Reviewerly/exHarmony huggingface dataset:
Vectorization of Floor Plans Based on EdgeGAN

A 2D floor plan (FP) often contains structural, decorative, and functional elements and annotations. Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D FPs. The detection results can be used to generate 3D models directly. The conventional pipeline of VFP often consists of a series of carefully designed complex algorithms with insufficient generalization ability and suffer from low computing speed. Considering the VFP is not suitable for deep learning-based object detection frameworks, this paper proposed a new VFP framework to solve this problem based on a generative adversarial network (GAN). First, a private dataset called ZSCVFP is established. Unlike current public datasets that only own not more than 5000 black and white samples, ZSCVFP contains 10,800 colorful samples disturbed by decorative textures in different styles. Second, a new edge-extracting GAN (EdgeGAN) is designed for the new task by formulating the VFP task as an image translation task innovatively that involves the projection of the original 2D FPs into a primitive space. The output of EdgeGAN is a primitive feature map, each channel of which only contains one category of the detected primitives in the form of lines. A self-supervising term is introduced to the generative loss of EdgeGAN to ensure the quality of generated images. EdgeGAN is faster than the conventional and object-detection-framework-based pipeline with minimal performance loss. Lastly, two inspection modules that are also suitable for conventional pipelines are proposed to check the connectivity and consistency of PFM based on the subspace connective graph (SCG). The first module contains four criteria that correspond to the sufficient conditions of a fully connected graph. The second module that classifies the category of all subspaces via one single graph neural network (GNN) should be consistent with the text annotations in the original FP (if available). The reason is that GNN treats the adjacent matrix of SCG as weights directly. Thus, GNN can utilize the global layout information and achieve higher accuracy than other common classifying methods. Experimental results are given to illustrate the efficiency of the proposed EdgeGAN and inspection approaches.
Hierarchical Quantum Network using Hybrid Entanglement

The advent of a new kind of entangled state known as hybrid entangled state, i.e., entanglement between different degrees of freedom, makes it possible to perform various quantum computational and communication tasks with lesser amount of resources. Here, we aim to exploit the advantage of these entangled states in communication over quantum networks. Unfortunately, the entanglement shared over the network deteriorates due to its unavoidable interaction with surroundings. Thus, an entanglement concentration protocol is proposed to obtain a maximally entangled hybrid Omega-type state from the corresponding non-maximally entangled states. The advantage of the proposed entanglement concentration protocol is that it is feasible to implement this protocol with linear optical components and present technology. The corresponding linear optical quantum circuit is provided for experimental realizations, while the success probability of the concentration protocol is also reported. Thereafter, we propose an application of maximally entangled hybrid state in the hierarchical quantum teleportation network by performing information splitting using Omega-type state, which is also the first hierarchical quantum communication scheme in the hybrid domain so far. The present hybrid entangled state has advantage in circumventing Pauli operations on the coherent state by polarization rotation of single qubit, which can be performed with lesser errors.
Youth Unemployment and Quality of Education in the MENA: An Empirical Investigation

The Middle East and North Africa (MENA) region has the highest rate of youth unemployment worldwide. High records of youth unemployment have increased the risk of political instability and other social concerns. Our study aims to investigate the role of quality of education in explaining the within country variation of youth unemployment in the MENA region. We use fixed effect regression methodology for a panel of 18 MENA countries from 2007 to 2017. Our results show a significant and robust negative (decreasing) effect of higher quality of education system on youth unemployment rate. This decreasing effect is robust to control of other drivers of youth unemployment, country and year fixed effects. Including full set of control variables, we show that a one unit increase in perceived quality of education systems from the Global Competitiveness Reports is associated with approximately 4 percentage points decline in youth unemployment. Our general specification explains approximately 70% of within-country variation of youth unemployment rate in the MENA region. Among control variables, only trade openness and foreign direct investment (FDI) share of GDP show statistically significant (negative) association with youth unemployment, meaning that trade and foreign investment openness contribute to employment of youth in the region. We suggest some recommendations for policy makers in the region.
Local Triangular Coded Pattern: A Texture Descriptor for Image Classification

Local binary descriptors are extensively used for image representation in many of the computer vision applications. A majority of these local binary descriptors exploit the intensity difference of the neighbouring pixels with respect to the centre pixel of the chosen region to formulate the representative value at the respective pixel position. In this paper, a novel descriptor, called Local Triangular Coded Pattern (LTCP), is introduced that utilises the relationship between a set of pixels in the triangular neighbourhood of a region to compute the descriptor. Unlike many of the other local binary descriptors, the proposed descriptor considers multiple pixels as centres within the given region to obtain the binary pattern. The performance of the LTCP descriptor is analysed by performing image classification in benchmarked texture datasets such as KTH-TIPS, Outex, Brodatz and Kylergb and in facial emotion datasets such as CK+, JAFFE, MUFE and Yale Face. The results indicate that LTCP with the Random Forest classifier gives an accuracy of 92.82%, 93.81%, 94.11% and 97.14%, respectively, on Brodatz, Outex, KTH-TIPS and Kylergb datasets for texture classification and 97.52%, 95.52%, 96.13% and 93.88%, respectively, on CK+, JAFFE, MUFE and Yale Face datasets for emotion classification. The experimental findings reflect the LTCP descriptor’s dominance and robustness over others.
Noise level estimation for effective blind despeckling of medical ultrasound images

Despeckling of medical ultrasound images is an essential pre-processing step in automated diagnosis systems. The performance of certain image despeckling methods is dependent on the noise level of an input image. In the design of despeckling algorithms, the noise level is assumed to be known. Therefore, it is essential to estimate the noise level from input ultrasound image for effective blind despeckling. In this paper, a novel noise level estimation (NLE) technique is proposed to estimate the speckle (signal-dependent) noise level from the noisy ultrasound images. The presented NLE technique uses noise level aware features obtained from the high-pass filtered noisy image. The extracted features are then used to train the support vector regression (SVR) model. The proposed feature extraction technique followed by the SVR model results in accurate NLE. In order to ensure the effectiveness, the proposed NLE technique is incorporated and validated with the NLE-dependent state-of-the-art despeckling methods. The combination of state-of-the-art despeckling methods with the proposed NLE technique shows superior despeckling performance when compared to existing NLE techniques. In this work, block matching based despeckling method is recommended in combination with the proposed NLE technique for better despeckling performance in the ultrasound images. The experimental results demonstrate that the proposed NLE technique shows better performance than existing NLE techniques in terms of average absolute deviation and execution time.
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks

In recent years, graph neural networks (GNNs) have gained increasing attention, as they possess the excellent capability of processing graph-related problems. In practice, hyperparameter optimisation (HPO) is critical for GNNs to achieve satisfactory results, but this process is costly because the evaluations of different hyperparameter settings require excessively training many GNNs. Many approaches have been proposed for HPO, which aims to identify promising hyperparameters efficiently. In particular, the genetic algorithm (GA) for HPO has been explored, which treats GNNs as a black-box model, of which only the outputs can be observed given a set of hyperparameters. However, because GNN models are sophisticated and the evaluations of hyperparameters on GNNs are expensive, GA requires advanced techniques to balance the exploration and exploitation of the search and make the optimisation more effective given limited computational resources. Therefore, we proposed a tree-structured mutation strategy for GA to alleviate this issue. Meanwhile, we reviewed the recent HPO works, which gives room for the idea of tree-structure to develop, and we hope our approach can further improve these HPO methods in the future.
A Private Key Recovery Scheme Using Partial Knowledge

In this paper we explore the problem of secure handling of private keys in blockchain applications. We present a novel approach, named "Partial Knowledge Recovery Scheme" (PKRS), which allows for the recovery of an encrypted private key through the use of personal security questions. In PKRS, an individual is asked a set of questions, and the answers to those questions are used to encrypt the input and produce a secured private key. Through the use of Shamir's secret sharing algorithm, the original private key can be recovered if the individual can answer correctly only a subset of the original questions. PKRS does not require any external services for the recovery process, since all the required information is stored within the secured private key itself. This approach tries to achieve a middle ground between security and usability. Security, where the private key needs to be encrypted and safely stored offline. Usability, where an individual wants to be able to recover their private key without the need of an easily forgotten passphrase and be able to store it in their personal cloud environments. We also discuss the correct design of personal security questions in social environments where an individual's personal data can be mined through public records and social networks. Finally, we present a blockchain Self-sovereign Identity use case, which was used for the integration and evaluation of PKRS within a real-world application.
Blockchain Technology Open Problems and Impact to Supply Chain Management in Automotive Component Industry

The study of the adoption of Blockchain Technology (BT) of the Supply Chain Management System (SCMS) automotive components industry still in infancy. BT's previous research topic mostly in virtual currency, security, and IoT. However, the automotive market's significant development causes several issues in the SCMS automotive components industry, such as fake components. Blockchain Technology (BT), as identified, might be possible as a way out for the development of SCMS automotive components. There are several research questions in this research, such as how BT can facilitate SCMS open issues? What is the BT impact in the SCM area? The study method used the systematic literature review (SLR) method to gather all information from 12 publishers and an international index journal. Seven open issues identified in this study and SCM's core operational aspect, such as supplier, logistics, manufacturer, distribution, and customer, have been identified impact with BT.
Detecting Sequentially Novel Classes with Stable Generalization Ability

Recognizing object of unseen novel classes is of great importance in real-world incremental data scenarios. Additionally, novel classes arise frequently in data stream mining, e.g., new topics in opinion monitoring, and novel protein families in protein sequence classification. Conducting streaming novel class detection is a complex problem composed of detection and model update. However, when updating the model solely with detected novel instances, it concentrates more on the novel patterns than known ones; thus the detection ability of the model may degrade consequently. This would exert harmful affections to further classification as the data evolving. To this end, in this paper, we consider the accuracy of the detection along with the robustness of model updating, and propose DEtecting Sequentially Novel clAsses with Stable generalization Ability (DesNasa). Specifically, DesNasa utilizes a prototypical network to reflect the structure information between scattered prototypes for novel class detection. Furthermore, the well-designed data augmentation method can help the model learning novel patterns robustly without degrading detection ability. Experiments on various datasets successfully validate the effectiveness of our proposed method.
Cif-Based Collaborative Decoding for End-to-End Contextual Speech Recognition

End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure and the E2E training hamper injecting context information into them for contextual biasing. Though contextual LAS (CLAS) gives an excellent all-neural solution, the degree of biasing to given contextual information is not explicitly controllable. In this paper, we focus on incorporating contextual information into the continuous integrate-and-fire (CIF) based model that supports contextual biasing in a more controllable fashion. Specifically, an extra context processing network is introduced to extract contextual embeddings, integrate acoustically relevant contextual information and decode the contextual output distribution, thus forming a collaborative decoding with the decoder of the CIF-based model. Evaluated on the named entity rich evaluation sets of HKUST/AISHELL-2, our method brings relative character error rate (CER) reduction of 8.83%/21.13% and relative named entity character error rate (NE-CER) reduction of 40.14%/51.50% when compared with a strong baseline. Besides, it keeps the performance on original evaluation set without degradation.
An Effective Method of Evaluating Pension Service Quality Using Multi-Dimension Attention Convolutional Neural Networks

How to make an accurate evaluation of the quality of pension service has become the most important task. However, in the real world, many customs always forget to rate pension service. They only leave a few short, less semantic, and discontinuous review words below the service. This paper will propose an effective multi-dimension attention convolutional neural networks (MACNNs) model to analyze customer review texts and predict the pension service quality. In MACNN, the emoticon feature, sentiment feature, and word feature can be extracted together to construct feature space. And then attention layer and convolution layer work together to predict the service quality. Compared with the traditional machine learning methods and neural network methods, this method is more objective and accurate to reflect consumers’ real evaluation of pension service.
Hardware-Oriented Algorithm for High-Speed Laser Centerline Extraction Based on Hessian Matrix

Centerline extraction is the basic and key procedure in line-structured laser 3-D scanners. In this article, we propose a hardware-oriented algorithm for fast and accurate extraction of a laser centerline based on the Hessian matrix. The algorithm is divided into three low-coupling modules that can be processed in parallel - coarse positioning, linewidth estimation, and precise positioning module. In the coarse positioning module, a window slider is used to traverse all image pixels in the raster-scan mode for collecting local image features, based on which the potential region of interest (ROI) containing laser lines is detected. In the linewidth estimation module, the second-order moment features in the detected ROI are calculated to estimate the linewidth according to the local rectangular similarity characteristics of the laser line. In the precise positioning module, the optimal Gaussian template estimated by the linewidth is convolved with the ROI to obtain the Hessian matrix. Based on the Hessian matrix, the normal direction of the laser line is obtained, and the second-order Taylor expansion is performed in that direction to determine the subpixel position of the center point. Finally, non-maximum suppression is used to remove noise points and obtain the most reliable single-pixel centerline. The proposed algorithm is evaluated using thousands of sample images with different materials, exposure times, and laser line shapes, and it presents high robustness for subpixel precision extraction. By implementing the proposed algorithm on a field-programmable gate array (FPGA), a high-speed, high-precision laser centerline extraction system is achieved, which operates at 1350 frames/s for a 1-million-pixel video stream.
Generating Short Video Description using Deep-LSTM and Attention Mechanism

In modern days, extensive amount of data is produced from videos, because most of the populations have video capturing devices such as mobile phone, camera, etc. The video comprises of photographic data, textual data, and auditory data. Our aim is to investigate and recognize the visual feature of the video and to generate the caption so that users can get the information of the video in an instant of time. Many technologies capture static content of the frame but for video captioning, dynamic information is more important compared to static information. In this work, we introduced an Encoder-Decoder architecture using Deep-Long Short-Term Memory (Deep-LSTM) and Bahdanau Attention. In the encoder, Convolution Neural Network (CNN) VGG16 and Deep-LSTM are used for deducing information from frames and Deep-LSTM combined with attention mechanism for describing action performed in the video. We evaluated the performance of our model on MSVD dataset, which shows significant improvement as compared to the other video captioning model.
Scalable Discriminative Discrete Hashing For Large-Scale Cross-Modal Retrieval

Cross-modal hashing has received increasing research attentions due to its less storage and efficient retrieval. However, most existing cross-modal hashing methods focus only on exploring multi-modal information, while underestimate the significance of local and Euclidean structure information on the hashing learning procedure. In this paper, we propose a supervised discrete-based cross-modal hashing method, named Scalable Discriminative Discrete Hashing (SDDH), for cross-modal retrieval, where 1) the discrete hash codes are directly obtained by multi-modal features and semantic labels so that the quantization errors are dramatically reduced, and 2) the discrete hash codes simultaneously preserve the heterogeneous similarity and manifold information in the original space by employing matrix factoring with orthogonal and balanced constraints. Moreover, an efficient optimization is introduced to tackle the discrete solution, which makes the SDDH scalable to large-scale cross-modal retrieval. Empirical results on three widely-used benchmark databases clearly demonstrate the effectiveness and efficiency of the proposed method in comparison with state-of-the-arts.
Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition

Automatic Speech Recognition (ASR) based on Recurrent Neural Network Transducers (RNN-T) is gaining interest in the speech community. We investigate data selection and preparation choices aiming for improved robustness of RNN-T ASR to speech disfluencies with a focus on partial words. For evaluation we use clean data, data with disfluen- cies and a separate dataset with speech affected by stuttering. We show that after including a small amount of data with disfluencies in the training set the recognition accuracy on the tests with disfluencies and stuttering improves. Increasing the amount of training data with disfluencies gives additional gains without degradation on the clean data. We also show that replacing partial words with a dedicated token helps to get even better accuracy on utterances with disfluencies and stutter. The evaluation of our best model shows 22.5% and 16.4% relative WER reduction on those two evaluation sets.
Privacy Preserved Secure Offloading in the Multi-access Edge Computing Network

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.
SRGCN: Graph-based multi-hop reasoning on knowledge graphs

Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reasoning based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first construct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the existing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outperforms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly efficient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance.