Reviewerly/exHarmony huggingface dataset:
Finding synergy between oral and visual narratives on memorable and meaningful tourism experiences

Personal technologies are widely used to capture the memorable and meaningful experiences travellers have during their journeys. These digital footprints serve as memorabilia for travellers to share and reminiscence about these special experiences. This study showcases an exploratory study of the creation of a travel diary from the traveller's digital footprints to facilitate the expression of and reminiscences about memorable and meaningful moments. In this study, 15 participants were asked to share their memorable and meaningful travel experiences and were then instructed to use their photos and a set of prototyping tools to create a paper-based visual diary of their experiences. The goal of this study was to examine the process of creating a visual diary to understand the differences in how these experiences are recounted and the ways they are expressed. We used a Labovian approach to compare and contrast the participants' oral narratives and visual diaries. As in comics studies, the visual diaries are analysed with respect to their spatio–temporal dimensions and the recurring patterns in these two narrative forms are discussed. Based on the results, recommendations are made regarding the future design of travel diary platforms.
Convolutional neural network applied to specific emitter identification based on pulse waveform images

To deal with problems of uncertain modulations and multiple pulse widths in pulse waveforms (PWs) during the identifying procedure, a novel specific emitter identification (SEI) method based on PW images (PWIs) and convolutional neural network is proposed. In the method, a more accurate signal model is built with considering the rising, steady and falling part of the whole PW based on actual radar pulse signals. PWI achieves transforming time-domain waveforms to 2D binary images as an SEI analysis feature. To match the PWI feature, a convolutional neural network with the small convolutional kernel is designed to extract the subtle features and finish the supervised training. By tuning the parameters of the convolutional neural network, it completes a balance of consuming time and identifying accuracy. Simulations and experiments indicate that the proposed method outperforms the existed methods on identifying radar individuals with uncertain modulations and multiple pulse widths in the intercepted pulse signals.
Towards Multi Robot Task Allocation and Navigation using Deep Reinforcement Learning

Abstract Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP) as two separate steps each with its own set of algorithms in which the MRTA algorithm assigns each robot to a task and the MRPP algorithm guides each robot through the environment towards the assigned goal position while avoiding both static and dynamic obstacles. In this paper, we present a method that combines both steps by using a deep reinforcement learning model. The model consists of a decentralized sensor level policy which outputs the robot’s velocity to guide it through the environment towards the selected goal position and avoiding collisions. The model was trained in a simulation environment and all the robots are homogenous differential drive robots. The objective is to ensure that each robot reaches a unique goal position with the number of goal positions is equal to the number of robots. The results of training the policy in an environment is presented with both static and dynamic obstacles with four robots and four goal positions.
Design of a Blockchain-based e-Tendering System: A Case Study in LPSE

The tender process is a long and complex business process to produce a series of legal obligations related to business contracts. The purpose of conducting a tender in government is to support the work program by finding the right tender (provider of goods and services) in the government procurement program so that it can be the basis of their decision to choose the most suitable tender. At the government in Indonesia, the tender process has been carried out using an e-tendering system with the use of internet technology through LPSE. The purpose of e-tendering is to facilitate the procurement process with related parties to increase productivity, effectiveness, and transparency in the tender process. However, problems in the e-tendering system are currently centralized, so that the local LPSE organization has full control of the database and the system which can pose a threat of vulnerability to fraud, collusion, and manipulation practices. The tender selection process that is full of competition must be supported by effective and transparent technology so that it does not cause fraud and suspicion among bidders. Blockchain technology is one of the solutions that allow integrated systems without a third party because it adheres to the decentralized nature of distributed databases so that it has a copy of many users. By adopting blockchain e-tendering can reduce the source of fraud, namely database manipulation. This research will explore the process of developing a blockchain-based system with e-tendering case studies on LPSE.
Improving Disentangled Representation Learning with the Beta Bernoulli Process

To improve the ability of variational auto-encoders (VAE) to disentangle in the latent space, existing works mostly focus on enforcing the independence among the learned latent factors. However, the ability of these models to disentangle often decreases as the complexity of the generative factors increases. In this paper, we investigate the little-explored effect of the modeling capacity of a posterior density on the disentangling ability of the VAE. We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter - if overly limited - creates an unnecessary competition with the data reconstruction objective in VAE. Therefore, if we preserve the independence but allow richer modeling capacity in the posterior density, we will lift this competition and thereby allow improved independence and data reconstruction at the same time. We investigate this theoretical intuition with a VAE that utilizes a non-parametric latent factor model, the Indian Buffet Process (IBP), as a latent density that is able to grow with the complexity of the data. Across two widely-used benchmark data sets (MNIST and dSprites) and two clinical data sets little explored for disentangled learning, we qualitatively and quantitatively demonstrated the improved disentangling performance of IBP-VAE over the state of the art. In the latter two clinical data sets riddled with complex factors of variations, we further demonstrated that unsupervised disentangling of nuisance factors via IBP-VAE - when combined with a supervised objective - can not only improve task accuracy in comparison to relevant supervised deep architectures, but also facilitate knowledge discovery related to task decision-making.
DeepTrust: A Deep User Model of Homophily Effect for Trust Prediction

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.
Quantitative assessment of capabilities of colour models for pen ink discrimination in handwritten documents

The addition of new words in handwritten documents such as bank cheques, bills, and notes is considered as common crime. Such immoral activities on handwritten documents have a bad effect on the victim in terms of mental and financial loss. For facilitating an impartial judicial process, it is important to differentiate between the used pen inks. Earlier, image processing and pattern recognition-based techniques for pen ink differentiation gained attention among researchers due to their non-destructive nature. Existing techniques use various colour models to represent the colour of pen inks. Thus, it is crucial to assess the capabilities of various colour models for differentiating pen inks in handwritten documents. In this paper, we propose a histogram distance based quantitative assessment technique for suitable colour model identification for differentiating pen inks. Seven blue and seven black pen ink samples are acquired on 112 cheque leaves. The k-means binarisation is used to identify pen ink pixels. colour model has been identified as the best colour model for this task. The statistical features of the ink colours in the identified colour model representation are extracted and a multi-layer perceptron (MLP) classifier validates the capability of the identified colour model for pen ink discrimination.
Data Augmentation using CA Evolved GANs

Mining medical data images have great potential for exploring hidden patterns in the medical domain. Medical data are heterogeneous which involves images to a great extent like MRI, ECG or Stroke effects etc. Knowledge discovery from such data can improve the diagnostic technique. However, to make the machine learn from such datasets requires large data. In the low-data regime, machine learning algorithms work poorly. Data Augmentation alleviates this by using existing data more effectively, but standard data augmentation produces only limited alternative data. Recent developments in Deep Learning field is noteworthy when it comes to learning probability distribution of points through neural networks, and one of key part for such progress is because of Generative Adversarial Networks(GANs). In this paper, we propose an evolutionary training technique using a cultural algorithm(CA) for neuro-evolution of deep task oriented GANs to find the best architecture for the given dataset. This architecture will help in generating similar but completely new data images which can be further used for training diagnostic Neural Networks. We have compared our approach with the Genetic Algorithm(GA) based neuro-evolution of GANs and show that CA based neuro-evolution of GANs evolves architecture which can generate a higher number of stroke-face images with better resolution when there is low data of original stroke faces.
Multi-step Pick-and-Place Tasks Using Object-centric Dense Correspondences

This paper presents an object-centric method for efficiently performing two types of challenging pick-and-place tasks, namely sequential pick and place and object sorting. We propose multiclass dense object nets (MCDONs) for learning object-centric dense descriptors that maintain not only intra-class variations but also inter-class separation. Intra-class consistency is also inherently learned and is useful for our pick-and-place tasks. All the tasks only require a single demonstration from users, which can then be generalized to all class instances. A dataset containing eight classes and a total of 52 objects was provided in this study. We obtained a task success rate of 93.33% on a five-block stacking task and 97.41% on a three-class object sorting task.
On Folded Graph Signals

Graph sampling allows a multidimensional signal generated on a graph to be represented by the signal at a smaller set of sampled nodes. On the other hand, self-reset analog-to-digital converters (ADCs) are used to sample high dynamic range signals resulting in modulo-operation based folded signals at the sampled nodes. In this paper, we study the problem of continuous-time graph signal recovery from the folded signals at discrete samples. We present a theoretical graph sampling rate that is sufficient for successful reconstruction of the graph signals from the folded signals. We deduce an optimal sample rate to recover a bandlimited continuous-time graph signal, such that integer programming can be applied for small graphs. To resolve the scalability issue of integer programming, we propose a sparse optimization based recovery method for graph signals satisfying certain conditions. Such an approach requires a novel graph sampling scheme that selects nodes with small signal variation. The proposed algorithm emphasizes that in our spatio-temporal sampling scenario, the inherent relationship among the graph nodes should be exploited in addition to the temporal correlation in the graph signal at different nodes to recover the signal.
Dynamically Adjusting Scale of a Kubernetes Cluster under QoS Guarantee

Nowadays, the container-based virtualization technologies have become very popular due to lightweight nature, scalability, flexibility and others. Kubernetes is one of the most popular container cluster management systems, which enables users to deploy applications on the container easily, so more and more web applications are deployed in a Kubernetes clusters. However, a Kubernetes cluster is generally designed to handle the peak of workloads, so that most of resources are idle in usual time, which results in an huge waste of resource. Hence, it is necessary to design a system to improve the cluster resource utilization and promise Quality of Service (QoS) in a Kubernetes cluster. In this paper, we propose a generic system to dynamically adjust the scale of a Kubernetes cluster, which is able to reduce the waste of resource on the premise of QoS guarantee. The proposed system contains four modules: monitor module, QoS module, scaling module, and executing module. First, the monitor module uses two open-source tools, Heapster and InfluxDB, to monitor and store real-time status of a Kubernetes cluster. Then, to guarantee QoS in the Kubernetes cluster, the QoS module presents a method to automatically decide a threshold of CPU utilization that is able to meet requirements of a specific application. Next, the scaling module provides a cluster scaling algorithm to get an ideal number of nodes in the Kubernetes cluster, which is used to allocate resources in a cluster-level allocation. Finally, according to the ideal number of nodes, the executing module adjusts the scale of the Kubernetes cluster to carry out the application. Extensive experiments in real environments show that, on average, the proposed system improves CPU utilization of a Kubernetes cluster by 28.99%.
Hierarchical Refined Local Associations for Robust Person Re-Identification

Person re-identification is the process to identify a person from images/ videos captured from different nonoverlapping cameras in an autonomous way. The biological vision scheme emphasis on local discriminative cues in addition to the global appearance of a person for re-identification. The local cues are helpful to identify a person even if viewed at different scales and with different backgrounds. To emphasize on local cues, in this paper we present a refined association scheme for local parts of the images. The proposed scheme eliminates the effects of scale differences and background noise for automated person re-identification. Our approach divides the image of a person in horizontal strips and vertical sub-patches. A hierarchical refined associations based network (HRAN) is introduced to establish the refined associations among local segments of given images. In the first phase, the associations are established among horizontal strips of two images. In the next phase, the vertical sub-patches of associated horizontal strips are aligned/ linked with each other. Background noise and scale differences between images are addressed effectively using the proposed two-step mechanism. The triplet loss is used to optimize the refined local associations among images. A different weighting scheme is used for local and global losses for optimization of proposed model. The evaluation results of proposed methodology on two publicly available large scale datasets Market-1501 and DukeMTMC-ReID verified the effectiveness of proposed refined alignment method.
Real-Time Automatic Modulation Classification

Deep learning based techniques have shown promising results over traditional hand-crafted methods for automatic modulation classification for radio signals. However, implementation of these deep learning models on specialized hardware can be challenging, as both latency and throughput performance are critical to achieving real-time response to over-the-air radio signals. In this work, we meet our targets by designing an optimized ternarized convolutional neural network that leverages the RF capabilities offered by the Xilinx ZCU111 RFSoC platform. The implemented networks achieve high-speed real-time performance with a classification latency of ≈8μs, and an operational throughput of 488k classifications per second. On the challenging open-source RadioML dataset, we achieve up to 81.1% accuracy, which is competitive to existing state-of-the-art software-only implementations.
Privacy Issues &amp; Security Techniques in Big Data

Big Data, as the name suggests is a process of collection of information in a very large amount and storing it in a multidimensional database of various organizations and thereby performing Analytical operations on it to increase their efficiency and enhance their ability of decision making. Strategies can be made using this technology which uses real time using Big Data Analytics. The main advantage of the technology of big data is that the user gets a complete and an accurate view of answers to all those questions raised by the user in processes related to business decisions. Moreover, the errors which exist within any organization can be identified with the help of this technology. This is due to the real time intuition which helps the organizations identify the errors. Also, in case if any on sanctioned user intends to cheat the organization with the valuable data stored at the database of the organization, the it can be detected at the instant it happens and so suitable measures can be taken against that unsanctioned user. Despite of having so many advantages, it has some limitations also. One of the main issues of concern of this technology is Privacy security. This paper focuses on identifying the threats related to data privacy information stored on the database of the organization and access control systems.
Graph Filtering with Quantization over Random Time-varying Graphs

Distributed graph filters can be implemented over wireless sensor networks by means of cooperation and exchanges among nodes. However, in practice, the performance of such graph filters is deeply affected by the quantization errors that are accumulated when the messages are transmitted. The latter is paramount to overcome the limitations in terms of bandwidth and computation capabilities in sensor nodes. In addition to quantization errors, distributed graph filters are also affected by random packet losses due to interferences and background noise, leading to the degradation of the performance in terms of the filtering accuracy. In this work, we consider the problem of designing graph filters that are robust to quantized data and time-varying topologies. We propose an optimized method that minimizes the quantization error, while ensuring an accurate filtering over time-varying graph topologies. The efficiency of the proposed theoretical findings is validated by numerical results in random wireless sensor networks.
DISC: A Large-scale Virtual Dataset for Simulating Disaster Scenarios

In this paper, we present the first large-scale synthetic dataset for visual perception in disaster scenarios, and analyze state-of-the-art methods for multiple computer vision tasks with reference baselines. We simulated before and after disaster scenarios such as fire and building collapse for fifteen different locations in realistic virtual worlds. The dataset consists of more than 300K high-resolution stereo image pairs, all annotated with ground-truth data for semantic segmentation, depth, optical flow, surface normal estimation and camera pose estimation. To create realistic disaster scenes, we manually augmented the effects with 3D models using physical-based graphics tools. We use our dataset to train state-of-the-art methods and evaluate how well these methods can recognize the disaster situations and produce reliable results on virtual scenes as well as real-world images. The results obtained from each task are then used as inputs to the proposed visual odometry network for generating 3D maps of buildings on fire. Finally, we discuss challenges for future research.
Machine Learning for RF Fingerprinting Extraction and Identification of Soft-Defined Radio Devices

Radio frequency (RF) fingerprinting technology has been developed as a unique method for maintaining security based on physical layer characteristics. In this paper, we propose the RF fingerprinting by extracting the parameter characteristics such as information dimension, constellation feature, and phase noise spectrum in the transmitted information when applied to the universal software radio peripheral (USRP) software-defined radio (SDR) platform. To achieve a great performance improvement of classification, not only the traditional support vector machine (SVM) classifier, but also the machine-based integrated classifier bagged tree and the adaptive weighting algorithm weighted k-nearest neighbor (KNN) are both discussed. It is demonstrated that the proposed method achieves good classification performance under different signal-to-noise ratios (SNR).
A Distributed Game Theoretic Approach for Blockchain-based Offloading Strategy

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.