Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Easy Sailing

Maritime navigation, and specifically sailing, is an activity influenced by many external factors such as weather and sea conditions, the presence of near obstacles, the state of the vessel; these factors determine the complexity of navigation, making it an activity dedicated to expert enthusiasts and professionist. We believe that the use of Augmented Reality in the field of maritime navigation can greatly help experienced navigators and make this activity more accessible even to the less experienced, while still ensuring its safety. The concept of Easy Sailing thus evolves/innovates, thanks to the fundamental instrument of AR used as an amplifier of the human senses, designed to inform the navigator, compensating for his lack of experience.
Can GAN-Generated Network Traffic be used to Train Traffic Anomaly Classifiers?

Recent attempts to introduce the Generative Adversarial Network (GAN) to the computer network traffic domain have shown promise, including several frameworks which generate realistic traffic. This paper presents the `GAN vs Real (GvR) score', a task-based metric which quantifies how well a traffic GAN generator informs a classifier compared to the original data. This metric is derived from the `Train-on-Synthetic, Test-on-Real' (TSTR) method, with the added step of comparing the TSTR accuracy to the performance of the same classifier trained on real data and tested on real data. We use this framework to evaluate the B-WGAN-GP model for generating NetFlow traffic records using several stock classifiers. Using GvR we conclude that it is possible to train accurate traffic anomaly detectors with GAN-generated network traffic data.
Special Session: Exploring the Ultimate Limits of Adiabatic Circuits

The field of adiabatic circuits is rooted in electronics know-how stretching all the way back to the 1960s and has potential applications in vastly increasing the energy efficiency of far-future computing. But now, the field is experiencing an increased level of attention in part due to its potential to reduce the vulnerability of systems to side-channel attacks that exploit, e.g., unwanted EM emissions, power supply fluctuations, and so forth. In this context, one natural question is: Just how low can the energy dissipation from adiabatic circuits, and the associated extraneous signal emissions, be made to go? We argue that the ultimate limits of this approach lie much farther away than is commonly appreciated. Recent advances at Sandia National Laboratories in the design of fully static, fully adiabatic CMOS logic styles and high-quality energy-recovering resonant power-clock drivers offer the potential to reduce dynamic switching losses by multiple orders of magnitude, and, particularly for cryogenic applications, optimization of device structures can reduce the standby power consumption of inactive devices, and the ultimate dissipation limits of the adiabatic approach, by multiple orders of magnitude as well. In this paper, we review the above issues, and give a preliminary overview of our group's activities towards the demonstration of groundbreaking levels of energy efficiency for semiconductor-based logic, together with a broader exploration of the ultimate limits of physically realizable techniques for approaching the theoretical ideal of perfect thermodynamic reversibility in computing, and the study of the implications of this technology direction for practical computing architectures.
Evaluation of Graphical User Interfaces Guidelines for Virtual Reality Games

Virtual Reality presents a new form of human-computer interaction for the video game world, introducing new challenges for various aspects of game development. Many of the traditional practices in the design and development of Graphical User Interfaces do not fit the context of Virtual Reality (VR), requiring adaptations or the creation of new solutions. This work proposes to analyze some of the GUI guidelines for VR, and from this, investigate the perception of players about the GUI guidelines. An analysis of some of the manufacturers and game engine guidelines for VR was carried out with the purpose of identifying which recommendations are more common. After that, a survey was applied with players to define, from the perspective of the user, the level of importance of each guideline. In addition to the analysis of the users' perspective, a set of games has been chosen and analyzed to understand the game's compliance with the guidelines. The results of the analysis showed that the games are respecting the guidelines, but users still perceive some issues with the VR games GUIs.
Neighborhood-aware autoencoder for missing value imputation

Missing values are a fundamental issue in many applications by constraining the application of different learning methods or by impairing the attained results. Many solutions have been proposed by relying on statistical or machine learning techniques. However, in most cases, the results are not yet satisfactory. Hence, motivated by the advent of deep learning, different solutions have also been proposed, such as by adopting autoencoders and adversarial training. However, in most of these solutions, the results are impaired by the network structure and training strategy, constraining the accuracy of missing value imputation. In this paper, we revisit autoencoder networks and show that through a careful selection of network structure and optimization strategy we outperform other deep learning solutions. We further study the impact of a previously proposed technique, stochastic corruption of inputs, to show that when the network is well designed and trained, it actually impairs the results.
Comparison of Face Detection Algorithms on Mobile Devices

Face detection is a fundamental task for many computer vision applications such as access control, security, advertisement, automatic payment, and healthcare. Due to technological advances mobile robots are becoming increasingly common in such applications (e.g. healthcare and security robots) and consequently there is a need for efficient and effective face detection methods on such platforms. Mobile robots have different hardware configurations and operating conditions from desktop applications, e.g. unreliable network connections and the need for lower power consumption. Hence results for face detection methods on desktop platforms cannot be directly translated to mobile platforms.We compare four common face detection algorithms, Viola-Jones, HOG, MTCNN and MobileNet-SSD, for use in mobile robotics using different face data bases. Our results show that for a typical mobile configuration (Nvidia Jetson TX2) Mobile-NetSSD performed best with 90% detection accuracy for the AFW data set and a frame rate of almost 10 fps with GPU acceleration. MTCNN had the highest precision and was superior for more difficult face data sets, but did not achieve real-time performance with the given implementation and hardware configuration.
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features

Tremendous amounts of multimedia associated with speech information are driving an urgent need to develop efficient and effective automatic summarization methods. To this end, we have seen rapid progress in applying supervised deep neural network-based methods to extractive speech summarization. More recently, the Bidirectional Encoder Representations from Transformers (BERT) model was proposed and has achieved record-breaking success on many natural language processing (NLP) tasks such as question answering and language understanding. In view of this, we in this paper contextualize and enhance the state-of-the-art BERT-based model for speech summarization, while its contributions are at least three-fold. First, we explore the incorporation of confidence scores into sentence representations to see if such an attempt could help alleviate the negative effects caused by imperfect automatic speech recognition (ASR). Secondly, we also augment the sentence embeddings obtained from BERT with extra structural and linguistic features, such as sentence position and inverse document frequency (IDF) statistics. Finally, we validate the effectiveness of our proposed method on a benchmark dataset, in comparison to several classic and celebrated speech summarization methods.
Remotely Controlled Surveillance Vehicle with Live Video Streaming

Technology is updating day by day so it also necessary to update according to it. These days anything can be done from home itself like meetings, online orders, and so on. So, the proposed project focusses on controlling the vehicle from anywhere remotely. This paper mainly concentrates on developing a surveillance vehicle that can be used to monitor dangerous areas, military bases, and other critical places using esp32 cameras. Our proposed design performs live streaming, including that it identifies the unauthorized person and stores it in SD card. Along with this, it also sends the image of that person to the authorized person’s email. Here we use a mobile phone to control the car from anywhere through Blink Application. Blynk App is used to control the camera movements. The main strength of this paper is security features.
Hybrid Outlier Detection in Healthcare Datasets using DNN and One Class-SVM

In general, a dataset may contain a minor portion of data objects, whose properties are not identical to the majority of its data objects. Such data objects are called outliers. Most of the data mining and machine learning tasks are aimed to detect the general properties of the dataset. Outlier detection plays an important role in various application domains such as malicious activity detection in software applications, fraud detection in financial transactions, intruder detection in communication systems etc. In this paper, the performance of two major outlier detection algorithms is analyzed for healthcare applications. A hybrid model is used for detecting outliers in healthcare systems by using one-class support vector machine model and the autoencoder model is also proposed. Experimental results using the benchmark healthcare datasets show that the proposed hybrid method performs better than the existing models for detecting outliers in these datasets.
Pattern Matching-based scraping of news websites

Abstract Web Scraping is the process of extracting content from human-readable websites in order to import it into local storage such as databases or CSV Files. The process of data extraction and its design is time-consuming requiring an analysis of the website, data representation of the objects comprising its structure (DOM), HTML tags, and the Cascading Style Sheets (CSS) classes. To support this process we aim at providing automation. In this paper, we propose a pattern mining technique to scrap news and blog websites by recognizing title and body based on a content structure pattern. This approach consists of three steps, i.e.: extracting news website structure, constructing a pattern of HTML content, and implementing the pattern as a set of rules in web scraping. Our approach is a simple, general, and straightforward way to extract articles that consist of the title, the body of any blogs, or news websites.
AR Circuit Constructor: Combining Electricity Building Blocks and Augmented Reality for Analogy-Driven Learning and Experimentation

We present AR Circuit Constructor (ARCC), an augmented reality application to explore and inspect electric circuits for use in educational settings. Learners use tangible electricity building blocks to construct a working electric circuit. Then, they can use a tablet device for exploring the circuit in an augmented reality visualization. Learners can switch between three distinct conceptual analogies: bicycle chain, water pipes, and waterfalls. Through experimentation with different circuit configurations, learners explore different properties of electricity to ultimately improve their understanding of it. We describe the development of our application, including a qualitative user study with a group of STEM teachers. The latter allowed us to gain insights into the qualities required for such an application before it can ultimately be deployed in a classroom setting.
Cognitive impact validation of requirement uncertainty in software project development

Currently, the most important factor which is receiving more attention is the requirements uncertainty in software projects development. Requirements uncertainty during the development of software project is identified as the most significant threat, and managing it is essential to achieve the successful completion of software projects. Requirements uncertainties have huge impact on budget, time and quality of the finished product. Several projects have either failed or challenged due to requirement uncertainty. Due to uncertainty in the requirements, it is very difficult to estimate the cost, schedule, efforts and its development approach in the early stages of software development process. This, in turn, will directly affect the development process. This may happen to be a strong reason for software project failure. Thus, uncertain requirements must be managed. The objective of this paper is to identify the relationships between uncertain requirements and other important parameters. Furthermore, the impact relationship between uncertain requirements and other parameters are established and validated. A conceptual research model is developed for relationship establishment as well as validated by an experimental study focused on a framework through online questionnaire survey. Responses are collected mostly from the Indian IT companies. The study results show a positive and significant relationship among requirements uncertainty and other important parameters. This research paper reveals the need of uncertainty analysis in the development process of software projects in order to recognize the role of uncertainty in software development process.
Power Equipment Recognition Method based on Mask R-CNN and Bayesian Context Network

In order to improve the accuracy of power equipment recognition, an image recognition method based on Mask RCNN and Bayesian Context Network is proposed. The two-layer network contains Mask R-CNN as the first layer, which gives preliminary recognition results, and Bayesian Context Network as the second layer, which utilizes context information to correct the preliminary results. Bayesian Context Network is designed to take the relationship of type, size, and spatial location between objects into account. Experiments on images of power equipment in substation show that the proposed method outperforms Mask R-CNN, Multi-task Network Cascades and Fully Convolutional Instance-aware Semantic Segmentation. Since power equipment in a picture shows a strong correlation, the proposed method is quite suitable for power equipment recognition.
Application of Differential Privacy Approach in Healthcare Data – A Case Study

With advancement in IoT and its application in various domains, huge amount of data is available and stored in databases both locally and on the cloud. Health care is one such domain. Patient medical data is stored and usually curator of the data is responsible for ensuring privacy of the patient. Privacy becomes the major concern in a scenario, where the data is shared with third party for further analysis or research purposes. To avoid any privacy breach in such or similar scenarios, this article discusses differential privacy approach. Major focus remains exploiting the unique property of differential privacy and its application to healthcare data. We discuss impact of amount of noise introduction in the original data, the relation between the added noise in the data, data utility, and the effect of data leakage to breach of privacy.
Robot Vision for Manipulation: A Trip to Real-World Applications

Along the last decades, Robotics research has taken a major turn from laboratories to factories and ordinary real-world environments. Consequently, new issues to be overcome have arisen, specially when autonomous, dexterous robots are in place. In this paper, we present this evolution in the case of robot vision for manipulation through several robot developments, by analysing their challenges and proposed solutions. This overview highlights the need of using different techniques depending on the task at hand and the scenario to work in.
The curved exponential family of a staged tree

Staged tree models are a discrete generalization of Bayesian networks. We show that these form curved exponential families and derive their natural parameters, sufficient statistic, and cumulant-generating function as functions of their graphical representation. We give necessary and sufficient graphical criteria for classifying regular subfamilies and discuss implications for model selection.
Stacked BiLSTM - CNN for Multiple label UAV sound classification

Recently the detection of drones using acoustic data has attracted the interest of researchers, because it is less expensive than other traditional methods. By using acoustic signature we can perform binary classification of UAVs, moreover we can identify if the drone has a load or not. Detection of UAVs with an additional load in the restricted and crowded areas is considered as an effective protection system. This paper considers Multiple label UAV sound classification task using LSTM-CNN architecture. The proposed architecture is composed of Stacked Bidirectional LSTM and CNN, which were learned on representations of the short-term power spectrum of UAV sounds (MFCCs). The results of our experiment show higher accuracy by using a combination of Stacked BiLSTM and CNN rather than using these architectures separately.
Learning from Noisy Labeled Data Using Symmetric Cross-Entropy Loss for Image Classification

While image classification with deep neural networks (DNNs) has made remarkable progress, learning from noisy labeled data degrades performance, and this problem remains challenging. Some previous methods, such as Co-teaching and MentorNet, use the memorization effects, which are characteristics of DNN training that first learn easy (most likely clean) samples and then difficult (most likely noisy) samples. These methods treat samples with small training loss as clean, and we call this idea the “small-loss criterion”. However, when there is a difference in learning speed between samples, a class including easy samples tends towards overfitting. In this paper, we propose a robust learning method by introducing a symmetric cross-entropy (SCE) loss to control the difference in learning speed between all classes. Experimental results on the CIFAR-100 image classification dataset containing 45% and 50% of noisy labels show the robustness of the proposed method.
Optical phase-shifting methods based on low coherence laser for large aperture Fizeau interferometer

PZT is commonly used as the phase shifter in interferometric systems with small apertures for decades. However, it becomes more difficult to be applied to the system with large aperture. In this paper, the phase shifting procedure is moved to the source module with a low coherence laser, which makes phase shifting independent of the large interference cavity. The path matching procedure based on intensity variation is introduced to compensate the path difference between the two arms. The experiments based on PZT and wavelength tuning methods are both carried out on a 600mm Fizeau interferometer for verification of the proposed method. The difference between the two results is 0.0015λ(PV). Besides, repeated experiments are performed, showing excellent repeatability and stability and the standard deviation of RMS is 0.0011λ via PZT method. The analysis of error sources is also performed, including the poor contrast and the nonlinearity of PZT. Both the experimental results and the error analysis verify the feasibility of the proposed method in large aperture interferometer.
Survey on cross virtual machine side channel attack detection and properties of cloud computing as sustainable material

Cloud computing dominates all kinds of computing in the network due to the service-oriented and on-demand nature of the model. The customer uses a cloud model for fulfilling the resource needs with minimum cost. Cloud security plays a vital role as it is securing the customer data in a more reliable manner. Various levels of security issues imposed by the cloud provider are still considered to be a problem in the resource level. Virtualization security technique is used to prevent the Virtual Machine (VM) and related information like data, state and shared resources. VM side channel attack leads to severe issues in the cloud because the attacker is performing access of the resources that causes the reliability problem. The main objective of this analysis is to explore various kinds of attacks and its solution model of the cloud. Side channel attacks are detected by using various techniques such as RSA and AES. Infrastructure level attacks are analyzed with various attack levels and rates. Hypervisor level protection is assessed by using the different classes with accurate parameters. The overall analysis provides the complete information about cloud resource level security and its solution.
Translation of Perceived Video Quality Across Displays

Display devices can affect the perceived quality of a video significantly. In this paper, we focus on the scenario where video resolution does not exceed screen resolution, and investigate the relationship of perceived video quality on mobile, laptop and TV. A novel transformation of Mean Opinion Scores (MOS) among different devices is proposed and is shown to be effective at normalizing ratings across user devices for in lab and crowd sourced subjective studies. The model allows us to perform more focused in lab subjective studies as we can reduce the number of test devices and helps us reduce noise during crowd-sourcing subjective video quality tests. It is also more effective than utilizing existing device dependent objective metrics for translating MOS ratings across devices.
Experience

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.
Observation of micro-characters using three-dimensional shape measurement method based on speckle interferometry

Speckle interferometry has long been used to measure the deformation of an object with a rough surface. Currently, it can be used to measure the shape of a three-dimensional microstructure by intentionally applying a lateral shift to the measured object and analysing the change in phase due to the shift. Furthermore, the validity of this measurement method has been verified in terms of the measurement of microstructures, such as micro-silica spheres and diffraction gratings. In this study, the validity of the measurement of an object possessing an artificial shape by this method in the micro-region is analysed using micro-characters drawn by an electron-beam lithography system. It is confirmed that this method can perform the shape measurement of three-dimensional microstructures that are artificially produced. This was achieved by observing the letter ‘U’, illustrated with a line width beyond the diffraction limit of the objective lens.