Reviewerly/exHarmony huggingface dataset:
Making sense of high dimensional concrete data – a statistical approach

Abstract Performance of concrete is dependent on a number of factors. It is difficult to understand the influence and interrelationship among these variables, when there are many. Dimensionality reduction techniques can yield the best possible data interpretation based on the variance in data, without loss of much of original information. This paper presents the application of dimensionality reduction technique for analysis of data and decision making in the field of Concrete Technology.
Extraction algorithm of image feature point based on multi-scale fusion information

In order to extract the key points of weak texture regions in two-dimensional image matching, a linear multi-scale image enhancement matching algorithm based on image fusion technology is proposed in this paper. The algorithm mainly generates the weighted image of the original image according to the saturation, brightness, contrast and other measurement factors, enhances the distribution weight of weak texture, and makes more detail information fusion into the original image through the reconstruction of Gauss pyramid. The experimental results show that the algorithm effectively enhances the details of the weak texture region of the image, and can obtain more feature points for subsequent image matching.
Multi-information Fusion Algorithm for Human Target Tracking

A human object tracking algorithm fused with depth information, color information and forecast information is presented in this paper, which enables follow-robot to track motion human object robustly. Firstly, Camshift algorithm is expended into three-dimensional space by color information and depth information. Secondly, the interference from complex background is eliminated from the projection drawing by depth image of motion human object in three-dimensional space. Finally, human object's location information is forecasted by Kalman algorithm and human's center of mass location is calculated in the corresponding depth image, human object's tracking is achieved now. Using Kinect sensor to capture RGB image and depth image, and human object tracking algorithm fused with Multi-information is achieved and applied to the follow-robot. The experimental result shows that the algorithm proposed in this paper can track motion human robustly in condition of complex background, uneven ambient light, deformed target and so on.
How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks

It is widely accepted that part-of-speech (POS) tagging and dependency parsing are highly related. Most state-of-the-art dependency parsing methods still rely on the results of POS tagging, though the tagger is not perfect yet. Inevitably, dependency parsing model will encounter performance degradation due to the error propagation problems. And it still remains uncertain about how important POS tagging is to dependency parsing. In this work, we propose a method to jointly learn POS tagging and dependency parsing so as to alleviate the error propagation problems. Our proposed method is based on transition system, which is capable to produce dependency tree efficiently and accurately. The results reported in the experiments support our idea that POS tagging is a crucial syntactic component for dependency parsing.
An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation

Rank aggregation is a popular problem that combines different ranked lists from various sources (frequently called voters or judges), and generates a single aggregated list with improved ranking of its items. In this context, a portion of the existing methods attempt to address the problem by treating all voters equally. Nevertheless, several related works proved that the careful and effective assignment of different weights to each voter leads to enhanced performance. In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query. The proposed method is based on the fact that if a voter has submitted numerous elements which have been placed in high positions in the aggregated list, then this voter should be treated as an expert, compared to the voters whose suggestions appear in lower places or do not appear at all. The algorithm iteratively computes the distance of each input list with the aggregated list and modifies the weights of the voters until all weights converge. The effectiveness of the proposed method is experimentally demonstrated by aggregating input lists from six TREC conferences.
Application Research of a Practical and New Intelligent Question Answering System

Abstract Intelligent question-answering system (IQAS) can help users quickly retrieve the answers to the required questions which is currently a popular research direction of NLP. This paper combines the research of relevant scholars and the analysis of their advantages and disadvantages, and an intelligent question-and-answer system is designed, which can automatically generate a question-and-answer knowledge base based on a given document. It can retrieve the knowledge base and recommend answers automatically, quickly and accurately according to the keywords provided by users. The system realized text semantic understanding and analysis, and its MRR value reached a satisfactory 0.7381. At the same time, the paper extends an automatic answer text generation technology based on Seq2Seq model, which can be a useful supplement to the traditional question-and-answer recommendation generation strategy.
Data Structures for Designing Interactions with Contextual Task Support

The diversity and the scale of available online instructions introduce opportunities but also user challenges in currently used software interfaces; Users have limited computational resources, and thus often make strategic decisions when browsing, navigating, and understanding instructions to accomplish a task. These strategic user interactions possess nuanced semantics such as users' interpretations, intents, and contexts in which the task is carried out. My dissertation research introduces techniques in constructing data structures that capture the diverse strategies users employ in which the collective nuanced semantics across multiple strategies are preserved. These computational representations are then used as building blocks for designing novel interactions that allow users to effectively browse and navigate instructions, and provide contextual task guidance. Specifically, I investigate 1) structure of instructions for task analysis at scale, 2) structure of collective user task demonstrations, and 3) structure of object uses in how-to videos to support tracking, guiding and searching task states. My research demonstrates that the user-centered organization of information extracted from interaction traces enables novel interfaces with contextual task support.
Design and Analysis of Sign Language Gesture Recognition as Text: A Redesigned Artificial Neural Network Approach

Numerous specialists tend to frame a gesture sign for words that may have discernible research result. In any case, because of alphabetic sign gesture prediction, numerous specialists neglects to close their exploration with huge result, this turns our research expectation towards an upgrade in compelling prediction over alphabetic sign gesture. In general, the researcher clear there research diagram in four stages information securing, preprocessing, feature extraction, and sign recognition. Moreover, with this consolidation of segmentation process before the feature, extraction will upgrade the viability of prediction accuracy definitely if there should be an occurrence of the ongoing procedure. The key procedure in this whole approach is sign recognition, where the specialist tend to use traditional systems in the midst of Artificial Neural Network (ANN) places an indispensable part. The proposed strategy raises the execution of the whole procedure in redesigning the genuine structure of ANN (hidden layer and neuron) by joining optimization techniques. This blend demonstrates a very clear outcome over customary procedures with its predicting accuracy and powerful execution. ANN in association with ABC reveals 96.71% for static data, while real time data set ANN associated with SSO reveal superior results of 83.73% over 75.75% by ANN-ABC.
Researching on HAZOP Information Standardization Based on Knowledge Ontology

The results of most traditional HAZOP analysis were recorded in documents. However, due to the large amount of data and the various formats of HAZOP analysis documents, the analysis data of the predecessors cannot be shared and reused. In order to solve this problem, the paper proposes a method to transform HAZOP analysis documents into knowledge ontology, which makes knowledge easy to store and share. We use the method to identify the content of the document, and then use the word segmentation tool ICTCLAS5.0 to process Chinese documents, including Chinese word segmentation, part-of-speech tagging and deletion of stop words, then the key information is extracted according to the IEC-61882 international standard and store it in the OWL document. Finally, we use Protégé software to intuitively manage OWL documents and validate knowledge. The final result shows that the method can well express the knowledge of HAZOP analysis and realize the integration, sharing and reuse of HAZOP analysis data.
Cervical Cell Recognition based on the Leave-one-out Feature Selection Algorithm

In order to reduce the mortality rate of cervical cancer, it is of great significance to implement timely and effective screening of cervical cancer for women. Cervical cell recognition based on image processing and computer science has become a trend, however, most methods extract too many cell features, resulting in lower recognition efficiency. In order to obtain effective cell features and represent the highest accuracy with the least features, a cervical cell recognition model based on the leave-one-out feature selection algorithm is proposed. Firstly, the improved Canny algorithm is used to extract the contour of the cells, and then color, texture and morphological features are extracted. In order to avoid low efficiency caused by feature redundancy, this paper uses the leave-one-out method to select the extracted features. Finally, this paper uses Support Vector Machine (SVM) to construct the recognition model. The experimental results illustrate that the improved Canny algorithm proposed in this paper can effectively extract precise cell edges, so that more accurate cell features are obtained. Leave-one-out method can select more representative features, maintaining the recognition accuracy while effectively reducing its running time.
Research on Smart Grid Load Forecasting Cloud Platform Based on Hadoop Architecture

Abstract In view of the shortcomings and shortcomings of traditional smart grid load forecasting system, this paper uses Hadoop architecture to build cloud computing platform, optimizes the layout and architecture of the entire smart grid load forecasting cloud platform, and forms the main framework. The basic coordination model is used to carry out multi-level coordination and optimization service, and its practicability is proved by an example. Through the research on the framework of the smart grid load forecasting platform based on cloud computing, improving the level of load forecasting can deepen the application of cloud computing in power system, lay a solid foundation for smart grid dispatching and load balancing, and provide a strong guarantee for the stability analysis of online upgrade of the system.
Specialized Car Detector for Autonomous Driving

Fast and accurate perception of the environment are important for autonomous vehicle research, especially the detection of other vehicles in the front. Different from object detection tasks in other fields where high accuracy and real-time detection speed are required, the model size of object detection system applied to autonomous vehicles should be as small as possible due to the limitations of vehicle-mounted embedded system. The object detection system of the autonomous vehicle takes too many pedestrians and other factors into account, which lead to a degradation in detection performance. In this work, we propose the so-called SpCarDet, a specialized car detector, which is a one-stage object detection network. Based on the Fire module, Stacked feature map and Shortcut connections, a new and small model size backbone network is built. We also present the bounding boxes generation network named AGnet that is optimized for car detection. The real-time accurate detection of the vehicle is achieved, and the model size of network is small to enable embedded system deployment. Experiments on the KITTI dataset demonstrate the effectiveness of our algorithm.
Tracking Jockeys in a Cluttered Environment with Group Dynamics

This project aims to detect and track jockeys at the turning point of the horse races. The detection and tracking of the objects is a very challenging task in a crowded environment such as horse racing due to occlusion. However, in the horse race, the jockeys follow each other's paths and move as a slowly changing group. This group dynamic gives an important cue to approximate the location of obscured jockeys. This paper proposes a novel approach to handle occlusion by the integration of the group dynamic into jockeys tracking framework. The experimental result shows the effect of group dynamics on the tracking performance against partial and full occlusions.
Neuro-Fuzzy-Rough Classification for Improving Efficiency and Performance in Case-Based Reasoning Retrieval

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for solving problems. The very fact that CBR draws on past experiences to solve a new problem makes it intuitively appealing as humans also have the same problem-solving behavior. Another advantage of CBR over other conventional AI techniques is that it can work on a shallow knowledge base to start with. These make CBR an excellent method to solve real-life problems and useful in the field like medical diagnosis, engineering diagnosis, product selection, weather prediction, aerospace applications, etc. Classification plays a vital role in the retrieval of cases, as a correct classification results in a correctly retrieved case, which eventually results in a correct solution given by the CBR system. Typically, case retrieval is similarity-based and uses a k-nearest neighbor (k-NN) algorithm. Retrieval aims to find among the stored cases the best match for a given new case. Typically, CBR systems use the nearest neighbor algorithm as a similarity metric for retrieving cases. In this paper, the researchers use the machine learning workbench WEKA to combine well-known classifiers multilayer perceptron and fuzzy-rough nearest neighbor and compare the performance of k-NN with them. They have used benchmark medical data sets to carry out the evaluation process. The experimental results show that the combination of multilayer perceptron and fuzzy-rough nearest neighbor outperforms k-NN to a significant extent for classification, thus effectively improving the case retrieval efficiency and performance.
Disruption of Object Recognition Systems

In recent times, deep neural networks are being used in a wide variety of applications such as autonomous vehicles, medical imaging and surveillance. While they are becoming increasingly powerful, it is possible to disrupt their task by crafting adversarial inputs. These inputs are essentially perturbations added to the original inputs so that the application using the network, such as an object recognizer, is unable to classify the object in the image. Crafting such inputs to disrupt such a recognition task is termed an adversarial attack. Here, we implement two disruption strategies, Fast Gradient Sign Method (FGSM) and generating perturbations using a generator network. While FGSM requires access to the gradient calculated by the classifier with respect to the input image, the generator trains simultaneously with the classifier network to learn how to craft perturbations. Once the generator network is trained with a particular classifier (say, VGG16), it can disrupt other classifier networks in a black-box fashion as well. Using the same dataset, in this case CIFAR-10, it is possible to adversarially train the classifier to make it more robust to perturbed images. This involves training the classifier on the CIFAR-10 images with both the original images and the ones perturbed by the generator. In experiments, the attack using the generator achieves higher disruption accuracies than FGSM on very deep networks.
Improved Theoretical Game Tree Model for the Study of “Knowing More is Less”

In the course of playing combinatorial games, players want to get better through knowing her opponent's information. However, knowing more does not always lead to higher probability of wining. In simulation of Five-in-a-Row (FIR), both phenomena of “knowing more is more” and “ knowing more is less” can happen under different conditions: the superior one wins more after knowing the other while the inferior one may lose more after knowing the other. A theoretical game tree model was built and it can show these phenomena whose determinant condition is independent with their opponents. This is inconsistent with our findings in FIR real games. In this paper we improve the model by introducing a parameter called correlated evaluation accuracy to represent the coupling relations between two players. Then we find the phenomena of “knowing more is less (more)” will happen when player's correlated evaluation accuracy is greater (lower) than 0.5. This is consistent with our finding in Five-in-a-row real games that the superiority between the player and her opponent determines “knowing more is less” or “knowing more is more This model is a general representation for many combinatorial games, thus we believe that “knowing more is less (more)” can be found in other real games. This finding displays the complex interaction in real games and offer a method to study coevolution of players.
S-Transform-Based Efficient Copy-Move Forgery Detection Technique in Digital Images

Copy-move forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. With the advent of high-performance hardware and the compact use of image processing software, it empowers creating image forgeries very easy which are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stockwell transform (S-transform) which hybrids the advantages of both scale invariant feature transform (SIFT) and wavelet transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks is measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel fuzzy min-max neural network-based decision tree (FMMNN-DT) classifier is used to recognize the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensures the significance of the proposed.
Exploring the Effects of Stereoscopic 3D on Gaming Experience Using Physiological Sensors

Past studies have shown that playing games in 3D stereo does not provide any significant performance benefits than with using a 2D display. However, most previous studies used games that were not optimized for stereoscopic 3D viewing and used self-reported data (excitement level, sense of engagement, etc.) to measure user experience. We propose to study games that are optimized for stereoscopic 3D viewing and use physiological sensors (an EEG and a heart rate monitor) to better gauge the user’s experience with these games. Our preliminary results reveal that stereo 3D does provide benefits in tasks where depth information is useful for the game task at hand. Additionally, participants in the 3D group had lower levels of stress and higher heart rates indicating a higher sense of engagement and presence under stereoscopic 3D conditions.
Motion Attitude Analysis Method of Bullets Based on Field Shadow Image

Abstract Aimed at obvious interference noise of shadow images of bullet shot in outside field, and larger extraction error of bullet attitude features of shadow images and other problems, intersection point location method based on axis line and edge is adopted to extract features of bullet tip, and the method based on ellipse fitting is adopted to extract features of bullet tail in the Paper, and then extracted feature information is used to calculate motion attitude of bullets. The method can be applied to shadow image analysis of bullets under field environment well with accurate feature extraction, good consistency and high analysis precision and other features.
Improving on Neighborhood based Recommendations with a Small World Network derived Novelty metric

In this work we present a novel recommendation methodology together with new complex network based proximity and novelty metrics. We base our approach on a neighborhood based collaborative filtering combined with the analysis of a small-world item-item network. Our approach uses implicit feedback from categorizations of content provided by users or content creators, instead of focusing on rating. However we build a ranking heuristic that mixes our proximity and novelty measures, in order to improve upon the recommendations made by a standard k-NN based collaborative filtering approach. We focus on the use case of gaming based platforms like Twitch.tv, where the content is classified by semi-free categories by the users. We also apply our methodology to the MovieLens dataset to try to predict the ratings of the users and provide a degree of comparability. We evaluate our results using a modified accuracy metric and also the normalized discounted cumulative gain when comparing with user ratings. We show that this heuristic more closely maps to the natural search behavior of users in content platforms than the classic neighborhood methods, and show how it can be used to improve recommendation, ad targeting, and content production.
A Real-Time Scene Recognition System Based on RGB-D Video Streams

Depth data captured by the cameras such as Microsoft Kinect can bring depth information than traditional RGB data, which is also more robust to different environments, such as dim or dark lighting conditions. In this technical demonstration, we build a scene recognition system based on real-time processing of RGB-D video streams. Our system recognizes the scenes with video clips, where three types of threads are implemented to ensure the realtime. This system first buffers the frames of both RGB and depth videos with the capturing threads. When the buffered videos reach the certain length, the frames will be packed into clips and forwarded in a pre-trained C3D model to predict scene labels with the scene recognition thread. Finally, the predicted scene labels and captured videos are illustrated in our user interface with illustration thread.