Reviewerly/exHarmony huggingface dataset:
D-SLAM: Diffusion Source Localization and Trajectory Mapping

We consider physical fields induced by a finite number of instantaneous diffusion sources, which we sample using a mobile sensor, along unknown trajectories composed of multiple linear segments. We address the problem of estimating the sources, as well as the trajectory of the mobile sensor. Within this framework, we propose a method for localizing sources of unknown amplitudes, and known activation times. The reconstruction method we propose maps the measurements obtained using the mobile sensor to a sequence of generalized field samples. From these generalized samples, we can then retrieve the locations of the sources as well as the trajectory of the sensor (up to a linear geometric transformation).
Mobile Money Wallet Security against Insider Attack Using ID-Based Cryptographic Primitive with Equality Test

An attack continuum perpetuated by an insider (Mobile Money Wallet Service Provider) is paramount in this era of data intelligent and analytics. Mobile Money Wallet Service Providers (MMWSP) are entrusted to keep our sensitive information such as tokens and other mobile financial transactions secured for users to query and exchange token information. However, it is possible for the mobile money wallet service provider (insider) to peddle with user's data stored on it's server for economic gains. we put forward a novel mobile money wallet security against insider attack using identity based cryptography. Our novel scheme resist an insider from peddling user's data for economic gains. The scheme adopts the witness based cryptographic primitive. Finally, The security of our scheme is demonstrated by the use of random oracle model.
Relevance Ranking Based on Query-Aware Context Analysis

Word mismatch between queries and documents is a long-standing challenge in information retrieval. Recent advances in distributed word representations address the word mismatch problem by enabling semantic matching. However, most existing models rank documents based on semantic matching between query and document terms without an explicit understanding of the relationship of the match to relevance. To consider semantic matching between query and document, we propose an unsupervised semantic matching model by simulating a user who makes relevance decisions. The primary goal of the proposed model is to combine the exact and semantic matching between query and document terms, which has been shown to produce effective performance in information retrieval. As semantic matching between queries and entire documents is computationally expensive, we propose to use local contexts of query terms in documents for semantic matching. Matching with smaller query-related contexts of documents stems from the relevance judgment process recorded by human observers. The most relevant part of a document is then recognized and used to rank documents with respect to the query. Experimental results on several representative retrieval models and standard datasets show that our proposed semantic matching model significantly outperforms competitive baselines in all measures.
A Survey on Video Dehazing Using Deep Learning

Abstract With the fast development of object recognition and detection in autonomous driving and video monitoring, image with haze or raindrop can affect the result a lot. As deep learning develops, the hazed and raindrop image can lower the accuracy of object recognition and detection significantly. While hazed images cannot be managed using other image refining process. Since the noise in hazed image is signal-dependent. The object degradation in hazed image is related to object depth. So, the dehazing process depends on the input image. This paper provides a survey on single image and video dehazing methods, from end-to-end system to distributed system. General methods based on deep learning of state-of-art papers from 2010 to 2018 are summarized and compared, accompanied with their datasets of the current progress in this field. The application of these methods and relationship between these methos are also discussed in this paper.
Phylogenetic Minimum Spanning Tree Reconstruction Using Autoencoders

The history of a shared and re-posted multimedia content can be reconstructed by analyzing the mutual relations between all of its near-duplicate copies and solving a minimum spanning tree (MST) problem, as shown by multimedia phylogeny research field, Unfortunately, MST estimation strategies are severely impaired by the noise affecting dissimilarity measures between pairs of near-duplicate contents, For this reason, researchers have recently been investigating robust dissimilarity metrics.This paper proposes a matrix denoising solution that both mitigates dissimilarity noise and reconstruct the desired phylogenetic tree at the same time, The proposed strategy is a first attempt to estimate a MST via a denoising autoencoder that returns an approximation of the adjacency matrix corresponding to the underlying tree, Experimental results prove that the proposed solution outperforms the previous approaches and easily adapts to different analysis scenarios.
Supervised deep semantics-preserving hashing for real-time pulmonary nodule image retrieval

Hashing-based medical image retrieval has drawn extensive attention recently, which aims at providing effective aided diagnosis for medical personnel. In the paper, a novel deep hashing framework is proposed in the medical image retrieval, where the processes of deep feature extraction, binary code learning, and deep hash function learning are jointly carried out in supervised fashion. Particularly, the discrete constrained objective function in the hash code learning is optimized iteratively, where the binary code can be directly solved with no need for relaxation. In the meantime, the semantic similarity is maintained by fully exploring supervision information during the discrete optimization, where the neighborhood structure of training data is preserved by applying a graph regularization term. Additionally, to gain the fine-grained ranking of the returned medical images sharing the same Hamming distance, a novel image re-ranking scheme is proposed to refine the similarity measurement by jointly considering Euclidean distance between the real-valued feature descriptors and their category information between those images. Extensive experiments on the pulmonary nodule image dataset demonstrate that the proposed method can achieve better retrieval performance over the state of the arts.
Image Representation Method Based on Relative Layer Entropy for Insulator Recognition

Deep convolutional neural networks (DCNNs) with alternating convolutional, pooling and decimation layers are widely used in computer vision, yet current works tend to focus on deeper networks with many layers and neurons, resulting in a high computational complexity. However, the recognition task is still challenging for insufficient and uncomprehensive object appearance and training sample types such as infrared insulators. In view of this, more attention is focused on the application of a pretrained network for image feature representation, but the rules on how to select the feature representation layer are scarce. In this paper, we proposed a new concept, the layer entropy and relative layer entropy, which can be referred to as an image representation method based on relative layer entropy (IRM_RLE). It was designed to excavate the most suitable convolution layer for image recognition. First, the image was fed into an ImageNet pretrained DCNN model, and deep convolutional activations were extracted. Then, the appropriate feature layer was selected by calculating the layer entropy and relative layer entropy of each convolution layer. Finally, the number of the feature map was selected according to the importance degree and the feature maps of the convolution layer, which were vectorized and pooled by VLAD (vector of locally aggregated descriptors) coding and quantifying for final image representation. The experimental results show that the proposed approach performs competitively against previous methods across all datasets. Furthermore, for the indoor scenes and actions datasets, the proposed approach outperforms the state-of-the-art methods.
Information Security Culture for Guiding Employee’s Security Behaviour: A Pilot Study

Experts and scholars have suggested that cultivation of a positive Information Security Culture (ISC) could improve employee's security behaviour in organization. However, specific ISC model for employee's security behaviour is limited. This paper discusses a pilot study of our research-in-progress that proposes a holistic ISC model to be used as guidance for employee's security behaviour in organization. ISC concept model developed in the study is represented by seven comprehensive dimensions formulated based on widely accepted concepts of Organizational Culture and ISC. These dimensions embody various aspects of ISC cultivation. The model was tested in a Malaysian public university. This study employed Partial Least Square Structural Equation Modelling (PLS SEM) using Smart PLS 3 software to analyze and validate the model. The findings proved that the ISC model is significant in influencing security compliance behaviour. Hence, this study contributes to ISC literature in terms of conceptualization and empirical validation of a new ISC model based on seven comprehensive dimensions in relation with ISP compliance behaviour.
Short-Term Solar Power Generation Forecasting Via Continuous Conditional Random Fields

Solar power generation with highly variable mode brings adverse effects on the grid. In order to reduce the negative impact on the grid, we use continuous conditional random fields (CCRF) to forecast solar generation. The CCRF is a powerful tool for relationship learning, which can capture the interaction between predicted solar generation. The potential function of the CCRF is designed as quadratic forms, which can transform the learning problems of the CCRF to convex optimization problems. In addition, it can perform probabilistic forecasting. To avoid over-fitting, the regularization of the weight is added to the loss function. We conduct the experiments on the freely available dataset to evaluate the forecasting performance. Experimental results show that the CCRF forecasting model can further improve the forecasting accuracy, compared with benchmarking forecasting method.
Effect of Choice of Probability Distribution, Randomness, and Search Methods for Alignment Modeling in Sequence-to-Sequence Text-to-Speech Synthesis Using Hard Alignment

Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. This research investigates various combinations of sampling methods and probability distributions for alignment transition modeling in a hard-alignment-based sequence-to-sequence TTS method called SSNT-TTS. We clarify the common sampling methods of discrete variables including greedy search, beam search, and random sampling from a Bernoulli distribution in a more general way. Furthermore, we introduce the binary Concrete distribution to model discrete variables more properly. The results of a listening test shows that deterministic search is more preferable than stochastic search, and the binary Concrete distribution is robust with stochastic search for natural alignment transition.
Clothing Image Detection and Recognition Based on Faster R-CNN

Abstract Clothing image detection and recognition algorithm is proposed in this paper, which involves the design of two neural network models based on improved Faster R-CNN [1]. In our work, in order to train neural networks, ModaNet [2] dataset is selected and improvements on it are made, that is, the improper bounding boxes of footwear and boots in ModaNet dataset are modified based on their polygon annotations. Moreover, by making the following improvements on Faster R-CNN, our two neural network models are established, which use VGG16[3] and the first 40 layers of ResNet50 [4] as feature extration network, respectively. When using ResNet50 for feature extraction, the last 10 layers of ResNet50 are selected as part of the final classification and regression network. In addition, the softmax activation function in RPN is replaced with sigmoid. Through experimental comparison, it is found that both of these two models can well accomplish the task.
A New Benchmark for Instance-Level Image Classification

Although fine-grained image classification is able to classify more fine-grained sub-categories compared to its coarse-grained counterpart, it often fails to identify individual instances. Therefore, we propose a new instance-level image classification task which further refines the granularity of fine-grained classification in order to identify unique instances rather than a sub-category containing multiple instances. In addition, we introduce an instance-level image classification dataset, AircraftCarrier, which contains 20 global aircraft carrier classes, as the first publically available dataset for instance-level image classification. The classification of instance-level aircraft carriers can prove to be a challenging task due to large intra-category differences as well as variations in the camera view, illumination, scale, and the presence of complex backgrounds. The AircraftCarrier dataset put forward here has the potential to improve the development of instance-level image classification. At the same time, we provide a Simple Classification Head (SCH) technique for the classification of aircraft carriers, with classical convolutional neural network models as the backbone network. The SCH has better performance than a direct classification head, and these results provide a benchmark performance result for researchers. Furthermore, we evaluate several fine-grained image classification methods and give their benchmark results. Finally, we present the challenges of instance-level classification and discuss further directions. This study provides the first publicly available instance-level image classification dataset and a performance benchmark for further research. The dataset and codes can be downloaded at https://github.com/tsingqsu/AircraftCarrier_Dataset/.
FIR Filter Design and Implementation for Phase-Based Processing

Complex steerable pyramid (CSP) is widely used to decompose images into muti-scale and oriented subbands for phase-based processing, such as video magnification, frame interpolation, and view synthesis. The conventional implementation is based on frequency-domain bandpass filtering which relies on fast Fourier transform (FFT). However, FFT requires high-precision computation and complex memory access for hardware implementation. In this paper, we study computation- and memory-efficient finite impulse response filter implementation for CSP. We use Kaiser windowing for filter designs and adopt 9-tap radial and 11-tap angular filters which can achieve 38.6 dB of PSNR for frame interpolation. We then discuss about VLSI architecture designs and, in particular, propose a stripe-based computation flow for 2-D CSP to reduce the line buffer size down to 15%. For evaluation, we implement two VLSI circuits using TSMC 40nm technology. One is a 1-D CSP engine working at 4K UHD 30 fps, and it saves 67.8% of logic gates compared to a FFT-based design. The other is a 2-D CSP engine working at FHD 60 fps. It uses 32-KB SRAM and 3.5M-gate logic. We also implement a FPGA system for 2-D CSP engine, and it operates at 80 MHz and provides 1024×1024 resolution video at 16 fps.
Gated Attentive Convolutional Network Dialogue State Tracker

In task-oriented dialogue systems, dialogue state tracking (DST) is an essential part which aims to estimate user goal at every turn 1 of the dialogue. At each turn, DST aims to estimate user goals by current user utterance and last system action. However, most current approaches encode these relevant sequences by recurrent networks, which is a challenge for these models to capture long-range dependencies. Besides predicting the current dialogue state is relevant to historical context and sometimes refers to the past utterances. In this paper, we propose the Gated Attentive Convolutional network Dialogue State Tracker (GAC) which overcomes these challenges by utilizing the gated attentive convolutional encoder and introducing historical information. We apply a gated attentive convolutional network encoder to learn the sequence representation and introduce historical dialogue utterances as evidence to track the user goal. Experiments show that our method improves joint goal accuracy on WoZ 2.0[1] and MultiWoZ 2.0[2] datasets respectively outperforms the previous state-of-the-art GLAD [3] and Trade [4] model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Sparse Modeling on Distributed Encryption Data

Big-data analysis by edge/cloud systems is becoming more important. However, when information may lead to personal identification, such information tends to be encrypted and restricted to its owners to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful analysis. As a result, the desired analysis accuracy may not be achieved. To deal with this issue, several studies have examined encryptions based on the random unitary transform. This is because the random unitary transform has lower computational complexity than other encryption schemes, and its encryption domain supports several signal processing algorithms. However, analysis models on distributed encrypted data, have not been studied deeply enough. In this paper, we construct an analysis model for data encrypted with the random unitary transform by deriving a LASSO solution for encrypted data. The analytical model can derive the same LASSO solution as that yielded by processing the original data (i.e. without encryption). The analytical model supports distributed encryption, where a data set consists of different components that are encrypted at different sites independently. The collaboration enables us to improve the accuracy of analysis for distributed privacy-sensitive information.
An Architectural Model for the Production of Virtual Reality Learning

Learning environments integrate multiple technology platforms allowing people to meet learning objectives through available content, resources, and integrated services. Learning environments allow users to cover learning objectives in different subject areas, such as social skills development, health, etc. This article presents a development model of Learning Environments through Virtual Reality (VR). This model incorporates new forms of interaction for learning, such as immersion in simulated scenarios that support users in attaining learning objectives. A VR tour of a university is presented as a case study, where distinct users, such as teachers, students, and general public interact with content, resources available and services to achieve the specific learning objectives.
Weakly Supervised Segmentation Guided Hand Pose Estimation During Interaction with Unknown Objects

Hand pose estimation is important for human computer interaction, but the performance is not satisfying when the hand is interacting with objects. To alleviate the influence of unknown objects, we propose a novel weakly supervised segmentation guided scheme to estimate hand poses. Approximate hand masks generated from annotations of sparse hand joints are used to supervise the segmentation task. Better features can be extracted since they are shared between the two tasks of hand segmentation and hand pose estimation. With the guidance of weakly supervised segmentation, the network can learn intermediate features balanced between focusing on the foreground and preserving contextual information. Finally the xy and z coordinates are estimated in different branches but utilizing shared feature maps. Experimental results of three different tasks on the publicly available FHAD dataset demonstrate the effectiveness of the proposed architecture.
Long-distance deformation object recognition by integrating contour structure and scale-invariant heat kernel signature

The tough challenges of object recognition in long-distance scene involves contour shape deformation invariant features construction. In this work, an effective contour shape descriptor integrating critical points structure and Scale-invariant Heat Kernel Signature (SI-HKS) is proposed for long-distance object recognition. We firstly propose a general feature fusion model. Then, we capture the object contour structure feature with Critical-points Inner-distance Shape Context (CP-IDSC). Meanwhile, we pull-in the SI-HKS for capturing the local deformation-invariant properties of 2D shape. Based on the integration of the above two feature descriptors, the fusion descriptor is compacted by mapping into a low dimensional subspace using the bags-of-features, allowing for an efficient Bayesian classifier recognition. The extensive experiments on synthetic turbulence-degraded shapes and real-life infrared image show that the proposed method outperformed other compared approaches in terms of the recognition precision and robustness.
Business Process Re-engineering and Agile Software Development: Applying the Story-Card Method

Enterprise designers need to continuously re-design their enterprise, re-evaluating the technologies that are available to digitize their operations. Although light-weight agile software development approaches are favored by software development service providers, additional requirements elicitation practices should be incorporated when scaling factors apply, since design team members need to have a shared understanding of the operating context and high-level requirements. Research indicated that the organization construction diagram (OCD) could be useful to create a shared context for enterprise operation, linking detailed functional requirements to this shared context during software development. Although the OCD is concise, its associated concepts are abstract and an additional story-card method (SCM) is needed to transform existing enterprise implementations into an OCD. Since additional evaluation of the SCM was required, this study focused on a real-world demonstration of the SCM at a Fintech company where an agile software development approach is applied. The results indicate that the SCM is useful when incorporated within an agile software development approach.
Improving Speaker-Attribute Estimation by Voting Based on Speaker Cluster Information

This paper proposes a general post-processing method for improving speaker-attribute estimation. Estimating speaker-specific attributes such as age and gender is an important task with a wide range of applications. While the recent proposed deep neural network-based end-to-end approach achieves high performance, the model tends to over-fit to specific speakers when the amount of training data is limited or imbalanced. To solve this over-fitting problem, we propose a general framework for correcting unreliable results. The proposed algorithm first clusters the target utterances into speaker clusters by speaker similarity based on i-vectors. Then, for each of the speaker cluster, the speaker-attribute class of the cluster is determined by voting on the utterances assigned to the cluster. By then replacing the result of each utterance with the clusters' speaker-attribute class, we can correct the result of unreliable utterances. We used two tasks to evaluate the proposed algorithm including age estimation using the NIST-SRE10 and age-gender classification using an in-house read speech corpus, yielding significant improvements in mean absolute and classification errors.