Reviewerly/exHarmony huggingface dataset:
Gamifying Security Awareness: A New Prototype

Data breaches within an organization have many causes. Social engineering attacks, ransom-ware applications and harmful spam email messages are data breach catalysts that are the result of human error. Human error is the leading cause of data breach and is also one of the more difficult factors for an organization to mitigate. Many users are unable to see how their role is impacted by organizational security policy, and therefor see no benefit to abide the policy. When employees use company devices to perform personal tasks, or use personal devices to perform business tasks, lines of ownership can be blurred and important organizational data assets can be put at risk. Training and awareness programs are too often treated as a bandage to fix a wound inflicted by a breach after the fact. If employees were trained effectively, the breach might not have occurred in the first place. This project and accompanying research paper will explore the gamification of the security training and awareness program. By developing role-based game modules to teach secure behavior to all organizational users, incentivizing secure behavior with real rewards that matter to participants and applying the training throughout the year, it can be possible to reinvent security awareness and prevent future data breaches.
SSH Key Management Challenges and Requirements

SSH (Secure Shell) uses public keys for authenticating servers and users. This paper summarizes progress in SSH key management so far, highlights outstanding problems, and presents requirements for a long-term solution. Proposals are solicited from the research community to address the issue. The problem is of high practical importance, as most of our critical Internet infrastructure, cloud services, and open source software development is protected using these keys.
A Literature Review of the Research on Interaction Mode of Self-driving Cars

With the developing of the technology on self-driving, more and more L3 driverless vehicles are launched in market, people get opportunities to experience the self-driving cars in their daily life. As a result, there is a growing demand for the autopilot experience. Natural and efficient human-computer interaction can not only improve the driving experience, but also accelerate the process of self-driving commercialization. This paper discusses eye-movement interaction, voice interaction and gesture interaction in self-driving car, and analyzes the technology, advantages and disadvantages of the existing interaction modes, and prospect the future development trend of self-driving human-computer interaction.
Exploiting Parallelism and Vectorisation in Breadth-First Search for the Intel Xeon Phi

Modern applications generate massive amounts of data that is challenging to process or analyse.Graph algorithms have emerged as a solution for the analysis of such data because they can represent the entities participating in the generation of large-scale datasets in terms of vertices and their relationships in terms of edges.Graph analysis algorithms are used for finding patterns within these relationships, aiming to extract information to be further analysed.The breadth-first search (BFS) is one of the main graph search algorithms used for graph analysis and its optimisation has been widely researched using different parallel computers.However, the parallelisation of BFS has been shown to be challenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability.This paper investigates the optimisation of the BFS on the Xeon Phi (Knights Corner), a modern parallel architecture provided with an advanced vector processor supporting the AVX-512 instruction set, using a bespoke development framework integrated with the Graph 500 benchmark.In addition, to demonstrate portability, we show results for a direct port of the algorithms to a more recent version of the Xeon Phi (Knights Landing) and to a Skylake CPU which supports most of the AVX-512 instruction set.Optimised parallel versions of two high-level algorithms for BFS were created using vectorisation, starting with the conventional top-down BFS algorithm and, building on this, a hybrid BFS algorithm.On the KNC our best implementations result in speedups of 1.37x (top-down) and 1.37x (hybrid), for a one million vertices graph, compared to the state-of-the-art.On the KNL and Skylake, the performance is higher than on KNC.In addition, we show results of our best hybrid algorithm on real-world graphs from the SNAP datasets with speedups up to 1.3x on KNC.Performance on KNL and Skylake is again higher, demonstrating the robustness and portability of our algorithm.The hybrid BFS algorithm can be further used to speed up other graph analysis algorithms and the lessons learned from vectorisation can be applied to other algorithms targeting existing and future models of the Xeon Phi and other advanced vector architectures.
End-to-End Feature Pyramid Network for Real-Time Multi-Person Pose Estimation

In computer vision, pose estimation system is widely used to construct human body transformation. However, it is hard to achieve these targets together: stable real-time speed, variance human number and high accuracy. This paper proposes an end-to-end pose estimation network. It contains a neural network friendly representation of human pose. Then it proposes a correspond real-time end-to-end pose estimation network based on feature pyramid network structure with attention-based detection modules. This network can detect multiple humans in more than 60 fps with 384 × 384 resolution on GTX 1070 with affordable accuracy. This work shows the potential of this network structure can perform both faster and better compared with state-of-the-art results.
A new detail enhancement method for high dynamic range infrared image

Due to the width limitation of 8-bit data used in traditional display device, the local details and contrast of the infrared image are compressed. To cope with these limitations, a novel infrared image enhancement method based on CLAHE and multi-scale approach of DoG is proposed. First, the guided image filter is utilized to separate the base layer and detail layer of the image. Then, the improved CLAHE is used to adjust the base layer global contrast and the multi-scale detail boosting is used to enhance the detail features; for the detail layer, multi-scale fast median filter is used for noise reduction. Finally, the two layers are fused according to the weight of the detail layers' noise level. Comparing with the real-time enhancement methods both in qualitative and quantitative aspects, our proposed method shows the better performance in experiment results.
Novel chaotic map‐based privacy‐preserving authenticated key agreement scheme without the electricity service provider involvement

Abstract Devising proper security measures to protect the usage reports of customers against unauthorized accesses is of prime importance. Authenticated key agreement schemes are one of the solutions, which are extensively used to provide secure data reading from the smart meters. Over the past decade, a number of key agreement protocols have been proposed for the smart meters that are covered by the smart grid data communication network. However, there are only two schemes, which have been presented quite recently particularly for the isolated smart meters. Since these schemes rely on the Internet and for each key agreement, the electricity service provider needs to involve, in this paper, we propose a novel anonymous authenticated key agreement scheme that is free from the electricity service provider entanglement. Further, the proposed scheme takes the advantage of substantial reduction in the communication overhead. The security of the presented scheme has been formally validated using the well‐known ProVerif tool and random oracle model and its efficiency has been analyzed in comparison to the related ones. The outcome indicates the superiority of the proposed scheme to be employed for the isolated smart meters.
Decision Tree Based Single Person Gesture Recognition

This chapter is aimed at gesture recognition using the Kinect sensor. The Kinect sensor generates the human skeleton with twenty different 3-dimensional coordinates corresponding to twenty body joints. The present work requires eleven out of these twenty joints: six joints about the right and the left hands and five upper body joints. A unique set of twenty three features has been extracted to distinguish between gestures corresponding to five basic human emotional states, namely, ‘Anger’, ‘Fear’, ‘Happiness’, ‘Sadness’ and ‘Relaxation’. The features are based on the distances, accelerations, and angle between the different joints. The goal of the proposed system is to classify an emotion as positive or negative and determine its intensity level from the corresponding gestures. A high overall recognition rate of 86.8% is obtained from the proposed system using a decision tree based classifier.
A Collusion Attack on Identity-Based Public Auditing Scheme via Blockchain

With cloud storage systems, users can access and update outsourced data remotely. Owing to the accompanying growth of the importance of data integrity, a great deal of attention has been focused on public auditing schemes. Identity-based public auditing (IBPA) scheme allows a third-party auditor (TPA) to verify the integrity of the outsourced data on behalf of users. However, malicious TPAs might collude with cloud servers and forge audit data to deceive users. In this paper, we first review the architecture of a traditional IBPA scheme and a novel IBPA scheme which try to solve the above problem via blockchain. Then, we analyze two main limitations in this newly proposed public auditing scheme against malicious auditors and illustrate our collusion attack on this IBPA scheme. Finally, we offer some suggestions to overcome the disadvantages and help to create a more trustworthy blockchain-based public auditing scheme.
A Foundation for Change: Using Challenges and Opportunities as Building Blocks for Collection Management

Large-scale collection management represents not just opportunities in terms of space, budgets, and personnel but also an important responsibility for the library’s ability to provide quality service and access to knowledge in the future. Libraries must address many challenges when managing collections, but by taking advantage of opportunities when presented, libraries can improve access for today and tomorrow. Using the University of Illinois at Urbana-Champaign as an example, this article will explore how opportunities like high-density storage, shared print initiatives, digitization efforts, and mass deduplication can address challenges facing libraries and improve current and future access to collections.
Immersive Analytics Through HoloSENAI MOTOR Mixed Reality App

This study examines the use of HoloSENAI MOTOR and LiteMap annotation as novel approach for preparing learners and professionals for the industry 4.0. This new mixed reality technology was developed with UNIT3D and C# language for the Microsoft HoloLens®. This educational resource enables the projection of 3D scenes of a real electric motor into the natural world environment. It was used by students from an Engineering course in Brazil. Our aim was to identify the potential benefits and barriers to promote immersive analytics and authoring skills through HoloSENAI MOTOR for learning and teaching. Immersive Analytics is a novel framework that combines real-time interaction with visualization techniques for students to explore and analyze information about the motor in their physical environment. This study is based on Responsible Research and Innovation approach. It revealed that the key benefits for learners were to increase their motivation, curiosity and understanding of features, properties and functionalities of the motor, including better acquisition of information and data analysis skills fun learning. The key barriers were the expensive equipment, time and staff for developing other APPs and new pedagogical approaches for assessment.
Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Abstract Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.
Immersive Community Analytics for Wearable Enhanced Learning

Nowadays, we can use immersive interaction and display technologies in collaborative analytical reasoning and decision making scenarios. In order to support heterogeneous professional communities of practice in their digital transformation, it is necessary not only to provide the technologies but to understand the work practices under transformations as well as the security, privacy and other concerns of the communities. Our approach is a comprehensive and evolutionary socio-technological learning analytics and design process leading to a flexible infrastructure where professional communities can co-create their wearable enhanced learning solution. In the core, we present a multi-sensory fusion recorder and player that allows the recordings of multi-actor activity sequences by human activity recognition and the computational support of immersive learning analytics to support training scenarios. Our approach enables cross-domain collaboration by fusing, aggregating and visualizing sensor data coming from wearables and modern production systems. The software is open source and based on the outcomes of several national and international funded projects.
Toward RNN Based Micro Non-verbal Behavior Generation for Virtual Listener Agents

This work aims to develop a model to generate fine grained and reactive non-verbal idling behaviors of a virtual listener agent when a human user is talking to it. The target micro behaviors are facial expressions, head movements, and postures. The following two research questions then emerge. Whether these behaviors can be trained from the corresponding ones from the user's behaviors? If the answer is true, what kind of learning model can get high precision? We explored the use of two recurrent neural network (RNN) models (Gated Recurrent Unit, GRU and Long Short-term Memory, LSTM) to learn these behaviors from a human-human data corpus of active listening conversation. The data corpus contains 16 elderly-speaker/young-listener sessions and was collected by ourselves. The results show that this task can be achieved to some degree even with the baseline multi-layer perceptron models. Also, GRU showed best performance among the three compared structures.
Implementation and Evaluation of Personal Ontology Building System with Virtual Agent

Integrating the virtual agent, IoT, and cloud technologies, we have been developing a communication care system, where a person with dementia can talk to an agent every day at home. In order to achieve active conversations with a person with dementia, it is essential to personalize topics to every individual person with dementia. In this paper, we propose a method that represents and manages "the knowledge of individual person" (Personal Ontology) with Linked Data, in order to generate person-centered topics to the individuals. The proposed method with Linked Data does not only enable flexible management of personal ontologies, but also does not only represent complex structure in the ontlogies. Moreover, connecting the data to the external Linked Open Data makes it efficient to obtain various knowledge relevant to the conversation from the wisdom of crowds in the Internet. Also, in this paper, based on the proposed method, we develop a prototype system which dynamically builds Personal Ontology through the conversations between the user and virtual agent. Specifically, virtual agent inquires of the user (U) the attribute (P) such as hobbies, favorite singers and any others. Then virtual agent collects the answers (O) of the user. After that, personal information represented by tuple $$\langle U, P, O \rangle $$ is added to Personal Ontology. Furthermore, we confirm collectability of Personal Ontology through the prototype system by experiments with a few subjects.
Performance of Hash Functions in Blockchain Applied to IoT Devices

The IoT (Internet of Things) is a network composed of several devices (things) connected to the Internet and to each other. IoT services are increasingly growing and are allowing companies to deploy scalable solutions with reduced costs and instantaneous data access. These solutions require seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such requirements can be given to a company's IoT solution by applying blockchain technology. This paper analyzes the blockchain technology and the advantages and challenges behind its implementation in an IoT environment. A blockchain in IoT scenario was developed to evaluate the performance of different cryptographic hash functions in the IoT device RaspberryPi. Conclusions were drawn when it comes to the viability of some hash functions mainly based on the low resource characteristic shared by the IoT devices, which compromises the performance of the hash function.
Naïve Approach for Bounding Box Annotation and Object Detection Towards Smart Retail Systems

It is becoming a trend that companies use smart retail stores to reduce the selling cost, by using the sensor technologies. Deep convolutional neural network models which are pre-rained for the Object detection task achieve state-of-the-art result in many benchmark. However, when applying these algorithms to the intelligent retail system to help automated checkout, we need to reduce the manual labelling cost of making retail data sets, and to achieve real-time demand while ensuring accuracy. In our paper, we propose a naive approach to get first portion of the bounding box annotations for a given custom image dataset in order to reduce manual cost. Experimental results show that our approach helps to label the first set of images in short time of period. Further, the custom module we designed helped to reduce the number of parameters by 41.77% for the YOLO model maintaining the original model's accuracy (85.8 mAP).
The Role of Named Entities in Linking News Articles During Preservation

In the recent, the World Wide Web has become a platform for online news publications. Many sources started publishing digital versions of news articles online to vast users through a variety of devices, i.e. television channels, magazines, and newspapers. It is observed that the news articles available can be very huge and recommendation systems can help to recommend relevant news to the news readers by filtering news articles based on some predefined criteria or similarity measure, i.e. collaborative filtering or content-based filtering approach. The paper presents named entities based similarity measure for linking digital news stories published in various newspapers during the preservation process in a digital news stories archive to ensure future accessibility. The study compares the similarity of news articles based on human judgment with a similarity value computed automatically using the proposed technique. The results are generalized by defining a threshold value based on multiple experimental results using different datasets of different size.
Extreme learning‐based non‐linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results

In this study, an extreme learning-based non-linear model predictive controller (NMPC) is proposed for path following planning of an autonomous underwater vehicle (AUV) using horizontal way-points. The proposed controller comprises a kinematic controller and a dynamic controller. The kinematic controller is designed by using back-stepping approach whilst the dynamic controller is designed by employing the NMPC approach. The dynamics of the AUV is identified in real-time by employing an extreme learning machine (ELM) structure. In view of achieving improved performance of the ELM structure, its hidden layer parameters are optimally determined by applying Jaya optimisation algorithm. The resulting ELM model is then used to design a NMPC considering the constraint on rudder planes. The tracking performance of the proposed controller is compared with that of two recently reported control algorithms namely, state feedback controller and inverse optimal self-tuning proportional–integral–derivative (PID) controller. The proposed controller is implemented using MATLAB and then in real-time on a prototype AUV developed in the authors’ laboratory. From both the simulation and experimental results obtained, it is observed that the proposed controller exhibits superior tracking performance compared to both state feedback controller and inverse optimal self-tuning PID controller.
Identity Verification Using Face Recognition for Artificial-Intelligence Electronic Forms with Speech Interaction

Concern over the decline in Japan's manufacturing competitiveness has increased in recent years. In particular, falsification of inspection data is a social problem that could undermine Japan's manufacturing industry, which is founded on a dedication to high quality. Falsification could be prevented by ensuring transparency of the inspection process by visualizing the process. End-to-end visualization facilitates early detection and prevention of various law infractions. In the workplace, visualization requires an efficient low-cost identity-verification method that ensures ease of visual confirmability for product traceability. We previously developed AI-forms, i.e., artificial-intelligence electronic forms, that provides a speech interface as a means of improving the standard work process in workplaces by making operations more efficient and visualizing processes. AI-forms improves production efficiency and visualizes the collected operation records by enhancing the readability and writability of records and handover operations that are not sufficiently supported by traditional electronic forms. To prevent falsification of inspections, it is necessary to use a widely deployed device and verification method in the workplace. We propose an identity-verification method for applying face recognition to AI-forms and developed a smartphone app for AI-forms. Preliminary feasibility testing involving 11 workers in an actual workplace confirmed that identity verification is possible when face recognition is carried out with frontal images of workers who are not wearing face masks. The face-recognition process completed within 0.4 s, enabling workers to seamlessly begin work with AI-forms. Recording both collation photos and worker names during identity verification also made it possible for a human to visually confirm a worker's identity. Discussion with workers and supervisors after the feasibility tests provided findings for improving our face-recognition app for closer integration of AI-forms and our identity-verification method at arbitrary times.