Reviewerly/exHarmony huggingface dataset:
Interview with Belinda Barnet

Belinda Barnet is Senior Lecturer in Media &amp; Communications at Swinburne University, Melbourne. She is the author of "Memory Machines: the Evolution of Hypertext" (Anthem Press UK, 2013), which can be obtained, e.g., via https://amzn.to/2KjqlYH. Alongside her research career, Dr Barnet has worked as Service Delivery Manager (Wireless Content Services) for Ericsson Australia. She convenes the Social Media Major at Swinburne (https://bit.lv/312ARJY). Her current projects include building a speech recognition program to help young children and toddlers with Cochlear Implants learn to speak, and she'd be interested in hearing from anyone working in this field. She's also investigating Shadow Data.
Teaching Assistant System of Lathe Turning Training Based on Mobile Augmented Reality

Lathe turning training is an important practical teaching content for students in engineering universities. The traditional teaching way are operation demonstration and theory explanation, and the teaching efficiency and teaching quality all need to be improved. Based on mobile augmented reality(AR) technology and combined with the characteristics of lathe turning training in engineering training, a teaching assistant system of lathe turning training is developed. The development content, development process, application involved in the system and student feedback are studied. By using Unity3D development engine and HiAR augmented reality platform, the real-time display models of lathe and its internal structure models are realized. At the same time, the models can be rotated, zoomed in, and zoomed out by touch screen operation. Therefore, the traditional teaching method is changed, the visualization of teaching content is realized. The effects of contextual learning and interactive learning are achieved.
An Intelligent Touring Recommendation System Using the Deep Learning and Augmented Reality Technology-Case Study of Toucheng Historic Street

Recently, it is more and more difficult to make a decision because we receive a large amount of information through the Internet every day. Therefore, the recommender system becomes more and more popular. It can help users making decisions effectively by providing suitable suggestions to users, and those suggestions were processed according to users' browsing history and transaction. Therefore, the development of the recommender system in this research is based on cloud platform and focuses on providing location-based search and intelligent recommendation for resource objects. First, we will automatically through the pre-processing process, crawler technology and APIs provided by social networking sites, and use these data descriptions to enhance data search with correctness. Moreover, we design and build an integrated tourism augmented reality navigation system with recommendation technology and dynamic route planning which covers all tourism activities. The service also provided various valued-added services, such as wikitude Augmented Reality, subject guidance including POI, LOI and SOI, and stored trajectory path of users. Therefore, the user may have a story-based touring guidance while surfing the cultural and historic contents in advance.
Statistical Analysis of Enhanced SDEx Encryption Method Based on SHA-256 Hash Function

In the paper, the author provides a statistical analysis of the enhanced SDEx method based on SHA-256 hash function, which is used to secure end-to-end encryption in data transferring. To examine the quality of the enhanced SDEx encryption algorithm, the ciphertext files were tested to check if they fulfill the conditions of pseudo-randomness. To test the pseudo-randomness of the encrypted files, a test package shared by NIST was used for this purpose. In addition, compression tests on ciphertext files were performed using the WinRAR software.
Triforce QNX Syscall Fuzzer

The task of mitigating kernel vulnerabilities in a RTOS kernel like QNX is of utmost importance in recent times. AFL is probably one of the most effective fuzzing tools available, with its functionalities for feedback driven and instrumented fuzzing. In this paper, we present our experience report on developing an environment for fuzzing QNX kernel using AFL.
Game Console Protection and Breaking It

This article surveys protection and attacks on various game consoles. Game consoles are a special kind of embedded system that are used primarily for entertainment. Virtually all game consoles include means for digital rights management (DRM) and virtually all have been attacked. The chapter describes the various motivations behind these attacks, the way the console manufacturer acted, and the way the hacking community reacted. This article is designed based on lectures published by the actual hackers of the machine describing their efforts. The article summarizes the hackers' activities on multiple consoles over the last 20 years and does not focus on specific bug or exploit.
Indoor fall detection system for the elderly using passive RFID sensor tags

This research aims at composing indoor fall detection system for the elderly at a nursing home. The system reduces their uncomfortable feeling by avoiding attaching sensors on the human bodies. The proposed system can detect falls of the elderly while walking with passive RFID sensor tags attached to a pair of room shoes in order to monitor their activities. This paper presents a method of data collection, feature extraction and machine learning techniques.
Cut-and-Choose for Garbled RAM

Garbled RAM, introduced by Lu and Ostrovsky in 2013, provides a novel method for secure computation on RAM (Random Access Machine) programs directly. It can be seen as a RAM analogue of Yao's garbled circuits such that the computational complexity and communication complexity only grow with the running time of the RAM program, avoiding the inefficient process of first converting it into a circuit. It allows for executing multiple RAM programs on a persistent database, but is secure only against semi-honest adversaries. In this work we provide a cut-and-choose technique for garbled RAM. This gives the first constant-round two-party RAM computation protocol secure against malicious adversaries which allows for multiple RAM programs being executed on a persistent database. Our protocol makes black-box use of the one-way functions, and security of our construction is argued in the random oracle model.
Bayesian Network Learning for Classification via Transfer Method

In classification problem, Bayesian networks play an important role because of its efficiency and interpretability. Bayesian networks learning methods require enough data to produce reliable results. Unfortunately, in practice, the training data are often either too few, expensive to label, or easy to be outdated. However, there may be sufficient labeled data that are available in a different but related domain. Learning reliable Bayesian networks from limited data is difficult; and transfer learning might be used to improve the robustness of learned networks by combining data from auxiliary and related labeled dataset. In this paper, we propose a novel transfer learning method for Bayesian networks for classification that considers both structure and parameter learning. Our solution is to first construct the initial Bayesian networks model for auxiliary labeled data, and then revise the model according to an Expectation-Maximization (EM) algorithm, structure and parameters are revised by turns, in order to make it applicable to the target unlabeled dataset. We mainly apply our method on a special type of Bayesian networks, namely tree-based Bayesian network. To validate our approach, we evaluated the method on a real and typical classification scenario - text classification problem. We compared our method with other transfer learning method as well as the traditional supervised and semi-supervised learning algorithms. The experimental results show that our algorithm is very effective and obtains a significant improvement when we transfer knowledge from related dataset.
A Meta-strategy for Multi-issue Negotiation

Automated negotiation is a process where multiple software agents interact with each other over certain matters through making offers and counteroffers. This paper considers a buyer agent negotiating with a seller agent over multiple issues. A meta-negotiation strategy is proposed to improve the negotiation process outcomes. The presented negotiation strategy is a cooperative negotiation one that uses both, preference-based and fuzzy similarity mechanisms. The preference-based mechanism is used for generating quantitative offers while the fuzzy similarity is used for in the process of generating qualitative offers. The preference-based mechanism considers the preferences of the opponent when making offers; the agent makes more concessions on the negotiation issues that are believed to be more preferred to the opponent. In the fuzzy similarity approach, the agent formulates an offer that is believed to be most similar to the one received from the opponent in the last negotiation round. The experimental results show that the proposed cooperative negotiation strategy outperforms the basic negotiation strategy in all the evaluation criteria such as utility, agreement rate and Nash product.
Which is Better for Mobile Robot Trajectory Optimization: PSO or GA?

In mobile robot research domain and especially in its path planning sub-domain, the optimization tools the most used are PSO (Particle Swarm Optimization) and GA (Genetic Algorithms). In fact, mobile robot path planning is a multi-objective optimization problem where at least these objectives are considered: the shortest and the safest path to the goal. Sometimes other objectives are added like travel duration. The subject of this paper is a comparison investigation concerning the efficiency of PSO and GA in mobile robot path planning optimization. We start with a comparison of evolutionary performances of PSO and GA. Then, we apply PSO and GA in the optimization of parameters of DVSF$$^{2}$$ (Dynamic Variable Speed Force Field) path planning approach. Different environments of different types, statics and dynamics are considered in these simulations to decide which is better for mobile robot path planning optimization: PSO or GA.
Experimental Evaluation of Computation Cost of FastSLAM Algorithm for Unmanned Ground Vehicles

Two decades ago, FastSLAM algorithm for mobile robots was introduced. Since then, dozens of research work focused on FastSLAM algorithm performance enhancement while keeping reduced computation cost. Since experimental evaluation of computation cost is dependent on the hardware capabilities of the platform, the present work introduces a quantitative theoretical method for predicting the computation cost of the FastSLAM algorithm. The method relies on the big (O) computation complexity which represents the worst case. The method was evaluated experimentally with different number of particles and different number of map features. The computation cost evaluation analysis was broken down into prediction, observation, data association and resampling computation cost evaluation. The proposed method was proven to be helpful in customization of FastSLAM parameters like number of particles and data association optimization for FastSLAM algorithm developers.
A Scalable Approach for Improving Implementation of a Frequent Pattern Mining Algorithm using MapReduce Programming

A Frequent pattern is a pattern (a set of items, subsequences, sub-graphs, etc.) that occurs frequently in a transactional database. Frequent pattern mining gives vast benefit in domains such as extracting knowledge from transactional data for market basket analysis or cross-marketing and selling. A number of important FIM (Frequent itemset mining) algorithms have been developed to speed up mining performance since its inception. Unfortunately, when the dataset size is massive, it can still be prohibitively expensive for communication cost, memory usage, balanced data distribution & I/O utilization. One of the existing frequent pattern mining algorithms called CATS Tree (Compressed and Arranged Sequences tree) can perform interactive mining by a single scan. In this work, we propose to parallelize a part of CATS-Tree algorithm on scattered machines, which will improve the overall performance of CATS-Tree for large transaction data. This algorithm partitions computation to execute an independent group of mining tasks on each machine. We present a comparison based on time complexity, algorithm complexity and performance on a different type of datasets. The result shows that the proposed parallel implementation of CATS-Tree provides better performance for massive datasets.
Sparse High-Level Attention Networks for Person Re-Identification

When extracting convolutional features from person images with low resolution, a large amount of available information will be lost due to the pooling, which will lead to the reduction of the accuracy of person classification models. This paper proposes a new classification model, which can effectively to reduce the loss of important information about the convolutional neural works. Firstly, the SE module in the Squeeze-and-Excitation Networks (SENet) is extracted and normalized to generate the Normalized Squeeze-and-Excitation (NSE) module. Then, 4 NSE modules are applied to the convolutional layers of ResNet. Finally, a Sparse Normalized Squeeze-and-Excitation Network (SNSENet) is constructed by adding 4 shortcut connections between the convolutional layers. The experimental results of Market-1501 show that the rank-1 of SNSE-ResNet-50 is 3.7% and 4.2% higher than that of SE-ResNet-50 and ResNet-50 respectively, it has done well in other person re-identification datasets.
Application Research of AR Holographic Technology based on Natural Interaction in National Culture

With the rapid development and wide popularization of computer technology, the most natural interaction modes between human and computer, such as voice interaction and somatosensory interaction, have become a new research field. Interactive AR holographic technology covers three international research hotspots: natural interaction, augmented reality and holographic projection. It has tremendous vitality and market prospects. The new interaction concept of AR holographic technology is that under the background of digitization and the promotion of new technologies of AR and holographic technology, the interaction subject has changed from passive and semi-active to real initiative. Into the comprehensive communication in the virtual world created by AR and holographic technology, people can watch and experience new experience environments in the situation. The protection of the national culture of AR holographic technology is being transformed from traditional technical means into digital protection means. With the help of digital images, three-dimensional display, virtual reality and other technologies, the digital protection, inheritance and development of national cultural heritage has become a research and development hotspot. In this paper, natural interaction, AR technology and holographic technology are applied to the inheritance and protection of national culture. A kinect-based AR holographic interactive display scheme is proposed. Taking Dai culture as an example, the key technologies are analyzed to prove its feasibility and effectiveness.
FDDS: An Integrated Conceptual FDDS Framework for DDS Based Middleware

Data Distribution Services (DDS) has changed the way of the development process for complex distributed systems by tightly coupling QoS policies for achieving desired functionality at execution time. DDS middleware can be configured to handle edge-to-edge quality of service (QoS) policies. However, unpredictable environment imposes a greater challenge in the design and execution of these systems. The publish/subscribe middleware has improved the scalability and interoperability of the distributed systems. The stringent Quality of Services (QoS) standards imposes a greater challenge to developers. We propose a framework based approach for designing, development, and testing of distributed systems on top of Real-Time Innovations (RTI) and Open-Splice (OSPL) DDS middleware. The framework consists of ACGF Module which generates the code for application. DCF Module provides the abstraction from core functionality of DDS by using templates. Finally, ATF Module will help developer to test their application. This work will be beneficial for the development of different types of DDS applications rapidly.
FORMAL QUALITATIVE PROBABILITY

Abstract Choices rarely deal with certainties; and, where assertoric logic and modal logic are insufficient, those seeking to be reasonable turn to one or more things called “probability.” These things typically have a shared mathematical form, which is an arithmetic construct. The construct is often felt to be unsatisfactory for various reasons. A more general construct is that of a preordering, which may even be incomplete, allowing for cases in which there is no known probability relation between two propositions or between two events. Previous discussion of incomplete preorderings has been as if incidental, with researchers focusing upon preorderings for which quantifications are possible. This article presents formal axioms for the more general case. Challenges peculiar to some specific interpretations of the nature of probability are brought to light in the context of these propositions. A qualitative interpretation is offered for probability differences that are often taken to be quantified. A generalization of Bayesian updating is defended without dependence upon coherence. Qualitative hypothesis testing is offered as a possible alternative in cases for which quantitative hypothesis testing is shown to be unsuitable.
Fake Review Prevention Using Classification and Authentication Techniques

Day by day people shopping via E-commerce sites is burgeoning. Decision of placing orders relies on product and service reviews provided by the customers. The importance of the reviews has increased tremendously because they provide information about the quality of product and service. This stimulates sellers to exploit these reviews to increase their sales by deceiving the customers with false information. Thus, detection and prevention of fake reviews becomes pivotal. This paper focuses on detecting and preventing fake reviews using classification and authentication techniques. Review content and Reviewer behavior-based features were used to train different machine learning algorithms such as SVM, Random forest, and Decision tree; among these, Random forest classification algorithm had the highest accuracy of fake review detection with 73.33%. Prevention of fake reviews is achieved by making sure the right person gets to write the review by sending the review writing link to the registered email-id. This research is also concerned over preventing bots to write review by examining the keyboard and mouse activities of the machine. Captcha authentication method has been adopted to prevent bots from writing reviews.
Digital Transformation: A Case Study for Providing E-services via Moodle in Universities

The evolution of digitization reaches a new stage with a focus on the overall transformation, where the so called “Intelligent Environment” such as “Smart City”, “Smart Campus” is in the heart. Today's institutions, which have not yet started the transformation towards a Smart Campus, are capable to exploit untapped ICT resources that could potentially lead to the optimization of the processes and services. In order to verify that a new service can be offered with the available ICT resources, a method of finding a subset satisfying some condition is used. The paper reports approach where elements of the main set include own resources and their functionalities, while needed resources to provide a new service build the subset elements. This work presents examples of developed e-services by using the open source Learning Management System (LMS) Moodle. The study demonstrates that Moodle and advanced ICTs are just as so an alternative for improving personal management and information systems in the means of e-services offering. The paper analyzes the results and evaluate the benefits from the digital transformation point of view. The conclusion summarizes features of the applications will contribute the way university interacts with students and staff.
Freedom of Expression

The rise of social media and populism around the world has given debates surrounding free speech a certain ferocity. Stereotypically, one side defends free speech as the bulwark of a free society a...
D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering

Multi-domain recommender systems can solve cold-start problems and can support cross-selling of products and services. We propose a model to address these difficulties by extracting homogeneous and divergent features from domains. Our Domain-to-Domain Translation Model (D2D-TM), which is based on generative adversarial networks (GANs) and variational autoencoders (VAEs), uses the user interaction history. Domain cycle consistency (CC) constrains the inter-domain relations. Results obtained from experimentation demonstrate the great effectiveness of the proposed system when compared to several state-of-the-art systems.
Face Recognition Using Segmentation Technology

Face recognition technology has become a popular research topic in the fields of pattern recognition, image processing, and computer vision. However, the face recognition accuracy rate will be confronted with challenges if the technology is trained on the unconstrained images with disguised and covered objects on a face area. This paper aims to propose a state-of-the-art face recognition methodology which could be applied in the crime field, especially in the cases of face-disguised crime. The main idea is to extract face pixels from a background and send them to a trained system to eliminate noise decrease meaningless contribution from useless pixels. This research combines two technologies: Fully Convolutional Networks (FCN) for face extraction from the background and disguised part and deep convolution neural network for face training and testing. The process of this research is to build a training set of segmentation of the face area with FCN as an input of Convolution Neural Network (CNN), and then train the CNN network with transfer learning from VGG-Face. Lastly, the classification function SoftMax is applied for face probability distribution. This algorithm has been experimented on a challengeable face dataset. Our experimental results show that the accuracy of recognition achieves better results compared to recognition without face segmentation.