entangled quantum channel

In this paper, we propose a novel scheme for deterministic hierarchical joint remote state preparation (HJRSP). Two senders and three receivers are involved. One sender has the amplitude information of the target state to be prepared at the receiver's port, while the other one has the phase information. There is a hierarchy among the receivers concerning their powers to reconstruct the target state. 6-particle partially entangled state is used as the quantum channel. Participants perform projective measurements under elaborate measurement bases. According to their measurement results, the receiver reconstructs the target state by means of appropriate unitary operation. The higher-power receiver reconstructs the target state independently, and the lower-power receiver reconstructs the target state if and only if all the other receivers help him (her). Different from the previous HJRSP scheme utilizing partially entangled channels, auxiliary qubit is not required in our scheme, and unit success probability can be achieved. It is shown that the success probability is independent of the parameters of the partially entangled quantum channel.
The Design and Research of Electromagnetic Compatibility in Digital T/R Module

Digital array radar technology has developed rapidly in recent years. Digital T/R module is the core of the digital array radar. Electromagnetic compatibility is an important signature in digital T/R module system. The electromagnetic interference can directly influence the successful operation of digital T/R module system. The relevant EMI concepts and the complex electromagnetic environment that digital T/R module faced is presented. The principle of digital T/R module based on direct digital synthesis (DDS) technology is proposed. The EMC design of T/R module was given and solving methods are proposed. The performances tests are carried out, which show that compared with absorbing materials, conduct material has remarkable advantages such as stretchable, compressible, light weight and easy to integrate with digital T/R module.
The Design of Smart Suitcase

With the development of the Internet of Things (IOT) and the wave of artificial intelligence (AI), more and more embedded systems combine AI to make life easier and more convenient for people. This paper integrates image recognition and object-following techniques to create an intelligent AI suitcase. By recognizing the back view of the user, the smart suitcase can move and follow the user with appropriate speed. When the user is too close, the suitcase performs corresponding actions to slow down or to stop to avoid collision; while the user is too far away, the suitcase will accelerate to maintain the distance between the users to avoid of losing connection with user. For image processing, we use Convolutional Neural Networks (CNN) as our algorithm and OpenCV for implementation to achieve object recognition and tracking. The Wi-Fi module is installed on the suitcase, it serves to detect whether the target is missing by connecting to the user's mobile phone. When the detected Wi-Fi signal is too low, the mobile phone will issue a warning to remind the user that the luggage distance is too far. In terms of control, we use fuzzy control and machine learning to control the motor to make tracking of the user more stable.
Learning Text Representations for Finding Similar Exercises

Mathematical Intelligent Tutor System brings great convenience for both teachers and students. A basic task in the system is to find similar exercises, which examine students the same skills or knowledge. Inspired by previous work, we propose a new model called Siamese based Bidirectional Encoder Representations from Transformer (SBERT). After training on our Chinese math exercises dataset, AUC(Area Under Curve) of SBERT model can reach up to 0.90, which is higher than that of existed models. Visualization analysis also proves that our model obtains better text representing performance of exercises than previous work.
UAV Autonomous Reconnaissance Route Planning Based on Deep Reinforcement Learning

In order to improve the autonomous reconnaissance efficiency of unmanned aerial vehicle (UAV) in an uncertain environment, situation and observation information acquired by UAV are input into the replay buffer. Model-free training is performed on the data of the replay buffer by deep reinforcement learning (DRL) method, so as to generate the corresponding network model. The reward function is designed for UAV regional reconnaissance missions to further improve the generalization ability of the model. The simulation results show that the UAV autonomous reconnaissance route planning algorithm based on DRL has a high degree of sustainable coverage and its patrol path is unpredictable.
TIC-TAC Based Live Acoustic Watermarking With Improved Forgery Detection Performances

Audio recording authentication becomes a necessity as the evolution of technology allowed the manufacturing of small and performant recording devices. The difficulty of editing audio recordings has decreased significantly, given the development of the audio editing software. The paper describes an improved version of a recently proposed live watermarking system based on the TIC-TAC signal. The version presented in this paper eliminates some detection flaws of the original version, studies the effect of the propagation of the original live watermark signal through various acoustic environments, and proposes an improved version that is less affected by those effects.
A High-Accuracy Algorithm Combing Shape Prior with Algebraic detected elliptical arc distance for Circle Pose Measurement

Existing the detected ellipse coefficients-driven circle-pose measurement algorithms suffer from low measurement accuracy as well as measurement failure in the presence of partial occlusions. This paper proposes a high-accuracy circle pose measurement algorithm that combing the shape prior constraint with the detected elliptical arc data and accurately obtain the image contour of the spatial circle to measure circle pose. This algorithm selects the spatial circle projection contour as the shape prior item; the algebraic detected elliptical arc distance is defined as the sum of points-to-contour distance between detected elliptical arc points and the spatial circle projection contour, which is elliptical arc data item. It defines the likelihood function based on these items so that the circle pose parameters can be obtained by minimizing the algebraic detected elliptical arc distance. The proposed algorithm avoids performing ellipse fitting; it utilizes the shape prior constraint of the spatial circle and the algebraic point-to-contour distance to effectively reduce the impacts of partial occlusions on the pose measurement results. Experiment results demonstrate that the algorithm can reliably and accurately measure the circle pose in presence of occlusions.
Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network

Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling based motion planners are proven to be the most effective for such high dimensional planning problems with real time constraints. Unluckily random stochastic samplers suffer from the phenomenon of `narrow passages' or bottleneck regions which need targeted sampling to improve their convergence rate. Also identifying these bottleneck regions in a diverse set of planning problems is a challenge. In this paper an attempt has been made to address these two problems by designing an intelligent `bottleneck guided' heuristic for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on relevant context extracted from the planning scenario using a 3D Convolutional Neural Network and it is also proven that the proposed technique generalizes to unseen problem instances. This paper benchmarks the technique (bottleneck guided RRT*) against a 10% Goal biased RRT* planner, shows significant improvement in planning time and memory requirement and uses ABB 1410 industrial manipulator as a platform for implantation and validation of the results.
Extraction of Characteristic Terms from Patent Documents for Technical Trend Analysis

In this paper, we propose a method that automatically extracts terms defined "characteristic terms" from several patent documents belonging to a certain technical field. The characteristic terms extracted by our method are useful for technology trend analysis. For example, in the case of patent documents relating to an organic electroluminescence (EL), the characteristic term is a word that expresses the characteristic of the technology shown in the patents, such as " Luminous efficiency (hakkoukouritu: luminous efficiency)" or " Luminance (koudo:brightness)". Finally, our method was evaluated and as a result of that, good results were obtained.
Multibiometrics User Recognition using Adaptive Cohort Ranking

Personal identification using multibiometrics is desirable in a wide range of high-security and/or forensic application as it can address performance limitations from unimodal biometrics systems. This paper presents a new scheme the multibiometrics fusion to achieve performance improvement for the user identification/recognition. We model the biometric identification solution using an adaptive cohort ranking approach, which can more effectively utilize the cohort information for maximizing the true positive identification rates. In contrast to the tradition cohort-based methods, the proposed cohort ranking approach offers merit of being matcher independence as it does not make any assumption on the nature of score distributions from any of the biometric matcher(s). In addition, our scheme is adaptive and can be incorporated for any biometric matcher/technologies. The proposed approach is evaluated on publicly available unimodal and multimodal biometrics databases, i.e., BSSR1 multimodal matching scores for fingerprint and face matchers and XM2VTS matching scores from synchronize databases of face and voice. In both the unimodal and multimodal databases, our results indicate that the proposed approach can outperform the conventional adaptive identification approaches. The experimental results from both public databases are quite promising and validate the contributions from this work.
ADREMOVER: THE IMPROVED MACHINE LEARNING APPROACH FOR BLOCKING ADS

There is an explosion in the advertisements over web nowadays. Most of the websites we visit contain ads, even Facebook, Google and Twitter. Sometimes, it could also appear that someone is spying on us because there are incidents like ads that show up with the content exactly what you have been searching not long ago. Such events happen as a result of Web Tracking. Initially, ads were meant to support businesses and companies to market their products and persuade the users to purchase them. Web Tracker were meant to track the user interaction with the website so it can improve the user experience. However, some of these have allowed ads as malvertisements, which may take advantage of these functionalities to steal the users sensitive information. To counter this tsunami of ads (malware), several ad blockers were created and are freely available as a browser extension and serve the purpose of blocking ads and trackers and most of them use the hand-crafted filter lists, some of them apply the machine learning approach. However, because of outdated filter-list or white-list and also inability to identify brand new ad signatures, most of them do not provide the depth of functionality and intelligence required to block all the undesirable content. After applying several machine learning approaches and comparing them, we propose our tool, AdRemover, that is a novel approach using the list of URLs which contains Ad and Non-Ad as the dataset. The classification applying Random Forest exceeds 98% with the splitted 20% testing data and 80% training data from our dataset.