Reviewerly/exHarmony huggingface dataset:
Case matching and syncretism in ATB‐dependencies

Abstract Syncretism has been reported to have the peculiar property of repairing violations of syntactic constraints, e.g. with agreement (Schütze 2003, Bhatt &amp; Walkow 2013) and case matching (Citko 2005, van Craenenbroeck 2012). This paper puts forward the view that in one well‐reported instance of syncretism repair of case‐matching violations with ATB‐movement, this repair follows directly from the nature of ATB movement. We pursue a novel movement‐based analysis in which ATB movement involves the actual fusion of two syntactic objects, via intersection of feature sets. As well as deriving the one‐to‐many relation between fillers and gaps in ATB, we show how the ‘repair’ effect of syncretism with case matching violations follows naturally under this approach.
A Graph-Based Method for Active Outlier Detection With Limited Expert Feedback

Labeled data, particularly for the outlier class, are difficult to obtain. Thus, outlier detection is typically regarded as an unsupervised learning problem. However, it still has an opportunity to obtain few labeled data. For example, a human analyst can give feedback to few data when he/she examines the results of an unsupervised outlier detection method. Moreover, the widely used unsupervised method for outlier detection cannot only take the labeled data into consideration nor use them properly. In this study, we first propose a graph-based method to endow the unsupervised method with the ability to consider few labeled data. Then, we extend our semi-supervised method to active outlier detection by incorporating the query strategy that selects top-ranked outliers. Comprehensive experiments on 12 real-world datasets demonstrate that our semi-supervised outlier detection method is comparable with the best of state-of-the-art approaches, and our active outlier detection method outperforms state-of-the-art methods.
Unsupervised representation learning with Laplacian pyramid auto-encoders

Scale-space representation has been popular in computer vision community due to its theoretical foundation. The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. Hence, it is reasonable to consider learning features with image pyramids generated by smoothing and down-sampling operations. In this paper we propose Laplacian pyramid auto-encoders, a straightforward modification of the auto-encoder framework, for unsupervised representation learning. The method uses multiple encoding–decoding sub-networks within a Laplacian pyramid framework to reconstruct the original image and the low pass filtered images. The last layer of each encoding sub-network also connects to an encoding layer of the sub-network in the next level, which aims to reverse the process of Laplacian pyramid generation. Experimental results showed that Laplacian pyramid leaded to a more stable and efficient training procedure and improved the performance of the learned representation with scale information.
A Document Ranking Method With Query-Related Web Context

In this paper, an approach is proposed to evaluate and rearrange web pages, based on the query-related web context. The contexts we focus on are the terms co-occurring with queries in microblogs. The proposed approach is based on the retrieved result by a search engine. If a query is given, it retrieves the search results, and checks whether the query is on a burst state or not. If the query is on a burst state (or popular state), our method applies the query-related context to the search results. Since context terms can reflect the current interest of people regarding the query, a web page can be considered within the current interest of people, if it has many of the context terms. Thus, the retrieved web pages are re-ranked based on the context terms, to present the search result in accordance with the current public interest. We present some observations that show microblog contents and search queries are strongly related, if queries are on a burst state. In order to verify the effect of context terms, we conduct experiments, and compare the result with Google using a questionnaire survey.
On Approaches to Discretisation of Stylometric Data and Conflict Resolution in Decision Making

The paper presents research on unsupervised and supervised discretisation of input data used in execution of stylometric tasks of authorship attribution. Basing on numeric characterisation of writing styles, recognition of authorship is performed by decision rules, as their transparent structure enhances understanding of discovered knowledge. The performance of rule classifiers, constructed in rough set approach, is studied in the context of a strategy employed for resolving conflicts. It is also contrasted with that of other selected inducers.
Occlusion-aware Hand Posture Based Interaction on Tabletop Projector

Conventional camera-based hand interaction technique suffered from self-occlusion among fingers, which lowers the detection accuracy of fingertip positions, leading to uncomfortable UI controls. Based on observations, self-occlusion depends on hand postures. We design an interaction framework in which interaction is decided in response to a recognized hand posture. Using a tabletop projection system that has a projector and a depth sensor, we implement the framework by integrating five touch and in-air interactions that balance its stability and usability.
Autonomous Vehicles Path Planning With Enhanced Ant Colony optimization

Reasonable optimal path planning is one of the core requirements of autonomous vehicles. Ant colony optimization is the most famous bionic optimization method which has been widely used in path planning. However, it has some drawbacks such as slow convergence speed and ease of falling into the local optimal solution. An enhanced ant colony optimization algorithm that adopts the new inspiration function and pheromone updating strategy is provided in this paper, which aims to improve the convergence speed and search ability. In addition, our ant colony algorithm integrates the non-uniform rational B-spline curve to smooth the path. The simulation experiments indicate that this algorithm is feasible and efficient in common scene and maze scenario. Compared with the traditional ant colony algorithm, our algorithm has shorter path length and fewer iterations to converge to the optimal path.
Erasure decoding of two-dimensional color codes

The quantum erasure channel models phenomena such as loss or leakage of qubits. Using quantum codes, we can recover from such errors. In this paper, we are interested in studying the performance of two-dimensional color codes over the quantum erasure channel. Our approach makes use of the local equivalence between color codes and surface codes. We propose a variety of decoding algorithms for color codes over the erasure channel. First, we propose algorithms that decode by projecting the erasures on the color to surface codes. Then, instead of directly decoding on the color code or on the equivalent copies of surface codes, we decode jointly on the color code and the equivalent surface codes. We observe a threshold of 44.3% for the color code on the square octagonal lattice.
Sentence guided object color change by adversarial learning

In this paper, we propose a novel problem that is sentence guided object color change. Based on an original sentence and the corresponding image, by modifying this original sentence, the object color of corresponding image is changed along with the modified target sentence. This problem has two difficulties: (1) How to design a model to learn the change of sentence? (2) How to balance the relationship between the local object and the whole image during learning process? Confronted with these difficulties, as far as we know, few existing methods deal with them effectively. Therefore, we propose a new cascaded model to solve this problem based on generative adversarial networks (GANs). We employ the adversarial game to build a cascaded model, which learns the changed information of a sentence. Then, we specially design a penalty balance term to balance the relationship between local object and entire image in the generated image. Finally, experimental results on the flower and bird datasets demonstrate the validity of the proposed model.
Identification and localization method of the insulator based on three-dimensional point cloud modeling

Insulator failure is one of the main factors that cause power system failure. The insulator will have a decrease in insulation performance due to contamination of the insulator skirt between the conductive material such as dirt, snow and icicles, which will damage the high voltage transmission line. Therefore, it is important to clean the insulator, which relies heavily on the effective identification and positioning of the insulator. In order to solve the influence of image background, contrast and sharpness on insulator recognition under complex environmental conditions this paper proposes an insulator identification and localization method based on three-dimensional point cloud modeling. First, the environment three-dimensional point cloud data is acquired by using a structured light camera. We extract the depth information from the point cloud data and represent it in grayscale, and binarize the grayscale result. Then we identify the insulator based on principal component analysis, and calculating the central axis of the insulator using the least squares method based on the contour point cloud data of the insulator. In this way we obtain the position of the insulator in three-dimensional space. It has been proved by experiments that this method can provide accurate information on the position of the insulator for cleaning tool and guide it to clean insulator.
NoSQL Database for Storing Historic Records in Monitoring Systems: Selection Process

The storage of historical information consists of the registration of large concentrations of data based on samples of information issued by a given system. Historical databases play a key role in industrial control systems that process and provide the information needed for the tasks of supervision, event records, management, and maintenance in the industrial sector. The purpose of the present research is to analyze the current state of monitoring and control systems, recognizing their advantages and disadvantages, and reaching the state of the art about the selection of a NoSQL database for the recording of time series. The development of the work focused on the comparison of column-oriented storage managers as HBase, Cassandra, and Big Table. After designing and implementing two cases of tests for comparing the behavior of both managers in the scenarios of Intensive Readings and Reading/Writing, it was concluded that both of them present excellent performance for storing historic records.
Long-term 3D Convolutional Fusion Network for Action Recognition

Applications of deep convolutional networks have achieved success in the field of video action recognition. However, there are still existing problems: 1) Most motion recognition only uses a single video segment as research object, which is difficult to represent the coherent process of the action in whole video because of the lack of ability to use the complementary information; 2) Most studies have not specifically enhanced the ability to characterize movements as time goes on, just put temporal information in the same class as spatial information. A Long-term 3D Convolutional Fusion Network (LT3D-CFN) is proposed to aim at these problems. LT3D-CFN could be summarized as the following two aspects: CNN in two-stream network has been replaced by 3DCNN because of the ability of extracting features from both the spatial and the temporal dimensions of one video clip. In addition, long-term associations between clips of one motion video are established by adding deep LSTM network. The proposed architecture LT3D-CFN, is trained and evaluated on UCF-101 dataset where the proposed architecture achieves superior performers.
Multi-Classification Model for Spoken Language Understanding

The spoken language understanding (SLU) is an important part of spoken dialogue system (SDS). In the paper, we focus on how to extract a set of act-slot-value tuples from users' utterances in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU). This paper adopts the pretrained BERT model to encode users' utterances and builds multiple classifiers to get the required tuples. In our framework, finding acts and values of slots are recognized as classification tasks respectively. Such multi-task training is expected to help the encoder to get better understanding of the utterance. Since the system is built on the transcriptions given by automatic speech recognition (ASR), some tricks are applied to correct the errors of the tuples. We also found that using the minimum edit distance (MED) between results and candidates to rebuild the tuples was beneficial in our experiments.
Neural CTR Prediction for Native Ad

Native ad is an important kind of online advertising which has similar form with the other content in the same platform. Compared with search ad, predicting the click-through rate (CTR) of native ad is more challenging, since there is no explicit user intent. Learning accurate representations of users and ads that can capture user interests and ad characteristics is critical to this task. Existing methods usually rely on single kind of user behavior for user modeling and ignore the textual information in ads and user behaviors. In this paper, we propose a neural approach for native ad CTR prediction which can incorporate different kinds of user behaviors to model user interests, and can fully exploit the textual information in ads and user behaviors to learn accurate ad and user representations. The core of our approach is an ad encoder and a user encoder. In the ad encoder we learn representations of ads from their titles and descriptions. In the user encoder we propose a mult-view framework to learn representations of users from both their search queries and their browsed webpages by regarding different kinds of behaviors as different views of users. In each view we learn user representations using a hierarchical model and use attention to select important words, search queries and webpages. Experiments on a real-world dataset validate that our approach can effectively improve the performance of native ad CTR prediction.
Edge Computing: Smart Identity Wallet Based Architecture and User Centric

The huge amount of exchanged data in the internet between entities and the quick development of edge computing has increased users frustration about the future generations. We expect a future where transactions will be based on clouds and virtual machines, gathered from sensors and IoT devices and processed by different artificial intelligence algorithms or agents. The speed and backlash change in the world has driven researchers to work on privacy and security for entities’ information and transactions. Different concepts arise and still in their early stage as digital identity, self sovereign identity, global unique identifier and identity of things (IDoT). Therefore, motivated by the recent explosion of interest around Blockchains, we examine whether they make a good fit for the Identity Internet of Things (IDoT) sector. Blockchain a major distributed peer-to-peer network where non-trusting members can interact together without a need for a trusted third party, it actually make available many advantages to providers and consumers and solve data protection features lifelong a transaction existing. As being immutable, transparent and trustful platform the Blockchain allows managing identities and privacy of its nodes information. Our contribution is a new architecture eventually using a public Blockchain and creating a smart identity wallet. It contains standard node data and will also integrate proactive data behavior and the reactive one. The main target is to protect users from sybil attack at early stage. We will define a new digital wallet based on entities behavior in order to prevent 51% attack and Sybil attack
Multi-Rotor UAVs Path Planning Method based on Improved Artificial Potential Field Method

A path planning method based on Biogeography-Based optimized (BBO) artificial potential field algorithm is presented. The parameters of artificial attraction potential field and artificial repulsion potential field is optimized through state feedback and cyclic iteration. The BBO algorithm is used for parameters optimizing. While involving the optimization, the complexity of the design of path planning algorithm is reduced. The simulation results under local minimum situation and target unreachable situation is done. The results illustrate that the BBO based path planning algorithm is able to effectively accomplish the 2D path planning.
Radar Emitter Sorting and Recognition Based on Time-frequency Image Union Feature

This paper proposed a radar emitter sorting and recognition algorithm based on Choi-Williams Distribution (CWD) time-frequency image union features for problem of low recognition rate and high complexity of radar signals soring and recognition algorithm at low signal-to-noise ratio (SNR). Firstly, the time-frequency image matrix of signals is obtained via CWD, then the time-frequency image is processed to extract the Gray Level Co-occurrence Matrix (GLCM), Zernike moment features and singular value entropy features of time-frequency image to form union features parameter vectors, and finally the automatic sorting and recognition of radar signals is achieved via support vector machine (SVM) classifier. Simulation results show that the overall average recognition rate of the proposed method for 6 types radar signals could reach above 97.3% under -6dB SNR, it also has a better recognition effect on the hybrid modulated signal.
Multi-parameter streaming outlier detection

Distance-based outlier detection techniques is a wide-spread methodology for anomaly detection. Despite their effectiveness, a main limitation is that they heavily rely on the dataset and the parameters chosen in order to establish the right status of each data point. These parameters typically include, but are not limited to, the neighborhood radius and threshold. In continuous streaming environments, the need for real-time analysis does not permit for an algorithm to be restarted multiple times with different parameters until the right combination is specified. This gives rise to the need for one technique that combines an arbitrary number of parameterizations with the use of minimal yet sufficient computer resources. In this work we both compare the state-of-the-art techniques for handling multiple queries in distance-based outlier detection algorithms and we propose a novel technique for multi-parameter distance-based outlier detection tailored to distributed continuous streaming environments, such as Spark and Flink.
Efficient and Accurate Non-exhaustive Pattern-Based Change Detection in Dynamic Networks

Pattern-based change detectors (PBCDs) are non-parametric unsupervised change detection methods that are based on observed changes in sets of frequent patterns over time. In this paper we study PBCDs for dynamic networks; that is, graphs that change over time, represented as a stream of snapshots. Accurate PBCDs rely on exhaustively mining sets of patterns on which a change detection step is performed. Exhaustive mining, however, has worst case exponential time complexity, rendering this class of algorithms inefficient in practice. Therefore, in this paper we propose non-exhaustive PBCDs for dynamic networks. The algorithm we propose prunes the search space following a beam-search approach. The results obtained on real-world and synthetic dynamic networks, show that this approach is surprisingly effective in both increasing the efficiency of the mining step as in achieving higher detection accuracy, compared with state-of-the-art approaches.
SDV-Net: A Two-Stage Convolutional Neural Network for Smoky Diesel Vehicle Detection

Smoky diesel vehicle which constantly emit a lot of black smoke is one of the major sources of urban air pollution. It is a timely-responsive, convenient and cost-effective way to detect smoky diesel vehicle utilizing electronic surveillance cameras at both urban and suburban traffic crossings. However, the water stain, shadow and inter-vehicle occlusion significantly hinder the usage of traditional image processing methods in smoky diesel vehicle detection. In this paper, a two-stage convolutional neural network is proposed, codenamed SDV-Net, for smoky diesel vehicle detection. Specifically, an object detection network is firstly presented to predict bounding boxes enclosing the whole outline and back contour of the diesel vehicle, and to locate the regions around exhaust pipes. Secondly, a multi-region convolutional tower network is proposed to conduct a fine-grained classification task on the cropped regions to further determine whether there are smoky diesel vehicle. Several experiments are implemented based on real surveillance images captured by electronic surveillance cameras mounted at different crossroads in Hefei during 2018, and the experimental results explicitly demonstrate the promising effectiveness of our proposed architecture.
Ethereum Blockchain for Securing the Internet of Things: Practical Implementation and Performance Evaluation

Blockchain technology is a distributed database `distributed ledger' and offers features such as autonomy, decentralisation and a trustless environment. These features make blockchain suitable to be applied to different applications within the Internet of Things (IoT) realm. This paper provides a practical implementation of Proof of Authority (PoA) Ethereum blockchain on an IoT system in a real-world use case. This implementation was practically accomplished in order to investigate and highlight some of the possible issues that could affect the integration of blockchain with IoT, to lay the ground for future research and possible solutions to these issues.
Deep Learning

Multimedia application is a significant and growing research area because of the advances in technology of software engineering, storage devices, networks, and display devices. With the intention of satisfying multimedia information desires of users, it is essential to build an efficient multimedia information process, access, and analysis applications, which maintain various tasks, like retrieval, recommendation, search, classification, and clustering. Deep learning is an emerging technique in the sphere of multimedia information process, which solves both the crisis of conventional and recent researches. The main aim is to resolve the multimedia-related problems by the use of deep learning. The deep learning revolution is discussed with the depiction and feature. Finally, the major application also explained with respect to different fields. This chapter analyzes the crisis of retrieval after providing the successful discussion of multimedia information retrieval that is the ability of retrieving an object of every multimedia.
Depth Estimation based on a Single Close-up Image with Volumetric Annotations in the Wild: A Pilot Study

A novel depth estimation technique based on a single close-up image is proposed in this paper for better understanding of the geometry of an unknown scene. Previous works focus mainly on depth estimation from global view information. Our technique, which is designed based on a deep neural network framework, utilizes monocular color images with volumetric annotations to train a two-stage neural network to estimate the depth information from close-up images. RGBVOL, a database of RGB images with volumetric annotations, has also been constructed by our group to validate the proposed methodology. Compared to previous depth estimation techniques, our method improves the accuracy of depth estimation under the condition that global cues of the scene are not available due to viewing angle and distance constraints.