Reviewerly/exHarmony huggingface dataset:
Interaction design and requirements elicitation integrated through SPIDe

SPIDe is a semio-participatory methodological process initially composed for the interaction (re)design of educational software. However, some SPIDe studies indicated that it is also possible to elicit requirements through its application due to its participatory characteristics. In this paper, we present an investigation about the feasibility of using SPIDe for requirements elicitation, presenting an exploratory case study of requirements elicitation integrated into interaction design. The data was collected in logbooks, semi-structured interviews, a technology acceptance model questionnaire, and analyzed using thematic analysis. The results show that the use of SPIDe for requirements elicitation integrated into interaction design is feasible. Besides that, they indicate some improvements to the overall process.
Efficiency evaluation of intelligent swarm based on AHP entropy weight method

Abstract With the rapid development of high-tech, UAV intelligent swarm is expected to become an important means to deal with the complex situation environment in the future due to its intelligent and high cost-effective characteristics. UAV intelligent swarm is a nonlinear complex system, and the traditional single effectiveness evaluation method can not effectively reflect the system capability. Therefore, this paper proposes an evaluation method based on AHP entropy weight method for UAV intelligent swarm system effectiveness evaluation. The final weight is obtained by combining the two weights, which can reduce the impact of subjective factors. Finally, the feasibility of the proposed method is verified by simulation experiments.
In situ capture of impact-induced progressive damage and delamination in fiberglass composite laminate with a high-speed optical imaging method

Delamination is one of the severest failure modes for laminated composite materials during applications, especially when the composites are subjected to dynamic loading like impact. The impact-induced delamination patterns in laminates have been studied before but the progressive delamination evolution during the impact process has rarely been revealed through experimental testing. Capturing the real-time delamination progression is of great significance for understanding the fundamentals of the composite performance and damage resistance during the dynamic loading process. This study investigates the feasibility of using a high-speed optical imaging method for in-situ capturing the impact-induced progressive damage and delamination evolution in fiberglass composite laminate [0/90/0/…]13. The progressive delamination areas are further quantified through a sequential series of captured images using a custom algorithm. The relations between the progressive delamination evolution and the real-time absorbed energy, as well as the impact force and deflection, have been investigated. This study provides insights into the experimental testing method of dynamic damage and failure process in materials and structures.
Game Theoretic Analysis of Reputation Approach on Block Withholding Attack

Bitcoin and the underlying technology blockchain introduced an open distributed system that incorporates Proof of Work and Nakamoto Consensus. Despite the broad adoption by enthusiasts, the consensus mechanism is vulnerable to certain issues, such as block withholding attack, selfish mining, and 51% attack. Various solutions have been proposed to address these problems. RepuCoin is one successful example, which claims to solve the selfish mining and 51% attack by adopting a novel reputation concept and modified BFT protocol. We generalize the reputation concept introduced in RepuCoin, and implement it in traditional Bitcoin system to analyze whether it can prevent the block withholding attack. We propose a reputation-based reward mechanism for the Bitcoin blockchain and a reward sharing schema for the mining pools. We model the utility of honest mining and block withholding attack for pools, and find that the inclusion of reputation in Bitcoin’s reward mechanism and pools’ reward schema can prevent mining pools from launching block withholding attack.
How a Quantum Computer Could Solve a Microkinetic Model

A CO oxidation microkinetic model is set up for a quantum circuit. The CO oxidation microkinetic model, and microkinetic models in general, exhibit an advantage of not requiring an encoding step because of being a subclass of systems of equations. The microkinetic model is cast as a nonlinear set of equations at first. Then, a linearizing approximation is made, and the resulting linear set of equations may be iterated to converge to the solution to the nonlinear set of equations. In this CO oxidation, the linearized set of equations is realized to chemical accuracy with one iteration. Current limitations in executing the quantum circuit to obtain the solution are discussed.
Optimizing a RFID-UAV cattle search tour

We present a path determination method for an unmanned aerial vehicle (UAV) equipped with a radio frequency identification (RFID) interrogation system. UAV path design is one of the main factors influencing the cost of UAV inventory management systems. A case study for using a path-optimized UAV-RFID system is presented. In this case study the RFID-UAV travels above the pastures of an extensive cattle ranch scanning the RFID tags attached to the cattle in order to detect their locations. Since the UAV battery limits the flight time, the UAV path must be segmented and landing locations for recharging or battery replacement must be determined. Scanning a cattle RFID tag requires the UAV to fly within a certain distance from the animal, as opposed to approaching the animal’s exact location. The reading distance (RD) of the RFID equipment determines the allowable distance. We use an easy-to-explain method to determine the UAV path. We generate a RD-based hexagonal tiling of the pasture and minimize the UAV path such that it visits all the tile center points. Based on the current UAV system configuration and environmental conditions, the optimal UAV tour allows for the scan of each pasture segment’s animals to be completed within the battery-limited flight time. A case study was performed at the California Polytechnic (Cal Poly) State University’s Escuela and Walters Ranches. The RFID-UAV system solution is estimated to be 62%faster than the current cattle scanning practice, which requires ranch employees to search for and scan each animal from close proximity.
Web-Based Canteen Payment and Ordering System

Abstract The purpose of making a web-based payment and ordering system is for tenants in the canteen to be able to access payment and ordering system simply through communication equipment, so that tenants can minimize calculation errors in the payment or ordering process. The data collection method used in this study is the SDLC (Software Development Life Cycle) method with the waterfall model. The result of the testing of this system shows that the web-based payment and ordering system is easy to use and is ready to be implemented. We conclude that a web-based payment and ordering system makes payment and ordering process in the canteen easy for tenants and customers alike.
A point cloud compression framework via spherical projection

In this paper, we propose a sphere-projection-based framework for point cloud geometry and attribute lossless and lossy coding. The original point cloud is adaptively divided into blocks, and then we create a fitting sphere in each block for modeling the local geometry structure of the point cloud. Sphere coordination transform and spherical projection scheme are introduced to transfer a 3D point cloud to a set of the range images. A novel compact representation of generated range images based on Morton codes is proposed to separate the range images into occupancy images and attributes vectors for further compression. Experimental results demonstrate that for the LiDAR point clouds datasets in lossless compression, the proposed method offers better performance than geometry-based point cloud compression (G-PCC). For the object point clouds datasets in lossy compression, the proposed method has better rate-distortion (R-D) performance than Draco.
Self-Paced Learning With Diversity for Medical Image Segmentation by Using the Query-by-Committee and Dynamic Clustering Techniques

Convolutional neural networks (CNNs), as a typical deep learning technique, have been widely used in image segmentation, but they often require a large amount of annotated data.However, the number of available pixel-wise labeled medical images is extremely small, and this prevents the application of CNNs in many medical image segmentation tasks.We proposed a self-paced learning with diversity SPLD) framework to boost the performances of medical image segmentation models with a limited amount of annotated data.Self-paced learning (SPL) is a learning regime that selects training samples in order from the easiest to the most difficult for model training.In addition, we took the diversity of the data into consideration.The proposed self-paced learning with diversity by query-by-committee (SPLD-QBC) algorithm dynamically and diversely selects the appropriate training data to boost the performance of an image segmentation model.SPLD-QBC incorporates the query-by-committee (QBC) technique for data selection and affinity propagation for optimizing the data diversity.By dynamically selecting the optimal sequence of training samples from different probability distributions, the segmentation models achieved improved performances.To verify the effectiveness of the proposed SPLD-QBC framework, we conducted experiments on three medical image segmentation tasks with five different datasets.The experimental results indicated that the proposed SPLD-QBC algorithm significantly improved upon the segmentation performances of the baseline models and resulted in a higher Dice score, surface distance and mean intersection over union (mIoU).The proposed SPLD significantly boosts the segmentation performances of models and is easily embedded into CNN-based image segmentation models.
Self-Attentive Normalization for Automated Gleason Grading System

Recently, convolutional neural networks (CNNs)- based automated Gleason grading system for prostate cancer has been widely researched. However, these systems still need further improvement to achieve pathologist-level performance. To this end, this paper introduces a novel self-attentive normalization (SAN) which is the first work to employ the attention mechanism for the automated Gleason grading system. Unlike conventional normalization techniques, e.g. batch normalization and instance normalization, which learn a single affine transformation, the proposed method can learn the elementwise affine transformation to focus on more informative regions of the feature map. Since SAN requires a small number of extra learning parameters, it can be integrated into existing automated Gleason grading systems seamlessly with negligible overheads. Extensive quantitative evaluations show that, by applying SAN to various CNN architectures, the diagnostic accuracy can be significantly improved. For instance, we raise VGG-16's diagnostic accuracy from 73.99% to 79.16% on the Harvard Dataverse.
Receptionist and Security Robot Using Face Recognition with Standardized Data Collecting Method

Face recognition has become the front runner for deep learning applications in the real world and this paper focuses on its implementation in a human-robot interaction and security system. For this specific project, it is inherent that restraints are created to allow the system to produce greater performance within the requirements of a receptionist and security robot. A k-nearest neighbors classifier is applied to further enhance the accuracy of face recognition. By sequencing images from videos, we create large datasets to train our own classifier in various conditions to increase its accuracy and lower false-positive rates in poor lighting environments. With the goal of creating a service robot, we have standardized our method of data collection for new inputs that will assist the recognition process in variable conditions of operation. The resulting product is a system that can accurately predict known and unknown faces with Asian features.
Blockchain Software System Proposal Applied to Electric Self-driving Cars Charging Stations: A TSP Academic Project

Nowadays principal carmakers and technology companies have been working to launch their own self-driving cars. However, a considerable quantity of challenges has to be resolved before total car autonomy can be reached. This paper shows a proposal about how a self-driving car could be able to charge energy with no human intervention. This proposal used blockchain technology and TSP to tackle this challenge. This paper shows how this software system prototype was developed as and academic project and why addressing these kinds of challenges will be a way to achieve fully self-driving cars in the future.
Using Augmented Reality to Explore Museum Artifacts

Many technologies have been used recently to enhance the tourism experience, and Augmented Reality (AR) has proven to be one of the more promising. This research study aims to use AR to enhance the tourism experience at heritage sites, and in particular to show that AR can provide a richer understanding of how historical artifacts were used. This research adopts an exploratory approach comprising: an exploratory survey; an exploratory interview study; interview study approach in Saudi Arabia; the design of a User Interface (UI); and an evaluation of user experience. The expected outcomes of this research are: conclusions drawn from the interview data; and secondly, a User Interface application for the General Entertainment Authority of audi Arabia.
UI Components Recognition System Based On Image Understanding

Before the release of mobile application products, a lot of repeated testing is often required. In the process of mobile application testing, the core problem is to locate the UI components on the mobile application screenshots. There are many methods to automatically identify UI components, but in some cases, such as crowdsourcing testing, it is difficult to use automatic methods to identify UI components. In view of this, the APP UI components recognition system based on image understanding provides new solutions and methods for application scenarios that are difficult to automatically locate components. We investigate Android UI component information, use image understanding analysis to extract component images on screenshot, design and implement a convolutional neural networks, and then use trained CNN to classify these images. The classification accuracy is up to 96.97%. In the end, we get the component information contained in screenshot.
Parallel based Hiding of Sensitive Knowledge

Nowadays, privacy preserving data mining is an increasingly popular field of data mining research. frequent itemset-based approaches comprise a large category of techniques where the hiding is exemplified through the Apriori algorithm. This paper extends the work on a previously presented hiding scheme that sanitizes the input database by extending it based on the ideal positive border that is computed through the application of a constraint-based frequent itemset mining algorithm. The goal of the proposed approach is to improve at large on computational aspects of the hiding methodology, and in particular to accommodate bigger datasets by customizing the hiding scheme and allowing it to run in parallel while ensuring the hiding of the sensitive knowledge in its entirety. The proposed approach is a significant acceleration factor provided to business user directly in the form of Software as a Service.
Virtual Reality in Education: A Case Study on Exploring Immersive Learning for Prisoners

Our research presented here tries to bridge the gap between technology-oriented lab work and the praxis of introducing VR technology into difficult to deploy-to contexts-in our case prisoners with high learning needs. We have developed a prototypical immersive VR application designed for delivering low-level literacy and numeracy content to illiterate adults. This development has been taken to the commercial sector and is currently under product development. The target population for the application are those currently held in a correctional facility, but who have the motivation and determination to educate themselves. In this paper we discuss the current lifecycle of this project including the development, initial tests, and an exploratory study we conducted. We conclude with a discussion of logistical issues, potential research opportunities, and current outcomes.
E2E-FS: An End-to-End Feature Selection Method for Neural Networks

Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms provide a clear explanation about which variables are being used to trigger a certain output, LASSO-like approaches sacrifice a detailed explanation in favor of increasing its accuracy. In this paper, we present a novel embedded feature selection algorithm, called End-to-End Feature Selection (E2E-FS), that aims to provide both accuracy and explainability in a clever way. Despite having non-convex regularization terms, our algorithm, similar to the LASSO approach, is solved with gradient descent techniques, introducing some restrictions that force the model to specifically select a maximum number of features that are going to be used subsequently by the classifier. Although these are hard restrictions, the experimental results obtained show that this algorithm can be used with any learning model that is trained using a gradient descent algorithm.
A Dense-Gated U-Net for Brain Lesion Segmentation

Brain lesion segmentation plays a crucial role in diagnosis and monitoring of disease progression. DenseNets have been widely used for medical image segmentation, but much redundancy arises in dense-connected feature maps and the training process becomes harder. In this paper, we address the brain lesion segmentation task by proposing a Dense-Gated U-Net (DGNet), which is a hybrid of Dense-gated blocks and U-Net. The main contribution lies in the dense-gated blocks that explicitly model dependencies among concatenated layers and alleviate redundancy. Based on dense-gated blocks, DGNet can achieve weighted concatenation and suppress useless features. Extensive experiments on MICCAI BraTS 2018 challenge and our collected intracranial hemorrhage dataset demonstrate that our approach outperforms a powerful backbone model and other state-of-the-art methods.
Secure graphical password based on cued click points using fuzzy logic

Abstract Nowadays authentication technology is the most important means to ensure security. Alphanumeric username and password procedure is the most common authentication method. Users typically choose memorable passwords that are easy for attackers to guess, and assigned passwords by the system are difficult for users to remember. We present a graphical password based on cued recall in which is secured from shoulder surfing attack. Also, we propose an image encryption technique using a 2D cat map and edge detection technique in which it uses fuzzy logic inference to detect the edge of images. Then the user password is encrypted before storing it in the database using this method. Experiments illustrate that the proposed method has good performance in usability and security.
Integrating Computer Vision and Natural Language Instruction for Collaborative Robot Human-Robot Interaction

In this paper, we report how we design a human-robot co-working scheme. We believe that natural language is the best way that human and machine can exchange messages. However, understand voice command is not enough, what object is referred in what location is also very import. To better understand a natural language instruction, a robot also require computer vision to resolve the real-world object referencing problem. In this paper we show how to integrate the Google voice recognition result with the YOLO computer vision software with the KINOVA robot arm and achieve a collaborative robot interaction.
Generating Procedural Dungeons Using Machine Learning Methods

Procedural content generation (PCG) is a powerful tool to optimize creation of content in the game industry. However, it can lead to lack of control and mischaracterization of the game design, creating unbalanced or undesired situations. To overcome such problems, machine learning can be used to map important patterns of a game design and apply them in the PCG. Considering such aspects, this paper proposes a strategy for procedurally generating dungeons using ML techniques. We use Unity ML-Agents tool for the implementation, since dungeons are environments largely used in the industry that also require more control over its creation. The strategy used in this paper has proven to generate dungeons that respect room positioning design choices and maintains the game characterization. We conclude, after conducting a survey with users, that the generated dungeons presented reliable maps and showed to be more enjoyable and replayable than manually generated ones following the same design principles.
A study on Time-series DBMS Application for EdgeX-based lightweight edge gateway

The EdgeX platform is an open source software project for edge gateways. Each function of edge gateway operates as a Docker container-based microservice, and at least 10 microservices operate simultaneously to perform a series of edge computing functions such as data collection, storage, analysis, and delivery. The load of each microservice is a very important issue because the lightweight edge gateway for relatively low-load data processing such as Smart Metering, Smart Home, and Smart Farm has low-end hardware capabilities for low price. In this paper, we describe the analysis results of the internal data storage of EdgeX, which has the most workload in the lightweight edge gateway. In addition, the test-bed application results with time-series DBMS are described and requirement issues are discussed.
Energy-efficient Inference Service of Transformer-based Deep Learning Models on GPUs

Inference-as-a-service (IAAS) has been recently launched by cloud service providers to support on-demand AI applications. Many natural language processing (NLP) services are based on the Transformer Sequence Transduction model. However, the inference process of the Transformer model consumes a significant amount of energy due to the large model size (e.g., billions of parameters) and tremendous computations. How to reduce the energy consumption of IAAS without violating the service-level agreement (SLA) becomes a practical challenge for service providers. In this work, we conduct a comprehensive study on the inference performance and energy efficiency of a Transformer model trained for the language translation service. First, we empirically characterize some essential performance metrics, including latency, throughput, and energy consumption on three different GPUs with diversified workload configurations. The detailed workload separation facilitates a thorough and deep understanding of the inference process of the Transformer model. Second, we provide an energy consumption model for the Transformer based on the observed data. Finally, we propose the Aligned scheduling scheme that optimizes throughput and energy efficiency with up to 2.86× and 2.73× improvement at the cost of 40% average latency loss. Our findings provide a full scope of Transformer inference, and suggest that the workload balancing and scheduling have great potentials to offer energy-efficient Transformer inference services.