Reviewerly/exHarmony huggingface dataset:
A Scene Classification Approach for Augmented Reality Devices

Augmented Reality (AR) technology can overlay digital content over the physical world to enhance the user’s interaction with the real-world. The increasing number of devices for this purpose, such as Microsoft HoloLens, MagicLeap, Google Glass, allows to AR an immensity of applications. A critical task to make the AR devices more useful to users is the scene/environment understanding because this can avoid the device of mapping elements that were previously mapped and customized by the user. In this direction, we propose a scene classification approach for AR devices which has two components: i) an AR device that captures images, and ii) a remote server to perform scene classification. Four methods for scene classification, which utilize convolutional neural networks, support vector machine and transfer learning are proposed and evaluated. Experiments conducted using real data from an indoor office environment and Microsoft HoloLens AR device shows that the proposed AR scene classification approach can reach up to \(99\%\) of accuracy, even with similar texture information across scenes.
Machine Learning Based Encoder-Decoder for Captcha Recognition

Captcha is widely used in modern websites in order to hinder illicit tasks. It provides a mechanism through which a robot and human can be differentiated. It is akin to a security system which aims at deterring fraud and automatic attack. Advanced technologies such as deep learning is capable of recognising captcha without human intervention. Needed to walk through how this security breach can happen. In addition to complex object detection and segmentation tasks, our work analysed how encoder and decoder models can be used in this task. Also,experimented with various encoders and decoders to contrast their efficiencies and to propose the best model. Typically, a convolutional neural network is an encoder and sequential model such as recurrent neural network acts as decoder. Our work proved that a combination of Resnet and Long short term memory has yielded higher accuracy. In the case of object detection approach, YOLO (achieved 0.710 mean average precision) performed better than Faster RCNN is observed. Grey scale images have also been considered as captcha recognition task is concerned with extraction of shapes rather than colours.
UAV 3D path and motion planning in unknown dynamic environments

Unmanned aerial vehicles (UAVs) are used in increasingly complex environments. This paper deals with path and motion planning, based on the local perception of the UAV, generating autonomously a safe 3D path in dynamic cluttered environments under different constraints. The latter are formulated in a multi-criteria real-time optimization process that aims to minimize an objective function while satisfying the dynamic constraints of the UAV and the safety requirements. The considered constraints impact the velocity and the acceleration of the UAV, but also its curvature along the trajectory. Safety constraints are ensured by a minimal-security distance and by locally shifting the heading of the UAV to avoid obstacles while maintaining attractiveness to the goal location. Simulation results under ROS/Gazebo show the effectiveness and robustness of the approach even in highly cluttered environments. We study the effect of several parameters on the optimization process and the resulting trajectories.
Incorporating Phrase-Level Agreement into Neural Machine Translation

Phrase information has been successfully integrated into current state-of-the-art neural machine translation (NMT) models. However, the natural property of the source and target phrase alignment has not been explored. In this paper, we propose a novel phrase-level agreement method to deal with this problem. First, we explore n-gram models over minimal translation units (MTUs) to explicitly capture aligned bilingual phrases from the parallel corpora. Then, we propose a phrase-level agreement loss that directly reduces the difference between the representations of the source-side and target-side phrase. Finally, we integrate the phrase-level agreement loss into the NMT models, to improve the translation performance. Empirical results on the NIST Chinese-to-English and the WMT English-to-German translation tasks demonstrate that the proposed phrase-level agreement method achieves significant improvements over state-of-the-art baselines, demonstrating the effectiveness and necessity of exploiting phrase-level agreement for NMT.
Virtual Material Modeling and Vibration Reduction Design of Electron Beam Imaging System

The electron beam imaging system is used for wafer inspection, and its imaging accuracy is directly affected by the vibration characteristics of the system. In order to explore the influence of the vibration characteristics of the electron beam imaging system on its imaging accuracy, the key vibration transmission path was selected, and the high-precision joint surface virtual material modeling method was introduced into the structure to establish the whole vibration transmission model. The first 10 natural frequencies, mode shapes and the harmonious response of the electron gun tip are obtained by the finite element method. Two schemes of lens tube materials were proposed to increase the natural frequency, and the second solution suppressed the maximum response displacement of the gun tip to 83% of the original structure. Then the structure of the chamber was redesigned to reduce the displacement response of the electron gun tip when the molecular pump was working. The weight of the chamber of the final scheme is 75.6% of the original structure, and the response displacement of the electron gun tip are suppressed to about 40% the original structure, therefore improving the imaging accuracy.
High‐order moment multi‐sensor fusion filter design of Markov jump linear systems

To solve the problem of high-order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high-order moment information obtained from sensor data is designed. To obtain high-order moment information, the multi-sensor MJLS is converted to a single-mode system composed of high-order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high-order moment information form according to the transformed single-mode deterministic system. Subsequently, a high-order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high-order moment information obtained from various sensors. Comparing the first- and second-order moment information obtained by traditional Gaussian distribution, the HGD introduces higher-order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.
A Data Driven Method to Detect First-Out Alarms Based on Alarm Occurrence Events

A first-out alarm is an alarm that comes first in a group of highly correlated alarms. To achieve alarm reduction, it is highly recommended in industrial alarm management standards to only present first-out alarms to operators, whereas consequential alarms should be silenced or suppressed using first-out display or logic alarm suppression in ANSI/ISA-18.2. However, to collect such groups of correlated alarms and identify first-out alarms from them usually relies on proficient knowledge and experience, and thus is time and resource intensive. This work proposes a data driven method to detect first-out alarms from historical alarm data. Two main steps are involved: 1) consequential alarms are detected for each unique alarm based on alarm occurrence events; 2) first-out rules are produced and pruned based on redundant alarm analysis. The effectiveness of the proposed method is demonstrated by an industrial case study involving alarm data collected from a real industrial facility. The detected first-out rules can be used as candidates to design first-out display or logic alarm suppression strategies in practice.
Do You See What I See?

A password is one of the oldest forms of authentication whose popularity has not waned. Passwords are easy to use, inexpensive to deploy, and are familiar by everyone. However, this comes with a cost. Passwords are easy to guess, difficult to remember when they are made complex and unique, and found everywhere on the Dark Web. The security usability paradox suggests that any improvements in security will result in a decrease in usability, and vice versa. In this paper, we examine the feasibility of a visual password system in which a traditional password is used, but with the additional capability of modifying the characters of the password to provide significantly higher levels of entropy.
Situation Alignment for Distributed Operations

In the absence of network and processing limitations, sharing all of the data collected to other nodes in the network will allow all nodes to produce the same understanding of all situations ongoing in the operating environment, i.e., all nodes situation awareness will be in alignment. However, when there are network or processing limitations across the network, not all collected data will be able to be shared to all nodes. This research is concerned with determining the subset of collected data that should be shared across the bandwidth constrained network in order to best align situational understanding across nodes of the network. Four mathematical formulations are derived, making use of Dempster-Shafer calculus, and applied to an illustrative example problem.
Referring Expression Generation via Visual Dialogue

Referring Expression Generation (REG) is to generate unambiguous descriptions for the referred object in contexts such as images. While people often use installment dialoguing methods to extend the original basic noun phrases to form final references to objects. Most existing REG models generate Referring Expressions (REs) in a "one-shot" way, which cannot benefit from the interaction process. In this paper, we propose to model REG basing on dialogues. To achieve it, we first introduce a RE-oriented visual dialogue (VD) task ReferWhat?!, then build two large-scale datasets RefCOCOVD and RefCOCO+VD for this task by making use of the existing RE datasets RefCOCO and RefCOCO+ respectively. We finally propose a VD-based REG model. Experimental results show that our model outperforms all the existing "one-shot" REG models. Our ablation studies also show that modeling REG as a dialogue agent can utilize the information in responses from dialogues to achieve better performance which is not available in the "one-shot" models. The source code and datasets will be seen in https://github.com/llxuan/ReferWhat soon.
Medical CT Image Super-Resolution via Cyclic Feature Concentration Network

Clear and high-resolution medical computed tomography (CT) images are of great significance for early medical diagnosis. In practice, there are many cases of medical CT images that lead to poor image quality which complicates the diagnosis process. This paper proposes a new type of neural network that enables low-resolution images to reconstruct high-resolution images and aids in the early diagnosis of medical CT images. The network mainly consists of a low-level feature extraction module, a cyclic feature concentration block, and a reconstruction module. First, the underlying feature extraction module extracts the effective feature information of the low-resolution image. Then, the cyclic feature concentration block further extracts the feature information. Finally, the reconstruction module reconstructs the high-definition image. It can be seen from the experiment results that the method proposed in this paper achieved the best results on two objective indicators.
A Comparison Analysis of Collaborative Filtering Techniques for Recommeder Systems

In E-commerce environment, a recommender system recommend products of interest to its users. Several techniques have been proposed in the recommender systems. One of the popular techniques is collaborative filtering. Generally, the collaborative filtering technique is employed to give personalized recommendations for any given user by analyzing the past activities of the user and users similar to him/her. The memory-based and model-based collaborative filtering techniques are two different models which address the challenges such as quality, scalability, sparsity, and cold start, etc. In this paper, we conduct a review of traditional and state-of-art techniques on how they address the different challenges. We also provide the comparison results of some of the techniques.
Military Vehicle Recognition with Different Image Machine Learning Techniques

Different neural network training systems are studied for image recognition of military vehicles, variable start layer transfer training models and own convolutional neural networks training from scratch. Since, there is limited openly available military recordings, labeled social media images are used for training. Furthermore, expanding the image-set by random data transformation. An implementation is made in terms of image augmentation handling as an internal loop that freezes all numerical parameters of the neural network training, while selecting continuously a slightly larger section of the training set including an increment part of artificial images added to the system. All models where trained for three vehicle and two situational environment classification cases. The transfer learning is based on two of the most widely used recognition networks, ResNet50 and Xception, with a variable number of last trained layers to max. twenty. The first being successfully transfer-trained with validation accuracy values of \({\approx }\)88%. In contrast Xception resulted on a over-fitted neural network with low validation accuracy and large loss values. Neither of the transferred schemes benefit from image augmentation. Moreover, in variable architecture training of convolutional networks, it was corroborated that different configurations of layers numbers/type/neurons adapt differently. Thus, a tailor-fit neural network combined with data augmentation strategy is the best approach with validation accuracy of \({\approx }\)86.4%, comparable to large transferred networks with a \({\approx }\)40 times smaller network architecture. Hence, requiring less computational resources. Data augmentation influenced an increment of validation accuracy values of \({\approx }\)9.2%, with the least accurate network trained gaining up to 20% on accuracy due inclusion of artificial images.
An Overview of Outliers and Detection Methods in General for Time Series from IoT Devices

As internet of things (IoT) devices are booming, a huge amount of data is sleeping without being used. At the same time, reliable and accurate time series analysis plays a key role in modern intelligent systems for achieving efficient management. One reason why the data are not being used is that outliers are preventing many algorithms from working effectively. Manual data cleaning is taking the majority time before one solution could really work on data. Thus, data cleaning, especially fully automated outlier detection is the bottleneck which should be resolved as soon as possible. Previous work has investigated this topic but lacks study on overview from outlier and detection categorization aspects at the same time. This works aims to start covering this topic and to find a direction regarding how to make outlier detection and labelling more automated and general to be suitable for most time series data from IoT devices.
Building Large-Scale Programmable Photonic Circuits Using Silicon Photonic MEMS

Programmable photonic circuits electronically reconfigure the flow of light on a chip. This requires hundreds or thousands of phase shifters and tunable couplers. Silicon photonic MEMS offer both high integration density and low power consumption.
Evaluation of Inpainting in Speckled and Despeckled 2D Ultrasound Medical Images

Image processing has paved its way through various applications in the medical field, where researches are being carried out to prompt and improvise the care and treatment given to patients in all aspects. Medical image processing applications concerns the need for obtaining images as per the requirement. Inpainting of markers on medical images generated using various modalities is crucial to process the images for numerous applications. One of the inherent and profoundly seen problem in medical images, especially in the ultrasound images is the speckle noise. It alters the edges, fine details and other features of organs that is required for interpretation. In this paper, the need for despeckling of 2D ultrasound images is studied by evaluating the performance metrics for inpainting based on fast marching algorithm of speckled and despeckled 2D ultrasound images.
Formalizing Open Source Software Quality Assurance Model by Identifying Common Features from Open Source Software Projects

Not long ago many scholars used to believe and write that open source software or projects do not follow any decisive software quality assurance methods as traditional projects do. They would argue that the style adopted by open source communities is more or less ad hoc. However successful open source collaborative projects have over the years developed a typical software development quality assurance methods of their own which may be coined as continuous develop and release state quality assurance model. As a result many have started acknowledging this change and important writings are being published. In this tune the present paper discusses the quality assurance model adopted by few famous open source projects and attempts to strike a formalization of the common steps involved from practices employed by these open source software projects.
On composition of bounded-recall plans

The article studies the ability of agents with bounded memory to execute consecutive composition of plans. It gives an upper limit on the amount of memory required to execute the composed plans and shows that the limit cannot be improved. Furthermore, the article shows that there are, essentially, no other universal properties of plans for bounded-recall agents expressible through the relation “there is a plan for an agent with a given memory size to navigate from one given set of states to another”.
Collaborative earthquake detection and response using smart devices

An earthquake detection and response system is crucial due to the fatal nature of earthquakes. Recently, extensive research efforts have been made to detect earthquakes in real time and give an alert by incorporating with server-based systems or by working as a stand-alone device. However, such approaches still have many challenges due to the cost of network and server infrastructures or the inaccuracy of detection algorithms. In this work, we introduce a collaborative approach using smart devices located in a near area to detect earthquakes and issue an alert with detailed action plans. A smartphone's accelerometer works as a seismic sensor to monitor ground motions when it is in a steady state. Once an earthquakelike motion is detected, it is sent to nearby devices through D2D communications to confirm it as an earthquake. Our experimental results show that the proposed approach can be effective in a home environment or an earthquake early warning system is not present.
Adversarial Path Sampling for Recommender Systems

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose <i>path2rec </i> a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e.g., social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed <i>path2rec</i> by applying it into top-<i>n</i> item recommendation, which reaches better performance than other counterparts.
Embedded object detection applying Deep Neural Networks in railway domain

In the last few years, research on deep learning application on the transportation industry has grown. One of the tasks afforded on those works is the object detection, a essential function in autonomous vehicles, including railway vehicles. While the application of deep learning for object detection is increasing in railway domain, proposed methods have to be yet tested on embedded hardware. This work explores the efficiency of the standard YoloV3 detector embedded on a NVIDIA Jetson AGX Xavier to infer traffic signals in the railway domain. Furthermore, different architectures of YoloV3 are analyzed and compared to find the best output for the used dataset. A data augmentation technique called RICAP-DET is developed to create the training dataset by generating labeled images from cutouts of a set of images. The results show that YoloV3 can be used to detect rail traffic-signals in real time on an embedded platform and that RICAP-DET is adequate to train YoloV3.
Developing random satisfiability logic programming in Hopfield neural network

The random satisfiability (RAN-SAT) is a problem that decides whether there exists a mapping of truth values to variables that makes a given random Boolean formula satisfiable. This paper gives a brief discussion on artificial neural network, Hopfield neural networks and logic programming integration. It proposed a method of optimizing random satisfiability in Hopfield neural networks based on the model energy minimization scheme. Computer simulation is carried out to demonstrate and verify the feasibility to carrying out random satisfiability in the Hopfield network.
COaaS

To quickly and securely deploy the latest version of hardware and software resources, DevOps communities use continuous delivery, continuous integration, and continuous deployment framework. This paper presents the methodology adopted by the m41ab team of the University of Applied Sciences to use the CI/CD framework in the development of workflows and shows the benefits that can be achieved using the CI/CD framework. Using High-Performance Computing (HPC) along with GitLab runners, we show that the use of Continuous Integration and Delivery framework can efficiently overcome software management challenges even without knowing much about HPC. We present our CI/CD infrastructure deployed for the transfer portal for m41ab, and explain how Gitlab-Runner allows end-user to adapt the environment to use HPC for computation diligently. The research, by use-case, shows the way forward to use the m41ab transfer portal for developing and maintaining the computing resources. Similarly traditional software, HPC software management is a complicated process that shares a number of objectives with enterprise-scale software development which obliges trustworthy updates and validation on a hard deadline.